|
on Market Microstructure |
| By: | Sergei Glebkin (INSEAD); Semyon Malamud (Ecole Polytechnique Federale de Lausanne; Centre for Economic Policy Research (CEPR); Swiss Finance Institute); Alberto Mokak Teguia (University of British Columbia (UBC)) |
| Abstract: | We develop a tractable model to study how asset concentration among a few large investors impacts asset prices and liquidity. Consistent with existing empirical evidence: (i) greater concentration is associated with higher volatility and returns, and (ii) large investors' turnover share is smaller than their proportion of total wealth. Surprisingly, higher concentration enhances liquidity, aligning with our new empirical findings. We show that increased concentration can benefit all investors in sufficiently non-competitive markets. We link the wedge between competitive and non-competitive outcomes to the Herfindahl-Hirschman Index measuring wealth concentration. The wedge can remain positive even in large markets. |
| Keywords: | Market Liquidity, Funding Liquidity, Price Impact, Strategic Trading |
| JEL: | G31 G32 G35 L11 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:chf:rpseri:rp2597 |
| By: | Maximiliano Dvorkin; Melanie LeTourneau |
| Abstract: | High-frequency data sources like Homebase offer a real-time look at trends in the U.S. labor market, which is very valuable when official data are unavailable. |
| Keywords: | labor markets; private-sector data; high-frequency data; hiring; job openings; job separations; labor turnover |
| Date: | 2025–11–07 |
| URL: | https://d.repec.org/n?u=RePEc:fip:l00001:102071 |
| By: | Dmitrii Vlasiuk; Mikhail Smirnov |
| Abstract: | We test the hypothesis that consecutive intraday price changes in the most liquid U.S. equity ETF (SPY) are conditionally nonrandom. Using NBBO event-time data for about 1, 500 regular trading days, we form for every lag L ordered pairs of a backward price increment ("push") and a forward price increment ("response"), standardize them, and estimate the expected responses on a fine grid of push magnitudes. The resulting lag-by-magnitude maps reveal a persistent structural shift: for short lags (1-5, 000 ticks), expected responses cluster near zero across most push magnitudes, suggesting high short-term efficiency; beyond that range, pronounced tails emerge, indicating that larger historical pushes increasingly correlate with nonzero conditional responses. We also find that large negative pushes are followed by stronger positive responses than equally large positive pushes, consistent with asymmetric liquidity replenishment after sell-side shocks. Decomposition into symmetric and antisymmetric components and the associated dominance curves confirm that short-horizon efficiency is restored only partially. The evidence points to an intraday, lag-resolved anomaly that is invisible in unconditional returns and that can be used to define tradable pockets and risk controls. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.06177 |
| By: | Andrea Macr\`i; Sebastian Jaimungal; Fabrizio Lillo |
| Abstract: | Reinforcement Learning (RL) applied to financial problems has been the subject of a lively area of research. The use of RL for optimal trading strategies that exploit latent information in the market is, to the best of our knowledge, not widely tackled. In this paper we study an optimal trading problem, where a trading signal follows an Ornstein-Uhlenbeck process with regime-switching dynamics. We employ a blend of RL and Recurrent Neural Networks (RNN) in order to make the most at extracting underlying information from the trading signal with latent parameters. The latent parameters driving mean reversion, speed, and volatility are filtered from observations of the signal, and trading strategies are derived via RL. To address this problem, we propose three Deep Deterministic Policy Gradient (DDPG)-based algorithms that integrate Gated Recurrent Unit (GRU) networks to capture temporal dependencies in the signal. The first, a one -step approach (hid-DDPG), directly encodes hidden states from the GRU into the RL trader. The second and third are two-step methods: one (prob-DDPG) makes use of posterior regime probability estimates, while the other (reg-DDPG) relies on forecasts of the next signal value. Through extensive simulations with increasingly complex Markovian regime dynamics for the trading signal's parameters, as well as an empirical application to equity pair trading, we find that prob-DDPG achieves superior cumulative rewards and exhibits more interpretable strategies. By contrast, reg-DDPG provides limited benefits, while hid-DDPG offers intermediate performance with less interpretable strategies. Our results show that the quality and structure of the information supplied to the agent are crucial: embedding probabilistic insights into latent regimes substantially improves both profitability and robustness of reinforcement learning-based trading strategies. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.00190 |
| By: | Peter Yegon; W. Brent Lindquist; Svetlozar T. Rachev |
| Abstract: | We present two models for incorporating the total effect of market microstructure noise into dynamic pricing of assets and European options. The first model is developed under a Black-Scholes-Merton, continuous-time framework. The second model is a discrete, binomial tree model developed as an extension of the static Grossman-Stiglitz model. Both models are market complete, providing a unique equivalent martingale measure that establishes a unique map between parameters governing the risk-neutral and real-world price dynamics. We provide empirical examples to extract the coefficients in the model, in particular those coefficients characterizing the influence of the microstructure noise on prices. In addition to isolating the impact of noise on the volatility, the discrete model enables us to extract the noise impact on the drift coefficient. We provide evidence for the primary microstructure noise we believe our empirical examples capture. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.00308 |