empirical evidence yet that reveals the relative performance of financial market institutions and that
can guide market design for financial securities – despite the fact that policy makers worldwide are
already taking actions intended to discourage high frequency trading. Zhang and Riordan (2011),
Brogaard et al. (2014), Menkveld and Zoican (2014), Benos and Sagade (2016), and Benos et al.
(2017), among others, provide evidence for the costs of aggressive sniping. However, this literature
comes from minor variants of the standard financial market design, and thus offers no direct evidence
about the costs and benefits of other platforms, engineered to eliminate the dilemma, as in Budish
et al. (2015) and Aquilina et al. (2020). Moreover, even though there are three decades of studying
financial markets in the laboratory (for surveys on experimental research in financial markets see
Friedman and Rust 1993, Friedman 2010, and Noussair and Tucker 2013), aside from particular
episodes such as the “Flash Crash” (Aldrich et al. 2016), little is known about the impact of sniping in
times of financial stress as opposed to normal times (but see Jagannathan 2019 for a step in this
direction). However, Aldrich and López Vargas (2019) recently conducted a laboratory market design
study on high-frequency trading that suggests that, relative to the continuous double auction, the
frequent batch auction exhibits less predatory trading behavior, lower investments in low-latency
communication technology, lower transaction costs, and lower volatility in market spreads and
liquidity. More studies on how financial market design affects sniping, market stability and market
resiliency are necessary.
Also, many other markets, as they move to real-time interaction, already see or will likely see
similar problems, and thus require new clever market design solutions. As an example, think about
electricity market design, where we are just starting to observe similar issues. One of the reasons is
the increasing share of intermittent renewables, which puts enormous stress on the system and
increases the risk of outages, so that both, improved investment incentives for reserve generation
capacity (Cramton and Ockenfels 2012, Cramton et al. 2013) and more liquid real-time trading is
needed. Yet, because the trend towards algorithmic trading in continuous electricity markets will also
lead to a wasteful race for speed, this is posing serious threats to the efficiency and reliability of these
markets (Neuhoff et al. 2016). Moreover, compared to financial markets, things tend to be more
complicated in electricity markets because of complementarities in electricity production (Wilson
2002 and Cramton 2017). For instance, the race for speed in electricity trading hampers efficient
pricing of transmission, which is often done on a first-come-first-serve basis in intraday trading. Also,
a race for speed complicates the formulation and consideration of multi-dimensional bids, which
consider the non-convex cost structure of electricity production.
Another interesting example for the relevance of timing in markets is auction design for
continuous sponsored search in the Internet, where other undesired bidding timing phenomenon
have been observed, such as bidding cycles with automated bidding agents, as well as various
attempts to address those (Edelman and Ostrovsky 2007, Edelman et al. 2007, Varian 2007, 2009,
Athey and Ellison 2011, Levin 2013 provides a survey). Clearly, taming sniping and improving price
formation will remain a critical aspect of market performance in modern market environments.