AlphaPortfolio: Goal-Oriented Investment Management Through Deep Reinforcement Learning -- by Lin William Cong, Ke Tang, Jingyuan Wang

We adapt attention-based neural networks and reinforcement learning to direct portfolio construction, allowing broader portfolio-management objectives (including non-time-additively separable ones) and in a data-driven way, searching over a much richer policy/strategy space than low-dimensional parametric rules or human-specified strategies. As arguably the first non-text-based, “large” GenAI model in Finance, AlphaPortfolio accommodates long- and short-range path dependence in firm and market states (e.g., using Transformer encoder), cross-asset information, flexible (path-dependent) objectives (incl. Sharpe ratio, which is non-additively separable across periods) for end-to-end (rather than step-by-step) optimizations. In U.S. equities, AlphaPortfolio yields superior out-of-sample performance (e.g., Sharpe ratio above two and risk-adjusted alpha over 13% with monthly rebalancing) robust under various market conditions and economic restrictions (e.g., exclusion of small/illiquid stocks) and over time. The gains come from the direct construction, effective sequence modeling, and cross-asset attention network. We further demonstrate AlphaPortfolio's flexibility to incorporate transaction costs, state interactions, and alternative objectives, before developing a polynomial-feature-sensitivity analysis to uncover key drivers of performance, including their rotation and nonlinearity.