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on Market Microstructure |
| By: | Zijie Zhao; Roy E. Welsch |
| Abstract: | Leveraging Deep Reinforcement Learning (DRL) in automated stock trading has shown promising results, yet its application faces significant challenges, including the curse of dimensionality, inertia in trading actions, and insufficient portfolio diversification. Addressing these challenges, we introduce the Hierarchical Reinforced Trader (HRT), a novel trading strategy employing a bi-level Hierarchical Reinforcement Learning framework. The HRT integrates a Proximal Policy Optimization (PPO)-based High-Level Controller (HLC) for strategic stock selection with a Deep Deterministic Policy Gradient (DDPG)-based Low-Level Controller (LLC) tasked with optimizing trade executions to enhance portfolio value. In our empirical analysis, comparing the HRT agent with standalone DRL models and the S&P 500 benchmark during both bullish and bearish market conditions, we achieve a positive and higher Sharpe ratio. This advancement not only underscores the efficacy of incorporating hierarchical structures into DRL strategies but also mitigates the aforementioned challenges, paving the way for designing more profitable and robust trading algorithms in complex markets. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.14927 |
| By: | Neil A. Chriss |
| Abstract: | This is the third paper in a series concerning the game-theoretic aspects of position-building while in competition. The first paper set forth foundations and laid out the essential goal, which is to minimize implementation costs in light of how other traders are likely to trade. The majority of results in that paper center on the two traders in competition and equilibrium results are presented. The second paper, introduces computational methods based on Fourier Series which allows the introduction of a broad range of constraints into the optimal strategies derived. The current paper returns to the unconstrained case and provides a complete solution to finding equilibrium strategies in competition and handles completely arbitrary situations. As a result we present a detailed analysis of the value (or not) of trade centralization and we show that firms who naively centralize trades do not generally benefit and sometimes, in fact, lose. On the other hand, firms that strategically centralize their trades generally will be able to benefit. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.13583 |
| By: | James Albrecht; Xiaoming Cai; Pieter Gautier; Susan Vroman; Pieter A. Gautier |
| Abstract: | This paper considers competitive search equilibrium in a market for a good whose quality differs across sellers. Each seller knows the quality of the good that he or she is offering for sale, but buyers cannot observe quality directly. We thus have a “market for lemons” with competitive search frictions. In contrast to Akerlof (1970), we prove the existence of a unique equilibrium, which is separating. Higher-quality sellers post higher prices, so price signals quality. The arrival rate of buyers is lower in submarkets with higher prices, but this is less costly for higher-quality sellers given their higher continuation values. For some parameter values, higher-quality sellers post the full-information price; for other values these sellers have to post a higher price to keep lower-quality sellers from mimicking them. In an extension, we show that if sellers compete with auctions, the reserve price can also act as a signal. |
| Keywords: | competitive search, signaling |
| JEL: | C78 D82 D83 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11309 |