Understanding Financial Market Behavior Through Empirical Game-Theoretic Analysis
Add to Google Calendar
Financial market activity is increasingly controlled by algorithms, interacting through electronic markets.
Unprecedented information response times, autonomous operation, use of machine learning and other adaptive techniques, and ability to proliferate novel strategies at scale are all reasons to question whether algorithmic trading may produce dynamic behavior qualitatively different from what arises in trading under direct human control.
Recent studies have turned to agent-based modeling to begin to understand modern market structure and behavior.
Given the high level of competition between trading firms and the significant financial incentives to trading, it is likely that most trading firms stand to gain little from altering their strategy, i.e. they are likely playing close to a Nash equilibrium.
This thesis builds on recent agent-based models of financial markets by imposing agent rationality and studying the models in equilibrium.
I use empirical game-theoretic analysis, a methodology for determining rational behavior in agent-based models, to evaluate three important aspects of market structure.
First, I evaluate the impact of strategic trading on agent welfare.
My results indicate that in many market environments, strategic misrepresentation of beliefs actually makes agents better off, even with the complexities of modern market interactions.
Next, I investigate the optimal clearing interval for a proposed market mechanism, the frequent call market.
There is significant evidence to support the idea that traders will benefit from trading in a frequent call market over traditional continuous double auction markets.
My results confirm this statement for a wide variety of market settings, but I also find a few circumstances, particularly when large informational advantages exist, or when markets are thin, that call markets consistently hurt welfare, independent of frequency.
I conclude with an investigation on the effect of trend following on market stability.
Here I find that the presence of trend followers alters a market's response to shock.
In the absence of trend followers, shocks are small but have a long recovery.
When trend followers are present, they alter background trader behavior resulting in more severe shocks that recover much more quickly.