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on Market Microstructure |
| By: | Alexander Barzykin; Robert Boyce; Eyal Neuman |
| Abstract: | As the FX markets continue to evolve, many institutions have started offering passive access to their internal liquidity pools. Market makers act as principal and have the opportunity to fill those orders as part of their risk management, or they may choose to adjust pricing to their external OTC franchise to facilitate the matching flow. It is, a priori, unclear how the strategies managing internal liquidity should depend on market condions, the market maker's risk appetite, and the placement algorithms deployed by participating clients. The market maker's actions in the presence of passive orders are relevant not only for their own objectives, but also for those liquidity providers who have certain expectations of the execution speed. In this work, we investigate the optimal multi-objective strategy of a market maker with an option to take liquidity on an internal exchange, and draw important qualitative insights for real-world trading. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.04603 |
| By: | Amit Kumar Jha |
| Abstract: | This paper develops a comprehensive theoretical framework that imports concepts from stochastic thermodynamics to model price impact and characterize the feasibility of round-trip arbitrage in financial markets. A trading cycle is treated as a non-equilibrium thermodynamic process, where price impact represents dissipative work and market noise plays the role of thermal fluctuations. The paper proves a Financial Second Law: under general convex impact functionals, any round-trip trading strategy yields non-positive expected profit. This structural constraint is complemented by a fluctuation theorem that bounds the probability of profitable cycles in terms of dissipated work and market volatility. The framework introduces a statistical ensemble of trading strategies governed by a Gibbs measure, leading to a free energy decomposition that connects expected cost, strategy entropy, and a market temperature parameter. The framework provides rigorous, testable inequalities linking microstructural impact to macroscopic no-arbitrage conditions, offering a novel physics-inspired perspective on market efficiency. The paper derives explicit analytical results for prototypical trading strategies and discusses empirical validation protocols. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.03123 |
| By: | Li, Zhouxin; Wang, Zhiguang; Diersen, Matthew |
| Abstract: | This study delves into the occurrence and differentiation of significant price jumps in agricultural commodity markets, challenging the conventional belief that such movements are solely driven by exogenous factors. Existing literature has primarily focused on the impact of news on agricultural commodity prices, neglecting the distinction between endogenous and exogenous price spikes. We aim to identify and categorize both types of price spikes in corn, soybean, and wheat futures markets. We propose a comprehensive methodology involving the collection of agricultural news, non-parametric price jump detection, and differentiation between exogenous (news-driven) and endogenous (non-news related) price spikes. By utilizing intraday price data from the CME Group, we will compare any two consecutive jumps specified by a Bernoulli null hypothesis, and aggregate single jumps into clusters of jumps. We investigate whether endogenous events result from a self-exciting stochastic process. This research lends support to both exogenous and endogenous jumps, providing insights into the efficiency of agricultural commodity markets. |
| Keywords: | Agricultural and Food Policy, Demand and Price Analysis |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ags:nccc24:379009 |
| By: | Juan C. King; Jose M. Amigo |
| Abstract: | The aim of this paper is the analysis and selection of stock trading systems that combine different models with data of different nature, such as financial and microeconomic information. Specifically, based on previous work by the authors and applying advanced techniques of Machine Learning and Deep Learning, our objective is to formulate trading algorithms for the stock market with empirically tested statistical advantages, thus improving results published in the literature. Our approach integrates Long Short-Term Memory (LSTM) networks with algorithms based on decision trees, such as Random Forest and Gradient Boosting. While the former analyze price patterns of financial assets, the latter are fed with economic data of companies. Numerical simulations of algorithmic trading with data from international companies and 10-weekday predictions confirm that an approach based on both fundamental and technical variables can outperform the usual approaches, which do not combine those two types of variables. In doing so, Random Forest turned out to be the best performer among the decision trees. We also discuss how the prediction performance of such a hybrid approach can be boosted by selecting the technical variables. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.02036 |