FactSet OnDemand – Web Services Reference Manual

FactSet OnDemand – Web Services

Reference Manual

Doc version - 2.0.2

2

Revision History

Effective Date

Version Number

All Sections

Changes Made

11/08/2021

2.0.2

Copyright and template

updated.

 
Copyright © 2021 FactSet Research Systems Inc. All rights reserved.        FactSet Research Systems Inc.   www.factset.com 

 
Table of Contents 
Revision History ....................................................................................................................................................... 2 
Notice ......................................................................................................................................................................... 5 
Trademarks ............................................................................................................................................................... 5 
FactSet Consulting Services .................................................................................................................................. 6 
Preface ...................................................................................................................................................................... 7 
Intended Audience ............................................................................................................................................... 7 
Introduction  .......................................................................................................................................................... 8 
1. FASTFetch Service ...................................................................................................................................... 8 
1.1. Factlets ................................................................................................................................................... 8 
HTTPS Requests and Responses .................................................................................................................... 8 
1. OnDemand URL Syntax .............................................................................................................................. 8 
2. Optional Query String Parameters ............................................................................................................. 9 
2.1. Example URLs ....................................................................................................................................... 9 
2.2. Format................................................................................................................................................... 11 
2.3. Orientation ............................................................................................................................................ 11 
FactSet Languages ........................................................................................................................................... 12 
1. FactSet Screening Language ................................................................................................................... 12 
2. FactSet Query Language .......................................................................................................................... 12 
3. Date Format ................................................................................................................................................ 12 
3.1. Absolute Dates .................................................................................................................................... 12 
3.2. Relative Dates ..................................................................................................................................... 13 
4. Understanding Rotated Databases.......................................................................................................... 13 
5. OnDemand Factlet Requests ................................................................................................................... 15 
5.1. Standard Factlets ................................................................................................................................ 15 
5.2. Specialized Factlets ............................................................................................................................ 15 
ExtractDataSnapshot ........................................................................................................................................ 17 
ExtractFormulaHistory ...................................................................................................................................... 23 
CorporateActionsDividends .............................................................................................................................. 27 
CorporateActionsSplits ..................................................................................................................................... 34 
ExtractBenchmarkDetail ................................................................................................................................... 38 
ExtractOFDBItem ............................................................................................................................................... 44 
ExtractScreenUniverse ................................................................................................................................... 50 
ExtractOptionsSnapshot ................................................................................................................................. 53 
ExtractSPARData ............................................................................................................................................ 55 
ExtractVectorFormula ..................................................................................................................................... 61 

 
Copyright © 2021 FactSet Research Systems Inc. All rights reserved.        FactSet Research Systems Inc.   www.factset.com 

ExtractEconData .............................................................................................................................................. 66 
ExtractAlphaTestingSnapshot ....................................................................................................................... 72 
LSD_Ownership ............................................................................................................................................... 78 
UploadToOFDB ............................................................................................................................................... 82 
1. Creating a New OFDB ............................................................................................................................. 82 
2. Modifying an Existing OFDB ................................................................................................................... 82 
EstimatesOnDemand ...................................................................................................................................... 84 
1. Estimates Report - Actuals ..................................................................................................................... 86 
2. Estimates Report – Broker Snapshot .................................................................................................... 89 
3. Estimates Report – Consensus ............................................................................................................. 90 
4. Estimates Report – Guidance ................................................................................................................ 91 
5. Estimates Report – Surprise ................................................................................................................... 92 
6. Estimates Report – Consensus Recommendation ............................................................................. 94 
7. Estimates Report – Detailed Recommendation ................................................................................... 95 
8. Appendix .................................................................................................................................................... 97 
 
   

5

Notice

This manual contains confidential information of FactSet Research Systems Inc. or its affiliates

("FactSet"). All proprietary rights, including intellectual property rights, in the Licensed Materials will

remain property of FactSet or its Suppliers, as applicable. The information in this document is subject to

change without notice and does not represent a commitment on the part of FactSet. FactSet assumes no

responsibility for any errors that may appear in this document.

Trademarks

information about he the third-party software that is delivered with the product, refer to the third-party

license file on your installation media that is specific to your release. All other brand or product names

may be trademarks of their respective companies.

FactSet is a registered trademark of FactSet Research Systems, Inc.

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All other brand or product names may be trademarks of their respective companies.

6

FactSet Consulting Services

North America - FactSet Research Systems Inc.

United States and Canada

+1.877.FACTSET

Europe – FactSet Limited

United Kingdom

0800.169.5954

Belgium

800.94108

France

0800.484.414

Germany

0800.200.0320

Ireland, Republic of

1800.409.937

Italy

800.510.858

Netherlands

0800.228.8024

Norway

800.30365

Spain

900.811.921

Sweden

0200.110.263

Switzerland

0800.881.720

European and Middle Eastern countries not listed above

+44.(0)20.7374.4445

Pacific Rim- FactSet Pacific Inc.

Japan Consulting Services (Japan and Korea)

0120.779.465 (Within Japan)

+81.3.6268.5200 (Outside Japan)

Hong Kong Consulting (Hong Kong, China, India, Malaysia,

Singapore, Sri Lanka, and Taiwan)

+852.2251.1833

Sydney Consulting Services

1800.33.28.33 (Within Australia)

+61.2.8223.0400 (Outside Australia)

E-mail Support

[email protected]

7

Preface

This document describes how to use the FactSet OnDemand DataFeed service that provides data and

calculations for client applications via a URL call to a web server at FactSet.

Intended Audience

The users should be familiar with the XML language and HTTPS protocol. This document will describe

the syntax needed for proper request formatting as well as the rules for processing responses. In addition,

complete code examples are included, which further illustrate the use of this service.

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1. Introduction

OnDemand provides synchronous access to data via the standard HTTPS protocol. Data can be returned

in several formats. You can make custom requests by changing the request URL to contain the

parameters you need.

1.1. FASTFetch Service

The OnDemand FASTFetch service from FactSet Research Systems provides data and calculations for

your applications via a URL call to a web server at FactSet. You receive data via pre-configured templates

that use FactSet FQL and Screening codes. Data is returned in various standard and customized formats,

such as XML or delimited text.

1.1.1. Factlets

The basic building block of FASTFetch is a FactSet Applet or “Factlet”. These Factlets are application

components that encapsulate business logic and data collection procedures. A Factlet can be a simple

data request or can invoke complex application logic. Factlets support multiple result formats that you

can choose from (e.g., XML, Delimited, Excel).

The key features of FASTFetch are:

• Adaptability – The flexibility of this model provides access to data beyond a simple security-based

requests, into requests for benchmark, aggregates, and economic data to name a few.

• Speed and Efficiency – FASTFetch is capable of cross referencing and dealing with time series for

a high amount of data in a relatively quick period of time. These can be simple requests of multiple

identifiers and codes over time or can be used for massive daily feeds, which would ordinarily take

days to run.

• Expanding Output Viewing – FASTFetch outputs can be changed via the orientation parameter of

the Factlets, allowing different ways of viewing data. Orientation is the term given to the rows and

columns retrieved by Factlets based on the entity, time, or item information. By changing the

orientation of these arguments, the data is returned in the way you desire.

2. HTTPS Requests and Responses

Receiving data via FASTFetch OnDemand is accomplished via simple URL requests that returns results

in flexible formats. A standard “name=value” pairing convention is used within the URL providing

consistency along with the power of customization.

The Factlet parameter specifies the stored procedure to generate the data. There are many standard

Factlets available.

2.1. OnDemand URL Syntax

A URL can be divided into the following arguments:

Example

https://datadirect.factset.com/services/FASTFetch?Factlet=ExtractFormu

laHistory&ids=fds

where:

Protocol

OnDemand information is transmitted via the HTTPS protocol for secure data delivery. In the above

example, the protocol argument is https.

Base URL

The base URL argument identifies the host web address and base path of a OnDemand service. In the

above example, the base URL is datadirect.factset.com/services.

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Service

The service argument identifies the OnDemand service being called. In the previous example, the service

argument is FASTFetch, which is a request to the FactSet OnDemand Service. Other OnDemand

services include, but are not limited to: DataFetch, DFSnapshot, Chart, and Research.

Factlet Name

The Factlet name argument identifies the name of the Factlet to call. Factlet name is a required argument;

if it is not defined, the URL will fail. In the above example, the Factlet name is

Factlet=ExtractFormulaHistory.

2.2. Optional Query String Parameters

Optional parameters can be supplied in the URL or in the POST part of the request, depending on the

length of their values (i.e. long lists of Ids are best sent with a POST).

Parameter

Description

ids

lists entity identifiers separated by commas

items

lists FactSet data items (e.g., P PRICE)

dates

lists the start date, end date, and frequency, separated by colons (:),

currency

specifies the currency the data is returned in, using a three-character ISO code

(e.g., ‘USD’ or ‘EUR’)

format

specifies the format of the data returned (e.g., “EXCEL”), default is XML

orientation

specifies the layout of the data returned (e.g., “EIT”), default is “None”

cutoff

specifies the maximum number of entities in a download, usually used with a

Factlet that returns a large universe

Ison

specifies the FQL value that extracts universe; e.g., ison_sp500 is entered as

ison=sp500 and

ISON_MSCI_WORLD(0,1) is written as ison=msci_world

isonParams

specifies ison codes that use parameters; e.g., ISON_MSCI_WORLD(0,1) is

written as isonParams=0,1

The optional query string specifies a list of service-specific parameters. The query string begins after the

Factlet name and contains a list of name=value pairs separated by ampersands (&).

2.2.1. Example URLs

The following examples explore how altering the URL will change the results returned for a given

FASTFetch call.

In the following FASTFetch example, company data is requested for the identifier FDS (FactSet) using the

ExtractFormulaHistory Factlet. The price at the end of the month between January and September of 2010

is requested. The data is oriented by equity, time, and item (i.e. price in this example).

https://datadirect.factset.com/services/fastfetch?factlet=ExtractFormulaHis

tory&ids=fds&dates=20100101:20100130&items=p_price&orientation=eti&format=x

ml

After providing authentication, data for the requested entities will be returned:

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This data can be returned in multiple formats, such as XML, PIPE, or CSV by changing the format

argument in the URL.

Special Characters in the URL

Notice the “%20” within the URL. This is a URL encoded space and is needed in most web browsers to

ensure that the URL is read correctly, and the data is returned in a proper manner. While some browsers

do support spaces in the URL, it is recommended to use “%20” in place of a space to avoid any data

retrieval issues.

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https://datadirect.factset.com/services/FastFetch?factlet=ExtractBenchmark

Detail&format=xml&ids=SP50&items=_SP_CLASS_GICS(0,,,%20%27%27SEC%27%27,%20

%27%27NAME%27%27)&dates=0

This is especially important when using certain formula libraries in your URL, some libraries have spaces

in their name, while other have underscores. Omitting the “%20” or the substitution of an underscore in

place of the “%20” can result in either a broken URL, or the retrieval of incorrect data so it is important

that the “%20” is used properly.

2.2.2. Format

The following formats are supported by FactSet OnDemand. Custom formats can be developed according

to an application’s specification. By default, format is set to return in XML.

Parameter

Description

XML

Extensible Markup Language

PIPE

Vertical bar delimited (“|”) rows and columns

CSV

Comma Separated Values in rows and columns with values appropriate for reading

into Microsoft Excel

HTML

HyperText Markup Language

2.2.3. Orientation

There are three main dimensions to the data returned by FASTFetch: entity, data item, and time. The

order of layout is controlled by the orientation parameter. The value should be set to some combination

of the letters, “E”, “I”, and “T”. For example, the “ETI” layout for a “PIPE” formatted file is shown below.

The first two dimensions appear in the first two columns and the last dimension is displayed along the

rows.

ETI Orientation with “PIPE” Format

Extract Data - Entity x Time X Item

Entity Id | Date | p_price | p_volume

Entity | Date | Double | Double

Id | Date | p_price | p_volume

IBM|3-Jan-2005|97.75|5301.4 IBM|4-Jan-2005|96.7|5711.

PG|3-Jan-2005|55.19|4858.5 PG|4-Jan-2005|54.5|5548.6

There is also a “none” orientation that places one value to an entry and labels the Data Items in their own

column. It is by default if no orientation is specified.

Sample None Orientation with “PIPE” Format

Extract Data

Entity Id | Date | Date Item_Value

Entity | Date | String | Variant

Id | Date | DataItem | Value

IBM | 1/3/2005 | p price|97.75

IBM | 1/3/2005 | p volume | 5301.4

For additional information, see Online Assistant page 14233.

12

3. FactSet Languages

FactSet stores all the available data in proprietary database structures on FactSet computers. This allows

FactSet to adjust the way data is stored, so that clients can access data as efficiently as possible.

Most datasets available on FactSet are stored in two different ways, so as to facilitate two different data

access methods. These two options use the FactSet Query Language (FQL) for timeseries requests and

the FactSet Screening Language (Screening) to efficiently extract data for a large universe of securities

as of a single date.

3.1. FactSet Screening Language

To facilitate efficient access to a data item of a single time period for a universe of securities, FactSet offers

an optimized cross-sectional data access method with the Screening Language. Given a data item, for

example EPS, and a time period. For example, Q4 2010, data for every entity in the specified universe can

be fetched using the Screening Language.

By default, the FactSet Screening Language does not allow iteration and therefore cannot be used to

return a time series of data with a single request code. To request data of a single historical date, it can

be specified either as an absolute or a relative date.

Note: Certain screening formulas are current only. If an option for a date argument is not available when

selecting a formula means that the formula does not accept a date reference.

3.2. FactSet Query Language

FQL is a proprietary data retrieval language used to access FactSet data.

The advantages of using FQL are:

• The ability to specify dates for any database using the same formats. With FQL, date formats are

flexible. You can use a number of consistent date formats (defined by FQL) for all databases which

makes using and combining data from different databases easier than ever.

• The ability to iterate items, formulas, and functions at any frequency. With FQL, you can iterate items,

formulas, and functions at any frequency. For example, you can request a series of weekly price to

earnings ratios.

• To request a time-series of data, a start date, end date and frequency needs to be specified. If a date

is not specified, data is returned from the most recent time period. The dates can be designated as

absolute dates or relative dates.

3.3. Date Format

The following sections explain how to define the Absolute and Relative dates.

3.3.1. Absolute Dates

FactSet Screening Language helps you define the absolute dates in the following manner:

Date Format

Description

Example

MM/DD/YYYY

Specific day

Note: DD/MM/YYYY is not a valid date

format

7/11/1999

MM/YYYY

Month-end

6/1999

YY/FQ or YYYY/FQ

Fiscal quarter-end

1999/1F, 2000/3F,

2001/2F

YY/CQ or YYYY/CQ

Calendar quarter-end

1999/1C, 00/3C,

2001/1C

YY or YYYY

Fiscal year-end

2000, 01, 1999

13

3.3.2. Relative Dates

Relative dates represent a date relative to the most recently updated period.

For example:

• 0 (zero) - represents the most recently updated period; The zero date is determined by the default

time period or the natural frequency of the data being requested. Zero (0) when used with monthly

data indicates the most recent month end.

• -1(Negative one) - represents the time period prior to the most recently updated. Negative one (-1)

when used with annual data indicates one fiscal year prior to the most recently updated fiscal year.

The following table lists the Relative Date Arguments and its descriptions.

Arguments

Description

D

0D is the most recent trading day, -1D is one trading day prior to most recent trading

date.

WE

0WE is the most recent trading weekend, -1AW is the one actual week (7 days) prior

to the most recent trading week.

W

0W is the last day of the most recent trading week (usually Friday), -1W is the last

trading day of the prior week.

AM

0AM is the most recent trading day, -1AM is the same day, one actual month prior to

most recent trading day.

M

0M is the last trading day of the most recent month, -1M is the last trading day of the

prior month.

AQ

0AQ is the most recent trading day, -1AQ is the same day 3 months prior to most

recent trading day.

Q

0Q is the last trading day of the company’s most recent fiscal quarter, -1Q is the last

day of the prior to most recent fiscal quarter.

CQ

0CQ is the last trading day of the most recent calendar quarter (March, June,

September, or December), -1CQ is the last trading day of the prior calendar quarter.

AY

0AY is the most recent trading day, -1AY is one actual year (365 days) prior to most

recent trading day.

Y

0Y is the last trading day of the company’s most recent fiscal year, -1Y is the last

trading day of the prior fiscal year.

CY

0CY is the last trading day of the most recent calendar year (the last trading day in

December), -1CY is the last trading day of the prior calendar year.

3.4. Understanding Rotated Databases

FactSet has two primary engines for retrieving data: Data Downloading and Universal Screening. The

Data Downloading engine is non-rotated and the Universal Screening engine is rotated.

Note: When you use Screening syntax to download data, the formula relies on the Screening engine;

therefore, it uses rotated data.

FactSet's databases containing company information are used in two different ways:

14

1. To generate reports or charts on data for a particular company.

Example:

Price History report - you may use this report to view High, Low, Close, and Volume information

for a single company, such as IBM.

2. To search through many companies' data in order to screen for companies or to generate aggregate

statistics on sets of companies.

Example:

Universal Screening application - you may use this application to find all companies with an EPS

greater than $2.

If only one database were available on FactSet, it would take too long to be able to accommodate both

of the above features in an efficient manner. FactSet needed to develop an intelligent method of laying

the data out on disk to make the "read operation" on the database as efficient as possible.

The solution was to have two copies of the database - one for each of the above desired features.

FactSet uses a non-rotated database for the single-company reports, and a rotated database for

Universal Screening and quantitative modeling applications, such as Alpha Testing.

FactSet first updates the single company database version. Next, a program runs to "rotate" the

database each night. The program reads through the single-company database (record by record) and

re-sorts the database by date to generate a rotated database file.

From a user's perspective, you are using the same database, only in different ways.

Example:

• Non-rotated database - The FactSet Daily Prices database is used in the Price History report.

All the data for IBM is consolidated in one part of the database, allowing FactSet to quickly read the

data from the disk and generate a report/chart, such as a price chart. (The data within the red box is

accessed in one "read" of the database, making the Price History report fast.)

• Rotated database - The FactSet Daily Prices database is used in the Universal Screening application.

In this database, the data is sorted by date and by type (basically, the non-rotated database is flipped

on its side). The data within the red box is accessed in one read. For example, in one read, you can

quickly get the high price for all companies in the database for 1/5/2001. If you used the non-rotated

database to perform this task, the process would take very long because every piece of data for each

company would need to be read.

Note: All databases created since 1994 (otherwise referred to as FDB), including OFDB databases, rotate

automatically.

15

3.5. OnDemand Factlet Requests

The following is a list of the Factlets available using OnDemand Web Service, MATLAB, R, Developer’s

Toolkit and SAS integrations. Not all Factlets are available in all integrations. The description for each

Factlet also highlights if the Factlet should be used with FQL or Screening syntax.

The Factlets should be chosen depending on the dataset required. There are general Factlets using either

actual screening or FQL codes as input (to find the correct code please use the FactSet Sidebar look-up

dialog) and specialized Factlets used for specific datasets.

3.5.1. Standard Factlets

The Standard Factlets below are used for Screening data, Economics data and FQL data. For the exact

input syntax, the FactSet Sidebar dialog box can be used.

3.5.2. Specialized Factlets

The specialized Factlets are developed for different content sets or specialized data structures. These

Factlets have been developed to simplify and standardize the data retrieval of more complex data

structures.

Factlet

FactSet syntax used

by Factlet

CorporateActionsDividends

Function is used for extracting stock dividend information.

FQL

CorporateActionsSplits

Function is used for extracting stock split information

FQL

EstimatesOnDemand

Function provides access to FactSet sourced company level estimates data.

The data is accessed through the following reports that are available with this

function: Actuals, BrokerDetail, BrokerSnapshot, Consensus, Guidance,

Surprise, Detailed Recommendations and Consensus Recommendations.

FQL

ExtractAlphaTestingSnapshot

Function provides access to data from AlphaTesting model results. Alpha

Testing is a tool available in the FactSet workstation used to assess the

relationship between one or more variables and subsequent returns over time.

A subscription to Alpha Testing in FactSet is necessary to extract this data in

the stat packages.

FQL

ExtractDataSnapshot

Function is used for extracting one or more items for a list of ids for 1 date,

both for equity or fixed income securities. Should be used to efficiently extract

data for a large universe of securities as of a single date.

The data can also be retrieved using a backtest date to avoid look-ahead bias

in the analysis. The backtest functionality is available to clients subscribing to

one of FactSet’s quantitative applications, such as Alpha Testing or Portfolio

Simulation.

Screening

ExtractEconData

Function provides access to a broad array of macroeconomic content,

interest rates and yields, country indices and various exchange rate

measures from both the FactSet Economics and the Standardized Economic

databases.

FQL

ExtractFormulaHistory

Function is used for extracting one or more items for one security, an index or

a list of securities over time.

FQL

Factlet

FactSet syntax

used by Factlet

16

Factlet

FactSet syntax used

by Factlet

ExtractBenchmarkDetail

Function is used for extracting multiple data items for a benchmark. Benchmark

data can be retrieved using other functions, such as with

ExtractFormulaHistory, but the ExtractBenchmarkDetail function allows a user

to retrieve a more comprehensive overview of the index constituent data,

without additional codes or calculations. In the default output, identifiers are

sorted in descending order by weight in the index and each row shows the

index id, company id, date, ticker, and weight. Additional items are displayed at

the end.

Screening

Note: The Extract-

BenchmarkDetail

function by default uses

Screening codes entered in

the Items argument of the

syntax. If using an FQL

code, enter an _ before the

FQL items code.

ExtractOFDBItem

Function provides access to a list of securities and multiple data items for a

range of dates uploaded into a single Open FactSet Database (OFDB).

Screening

Note: The Extract-

OFDBItem function by

default uses Screening.

FQL should be used when

using ids with spaces or

short positions, indicated in

the OFDB with an _S.

ExtractOFDBUniverse

Function provides access to a list of securities belonging to a single Open

FactSet Database (OFDB) file as of a single date.

FQL

ExtractScreenUniverse

Function used for extracting a list of Identifiers stored in a single FactSet

screen. In the FactSet workstation, a user can screen for securities based on

specified criteria and store the result using FactSet Universal Screening for

equity or debt securities.

Screening

ExtractOptionsSnapshot

Function is used for extracting options data for one or more conditions from the

FactSet-Options Derived Values database

FQL

ExtractSPARData

Function is used for displaying SPAR data for specified funds from databases

that includes S&P, Lipper, Morningstar, Russell, eVestment, Nelson,

Rogerscasey, and PSN. A subscription to SPAR in FactSet is necessary to be

able to extract this data in stat packages.

FQL

ExtractVectorFormula

ExtractVectorFormula function is used for extracting FactSet data that is stored

in a vector data format, where the data array does not have a predefined size

and is organized by the vector position. A vector can be thought of as a list that

has one dimension, a row of data. A vector position allows for a particular

element of the array to be accessed.

ExtractVectorFormula handles non-sequential data with support for matrix or

vector output. The nature of the data determines if the output is a matrix or

vector, it is not specified in the function to choose which format the data is

returned in. This type of data includes corresponding geographic or product

segment breakdowns for a company or detailed broker snapshot or history

estimates/analyst information.

FQL

17

Factlet

FactSet syntax used

by Factlet

LSD_Ownership

FactSet Ownership database collects global equity ownership data for

institutions, mutual fund portfolios, and insiders/stake holders. Detailed

ownership data can be extracted by company or by holder (institution, mutual

fund, and insider/stake). The LSD_Ownership function is used for extracting

one or more data items from the FactSet Ownership database for one or

multiple securities or holders.

FQL

4. ExtractDataSnapshot

The ExtractDataSnapshot function is used to efficiently extract data for multiple ids for a single date. This

function uses FactSet Screening Language. The FactSet Screening Language is a way to efficiently

extract data for a large universe of securities as of a single date.

The data can also be retrieved using a backtest date to avoid having look-ahead bias in the analysis. The

backtest functionality is available to clients who subscribe to FactSet’s quantitative applications in the

workstation, such as Alpha Testing and Portfolio Simulation.

The syntax for the ExtractDataSnapshot function is-

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractDataSnapshot&ids=&items=&date=&o

ptioanal_arguments.

where,

Optional arguments

curr

The currency in which the data is to be returned, using a string with the three-

character ISO code (e.g. ‘USD’ or ‘EUR’).

cal

Calendar setting, arguments include:

• FIVEDAY: Displays Monday through Friday, regardless of whether there

were trading holidays.

• FIVEDAYEOM: Displays Monday through Friday including a weekend date if it

falls on the last day of the month. When the month-end does not fall on a

weekend, the calendar will act just as the standard five-day calendar.

• SEVENDAY: Displays Monday through Sunday.

• AAM: For Exchange code, uses the calendar of a specific exchange,

represented by the exchange code. If there is no calendar available for a

specific exchange, the calendar will default to FIVEDAY.

universe

Screening expression to limit the universe

ison

Ison-codes can be used to limit the universe ISON_MSCI_WORLD(0,1) is written

as ‘ison’,’msci_world’,’isonParams’,’0,1’

isonParams

The arguments within brackets in the ison-code

data

variable name for the data returned

ids

CellString array with a list of one or multiple security identifiers

items

CellString array with a list of one or more FactSet data items in the Screening language

backtestDate

The backtest date for which the data is retrieved. If no date is specified, a backtest date will

not be set. The date can be entered using a relative date or absolute date.

18

OFDB

Universe is the constituents of an OFDB file, default directory is Client, if the OFDB

is stored in another location the path must be included

OFDBDate

Specific date for the constituents of the OFDB

universeGroup

Specifies what mode of screening to use. The default screening mode is Equity. For

Fund screening and Debt screening the universeGroup argument has to be used

with either FUND or DEBT respectively.

decimals

Positionally set according to the items in the selection, i.e. ‘decimals’,’,,3,4,3’

Example 1

This example uses the standard Screening syntax to retrieve the quarterly sales value from the FactSet

Fundamentals database for IBM using the Screening code FF_SALES (QTR,20110401,RF,EUR). The

data is retrieved in currency set to Euro, as of 04/01/2011. The RP default argument in the FactSet

Fundamentals database codes reflects that the data is the Latest Preliminary for the Reported Period

(alternative arguments could be for example RF, for the Latest Fully Reported Period, among others).

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractDataSnapshot&ids=ibm&items=FF_S

ALES(QTR,20110401,RP,EUR)&dates=20110401

Output

Example 2

In this example, instead of specifying securities in the ids field, as was done in Example 1 above, the

universe is specified as the constituents of an index using a so called ISON-code, here S&P 500. The

items specified - price and sales, are extracted for all constituents of the index. The syntax to extract the

price from the pricing database is using the code P_PRICE(20110401) and sales from the FactSet

Fundamentals database is using the code FF_SALES(QTR,20110401). The code to retrieve the current

constituents of S&P 500 is ISON_SP500.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractDataSnapshot&items=FF_SALES(QT

R,20110401),P_PRICE(20110401)&dates=20110401&ison=SP500

The universe is specified at the end of the code with the ison and sp500 arguments, which are broken

down from the actual Screening syntax for this universe which is using the code ISON_SP500.

19

Output

20

Example 3

In this example, the latest quarterly sales with the FactSet Fundamentals code FF_SALES in local

currency for the specified universe as the constituents of the MSCI EAFE index is retrieved. The

Screening code for this universe is ISON_MSCI_EAFE(0,1).

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractDataSnapshot&format=xml&items=FF

_SALES(QTR,0)&date=0&ison=msci_eafe&isonparams=0,1

Output

Note: The isonParams part of the code is used to specify the arguments within the brackets of the

ISON_xxx code, here 0,1.

21

Example 4

In this example, the latest closing price for the constituents of the MSCI USA index, using the pricing

database code P_PRICE is extracted. The Screening code for this Universe is

ISON_MSCI_COUNTRY(984000,0,CLOSE,OFF).

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractDataSnapshot&format=xml&items=P_

PRICE(0)&date=0&ison=msci_country&isonparams=984000,0,close,OFF

Output

22

Example 5

In this example, extract the decile ranking of the S&P 500 companies based on the most recently reported

quarterly earnings per share (EPS) using the FactSet Fundamentals formula FF_EPS. The FactSet

UDECILE function returns the decile rank (1-10) of a company against a specified universe when both

the company and the universe are evaluated for the same formula. The number 1 is the highest rank and

is assigned to the companies which fall within the top decile of the specified universe, in this case the

S&P 500.

URL:

https://datadirect.factXset.com/services/FastFetch?factlet=ExtractDataSnapshot&format=xml&items=

UDECILE(ISON_SP500,FF_EPS(QTR,0))&date=0&ison=SP500

Output

23

5. ExtractFormulaHistory

The ExtractFormulaHistory function is used for extracting one or more items for one security, an index or

a list of securities over time. The function is using the FactSet Query Language (FQL), which is a

proprietary data retrieval language used to access a time-series of FactSet data.

The syntax for the ExtractFormulaHistory function is-

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractFormulaHistory&ids=?&items=?&date

s=?&optional_arguments=?....

where,

data

variable name for the data returned

ids

CellString array with a list of one or multiple security identifiers

items

CellString array with a list of one or more FactSet data items in the Screening language

dates

Date range and frequency entered using actual or relative dates. A valid FactSet frequency

(e.g. ‘d’dates Alternate method of entering dates entered in start:end:freq format. (e.g.

‘20101215:20110115:d’)

Optional arguments

curr

The currency in which the data is to be returned, using a string with the three-character ISO

code (e.g. ‘USD’ or ‘EUR’).

cal

Calendar setting, arguments include:

• LOCAL: Uses the local trading calendar for each security. Local exchange holidays

will be skipped

• FIVEDAY: Displays Monday through Friday, regardless of whether there were

trading holidays.

• FIVEDAYEOM: Displays Monday through Friday including a weekend date if it falls

on the last day of the month. Where the month-end does not fall on a weekend, the

calendar will act just as the standard five-day calendar.

• SEVENDAY: Displays Monday through Sunday.

• AAM: For Exchange code uses the calendar of a specific exchange, represented

by the exchange code. If there is no calendar available for a specific exchange, the

calendar will default to FIVEDAY.

universe

Screening expression to limit the universe

ison

Ison-codes can be used to limit the universe ISON_MSCI_WORLD(0,1) is written as

‘ison’,’msci_world’,’isonParams’,’0,1’

isonParams

The arguments within brackets in the ison-code

OFDB

Universe is the constituents of an OFDB file, default directory is Client, if the OFDB is stored

in another location the path must be included

OFDBDate

Specific date for the constituents of the OFDB

decimals

Positionally set according to the items in the selection, ie ‘decimals’,’,,3,4,3’

dataType

The optional argument allows users to define a data type for a data item column that is NA

for the entire column. This option must be defined for every column/data item requested in

the command if it is used at all.

feelback

Setting to control data is not falling forward and display NAs instead of carrying forward

values, for those databases that do so (using ‘feelback’,’n’).

refresh

This will refresh the connection to FactSet servers to capture the latest database updates.

This only needs to be used when a refresh is necessary. It is not recommended to leave this

argument in every request made. To use this, the refresh argument should be paired with

the value “Y”.

24

Example 1

In this example extract the last 6 quarters EPS for Exxon Mobile (ticker XOM) using the FQL code

FG_EPS. The date argument is using relative rather than absolute dates. To specify relative dates, enter

the number of periods and a period code, such as D for days, W for weeks, or Q for quarters and Y for

years. When using relative dates, "0" refers to the most recent time period. Therefore, 0Q refers to the

most recent quarter end, while -1Q refers to two quarters ago.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractFormulaHistory&format=xml&ids=X

OM&items=FG_EPS(0Q,-5Q,Q)&dates=0Q:-5Q:Q

Output

25

Example 2

In this example, extract the last 6 quarters of pricing and sales data for Microsoft and IBM using the pricing

database with the FQL code P_PRICE and the FactSet Fundamentals database for sales data with the

FQL code FF_SALES. Both P_PRICE and FF_SALES in this example are used in the Items parameter.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractFormulaHistory&format=xml&ids=

MSFT,IBM&items=P_PRICE(-5,0,Q,USD),FF_SALES(QTR,-5,0,Q,,USD)&dates=-5:0:Q

Output

Note: To most efficiently ensure that that the dates for the different items (here price and sales) align

correctly with the dates field, the dates should be included both in the FQL code and in the dates

parameter as specified above.

Example 3

In this example, extract the price for Apple for the date range 12/31/1975 until 12/31/2001 on a monthly

frequency. Since there is no available price data for Apple starting in 1975, the data would be NA. When

using the ExtractFormulaHistory function the data type can be specified for treatment of NA’s, for example

as a double or integer.

26

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractFormulaHistory&format=xml&ids=AA

PL&items=P_PRICE(12/31/1989,12/31/2001,M)&dates=12/31/1989:12/31/2001:M&datatype=double

Output

Note: By default, the data type returned is determined by the first value of the items being returned. In

this case the p_price code returned as a character by default because the values for APPLE are NA (if

the request is made for just IBM with the same date range the p_price data is returned as a double since

the data is available for IBM). But with the addition of the ‘datatype’ optional argument, it is possible to

specify how the data is returned.

Example 4

In this example, retrieve the 60-month beta coefficient for Exxon Mobile relative to the S&P 500. The

FactSet BETA function measures a security or portfolio's volatility relative to an index. If a security has a

beta coefficient greater than one, it is considered more volatile. If a security has a beta coefficient of less

than one, its price can be expected to rise and fall more slowly. In the FQL syntax, the BETA function

returns the coefficient relative to an index and over any period of time that you specify.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractFormulaHistory&format=xml&ids=XO

M&items=BETA(%27SP50%27,-0,-59M,M)

27

Output

6. CorporateActionsDividends

The CorporateActionsDividends function is used for extracting stock dividend information.

The retrieved stock dividend information using the CorporateActionsDividends function includes special

dividends, which are defined as nonrecurring distribution of assets by a company to its shareholders in

the form of cash. Since it is unlikely to be repeated, it is often used in conjunction with a spinoff.

It also includes stock dividends, which are represented as forward stock splits, not regular cash

distributions.

The policy is, only actions affecting the pro-rata adjustment will be reflected. Because employee bonus

shares are not included in the pro-rata element announced by the company, the policy is to not include

adjustment for employee bonus shares as a part of the stock dividend amount.

The syntax for the CorporateActionsDividends function is:

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=CorporateActionsDividends&ids=&start=&en

d=&optional_arguments...

Where,

data

Variable name for the data returned

ids

Array with a list of one or more security identifiers.

start

Start date from which dividend data should be retrieved. Method of entering date is

in MM/DD/YYYY format.

end

End date for period during which dividend data should be retrieved. The end date

field is for entering a future date for which the dividend data is accessed. It can be

entered as a future date in MM/DD/YYYY format or as a number, e.g. 50, which

reflects 50 days from today which is set as the end date.

Note: When entering number of days, the maximum value that can be entered is

50.

28

Optional arguments

splitadj

Allows for split adjustment to be specified. This argument must be entered as:

'splitadj','9' to retrieve unadjusted dividends.

ngflag

Specify 'ngflag ','y ' to return a flag that indicate whether the dividend rate

returned is a net or gross. The output would be a G or N flag.

symbol

Argument allows for the CUSIP to be retrieved as the last column (by default

SecId is the first field that is retrieved when running a CorporateActionsDividends

function). This argument must be entered as 'symbol', 'y'.

cur

The optional currency argument to specify the currency in which the stock

dividend data is returned.

universe

Screening expression to limit the universe

secId

Currently, the stat packages display the ticker by default in the first column but

will now display whatever values are entered in the ids= argument. The secId=Y

parameter will now be used to display whatever is entered in the ids= argument.

summary

When ‘summary’ and ’Y’ is used as an argument, it will display a more detailed

view including dividend description and will group dividends paid at the same time

together. This is more common for Australian securities.

Example 1

In this example, extract the stock dividend information for Volkswagen from 1/1/2011 up to 1 day from

today.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=CorporateActionsDividends&format=xml&ids

=VOW-DE&start=1/1/2011&end=1

29

Output

30

Example 2

In this example, extract the stock dividend information for multiple securities – Ericson and Nokia from

1/1/1990 up to 50 days going forward from today.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=CorporateActionsDividends&format=xml&id

s=ERIC.B-SE,%20NOK1V-FI&start=1/1/1990&end=50

Output

31

Example 3

In this example, extract the stock dividend information for Coca-Cola over the last two years, retrieving

the unadjusted dividend.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=CorporateActionsDividends&format=xml

&ids=KO&start=-2AY&end=0&splitadj=9

Output

32

Example 4

In this example, extract for Vodafone the dividend information over the last 20 years that is flagged for a

dividend rate returned that is a net or gross marker.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=CorporateActionsDividends&format=xml&ids

=VOD-GB&start=0&end=-20Y&ngflag=y

Output

33

Example 5

In this example, the CUSIP is displayed with the result here for the dividends for the current constituents

of S&P 500 from 1/1/2000 to 12/31/2005.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=CorporateActionsDividends&format=xml&

start=1/1/2000&end=12/31/2005&symbol=y&universe=(ison_sp500=1)

Output

34

7. CorporateActionsSplits

The CorporateActionsSplits function is used for extracting stock split information.

Corporate Actions - FactSet Stock Split Methodology

The retrieved stock split information using the CorporateActionsSplits function is by ex-date.

The timing of adjustments to historical prices is based on regional settings. For more comprehensive

details regarding split rollover times by region, refer to Online Assistant page 14178.

The syntax for the CorporateActionsSplits function is:

URL:

https://datadirect.factset.com/services/FastFetch?factlet=CorporateActionsSplits&ids=&start=&end=

&optional_aruguments.....

where,

data

Variable name for the data returned

ids

Array with a list of one or more security identifiers.

start

Start date from which split data should be retrieved. Method of entering date is in

MM/DD/YYYY format.

end

End date for period during which dividend data should be retrieved. The end date

field is for entering a future date for which the split data is accessed. It can be

entered as a future date in MM/DD/YYYY format or as a number, e.g. 50, which

reflects 50 days from today which is set as the end date.

Note: When entering number of days, the maximum value that can be entered is

50.

Optional arguments

symbol

Argument allows for the CUSIP to be retrieved as the last column (by default

SecId is the first field that is retrieved when running a CorporateActionsSplits

function). This argument must be entered as 'symbol', 'y'.

universe

Screening expression to limit the universe

secId

Currently, the stat packages display the ticker by default in the first column but will

now display whatever values are entered in the ids= argument. The secId=Y

parameter will now be used to display whatever is entered in the ids= argument.

35

Example 1

In this example, extract the stock split information for Exxon Mobil from 1/1/1990 up to 1 day later from

today.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=CorporateActionsSplits&format=xml&ids=X

OM&start=1/1/1990&end=1

Output

Note: The retrieved items with this function are the split factor, the split ratio and any available split

comments.

36

Example 2

In this example, extract the stock split information for multiple securities – Ericson and Nokia from

1/1/1990 up to 50 days from today.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=CorporateActionsSplits&format=xml&ids=E

RIC.B-SE,%20NOK1V-FI&start=1/1/1990&end=50

Output

37

Example 3

In this example the CUSIP is displayed with the result, here for the splits for the current constituents of

S&P 500 from 1/1/1990 to 12/31/2012.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=CorporateActionsSplits&format=xml&start

=1/1/2000&end=12/31/2012&symbol=y&universe=(ison_sp500=1)

Output

38

8. ExtractBenchmarkDetail

The ExtractBenchmarkDetail function is used for extracting multiple data items for a benchmark.

Benchmark data can be retrieved using other functions such as ExtractFormulaHistory, but the

ExtractBenchmarkDetail function allows a user to retrieve a more comprehensive overview of the index

constituent data, without additional codes or calculations. In the default output, identifiers are sorted in

descending order by weight in the index and each row shows the index id, company id, date, ticker, and

weight. Additional items are displayed at the end.

Benchmark Data

FactSet clients have access to Equity and Fixed Income Benchmarks, which include Dow Jones, FTSE,

MSCI, Russell, S&P, Barclays, and BofA Merrill Lynch, among a number of others. Access to benchmarks

is based on client subscription to various benchmark providers.

In addition, FactSet Market Aggregates (FMA), combines data from FactSet Fundamentals, Estimates

and Prices to calculate ratios and per share values on an aggregate level. FMA comprises over 3,500

benchmarks including S&P, Russell, MSCI Global, FTSE, STOXX, TOPIX, and many local exchanges.

Benchmarks also include specific sector and industry level indices. This number is constantly expanding

based on client demand.

To request benchmark data as of a single date or as a time-series, dates can be designated as absolute

dates or relative dates. See section 3.3

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractBenchmarkDetail&ids=&items=&d

ates=&optional_arguments.....

where,

data

Variable name for the data returned

ids

Array with a list of one or more benchmark identifiers.

dates

One or more dates; Dates should be entered in start:end:freq format. (e.g.

'20101215:20110115:d')

items

One or more items in Screening syntax, if FQL syntax is required it may be used with an

underscore needs to be appended at the beginning of the code, i.e _P_PRICE

Optional arguments

cutoff

Number of constituents to display; default displays all instances

useBTD

To control the alignment of historical stitching following a merger the useBTD parameter

is used. When FactSet and a benchmark vendor make different choices in picking a

surviving entity symbols can be returned as a dummy ticker to be used as a placeholder.

To return the symbol as of the back test date 'useBTD','ON' should be used.

39

Example 1

In this example, the constituents of the S&P 500 is being extracted, the default columns will always be

available for this Factlet.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractBenchmarkDetail&format=xml&ids=S

P50

Output

40

Example 2

In this example, extract the top 10 holdings for the France CAC 40 index and display the companies’

securities price using the pricing database with the code P_PRICE and the company name using the

code PROPER_NAME.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractBenchmarkDetail&format=xml&ids=1

80454&items=P_PRICE,PROPER_NAME&date=0&cutoff=10

Output

41

Example 3

In this example, extract the price using the pricing database code P_PRICE for the CAC 40 constituents

for 5 days in January in 2011.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractBenchmarkDetail&format=xml&ids=1

80454&items=P_PRICE&dates=20110115:20110120:d

Output

Note: The Constituents are as of the date specified in the dates argument, i.e. if any constituents are

added or removed over the time period this will be reflected in the output.

42

Example 4

In this example the top 10 constituents for the fixed income index Barclays Capital EUR Corporate (1-5Y)

together with the names of the constituents which include the fixed income securities coupon rate and

maturity date. The index identifier is LHMN6732.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractBenchmarkDetail&format=xml&ids=l

hmn6732&items=lbc_name&universeGroup=debt&cutoff=10

Output

Note: Fixed income indices need to use the ‘universeGroup’,’debt’ argument.

43

Example 5

In this example, extract the S&P GICS classified sector names for the constituents of the S&P 500.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractBenchmarkDetail&format=xml&ids=

SP50&items=_SP_CLASS_GICS(0,,,%20%27%27SEC%27%27,%20%27%27NAME%27%27)&da

tes=0

Output

Note: The ExtractBenchmarkDetail Factlet, by default, works with Screening codes entered in the Items

argument of the syntax. If using an FQL code, enter an _ argument before the FQL items code, as

illustrated in this example using _SP_CLASS_GICS.

44

9. ExtractOFDBItem

The ExtractOFDBItem function provides access to a list of securities and multiple data items for a range

of dates uploaded into a single Open FactSet Database (OFDB).

Open FactSet Database (OFDB)

OFDB is a high-performance multi-dimensional database system used to securely store proprietary

numeric and textual data on FactSet. It is ideal for users who manage large portfolios or maintain

extensive historical proprietary databases. OFDB optimizes large, multi-dimensional databases, giving

FactSet users highly flexible, fast access to large volumes of complex data that can be used in many

different applications. OFDB is based upon Online Analytical Processing technology, which is the basis

for multi-dimensional databases.

The syntax for the ExtractOFDBItem function is:

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractOFDBItem&ofdb=&ids=&items=&d

ates=&optional_arguments.....

where,

data

Variable name for the data returned

OFDB

OFDB file from which the items should be used. The default directory is Client if

other locations are used the path must be specified i.e personal:MyOFDB

ids

Array with a list of securities to extract the data for. If left blank data for all

securities in the OFDB will be extracted.

dates

One or more dates; Dates should be entered in start:end:freq format. (e.g.

'20101215:20110115:d')

items

One or more items from the OFDB

Optional arguments

datesOnly

Displays only the dates that are in an OFDB with the parameter datesOnly’,’Y’

universe

Screening expression to limit the universe

feelback

If the feelback argument is not used, the returned data series will "feel back"

over NAs to find the last actual data point and carry this data forward over the

NAs. For the data not to carry forward, use 'feelback', 'N'. The data is then

returned as it is in the database.

fqlflag

Optional argument that is necessary because by default, the ExtractOFDBItem

factlet goes through screening, but when there are _S in the Identifier or

spaces between the identifiers, it is necessary to extract the data through FQL

to get the values. Need to specify 'fqlflag','y'.

cal

Calendar setting, arguments include:

FIVEDAY: Displays Monday through Friday, regardless of whether there were

trading holidays.

FIVEDAYEOM: Displays Monday through Friday including a weekend date if it

falls on the last day of the month. Where the month-end does not fall on a

weekend, the calendar will act just as the standard five-day calendar.

SEVENDAY: Displays Monday through Sunday.

AAM: For Exchange code uses the calendar of a specific exchange,

represented by the exchange code. If there is no calendar available for a

specific exchange, the calendar will default to FIVEDAY.

unsplit

Displays prices with split adjustments in unsplit form.

currency

The currency in which the data is to be returned, using a string with the three-

character ISO code (e.g. ‘USD’ or ‘EUR’). This will only work when “Currency

Mapping” is used in the OFDB.

45

Example 1

In this example, retrieve the price and shares data uploaded into the OFDB file titled MyPortfolio for

Microsoft as of 4 trading days ago, denoted with the date argument -3D.

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractOFDBItem&ofdb=MyPortfolio&ids

=MSFT&items=PRICE,SHARES&date=-3D

Output

46

Example 2

In this example, retrieve the uploaded shares and price data for the securities Microsoft and IBM OFDB

file titled MyOFDB saved in the Personal folder for a relative date range, starting 2 trading days ago and

going back 6 trading days ago.

URL

:https://datadirect.factset.com/services/FastFetch?Factlet=ExtractOFDBItem&ofdb=Personal:MyOF

DB&ids=MSFT,IBM&items=PRICE,SHARES&date=-1:-5D:D

Output

47

Example 3

In this example, retrieve the uploaded shares and price data for the securities IBM and GM from an OFDB

file titled MyOFDB for an absolute date range, starting January 2009 and ending December 2011 on a

daily frequency, with the calendar set to FIVEDAY.

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractOFDBItem&ofdb=Personal:MyOFD

B&ids=MSFT,IBM&items=PRICE,SHARES&date=20090101:20111231:D&cal=FIVEDAY

Output

Note: If during this date range the OFDB stores a value on a date that falls on a US holiday, by default

the value will be returned as an NA. However, by setting the calendar in this case to FIVEDAY this will

override the default and bring back the value.

48

Example 4

In this example, extract the universe of securities stored in the OFDB file titled Europe stored in the

subfolder Client:/Regions and their corresponding shares and price data for the last five days.

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractOFDBItem&ofdb=Client:/Regions/

MyOFDB&ids=MSFT,IBM&items=PRICE,SHARES&date=0:-5D:D

Output

49

Example 5

In this example the OFDB contains either symbols with spaces or short positions (symbols denoted with

_S) so the fqlFlag parameter must be used.

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractOFDBItem&ofdb=Personal:MyOF

DB&ids=MSFT,IBM&items=PRICE,SHARES&date=0:-5D:D&fqlFlag=Y

Output

50

10. ExtractScreenUniverse

The ExtractScreenUniverse function is used for extracting a list of CUSIPS stored in a single FactSet

screen. In the FactSet workstation, a user can screen for equity securities based on specified criteria and

store a list of companies using FactSet Universal Screening for equity or debt securities.

FactSet Universal Screening

Universal Screening in the FactSet workstation allows users to test investment strategies across all

databases simultaneously. It is possible to screen on a predefined investable universe or on tens of

thousands of companies worldwide using data items available on FactSet as the screening criteria.

For a more comprehensive overview of Universal Screening refer to Online Assistant page 20593.

The syntax for the ExtractScreenUniverse function is:

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractScreenUniverse&screen=&optiona

l_arguments....

where,

data

Variable name for the data returned

screen

Universal Screen for which the universe should be extracted. The default location is

Client: for any other location the path must be specified.

name

Optional parameter to display the name of the securities extracted. Specified as

'name', 'Y'.

All

Pulls all of the columns from a saved screen.

backtestDate

Ability to set a backtest date dynamically within the stat packages. This requires an

additional subscription to FactSet’s backtesting utilities.

removeColumns

Ability to hide specific columns from being displayed in the output. Requires the use of

the “All” parameter as well.

includeColumns

Ability to select specific columns to display in the output. Requires the use of the “All”

parameter as well.

Example 1

In this example, retrieve the securities stored in the screen titled MyScreen. The output displays the

CUSIPS for each security.

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractScreenUniverse&screen=MyScreen

Output

51

Example 2

In this example, retrieve all of the securities and parameters saved in the screen. Also, set a backtest

date to 6/30/2014.

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractScreenUniverse&screen=Personal:

MyScreen&all=Y&backtestdate=20140630

Output

52

Example 3

In this example, retrieve all of the securities returned by the screen, as well as only the first 3 parameters.

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractScreenUniverse&screen=Personal

:PORTFOLIO%20BUILDING&all=Y&includecolumns=1,3,5

Output

53

11. ExtractOptionsSnapshot

The ExtractOptionsSnapshot function is used for extracting options data for one or more conditions from

the FactSet-Options Derived Values database.

FactSet-Options Derived Values

The FactSet-Options derived Values provides access to expired options data such as historical pricing,

strike, expiration date, call or put, contract size, option type (equity, index), option style (American or

European), FactSet calculated Greeks (Delta, Theta, Vega, Rho, Gamma), and volatilities (Implied

Volatility, At-the-money Volatility).

The codes that are available for use in statistical packages provide access to option chain symbols for

both actively traded and expired options.

The syntax for the ExtractOFDBItem function is:

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractOptionsSnapshot&items=&date=&op

tional_arguments.....

where,

data

Variable name for the data returned

items

One or more items separated by a comma.

date

One or more dates; Dates should be entered in start:end:freq format. (e.g.

'20101215:20110115:d')

cond1/2/3

Screening condition with "=" or ">" or "<";

P_OPT_UNDERLYING_SECURITY=(default);P_OPT_ALL_VOLUME>

compval1/2/3

Value that meets cond1/2/3

54

Example 1

In this example a put or call flag, closing price, expiry date and delta is extracted for the options passing

the screening conditions that FactSet (FDS) is the underlying security and the expiration date is before

20190901.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractOptionsSnapshot&items=P_OPT_C

ALL_OR_PUT,P_OPT_CLOSE_PRICE,P_OPT_EXP_DATEN,P_OPT_DELTA&ids=FDS&P_OPT_E

XP_DATEN>=20190901

Output

55

12. ExtractSPARData

The ExtractSPARData function is used for displaying SPAR data for specified funds from databases that

includes S&P, Lipper, Morningstar, Russell, eVestment, Nelson, Rogerscasey, and PSN. A subscription

to SPAR in FactSet is necessary to be able to extract this data in the Statistical Package.

SPAR (Style, Performance, and Risk)

SPAR, FactSet’s returns-based portfolio analysis application, provides reports and charts that can be

used to determine the style, performance, risk, and peer group analysis of selected portfolios,

benchmarks, and competitor funds. SPAR incorporates the industry-standard methodology developed by

Nobel Laureate William Sharpe for determining the style of a portfolio.

Note: For more information on William Sharpe's methodology, refer to

www.stanford.edu/~wfsharpe/art/sa/sa.htm.

SPAR is similar to “Consumer Reports” magazine that ranks automobiles based on a list of criteria such

as safety, price, and gas mileage. SPAR does a similar role for money management firms that want to

sell their funds to their clients. The only difference is the criteria our clients use is annualized return,

standard deviation for risk, and peer rankings relative to the competition. There are literally thousands of

money management firms that individuals or institutions can select to manage their money. Typically, you

want to select a manager that has consistently beaten the benchmark while managing the proper amount

of risk.

The SPAR application thus allows users to analyze their portfolio’s returns against 20,000 equity and

fixed income benchmarks. Also, you can look at over 70 Modern Portfolio Theory risk statistics such as

beta, standard deviation, r-squared, alpha, and tracking error. SPAR allows you to determine the peer

rankings of your portfolio and the benchmark against the various mutual fund, institutional, and consultant

databases. The specific peer universe data available is S&P, Lipper, Morningstar, Russell, eVestment,

Nelson, Rogerscasey, and PSN.

URL :

https://datadirect.factset.com/services/FastFetch?factlet=ExtractSPARData&ids=&items=&date=&opti

onal_arguments....

where,

ids

CellString array array with a list of one or more benchmarks or funds.

items

CellString array with a list of one or more FactSet data items to display for the

selected benchmarks or funds

date

One or more dates; Dates should be entered in start:end:freq format. (e.g.

'20101215:20110115:d')

OFDB

OFDB file used to limit the universe

cal

Calendar setting, arguments include:

FIVEDAY: Displays Monday through Friday, regardless of whether there were

trading holidays.

SEVENDAY: Displays Monday through Sunday.

56

Example 1

In this example, for the two specified Morningstar funds retrieve the fund family name and the benchmark

name.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractSPARData&ids=MEUR:F0GBR04A

WX,MEUR:F000000GJF&items=SPAR_FUND_FAMILY,SPAR_MEUR_BM_NAME1

Output

Example 2

In this example, for the two specified Morningstar funds retrieve the International Securities Identification

Number (ISIN), the inception date, location of where the funds are domiciled and the management fee.

The management fee data is a percentage (%).

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractSPARData&ids=MEUR:F0GBR04A

WX,MEUR:F000000GJF&items=SPAR_MEUR_ISIN,%20SPAR_MEUR_INCEPTION_DATE,SPAR

_MEUR_DOMICILE,SPAR_MEUR_MGMTFEE

57

Output

58

Example 3

In this example, for the two specified Lipper TASS funds retrieve the company name of the fund, the

management fee, and location city of where the fund is based.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractSPARData&ids=LT:LT001929,LT:LT

073672&items=SPAR_LT_COMPANY_NAME,%20SPAR_LT_MGMNT_FEE,SPAR_LT_CITY

Output

59

Example 4

In this example, for the four specified Lipper US funds retrieve the category for these funds.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractSPARData&ids=LDMF:AMDXX,L

DMF:AIAGX,LDMF:ASMTX,LDMF:LS98372&items=SPAR_LIPPER_CATEGORY

Output

60

Example 5

In this example, instead of specifying the list of funds by using their ids, the universe is specified by an

OFDB file containing a list of funds. For those funds, extract the fund family name and the Morningstar

classified fund category.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractSPARData&items=SPAR_FUND_FA

MILY,SPAR_MEUR_CATEGORY&ofdb=personal:MyFUNDS

Output

61

13. ExtractVectorFormula

The ExtractVectorFormula function is used for extracting FactSet data that is stored in a vector data

format, where the data array does not have a predefined size and is organized by the vector position

(compared to much of FactSet data that is retrieved through FactSet OnDemand that is indexed by entity,

data item, and date, such as 5 years of sales history for a security). A vector can be thought of as a list

that has one dimension, a row of data. A vector position allows for a particular element of the array to be

accessed.

ExtractVectorFormula handles non-sequential data with support for matrix or vector output. The nature

of the data determines if the output is a matrix or vector, it is not specified in the function to choose which

format the data is returned in. This type of data includes corresponding geographic or product segment

breakdowns for a company or detailed broker snapshot or history estimates/analyst information.

For example, the FactSet Fundamentals business or geographic segment data for a specified data item

would be in a vector output given that IBM has 5 business segments whereas GE has 8 segments.

Alternatively, if requesting FactSet Mergers data, such as the current identifier of selected participant in

deals. The default output of this FactSet data is in a matrix format, because if there are multiple companies

playing for the sale role on the transaction, an array of data is returned. Therefore, when extracting this

data using the ExtractVectorFormula function, it will automatically retrieve it in a matrix format.

The syntax for the ExtractVectorFormula function is:

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractVectorFormula&ids=&items=&opti

onal_arguments....

where,

data

Variable name for the data returned

ids

CellString array with a list of one or multiple security identifiers

items

CellString array with a list of one or more FactSet data items in the FQL language

Optional arguments

universe

Screening expression to limit the universe

ison

Ison-codes can be used to limit the universe ISON_MSCI_WORLD(0,1) is written

as ‘ison’,’msci_world’,’isonParams’,’0,1’

isonParams

The arguments within brackets in the ison-code

OFDB

Universe is the constituents of an OFDB file, default directory is Client, if the

OFDB is stored in another location the path must be included

OFDBDate

Specific date for the constituents of the OFDB

combinedOutputTypes

Required argument when matrix and vector output formats are requested in the

same call.

Example 1

In this example, extract the business segment sales breakdown, with labels, as of the most recent fiscal

year end for IBM and GE using the FactSet Fundamentals database. The ExtractVectorFormula

function is used to extract this data because the output is a row of data, and it is not indexed by Id, data

item and date. Rather it is a list where IBM has 5 business segments and GE has 8 segments.

62

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractVectorFormula&ids=IBM&items=FF

_SEGMENT_RPT_DATA(ANN,0,,,,%27SALES%27,,BUS,%27SEG%27),FF_SEGMENT_RPT_LA

BELS(ANN,0,,,,,BUS,%27SEG%27)

Output

Example 2

In this example, extract the business segment sales data for the universe of securities stored in the

Open FactSet Database (OFDB) titled MyOFDB.

URL:

63

https://datadirect.factset.com/services/FastFetch?factlet=ExtractVectorFormula&ofdb=MyOFDB&ite

ms=FF_SEGMENT_RPT_LABELS(ANN,0,,,,,BUS,%27SEG%27)&new=y

Output

Example 3

In this example, extract the offer date (with the offer type being the follow-ons) from the FactSet New

Issues database based on the pricing date for security associate with the specified company identifier.

The specified universe is the constituents of the MSCI AC World Index. The code for this universe is

ISON_MSCI_REGION. In the output, the ID field brings back the CUSIPs of the companies in the index.

64

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractVectorFormula&items=FNI_OFFR_D

ATE_CO(%27FO%27,%27PRC%27,3,1)&ison=msci_region&isonparams=892400,0,close&new=y

Output

Example 4

In this example, extract from the FactSet Mergers database, the current identifier of the seller participant

on the deal for all of the most recent deals for IBM. If there are multiple companies playing for the same

role on the transaction, an array of data will be returned. For example, if there are two buyers on the deal,

two data points will return from this formula. The default output format of this FactSet data is in a matrix

format, and the ExtractVectorFormula function automatically retrieves this data as a matrix.

65

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractVectorFormula&ids=IBM&items=FM

G_ID_CO_CO(-1,%27S%27,%27TICKER%27,0)

Output

Example 5

In this example, extract the Northfield Correlation Matrix of Factors using the NIS US Fundamental Model.

URL:

66

https://datadirect.factset.com/services/FastFetch?factlet=ExtractVectorFormula&items=NIS_CORR

EL_MATRIX(%27NIS:FUND%27,0)&new=y

Output

14. ExtractEconData

The ExtractEconData function provides access to a broad array of macroeconomic content, interest rates

and yields, country indices and various exchange rate measures from both the FactSet Economics and

the Standardized Economic databases.

67

FactSet Economics Database

FactSet Economics is a database of primary-sourced information on the global economy. The database

includes specialty sources such as Eurostat, ICIS, IMF, LME, NYMEX, and OECD.

Each data series from the database has a mnemonic identifying that requested series. For example, GDP

published by the National Bureau of Statistics of China Seasonally Adjusted, will have an id or mnemonic

assigned to it that’s different from a mnemonic assigned for China’s GDP value that is not seasonally

adjusted. To find the mnemonic for a series, use the Series Lookup. For a demonstration covering how

to use the Series Lookup please refer to FactSet Online Assistant page 15694.

Standardized Economic Data

FactSet's Standardized Economic database includes a wide variety of commonly-used economic items

that are consistent across countries, letting you integrate country-level and company-level economic data

into FactSet reports and screens.

Different reporting standards across countries can present challenges to working with economic data. For

example, United States Real GDP may have a base year of 2005, whereas Canada's Real GDP may

have a base year of 2002. Items may also be reported in different currencies, making direct country-to-

country comparisons difficult.

FactSet's Standardized Economic Data solves these challenges by creating one set of commonly-used

economic items that are standardized and comparable across 95 countries plus five country aggregates.

The database's rebase year is 2005. Please refer to Online Assistant page 2022 for further information.

The syntax for the ExtractEconData function is:

URL:

https://datadirect.factset.com/services/factlet=FastFetch?&ids=&items=&optional_arguments......

where,

data

Variable name for the data returned

ids

CellString array with a list of the country identifiers when used for the standardized economic

database only, if other databases the ids argument should be left blank.

items

CellString array with a list of one or more FactSet data items from the Economic database

Optional arguments

date

One or more dates; Dates should be entered in start:end:freq format. (e.g.

'20101215:20110115:d')

NFB

NFB is the optional "no feel back" argument in FQL codes. If you do not use the NFB

argument, the returned data series will contain NAs where the data is not available (default is

NFB=1). If you want the data to "feel back" over NAs to find the last actual data point and carry

this data forward, set the NFB argument to either 0 or 2.

TSName

Used to display the time series value of the item in the label of the column where the data is

being displayed. Ie SPEC_ID_DATA('WTI-FDS:FG_PRICE',-121,-1,M) is displayed in the

column label as WTI-FDS. Specified as 'TSName','Y'

decimals

Positionally set according to the items in the selection, ie ‘decimals’,’,,3,4,3’

Example 1

This example retrieves industrial production data for the United States using the FactSet Economics

database, starting 122 months ago (denoted with -121) until two months ago (denoted with -1).

URL:

68

https://datadirect.factset.com/services/FastFetch?factlet=ExtractEconData&items=FDS_ECON_DA

TA(%27FRBIPSB50001%27,-121,-1,M,STEP,AVERAGE,1)

Output

Example 2

In this example, extract multiple data series from the IMFdatabase – population for the United States,

denoted with series IMF_IFS[11199Z_F], and for the UK, denoted with series IMF_IFS[11299Z_F].

69

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractEconData&items=IMF_IFS_DATA(

%2711199Z_F%27,31/12/2000,-1,y),IMF_IFS_DATA(%2711299Z_F%27,31/12/2000,-

1,y)&tsName=Y

Output

Note: The optional argument tsName here is used to display the mnemonics for each series.

Example 3

In this example, extract multiple data series from the Eurostat and FactSet Economics database sources.

The series are - Consumer Survey Consumer confidence indicator Balance for the Euro Zone, denoted

70

with series EURO_STAT[CONSCONFBAL@EUZ], and US Consumer Confidence, denoted with series

TCB_CCI[CCI].

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractEconData&items=EURO_STAT_DA

TA(%27CONSCONFBAL@EUZ%27,0,-11,M),TCB_CCI_DATA(%27CCI%27,0,-11,M)

Output

Example 4

71

This example retrieves the z-score of the Greece unemployment rate with FactSet Economics series

FDS_ECON[GRLM0347861]

URL:

https://datadirect.factset.com/services/FastFetch?factlet=ExtractEconData&items=ECON_EXPR_D

ATA(%27ZSCORE(FDS_ECON[GRLM0347861])%27,-10,0,M,STEP,AVERAGE)

Output

The FactSet ZSCORE function, expressed in units of the distribution's standard deviation calculates how

far and in what direction the specified series deviates from the distribution mean of data points in the

array.

72

15. ExtractAlphaTestingSnapshot

The ExtractAlphaTestingSnapshot function provides access to data from AlphaTesting model results.

Alpha Testing is a tool available in the FactSet workstation used to assess the relationship between one

or more variables and subsequent returns over time. A subscription to Alpha Testing in FactSet is

necessary to extract this data in the statistical package.

Alpha Testing

The Alpha Testing application in FactSet is used to build models specifying the factors to test, the

historical context, and customizing fractile assignments. After building and running a model, the data can

be viewed in the FactSet workstation in overview charts, an overall report or in detailed reports for any

specific fractile or time period.

For a more comprehensive overview of Alpha Testing refer to FactSet Online Assistant page 20828.

The syntax for the ExtractAlphaTestingSnapshot function is:

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractAlphaTestingSnapshot&useStat=&h

eaders=&model=&report=&items=&security=&date=&resultType=&sortOrder=&sortCol=&reportSettin

gName

Note: The ExtractAlphaTestingSnapshot function is used for extracting model results that use the Alpha

Testing codes AT3_RESULT_DATA or AT3_RESULT_STAT.

where,

data

Variable name for the data returned

useStat

Blank or N. Leave a blank in quotes (‘’) to extract the main report data and extracted

with the code AT3_RESULT_STAT. Specify (N) to extract the constituent data for the

report, with each security/period in each row and each data item result in each

column. This includes the raw, universe return and fractile data to display the raw

data available for the companies in the specified universe, compared to the data

available if outlier limitations are set within the model, along with the fractile values.

This company level data is extracted with the code AT3_RESULT_DATA and goes

into the aggregate calculation extracted with AT3_RESULT_STAT.

headers

Y or N. Specify if headers are required. If “Y” is specified, this will return ONLY

headers in the result. No other data will be retrieved.

model

String specifying a AT3 model. Format as client:model name.

report

Name of the report to be extracted, i.e. CONSITUENTS, FRACTILES or PERIODS

etc.

items

CellString specifying items (headers) or column numbers, ALL will return all items in

report.

security

A single security can be specified.

date

A single date can be specified.

resultType

M or S. Main or Summary data respectively. Defaults to M.

sortOrder

A or D. Displays data in either Ascending or descending order.

sortCol

Column from which to sort the data

reportSettingName

String specifying name of the report setting or template.

73

Example 1

In this example, extract column 6 and 7 from the Constituents report sorted by column 6 of the Alpha

Testing model titled Calculation Example Model.

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractAlphaTestingSnapshot&useStat=N

&headers=N&model=Factset:Calculation%20Example%20Model&report=CONSTITUENTS&items=

6,7&sortOrder=D&sortCol=6

Output

74

Example 2

In this example, extract all of the columns of data from the Constituents report of the Alpha Testing model

titled Calculation Example Model.

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractAlphaTestingSnapshot&useStat=N&

headers=N&model=Factset:Calculation%20Example%20Model&report=CONSTITUENTS&items=AL

L

Output

75

Example 3

In this example, extract specifically the columns titled Weight and Market Capitalization from the

Constituents report of the Alpha Testing model titled Calculation Example Model.

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractAlphaTestingSnapshot&useStat=N&

headers=N&model=Factset:Calculation%20Example%20Model&report=CONSTITUENTS&items=We

ight,Market%20Capitalization

Output

76

Example 4

In this example data from the Constituents report for the company Biacore International (ID: 08865810)

of the Alpha Testing model titled Calculation Example Model is extracted.

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractAlphaTestingSnapshot&useStat=N&

headers=N&model=Factset:Calculation%20Example%20Model&report=CONSTITUENTS&items=all

&security=08865810

Output

77

Example 5

In this example summary data for columns 5 and 7 from the Periods report of the Alpha Testing model

titled Calculation Example Model is extracted.

URL:

https://datadirect.factset.com/services/FastFetch?Factlet=ExtractAlphaTestingSnapshot&headers=N

&model=Factset:Calculation%20Example%20Model&report=PERIODS&items=1,2,7&resulttype=S

Output

78

16. LSD_Ownership

The FactSet Ownership database collects global equity ownership data for institutions, mutual fund

portfolios, and insiders/stake holders. Detailed ownership data can be extracted by company or by holder

(institution, mutual fund, and insider/stake). The LSD_Ownership function is used in for extracting one or

more data items from the FactSet Ownership database for one or multiple securities or holders.

FactSet Ownership Database

The FactSet Ownership database provides detailed share ownership data including shares held, position

change, market value adjusted for daily pricing and corporate actions, percent of both portfolio and shares

outstanding, source, metro region, state, style, and turnover. For a more comprehensive overview of the

FactSet Ownership database and data collection methodology, refer to Online Assistant page 17615.

For details on the Request Code syntax for ownership code see Online Assistant page 11728.

The syntax for the FDS.LSD_Ownership function is:

URL:

https://datadirect.factset.com/services/FastFetch?factlet=LSD_ownership&ids=&items=&Optional_arg

uments.....

where,

data

Variable name for the data returned

ids

CellString array with one or more identifiers for securities or holders.

items

CellString array with a list of one or more FactSet data items from the FactSet

Ownership Database

Optional arguments

combinedOutputTypes

Required argument when matrix and vector output formats are requested in the

same call.

79

Example 1

In this example, extract the names in English of the top 3 institutional (signified by the F in the request

code) holders (signified by the H in the request code) for Apple using the code LSD_NAME_TOP_HLDR.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=LSD_ownership&ids=AAPL-

US&items=OS_TOP_HLDR_NAME(3,0D,,MTD,,F,SEC,%27EN%27)

Output

80

Example 2

In this example, extract the names of all (signified by the -1 in the first position of the code) of the

institutional holders for IBM and GE using the code LSD_NAME_TOP_HLDR and the report date of the

institutional holders ownership using the code LSD_RD_TOP_HLDR.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=LSD_ownership&ids=IBM,GE&items=OS_T

OP_HLDR_NAME(ALL,0D,,MTD,,F,SEC,%27EN%27),OS_TOP_HLDR_RDATE(ALL,0D,,MTD,,F,%

27DATE%27)

Output

81

Example 3

It is also possible that instead of extracting the holders to extract the holdings as in this example where

the top 10 holdings for Fidelity Management and Research (identifier: F16925) is extracted with the

security identifiers, name and the percent position.

URL:

https://datadirect.factset.com/services/FastFetch?factlet=LSD_ownership&ids=F16925&items=OS

_TOP_HLDG_ID(10,0D,,MTD,,ALL),OS_TOP_HLDG_NAME(10,0D,,MTD,,ALL),OS_TOP_HLDG

_PCTOS(10,0D,,MTD,,ALL)

Output

82

17. UploadToOFDB

The UploadToOFDB functionality allows clients to upload data into an OFDB file stored in Data Central

in the FactSet workstation.

OFDB, which stands for Open FactSet Database, is a high-performance multi-dimensional database

system used to securely store proprietary numeric and textual data on FactSet. OFDB is ideal for users

who manage large portfolios or maintain extensive historical proprietary databases. OFDB optimizes

large, multi-dimensional databases, giving FactSet users highly flexible, fast access to large volumes of

complex data that can be used in many different applications.

Note: The optimal use of the UploadToOFDB functionality from MATLAB or R is for ad-hoc and smaller

scale data uploads and would not replace a client’s needs for FTP processes or production services, for

larger scale or holdings uploads into FactSet.

Requirements for UploadToOFDB

The following are the necessary requirements to upload data into an OFDB:

• Data set must have at least ID, Date and Items field

• Fields uploaded can be iterated of any frequency or non-iterated

• Date types can be High Precision, Integer, or Text

• MATLAB - Dates need to be uploaded as integers in YYYYMMDD format for MATLAB or a MATLAB

native date format

• R – Dates can be uploaded as yyyymmdd and mm/dd/yyyy formats.

• Data Central subscription in the FactSet workstation is necessary:

o Basic data storage access available to all clients with a premium FactSet workstation in Data

Central is 1GB of storage space. Additional data storage is available and should be discussed

with a FactSet sales representative.

o If attempting to upload data to a full OFDB file and thus exceeding data storage space access,

there will be an error message, "Client Data Space is Full", after running an upload from

MATLAB/R.

• FactSet does not need to be launched when uploading data into an OFDB.

17.1. Creating a New OFDB

The following details are regarding the behavior of an OFDB file that is created through

UploadToOFDB:

• If the OFDB does not already exist, it will be created.

• OFDBs created by UploadToOFDB have all fields iterated with Daily Frequency and data type High

Precision for numbers and Text(32) for strings.

• OFDB schemas define the database. If another schema is required for the OFDB file, it should first

be created in Data Central. For more details regarding creating or editing schemas refer to Online

Assistant page 11502.

• Once an OFDB is created, no changes to the schema can be made through the UploadToOFDB

functionality. A new OFDB would need to be created to make the necessary changes.

17.2. Modifying an Existing OFDB

The following details are regarding modifying an OFDB file through UploadToOFDB, when adding

additional dates or values to that file:

83

• Data for an additional date can be appended for existing sssecurities in an OFDB file.

• Data for additional securities can be appended to an OFDB file for the existing dates in the file or for

a new date range.

• The headers of the data uploaded must match the existing column names.

• New Data items cannot be appended to an existing OFDB through the UploadtoOFDB functionality.

UploadToOFDB Syntax

Before uploading data into an OFDB file, it is necessary to first create a structure, similar to the structure

of the results returned by a factlet request.

The syntax for the UploadToOFDB functionality is:

URL:

https://datadirect.factset.com/services/fastfetch?factlet=uploadtoofdb&ofdb=&columnnames=&colum

ntypes=&data=......

where,

OFDB

The name of the OFDB file to which the

data is getting uploaded (default directory is

Personal, for other locations the path must

be specified).

data

The Data structure that is uploaded to the

OFDB.

columnName

Names of the columns

columnTypes

Data Structure of the column

84

18. EstimatesOnDemand

The EstimatesOnDemand function provides access to FactSet sourced company level estimates data.

The data is accessed through the following reports that are available with this function: Actuals, Broker

Detail, Broker Snapshot, Consensus, Guidance, Surprise, Consensus Recommendations, Detailed

Recommendations and Broker Coverage.

FactSet Estimates

FactSet Estimates provides consensus- and detail-level estimates and statistics from leading investment

banks and research firms. With over 780 contributing brokers globally, FactSet Estimates covers more

than 16,300 active global companies and 100 data items. Categories of data include sector specific items,

commodity estimates, EPS, DPS, guidance and more. Global scope of companies covered is

approximately 31% from North America, 27% from Europe, and 35% from Asia. Historical information is

available from 1997 for European companies and 2000 for companies in the Americas and Asia. A

subscription to the FactSet Estimates database is necessary to be able to extract this data.

The manner in which contributed content is displayed and available on FactSet for individual users and

user groups is ultimately determined by the contributing partner.Several of FactSet brokerage partners

have additional restrictions on their data. Clients can request that the broker allow greater entitlements

and/or greater access to their supplied data on FactSet. Please contact your FactSet representative for

additional details.

For more information regarding thesss FactSet Estimates database refer to Online Assistant page 13369.

For a list of active brokers available in FactSet Estimates refer to Online Assistant page 14706.

The syntax for the EstimatesOnDemand function is:

URL:

https://datadirect.factset.com/services/fastfetch?factlet=EstimatesOnDemand&report=&ids=&items=&

startdate&optional_arguments.....

Output

data

variable name for the data returned

ids

CellString array with a list of one or multiple security identifiers

items

CellString array with a list of one or more FactSet data items from the FactSet Estimates

database (e.g., EPS, Sales, Net Debt).

Note: Table 1 in Appendix has a comprehensive list of items for which estimates are

available using this function.

report

Allows specification of the types of estimates report through which the data is retrieved.

The available reports as Actuals, BrokerDetail, BrokerSnapshot, Consensus, Guidance,

Surprise, Consensus Recommendations, Detailed Recommendations, and Broker

Coverage.

startDate

The start date as of which the estimate data is retrieved.

Optional arguments

end

The end date as of which the estimate data is retrieved

freq

The frequency of which the estimate data is retrieved

fiscalPeriod

The fiscal period for the estimate item. The option is available of looking at historical,

current, or future fiscal periods. The fiscal period can be specified using relative dates. The

arguments entered as relative dates represent a date relative to the most recently updated

period. For example, 0 (zero) represents the most recently reported period; -1 represents

the time period prior to the most recently reported period. Arguments entered

can be -1, 0, 1, 2, etc.

periodType

The argument can be entered as “annual”, “quarterly”, or “semi”, depending on the type

of estimates data request. Not all equities have estimates for all period types.

85

fields

Specification of a select number of fields to extract.

Note: Each section provides a detailed list of the output fields associated with each

FactSet Estimates report.

timeStamp

Display the publication time associated with the publication date. The argument would be

set up as: 'timestamp', 'y' and it can be used with an actuals report.

reportDate

Display report date. The argument would be set up as: 'reportDate', 'y' and can be used

with the Broker Detail report.

previousDates

Used with the Consensus report and refers to previous date as of which estimates can

be retrieved and compared to the estimates retrieved as of the date argument. For

example, if EPS estimates are displayed as of now, allows clients to compare the EPS

estimates as of

i.e. 30 days ago.

display

Used with the Broker Detail report. If utilizing HISTO for the historical look an ‘end date’

argument must be entered. If utilizing the SNAP mode, an ‘end date’ parameter is not

needed unless looking for the current consensus less than 100 days old. Otherwise

SNAP

will bring back the current consensus as of the last 100 days.

statistic

Used with the Surprise report. There are a number of different statistics that the client

can bring back using the Surprise Report. They have the ability to specify which one they

prefer. The list includes: Mean, Median (MED), High Estimate (HIGH), Low Estimate

(LOW),

Sigma and Standard Deviation (STDDEV).

offset1/offset2

Used with the Surprise report. This parameter is to change the number of days used

before and after the report date to calculate price impact. The argument would be set up

as: 'offset1', 'offset2'.

currency

Allows all values to be changed to the specified currency. By default, the currency is the

value of the security.

meanText

To display the Rating Name. The argument would be set up as: 'meanText', 'y' and can

be

used with the Consensus Recommendation report.

estCurrency

In cases where the security’s local currency does not match the Currency of the

estimates the argument 'estCurrency', 'Y' can be used, this changes the currency field to

display the

Estimate Currency. Also, the field heading changes to EST_CURRENCY.

showExcluded

Available for BrokerDetail and BrokerSnapshot, specifying this to N will only display the

broker estimates that are included in the consensus; default is to show all values.

universe

Screening expression to limit the universe

ison

Ison-codes can be used to limit the universe ISON_MSCI_WORLD(0,1) is written as

'ison',

'msci_world', 'isonParams', '0,1'

isonParams

The arguments within brackets in the ison-code

OFDB

Universe is the constituents of an OFDB file, default directory is Client, if the OFDB is

stored in another location the path must be included

OFDBDate

Specific date for the constituents of the OFDB

cal

Calendar setting, arguments include:

FIVEDAY: Displays Monday through Friday, regardless of whether there were trading

holidays.

SEVENDAY: Displays Monday through Sunday.

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18.1. Estimates Report - Actuals

The FactSet Estimates Actuals report provides access to the income statement, balance sheet, cash flow

statement and per share data for all companies covered by FactSet Estimates; as well as the median

value of the post-event consensus, known as the Broker Actual.

The data extracted by this report is accessible by using other functions such as ExtractFormulaHistory

and ExtractDataSnapshot, but the value added of this function is that the default output includes a more

comprehensive overview of the estimate actuals value, date and a flag explaining from where the actuals

value is extracted. This default output would entail making multiple requests using the other functions.

Actuals Methodology

Estimates are data points representing information about a future period: FY1, or FQ1, and beyond.

Actuals are data points representing information about the past: FY0, FQ0, or earlier.

An "Actuals" can have two forms:

• The value collected directly from the company’s income statement, balance sheet, cash flow

statement, known as the Actual.

• The median value of the post-event consensus, known as the Broker Actual.

o Mean can be used to calculate Broker Actual if desired.

o The Broker Actual is the default value for the European zone, even if an Actual is present.

FactSet Estimates actuals data is collected through a variety of channels, but the primary source is

financial statements published by the company. For the U.S., European, and Japanese sources

mentioned below, FactSet collects earnings announcements as soon as the data is made available to

these news services. Depending on local regulations, this can be anywhere from one to six months after

the end of the fiscal period.

These sources include:

For U.S. Companies:

• CallStreet Transcripts

• PR Newswire

• Business Wire

• CCN Matthews

• GlobeNewswire

• Market Wire

• CallStreet

For European Companies:

• Financial Express Company Announcements

• Europe PR Newswire

• Hugin Southern Europe

• Hugin

• Europe Business Wire for Japanese Companies:

• TDNet

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Data Fields extracted with the FactSet Estimates Actuals Report

The following table provides a detailed description of each of the 9 data fields that are by default retrieved

when using the FactSet Estimates Actuals Report.

Field Name

Description

SecId

The security identifier.

CURRENCY

The currency in which the selected estimate actual item is displayed.

FE_ITEM

The estimates actual item that is being retrieved, i.e. EPS.

FE_PER_REL

The relative fiscal period that is specified in the syntax. For example, when the

syntax specified fiscal period=1 and period type=annual, the data is retrieved for the

current unreported fiscal year for the company. This field retrieves a 1 for this

example since the fiscal period argument is 1. If the argument is for the current

reported fiscal year or quarter it would be FY0 or FQ0, respectively.

FE_REPORT_FY

The actual report date.

PUBDATE

The date when the company actually release their data.

Date

The period ending date.

FE_ACTUAL

The actual value.

FE_ACTUAL_FLAG

The Flag for the actual report type being retrieved.

+ 1 is returned if an actual is available outside of Europe (U.S., Canada, Latin

America, Asia/Pacific, and Australia).

Note: This is not a broker actual.

+ 2 is returned if a European actual is available.

Note: This is not a broker actual.

+ 3 is returned if the data is a broker actual (consensus coverage).

Estimates Report – Broker Detail

The BrokerDetail report provides access to detail level broker estimates from the FactSet Estimates

database. The data extracted by this report is accessible by using other functions such as

ExtractVectorFormula, but the value added of this function is that the default output includes more

comprehensive broker detail information in terms of the Brokers, Analysts and the change from their

historical estimates.

Broker Detail Methodology

The methodology used with the FactSet Estimates database is to group consensus estimates classes

into estimate groups, according to the different accounting methodologies used by various brokers. The

default consensus (class 0) regroups estimates according to FactSet Estimates methodology.

The goal of FactSet Estimates consensus classes is to identify and exclude brokers that use a different

methodology from the default methodology used by FactSet Estimates.

A consensus estimate is calculated for one class at a time because creating an average across different

classes can be misleading. FactSet Estimates provides a more meaningful consensus estimate figure

through the consensus class functionality.

For example, in the insurance sector, some brokers make an estimate based on gross premium and

others on net premium. If the FactSet Estimates methodology uses net premium as a default, then the

estimates of the brokers who use gross premium will belong to a new class of consensus which will be

different from the default class.

88

Data Fields extracted with the FactSet Estimates BrokerDetail Report

The following table provides a detailed description of each of the 18 data fields that are by default retrieved

when using the FactSet Estimates Broker Detail report.

Field Name

Description

SecId

The security identifier.

FE_FP_END

The date corresponding to the fiscal period type that is entered. For example, if

the arguments entered in the syntax are fiscal period=1 and period type=annual,

the relative date is FY1 which is the current unreported fiscal year for the

company.

CURRENCY

The currency in which the selected estimate item is displayed.

FE_ITEM

The estimates item that is being retrieved, i.e. EPS.

FE_PER_REL

The relative fiscal period that is specified in the syntax. For example, when the

syntax specified fiscal period=1 and period type=annual, the data is retrieved for

the current unreported fiscal year for the company. This field retrieves a 1 for this

example since the fiscal period argument is 1.

Date

The research date for the estimate item. This corresponds to the date of the

report issued by a broker. Whenever a broker sends a new estimate or opinion, it

is considered a research date. It reflects the date indicated in the actual report

issued by the broker, not the date FactSet received it.

FE_BROKER

The FactSet Estimates Broker code. For a list of active brokers available in

FactSet Estimates and their corresponding codes refer to Online Assistant page

14706.

FE_BROKERNAME

The Broker Name, i.e. Goldman Sachs. See Online Assistant page 14706 for a

full list.

FE_ANALYST

The code for the analyst. The code is based on a FactSet people map and

allows brokers to control readership entitlements. See Online Assistant page

14706 for a full list of Broker codes

FE_ANALYSTNAME

The name of the Analyst making providing the estimate.

ENTRY_DATETIME

The entry date of the estimate.

FE_ESTIMATE

The detail estimate history from contributing brokers over specified date range

for the specified period (i.e. EPS for FY1).

OTHER_CC

Consensus Class that pertains to a particular estimate. The details of this

methodology described in section 2 above.

FE_SECTION

Indicates if according to the default FactSet Estimates consensus methodology

the broker is included or excluded from the calculation.

FE_STATUS

Displays exclusion information. Explains the reason for the exclusion (i.e.

Dropping Coverage).

FE_EST_REV_VAL

The previous estimate value from the same analyst, for the same fiscal period.

FE_EST_REV_VAL_ARROW

Retrieves a -1, 0, 1 or NA to indicate the direction of the estimate change from

the analyst. A -1 indicates that the latest estimate value retrieved with

FE_ESTIMATE is lower than the value retrieved with the previous estimate,

retrieved with the field FE_EST_REV_VAL. A 0 indicates that there has been no

change in the estimate. A 1 indicates that the latest estimate is higher than the

previous value from the same analyst. An NA indicates that there was no

previous value from that analyst for this security.

FE_EST_REV_VAL_DATE

Retrieves the research date of the previous estimate value that corresponds to

FE_EST_REV_VAL

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18.2. Estimates Report – Broker Snapshot

The Broker Snapshot function provides access to a historical snapshot of detail level broker estimates

from the FactSet Estimates database. The difference between the FactSet Estimates BrokerSnapshot

and the FactSet Estimates BrokerDetail reports is that the BrokerSnapshot provides a snapshot only and

does not accept a date range, but the snapshot is an annual or quarterly roll argument to look at historical

estimates. Estimates on a rolling basis return data for the current unreported fiscal year or quarter as of

the date entered.

Data Fields extracted with the FactSet Estimates Broker Snapshot Report

The following table provides a detailed description of each of the 18 data fields that are by default retrieved

when using the Broker Snapshot report.

Field Name

Description

SecId

The security identifier.

FE_FP_END

The date corresponding to the fiscal period type that is entered. For example, if the

arguments entered in the syntax are fiscal period=1 and period type=annual, the

relative date is FY1 which is the current unreported fiscal year for the company.

CURRENCY

The currency in which the selected estimate item is displayed.

FE_ITEM

The estimates item that is being retrieved, i.e. EPS.

FE_PER_REL

The relative fiscal period that is specified in the syntax. For example, when the syntax

specified fiscal period=1 and period type=annual, the data is retrieved for the current

unreported fiscal year for the company. This field retrieves a 1 for this example since

the fiscal period argument is 1.

Date

The research date for the estimate item. This corresponds to the date of the report

issued by a broker. Whenever a broker sends a new estimate or opinion, it is

considered a research date. It reflects the date indicated in the actual report issued by

the broker, not the date FactSet received it.

FE_BROKER

The FactSet Estimates Broker code. For a list of active brokers available in FactSet

Estimates and their corresponding codes refer to Online Assistant page 14706.

FE_BROKERNAME

The Broker Name, i.e. Goldman Sachs.

FE_ANALYST

The code for the analyst. The code is based on a FactSet people map and allows

brokers to control readership entitlements.

FE_ANALYSTNAME

The name of the Analyst making providing the estimate.

ENTRY_DATETIME

The entry date of the estimate.

FE_ESTIMATE

The detail estimate history from contributing brokers over specified date range for the

specified period (i.e. EPS for FY1).

OTHER_CC

Consensus Class that pertains to a particular estimate. The details of this methodology

described in section 2 above.

FE_SECTION

Indicates if according to the default FactSet Estimates consensus methodology the

broker is included or excluded from the calculation.

FE_STATUS

Displays exclusion information. Explains the reason for the exclusion (i.e. Dropping

Coverage).

FE_EST_REV_VAL

The previous estimate value from the same analyst, for the same fiscal period.

FE_EST_REV_VAL_AR

ROW

Retrieves a -1, 0, 1 or NA to indicate the direction of the estimate change from the

analyst. A -1 indicates that the latest estimate value retrieved with FE_ESTIMATE is

lower than the value retrieved with the previous estimate, retrieved with the field

FE_EST_REV_VAL. A 0 indicates that there has been no change in the estimate. A 1

indicates that the latest estimate is higher than the previous value from the same

analyst. An NA indicates that there was no previous value from that analyst for this

security.

FE_EST_REV_VAL_DA

TE

Retrieves the research date of the previous estimate value that corresponds to

FE_EST_REV_VAL.

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18.3. Estimates Report – Consensus

The Consensus report provides access to consensus level estimates from the FactSet Estimates

databaseThe data extracted by this report is accessible by using other functions such as

ExtractFormulaHistory and ExtractDataSnapshot, but the value added of this function is that the

defaultoutput includes more comprehensive consensus information in terms of the mean, median, high,

low and standard deviation of estimates. This default output would entail making multiple requests using

the other functions.

Consensus Methodology

The methodology used with the FactSet Estimates database is to group consensus estimates classes

into estimate groups, according to the different accounting methodologies used by various brokers. The

default consensus (class 0) regroups estimates according to FactSet Estimates methodology. The goal

of FactSet Estimates consensus classes is to identify and exclude brokers that use a different

methodology from the default methodology used by FactSet Estimates.

A consensus estimate is calculated for one class at a time because creating an average across different

classes can be misleading. FactSet Estimates provides a more meaningful consensus estimate figure

through the consensus class functionality. For example, in the insurance sector, some brokers make an

estimate based on gross premium and others on net premium.

If the FactSet Estimates methodology uses net premium as a default, then the estimates of the brokers

who use gross premium will belong to a new class of consensus which will be different from the default

class.

Broker estimates can be received and processed in a multitude of formats of the brokers choosing. The

main two types of formats are manual contribution and automatic contribution. FactSet Estimates does

not make or alter estimates received from contributors, but does however, convert currency (i.e., USD to

EUR) and convert units (i.e., KM to Miles, Cubic feet to Barrels of Oil (BOE), etc.) when appropriate.

The “consensus window” refers to the time period associated with estimates used in the consensus. By

default, consensus estimates calculated by FactSet are based on estimates that have been validated via

broker research within the past 100 days. When an estimate does not exist in the past 100 days, typically

for small cap companies, FactSet Estimates automatically selects the latest estimate received within a

predetermined time period. This window is used to ensure that clients are analyzing meaningful

consensus estimates.

Data Fields extracted with the FactSet Estimates Consensus Report

The following table provides a detailed description of each of the 17 data fields that are by default retrieved

when using the Consensus report.

Field Name

Description

SecId

The security identifier.

FE_FP_END

The date corresponding to the fiscal period type that is entered. For example, if the

arguments entered in the syntax are fiscal period=1 and period type=annual, the

relative date is FY1 which is the current unreported fiscal year for the company.

CURRENCY

The currency in which the selected estimate item is displayed.

FE_ITEM

The estimates item that is being retrieved, i.e. EPS.

FE_PER_REL

The relative fiscal period that is specified in the syntax. For example, when the syntax

specified fiscal period=1 and period type=annual, the data is retrieved for the current

unreported fiscal year for the company. This field retrieves a 1 for this example since

the fiscal period argument is 1.

Date

The research date for the estimate item. This corresponds to the date of the report

issued by a broker. Whenever a broker sends a new estimate or opinion, it is

considered a research date. It reflects the date indicated in the actual report issued by

the broker, not the date FactSet received it.

91

Field Name

Description

FE_MEAN

Consensus – Estimate Mean

FE_MEDIAN

Consensus – Estimate Median

FE_NUM_EST

Consensus – Number of Estimates

FE_LOW

Consensus – Lowest Estimate

FE_HIGH

Consensus – Highest Estimate

FE_STD_DEV

Consensus – Standard Deviation from Estimate

FE_UP

Consensus – Number of Estimates Revised Up

FE_DOWN

Consensus – Number of Estimates Revised Down

FE_UNCHANGED

Consensus – Number of Estimates Unchanged Revisions

FE_TOTAL

Consensus – Number of Total Estimates Revised

FE_MEPS_INFO

Estimate Description Label

18.4. Estimates Report – Guidance

The Guidance report provides access to the estimates guidance that companies provide as an indication

or estimate of their future earnings. FactSet Estimates provides high, low, and mean guidance estimates

for companiesThe data extracted by this function is accessible by using other functions such as

ExtractFormulaHistory and ExtractDataSnapshot, but the value added of this report is that the default

output includes information in terms of the mean, high and low guidance values compared to the mean

estimate based on the broker contributions. This default output would entail making multiple requests

using the other functions.

Source of Guidance

Companies provide guidance as an indication or estimate of their future earnings.

The estimate guidance is collected by FactSet from the following sources:

For U.S Companies:

• CallStreet Transcripts

• PR Newswire

• Business Wire

• CCN Matthews

• GlobeNewswire

• Market Wire

For European Companies:

• Financial Express Company Announcements

• Europe PR Newswire

• Hugin Southern Europe

• Hugin

• Europe Business Wire for Japanese Companies:

• TDNet

92

Data Fields extracted with the FactSet Estimate Guidance Report

The following table provides a detailed description of each of the 12 data fields that can be retrieved when

using the Guidance report.

Field Name

Description

SecId

The security identifier.

CURRENCY

The currency in which the selected estimate guidance item is displayed.

FE_ITEM

The estimates guidance item that is being retrieved, i.e. EPS.

FE_PER_REL

The relative fiscal period that is specified in the syntax. For example, when the

syntax specified fiscal period=1 and period type=annual, the data is retrieved for

the current unreported fiscal year for the company. This field retrieves a 1 for this

example since the fiscal period argument is 1.

FE_MEAN_DATE

The research date for the estimate item. This corresponds to the date of the report

issued by a broker. Whenever a broker sends a new estimate or opinion, it is

considered a research date. It reflects the date indicated in the actual report issued

by the broker, not the date FactSet received it.

Guidance Min

Guidance – Low Estimate

Guidance Max

Guidance – High Estimate

Guidance Mean

Guidance - Mean of High and Low

FE_MEAN

Consensus - Mean of Estimates

Guidance Min Date

Guidance Min Record Date

Guidance Max Date

Guidance Max Record Date

Guidance Mean Date

Guidance Mean Record Date

18.5. Estimates Report – Surprise

The Surprise report provides data to measure adjustments made to the consensus vis-à-vis corporate

announcements. The data extracted by this function is accessible by using other functions such as

ExtractFormulaHistory and ExtractDataSnapshot, but the value added of this report is that the default

output includes more comprehensive overview of the change in consensus estimates before and after

the surprise event as well as the effect on the security price. This default output would entail making

multiple requests using the other functions.

Surprise Methodology

There are two types of Surprise calculations, either using the Actual or the post-event consensus. The

Actual is used as the default calculation for Australia, Japan, and the US geographic regions.

The post-event consensus is used for all other regions, primarily Europe. However, if there is no Actual

present, then the post-event consensus will be used.

Surprise calculations are triggered by events, which include profit warnings, preliminary releases, or an

earnings release, whether quarterly, semi-annual, or annual. The first event of the quarter will trigger the

surprise calculation.

Thus, there can be more than one surprise calculation within a single quarter. Only after a company rolls

will an Actual or Broker Actual be used. The Surprise Event is, by default, the first event of the quarter.

In this case, the surprise calculation can be based on a profit warning if available, instead of a publication

date.

93

The post-event consensus is continuously updated as relevant data is received until 100 days after the

event. At that point the post-event consensus is finalized and thus the Surprise value for that fiscal period

will remain static unless there is more than one event in the same quarter. If the two events occur within

the same quarter and they are not 100 days apart, the first post-event consensus will be finalized as of

just before the release of the second event.

Both annual and quarterly surprise values are calculated for every event. Either the quarterly or annual

calculation must be designated by the user. Annual surprises are recalculated quarterly. The FactSet

Estimates database assumes that recent quarterly results affect annual estimates. Thus, to retrieve a

surprise figure as of the year end, the last fiscal quarter in the FactSet Estimates code should be

referenced.

Data Fields extracted with the FactSet Estimates Surprise Report

The following table provides a detailed description of each of the 13 data fields that are by default retrieved

when using the Surprise report.

Field Name

Description

SecId

The security identifier.

CURRENCY

The currency in which the selected estimate item is displayed.

FE_ITEM

The estimates item that is being retrieved, i.e. EPS.

FE_PER_REL

The relative fiscal period that is specified in the syntax. For example, when the

syntax specified fiscal period=1 and period type=annual, the data is retrieved for

the current unreported fiscal year for the company. This field retrieves a 1 for

this example since the fiscal period argument is 1.

Surprise_Before_Event

Displays the Consensus figure one day prior to the surprise event. It can be

displayed in several forms: median, mean, low, high, standard deviation, and

number of estimates.

Surprise_After_Event

Displays the Consensus figure post the surprise event. It can be displayed in

several forms: median, mean, low, high, standard deviation, and number of

estimates.

Surprise_Amount

Displays the value of surprise after minus surprise before.

Surprise (%)

Displays the Surprise percentage, calculated as Surprise Amount/Surprise

Before.

Price_Impact (%)

Displays the Impact Surprise amount has on the Stock Price. It is the

percentage in price change between the dates before the report date and after.

By default, the price impact will calculate 1 day before and 0 day after the report

date.

Surprise_Date

Surprise event date.

Surprise_Event

Description of the event surprise that the figures are based on.

Surprise_Period

Displays the fiscal period related to the surprise date.

Surprise_Date_Before_Event

Displays the date one day prior to a surprise event.

94

18.6. Estimates Report – Consensus Recommendation

The Consensus Recommendation report provides access to the number of different recommendations

given by brokers as well as the mean recommendation based on the recommendation mark mapping.

Recommendation Methodology

Recommendation data covers all broker recommendations received over the past 100 days. When a

broker issues several recommendations over the past 100 days, only the most recent is retained.

Recommendations are divided into five broad categories: Buy, Overweight, Hold, Underweight, and Sell.

Then, a rating of between 1 and 3 is attributed to each category according to the table below.

Recommendation Mark

Recommendation Name

1

Buy

1.5

Overweight

2

Hold

2.5

Underweight

3

Sell

The methodology used with the FactSet Estimates database is to keep recommendations consistent

across the FactSet database. Not every broker uses the same recommendations that FactSet has in

place. Therefore, FactSet works with all its contributors in order to correctly map their recommendations.

The Estimates database builds out a recommendation dictionary for each broker which tells exactly how

each of their recommendations corresponds to FactSet's own categories. These recommendations can

be changed at any time should a contributor begin to give new recommendations or want to change their

existing mapping. By doing so, FactSet ensure that its contributor recommendations are captured

correctly in the Estimates Database.

Data Fields extracted with the FactSet Estimates Consensus Recommendation Report

The following table provides a detailed description of each of the 10 data fields that are retrieved when

using the FactSet Estimates Consensus Recommendation report.

Field Name

Description

SecId

The security identifier.

Consensus Date

The consensus date for the mean recommendations.

FE_BUY

The aggregate number of buy recommendations.

FE_OVER

The aggregate number of overweight recommendations.

FE_HOLD

The aggregate number of hold recommendations.

FE_UNDER

The aggregate number of underweight recommendations.

FE_SELL

The aggregate number of sell recommendations.

FE_TOTAL

The aggregate number of recommendations.

FE_MARK

The mean recommendation.

FE_MARK_TEXT

The mean recommendation with text string; accessible only with ‘meanText’,’Y’

FE_NO_REC

The aggregate number of brokers covering the security that are not providing a

recommendation for the particular period.

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18.7. Estimates Report – Detailed Recommendation

The Detailed Recommendation report provides access to the number of different recommendations given

by brokers.

Recommendation Methodology

Recommendation data covers all broker recommendations received over the past 100 days. When a

broker issues several recommendations over the past 100 days, only the most recent is retained.

Recommendations are divided into five broad categories: Buy, Overweight, Hold, Underweight, and Sell.

Then, a rating of between 1 and 3 is attributed to each category according to the table below.

Recommendation Mark

Recommendation Name

1

Buy

1.5

Overweight

2

Hold

2.5

Underweight

3

Sell

The methodology used with the FactSet Estimates database is to keep recommendations consistent

across the FactSet database. Not every broker uses the same recommendations that FactSet has in

place. Therefore, FactSet works with all its contributors in order to correctly map their recommendations.

The Estimates database builds out a recommendation dictionary for each broker which tells exactly how

each of their recommendations corresponds to FactSet's own categories. These recommendations can

be changed at any time should a contributor begin to give new recommendations or want to change their

existing mapping. By doing so, FactSet ensure that its contributor recommendations are captured

correctly in the Estimates Database.

Data Fields extracted with the FactSet Estimates Detailed Recommendation Report

The following table provides a detailed description of each of the 10 data fields that are by default retrieved

when using the FactSet Estimates Detail Recommendation report.

Field Name

Description

SecId

The security identifier.

FE_BROKER

The FactSet Estimates Broker code. For a list of active brokers available in

FactSet Estimates and their corresponding codes refer to Online Assistant

page 14706.

FE_BROKERNAME

The Broker Name, i.e. Goldman Sachs.

FE_ANALYST

The code for the analyst. The code is based on a FactSet people map and

allows brokers to control readership entitlements.

FE_ANALYSTNAME

The name of the Analyst making providing the estimate.

FE_ESTIMATE

The detailed recommendation mark from contributing brokers over specified

date range for the specified period (i.e. EPS for FY1).

FE_ESTIMATE_VALUE

The detailed recommendation name from contributing brokers over specified

date range for the specified period (i.e. EPS for FY1).

FE_EST_REV_VAL

The previous estimate value from the same analyst, for the same fiscal period.

96

Field Name

Description

FE_EST_REV_VAL_ARROW

Retrieves a -1, 0, 1 or NA to indicate the direction of the recommendation

change from the analyst. A -1 indicates that the latest recommendation value

retrieved with FE_ESTIMATE is lower than the value retrieved with the

previous estimate, retrieved with the field FE_EST_REV_VAL. A 0 indicates

that there has been no change in the recommendation. A 1 indicates that the

latest recommendation is higher than the previous value from the same

analyst. An NA indicates that there was no previous value from that analyst for

this security.

97

18.8. Appendix

Following is a list of Items for estimates available with EstimatesOnDemand

Description

Item Code

Adjusted Funds From Operations

AFFO

Annual Subscription Value

ASV

Tangible Book Value per Share

BPS_TANG

Book Value Per Share

BVPS

Capital Expenditures

CAPEX

Cash Flow From Financing

CFF

Cash Flow From Investing

CFI

Cash Flow From Operations

CFO

Cash Flow Per Share

CFPS

CurrentAssets

CURRENTASSETS

CurrentLiabilities

CURRENTLIABILITIES

EPS - Non-GAAP

CUSTOM_EPS

Dividends Per Share

DIV

Reported Earnings Per Share

EAG

Earnings Per Share Excluding Exceptions

EBG

EBIT

EBITDA

Earnings Per Share

EPS

EPS - Non-GAAP ex. SOE

EPSA

EPS - GAAP

EPSR

Stock Option Expense

FASB123IMP

Free Cash Flow

FCF

Free Cash Flow Per Share

FCFPS

Funds From Operations

FFO

Adjusted Funds From Operations

FFOA

Gross Income

GROSSINCOME

Interest Expense

INTEXP

Long Term Growth

LTG

Number of Shares

NBTITB

Number of Shares Basic

NBTITBAS

Net Income - Non-Consolidated

NET_P

Net Profit Adjusted

NETBG

Net Debt

NETDEBT

Declared Dividend Per Share

NETDIV

Net Profit

NETPROFIT

Net Income Adjusted

NETPROFITA

Pretax Income

PTP

Pre-Tax Income - Non-Consolidated

PTP_P

Pre-Tax Profit Reported

PTPBG

98

Description

Item Code

Pretax Income - Reported

PTPR

Research And Development

RD_EXP

Selling and Marketing

S_M_EXP

Sales

SALES

Sales - Non-Consolidated

SALES_P

Same Store Sales

SAMESTORESALES

Selling, General and Administrative Expense

SGA

Shareholder's Equity

SH_EQUITY

Shares Basic

SHARB

Shares Diluted

SHARD

Shareholder's Equity

SHEQUITY

Shares Dilute

SHR

Shares Basic

SHRB

Number of Shares Basic

SHRBLA

Shares

SHRLA

Stock Option Expense

SOE

Tax Expense

TAX_EXPENSE

Book Value per Share - Tangible

TBVPS

Target Price

TGP

Total Debt

TOTALDEBT

Total Assets

TOTASSETS

Total Goodwill

TOTGW

Total Revenue

TOTREV

Airlines

Airlines - Available Seat Km

AVAILABLESEATKM

Airlines - Load Factor

LOADFACTOR

Airlines - Operating Expenses per ASK

OPEX_ASK

Airlines - Passenger Revenue Km

REVPASSENGERKM

Airlines - Passenger Revenue per ASK

PASS_REV_ASK

Airlines - Passenger Revenue per RPK

PASS_REV_RPK

Airlines - Revenue Passenger

REV_PASSENGER

Airlines - Total Revenue per ASK

TOT_REV_ASK

Airlines Operating Expenses per ASK excluding fuel costs

OPEX_ASK_X

Banks

Bank - ASSETS_NONPERF

ASSETS_NONPERF

Bank - Average Earnings Assets

AVG_EARN_ASSETS

Bank - AVG_EARN_ASSETS

AVG_EARN_ASSETS

Bank - DEPS_AVG

DEPS_AVG

Bank - INT_INC_MARGIN

INT_INC_MARGIN

Bank - LOAN_NET_AVG

LOAN_NET_AVG

Bank - Net Charge Offs

NET_CHARGE_OFFS

Bank - Net Interest Margin

INT_INC_MARGIN

99

Description

Item Code

Bank - NET_CHARGE_OFFS

NET_CHARGE_OFFS

Bank - Non performing Loans

LOAN_NONPERF

Bank - Non-Performing Assets

ASSETS_NONPERF

Bank - Operating Expense

OperExpen

Bank - Tier 1 Common Capital Ratio

COMCAP_RATIO_TIER1

Bank - Capital Adequacy Ratio - Tier 1 - Banks

CAP_RATIO_TIER1

Bank - Cost to Income

COST_INCOME

Bank - Income from Fees & Commissions

INC_FEES

Bank - Net Interest Income

NetInterestInc

Bank - Net Loans

LOAN_NET

Bank - Provisions for Credit Losses

ProvLoans

Bank - Risk Weighted Assets

ASSETS_RISK_WGHT

Bank - Total Deposits

DEPS

Bank - Trading Income

TradInc

Education

Education - New Student Enrollment

STUDENTENROLL_NEW

Education - Total Student Enrollment

STUDENTENROLL_TOT

Commodities

Commodities - Mean Target Price

MTGP

Home Builders

Home Builders - Backlog Avg Price

BACKLOG_AVG_PRICE

Home Builders - Backlog Units

BACKLOG_UNITS

Home Builders - Backlog Value

BACKLOG_VALUE

Home Builders - Deliveries Average Price

DELIVERIES_AVG_PRICE

Home Builders - Deliveries Units

DELIVERIES_UNITS

Home Builders - Financial Services

FIN_SERVICES

Home Builders - Home Sales

HOME_SALES

Home Builders - Land Sales

LAND_SALES

Home Builders - Orders Avg Price

NEW_ORDERS_AVG_PRICE

Home Builders - Orders Units

NEW_ORDERS_UNITS

Home Builders - Orders Value

NEW_ORDERS_AVG_VALUE

Hospitals

Hospitals - Other Operating Expenses

OTHER_OPEX

Hospitals - Provision for Bad Debt

BAD_DEBT_PROV

Hospitals - Salaries and Benefits

SAL_BENEFITS

Hospitals - Same Store Adjusted Admissions

SS_ADJ_ADM

Hospitals - Same Store Admissions

SS_ADM

Hospitals - Same Store Revenue per Adjusted Admissions

SS_REV_PER_ADJ_AM

Hospitals - SUPPLIES

SUPPLIES

Hotels

Hotels - Revenue per Available Room-International

RevPar_intl

Hotels - ADR

Adr_Tot

100

Description

Item Code

Hotels - ADR - Dom.

ADR_Dom

Hotels - ADR - Intl.

ADR_Intl

Hotels - Occupancy % Dom

Occupancy_dom

Hotels - Occupancy % Intl

Occupancy_intl

Hotels - Occupancy % Total

Occupancy_tot

Hotels - RevPAR

RevPar_tot

Hotels - RevPAR - Dom

RevPar_Dom

Insurance

Combined Ration

COMBINED_RATIO

Embedded Value

EMBEDDED_VALUE

Insurance - Gross Premiums Written

GROSS_PREM_WRITTEN

Insurance - Net Investment Income

RevPar_intl

Insurance - Net Premiums Earned

PREM_EARN

Insurance - Net Premiums Written

PREM_WRITTEN

Mining

Mining - Cash Cost

CASH_COST

Mining - Realized Price

REAL_PRICE

Mining - Total Production

TOTAL_PROD

Multi Financial

Multi Financial - Asset Under Management Average

AUM_AVG

Multi Financial - Asset Under Management End of the Period

AUM

Multi Financial - Long Term Flows

LT_FLOWS

Multi Financial - Net Flows

NETFLOWS

Oil Companies

Debt-Adjusted Cash Flow

DACF

Oil companies - 1P Proved Reserves

Proved_1P

Oil companies - 2P Proved and Probable Reserves

Proved_2P

Oil companies - 3P Proved Probable and Possible Reserves

Proved_3P

Oil companies - Chemicals Income

Chemicals_OpInc

Oil companies - Chemicals Income - Dom

CHEM_DOM

Oil companies - Chemicals Income - Intl

CHEM_INTL

Oil companies - Downstream Income - Dom

R_M_DOM

Oil companies - Downstream Income - Downstream

R_M_OPINC

Oil companies - Downstream Income - Intl

R_M_INTL

Oil companies - Exploration Expense

Exploration_Exp

Oil companies - OPEX Per Unit

OPEX_UNIT

Oil companies - Production Per Day

PRODPERDAY

Oil companies - Production Per Day - Natural Gas

PROD_DAY_GAS

Oil companies - Production Per Day - Oil & NGLs

PROD_DAY_OIL

Oil companies - Realized Price

REAL_PRICE

Oil companies - Realized Price - Natural Gas

REAL_PRICE_GAS

Oil companies - Realized Price - Oil & NGLs

REAL_PRICE_OIL

101

Description

Item Code

Oil companies - Upstream

E_P_OPINC

Oil companies - Upstream Income - Dom

E_P_DOM

Oil companies - Upstream Income - Intl

E_P_INTL

Total Production

TOTAL_PROD

Real Estate

Real Estate - Adjusted Funds from Operations

AFFO

Real Estate - Funds from Operations

FFO

Real Estate - Net Asset Value per Share

NAVPS

Real Estate - Net Asset Value per Share - NTM

RNAVPS

Retailers

Retailers - Total Production

StoresEnd

Retailers - # of Stores Opened

StoresClosed_I

Retailers - # Stores at Period End

StoresEnd

Retailers - # Stores at Period End - Intl.

StoresEnd_I

Retailers - # Stores at Period End- Dom.

StoresEnd_D

Retailers - # Stores Closed During Period

StoresClosed

Retailers - # Stores Closed During Period - Dom.

StoresClosed

Retailers - # Stores Closed During Period - Intl.

StoresClosed

Retailers - # Stores Opened During Period - Dom.

StoresOpened_D

Retailers - # Stores Opened During Period - Intl.

StoresOpened_I

Retailers - # Stores Relocated During Period

StoresReloc

Retailers - # Stores Relocated During Perioud - Dom.

StoresReloc_D

Retailers - # Stores Relocated During Perioud - Intl.

StoresReloc_I

Retailers - Net Sales per Retail Square Foot

NetSalesRetailSq

Retailers - Same Store Sales

SameStoreSales

Retailers - Same Store Sales Dom.

SameStoreSales_D

Retailers - Same Store Sales INTL.

SameStoreSales_I

Retailers - Same Store Sales Monthly

SAMESTORESALESM

Retailers - Selling Space Sq. Ft. (Gross)

SellingSpace

Retailers - Selling Space Sq. Ft. (Gross)- Dom

SellingSpace_D

Retailers - Selling Space Sq. Ft. (Gross)- Intl

SellingSpace_I

SSS_WMT

SSS_WMT_samsclub

Telecom

ACCESS LINES

ACCESS_LINES

Average Revenue Per User

ARPU

CHURN

Cost per Gross Add

CPGA

Gross Adds

GROSS_ADDS

Minute of Use

MOU

Net Adds

NET_ADDS

Number of Subscribers

SUBSCRIBERS_NB

102

Description

Item Code

Subscriber Acquisition Cost

SAC