Problem
Research questions and friction points this paper is trying to address.
Portfolio Optimization
Non-Stationarity
Noisy Data
High Transaction Costs
Financially Guided Deep Portfolio Optimization
🤖 AI Summary
本文针对金融市场的非平稳性、噪声数据及高交易成本问题，提出了一种端到端的深度投资组合优化框架，通过直接优化财务指标的可微代理来学习投资权重。
📝 Abstract
Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction costs. Standard predict-then-optimize methods first forecast returns and then solve for weights, compounding prediction errors and often failing under regime shifts. We propose an end-to-end framework that directly optimizes differentiable surrogates of key financial metrics - Sharpe ratio, Omega ratio, Conditional Value-at-Risk (CVaR), and Risk Parity - allowing neural networks to learn portfolio weights via backpropagation. Our expanding-window walk-forward procedure, applied to 50 S&P 500 stocks from 2007 to 2023, incorporates realistic bid-ask spread costs and rebalances quarterly. On the challenging out-of-sample test period (2022-2023), the best model - an AttentionLSTM with the Omega-CVaR-RiskParity loss - achieves an annualized Sharpe of 0.29 and a total compounded return of +7.86%, while the S&P 500 delivers -4.52% total return and an annualized Sharpe of -0.02. This outperforms the S&P 500 by 12.38 percentage points (a relative improvement of over 270%), while keeping tail risk (CVaR) nearly unchanged. The framework consistently outperforms the equal-weight portfolio, S&P 500, and traditional methods (MVP, HRP, NCO), demonstrating that embedding financial objectives directly into model training yields robust, economically meaningful outperformance even in adverse market conditions.
Innovation
Methods, ideas, or system contributions that make the work stand out.
end-to-end framework
differentiable surrogates
financial metrics
backpropagation
AttentionLSTM
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