The Rise of Algorithmic Trading: Implications for Price Elasticity and Market Competitiveness [Paper]
Job Market Paper
Financial markets’ demand elasticity has decreased substantially over the last 20 years, leading to increased volatility and undermining price stability. This paper investigates the impact of the use of AI traders that make unsupervised trading decisions on aggregate stock elasticity. Building on Haddad et al. (2021), I estimate investors’ demand and then I simulate a fictitious financial market populated by artificial intelligence (AI) traders, whose investment decisions are governed by a neural network-based reinforcement learning algorithm. The introduction of AI traders has non-trivial effects on elasticity: on the one hand, because of their high individual-specific elasticity they increase aggregate elasticity compared to a market that is not populated by algorithmic traders. On the other hand, when these agents reduce their exposure on the risky asset, aggregate elasticity drops by around 2.5%. The last is larger when other investors demand is more sensitive to aggregate market elasticity.
Artificial Intelligence, Algorithmic Trading, and Market Stability (with E. Tarantino and F. Sangiorgi) [SSRN]
We investigate how AI-driven investors, modeled via deep reinforcement learning, operate in a calibrated financial market with realistic return predictability and endogenous price impact. We examine whether these agents can learn to detect and exploit return predictability from public signals, decode prices to infer latent demand, and adjust for price impact. To evaluate performance, we compare AI traders to a rational benchmark representing the optimal policy under full knowledge of the data generating process. In simulations, AI traders qualitatively match the benchmark. Quantitatively, however, they fall short when many interact. The presence of other AI traders injects noise into the price process through their exploration, distorting the portfolio-return signals each agent learns from and thus impairing learning. This negative learning externality reduces trading profits, lowering market efficiency and liquidity relative to the benchmark. Our findings suggest caution when extrapolating from partiale-quilibrium analyses of AI trading’s profitability and impact on market quality.
Robust Identification in Repeated Games: An Empirical Approach to Algorithmic Competition (with A. Cozzolino, C. Gualdani, N. Lomys, and L. Magnolfi)