We present an agent-based model of manipulating prices in financial markets through spoofing: submitting spurious orders to mislead traders who learn from the order book. Our model captures a complex market environment for a single security, whose common value is given by a dynamic fundamental time series. Agents trade through a limit-order book, based on their private values and noisy observations of the fundamental. We consider background agents following two types of trading strategies: the non-spoofable zero intelligence (ZI) that ignores the order book and the manipulable heuristic belief learning (HBL) that exploits the order book to predict price outcomes.more »
Evaluating the Stability of Non-Adaptive Trading in Continuous Double Auctions
The continuous double auction (CDA) is the predominant mechanism in modern securities markets. Many agent-based analyses of CDA environments rely on simple non-adaptive trading strategies like Zero Intelligence (ZI), which (as their name suggests) are quite limited. We examine the viability of this reliance through empirical game-theoretic analysis in a plausible market environment. Specifically, we evaluate the strategic stability of equilibria defined over a small set of ZI traders with respect to strategies found by reinforcement learning (RL) applied over a much larger policy space. RL can indeed find beneficial deviations from equilibria of ZI traders, by conditioning on signals of the likelihood a trade will execute or the favorability of the current bid and ask. Nevertheless, the surplus earned by well-calibrated ZI policies is empirically observed to be nearly as great as what the adaptive strategies can earn, despite their much more expressive policy space. Our findings generally support the use of equilibrated ZI traders in CDA studies.
- Award ID(s):
- 1741190
- Publication Date:
- NSF-PAR ID:
- 10105519
- Journal Name:
- 17th International Conference on Autonomous Agents and MultiAgent Systems
- Sponsoring Org:
- National Science Foundation
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