The Rise of Algorithmic Trading: Implications for Price Elasticity and Market Competitiveness [Link]
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) [Link]
We develop an econometric framework for recovering structural primitives---such as marginal costs---from price or quantity data generated by firms whose decisions are governed by reinforcement-learning algorithms. Guided by recent theory and simulations showing that such algorithms can learn to approximate repeated-game equilibria, we impose only the minimal optimality conditions implied by equilibrium, while remaining agnostic about the algorithms’ hidden design choices and the resulting conduct---competitive, collusive, or anywhere in between. These weak restrictions yield set identification of the primitives; we characterise the resulting sets and construct estimators with valid confidence regions. Monte~Carlo simulations confirm that our bounds contain the true parameters across a wide range of algorithm specifications, and that the sets tighten substantially when exogenous demand variation across markets is exploited. The framework thus offers a practical tool for empirical analysis and regulatory assessment of algorithmic behaviour.
Passive investing, diversification risk and financial stability (draft coming soon!)
This paper examines how passive investing affects financial stability by altering the composition of equity market risk. Using a valuation-adjusted Bartik instrument, I show that passive ownership causally increases stock return correlations by 3.5 percentage points per standard deviation increase. I decompose diversification benefits and portfolio risk into correlation-driven and idiosyncratic-volatility components using a structural demand system. The analysis reveals opposing effects: passive investing erodes diversification benefits by 3% through increased correlations, while dampening individual stock volatility. The correlation effect dominates—minimum achievable portfolio volatility is 20% higher with passive investors. Overall, passive investing reallocates risk from idiosyncratic to aggregate, non-diversifiable components, reducing the equity market's capacity to absorb shocks.