英文标题：
《Risk-Sensitive Compact Decision Trees for Autonomous Execution in
  Presence of Simulated Market Response》
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作者：
Svitlana Vyetrenko, Shaojie Xu
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最新提交年份：
2021
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英文摘要：
  We demonstrate an application of risk-sensitive reinforcement learning to optimizing execution in limit order book markets. We represent taking order execution decisions based on limit order book knowledge by a Markov Decision Process; and train a trading agent in a market simulator, which emulates multi-agent interaction by synthesizing market response to our agent\'s execution decisions from historical data. Due to market impact, executing high volume orders can incur significant cost. We learn trading signals from market microstructure in presence of simulated market response and derive explainable decision-tree-based execution policies using risk-sensitive Q-learning to minimize execution cost subject to constraints on cost variance.
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中文摘要：
我们展示了风险敏感强化学习在限额订单市场中优化执行的应用。我们用马尔可夫决策过程表示基于极限订单书知识的订单执行决策；并在市场模拟器中培训交易代理，该模拟器通过从历史数据中综合市场对代理执行决策的反应来模拟多代理交互。由于市场影响，执行大量订单可能会产生巨大的成本。我们在模拟市场反应的情况下，从市场微观结构中学习交易信号，并使用风险敏感Q-学习推导基于可解释决策树的执行策略，以最小化成本方差约束下的执行成本。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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