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on Market Microstructure |
| By: | Briola, Antonio; Bartolucci, Silvia; Aste, Tomaso |
| Abstract: | We exploit cutting-edge deep learning methodologies to explore the predictability of high-frequency Limit Order Book mid-price changes for a heterogeneous set of stocks traded on the NASDAQ exchange. In so doing, we release ‘LOBFrame’, an open-source code base to efficiently process large-scale Limit Order Book data and quantitatively assess state-of-the-art deep learning models' forecasting capabilities. Our results are twofold. We demonstrate that the stocks' microstructural characteristics influence the efficacy of deep learning methods and that their high forecasting power does not necessarily correspond to actionable trading signals. We argue that traditional machine learning metrics fail to adequately assess the quality of forecasts in the Limit Order Book context. As an alternative, we propose an innovative operational framework that evaluates predictions' practicality by focusing on the probability of accurately forecasting complete transactions. This work offers academics and practitioners an avenue to make informed and robust decisions on the application of deep learning techniques, their scope and limitations, effectively exploiting emergent statistical properties of the Limit Order Book. |
| Keywords: | deep learning; econophysics; high frequency trading; limit order book; market microstructure |
| JEL: | J1 F3 G3 |
| Date: | 2025–07–22 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:128950 |
| By: | Patrick Chan; Ronnie Sircar; Iosif Zimbidis |
| Abstract: | We consider a dynamic portfolio optimization problem that incorporates predictable returns, instantaneous transaction costs, price impact, and stochastic volatility, extending the classical results of Garleanu and Pedersen (2013), which assume constant volatility. Constructing the optimal portfolio strategy in this general setting is challenging due to the nonlinear nature of the resulting Hamilton-Jacobi-Bellman (HJB) equations. To address this, we propose a multi-scale volatility expansion that captures stochastic volatility dynamics across different time scales. Specifically, the analysis involves a singular perturbation for the fast mean-reverting volatility factor and a regular perturbation for the slow-moving factor. We also introduce an approximation for small price impact and demonstrate its numerical accuracy. We formally derive asymptotic approximations up to second order and use Monte Carlo simulations to show how incorporating these corrections improves the Profit and Loss (PnL) of the resulting portfolio strategy. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.17162 |
| By: | Papastaikoudis, I.; Watson, J.; Lestas, I. |
| Abstract: | This paper presents a cyber-physical systems (CPS) framework to model the interplay between market price dynamics and social belief formation in a decentralized setting. The physical layer captures the evolution of prices through a networked market system governed by linear supply, demand, and crossprice elasticity relationships. The cyber layer represents belief formation via a hypergraph-structured learning model, where agents update expectations through distributed Kalman filters based on noisy price observations and group-level interactions. We analyze how informational frictions—driven by social structure, media influence, or cognitive limitations—induce delays in belief con-vergence to equilibrium prices. These delays, in turn, generate dynamic welfare losses due to suboptimal economic decisions. By linking convergence rates to hypergraph Laplacian spectra, we show how group-level information structures determine the speed and equity of learning processes. Our findings provide a theoretical foundation for studying misinformation and its economic costs in markets shaped by decentralized learning and social influence. |
| Keywords: | Cybernetics of Economic Networks, Distributed Kalman Filter, Social Welfare |
| JEL: | C32 D47 D85 |
| Date: | 2025–06–20 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camjip:2518 |