内容简介:

This paper introduces a reinforcement learning framework that non-parametrically estimates the optimal multi-period portfolio strategy for a given investment horizon, subject to realistic and predictable trading costs. Conditioning on a comprehensive set of stock characteristics and macroeconomic indicators, the trading-cost-aware portfolio strategy substantially outperforms market benchmarks in out-of-sample tests, and is robust to various limits-to-arbitrage constraints. I demonstrate that incorporating explicit trading-cost penalty is critical to avoid extracting performance from small and illiquid stocks, better capture market stress periods, and allocate assets based on more fundamental signals.

演讲人简介：

Chengyu Zhang is currently finalizing a Ph.D. in finance at McGill University. His research focuses on empirical asset pricing, machine learning and big data, financial derivatives, and market microstructure. Chengyu has developed significant expertise in applying reinforcement learning to portfolio strategies, analyzing option illiquidity, and exploring AI's role in textual analysis. He received an honors bachelor’s degree in statistics from the University of Toronto, and a master’s degree in finance from McGill University.

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