论文标题
不确定性感知到定量投资的lookahead因素模型
Uncertainty-Aware Lookahead Factor Models for Quantitative Investing
论文作者
论文摘要
公开交易的公司定期报告基本面，财务数据，包括收入，收益，债务等。定量金融研究已经确定了几个因素，这是与股票市场绩效相关的报告数据的功能。在本文中，我们首先通过模拟表明，如果我们可以通过根据未来基本原理计算的因素（通过Oracle）选择股票，那么我们的投资组合将远远超过标准因素模型。在这种见识的激励下，我们训练深网，以预测未来5年历史的未来基本面。我们提出了LookAhead因子模型，该模型将这些预测的未来基本面插入传统因素。最后，我们从神经异质分析回归和基于辍学的启发式术中结合了不确定性估计，通过调整投资组合以避免风险来提高性能。在回顾性分析中，我们利用行业级的投资组合模拟器（Backtester）同时改善了年度回报和夏普比率。具体而言，不确定性感知模型的模拟年度回报率为17.7％（标准因子模型为14.0％），夏普比率为0.84（vs 0.52）。
On a periodic basis, publicly traded companies report fundamentals, financial data including revenue, earnings, debt, among others. Quantitative finance research has identified several factors, functions of the reported data that historically correlate with stock market performance. In this paper, we first show through simulation that if we could select stocks via factors calculated on future fundamentals (via oracle), that our portfolios would far outperform standard factor models. Motivated by this insight, we train deep nets to forecast future fundamentals from a trailing 5-year history. We propose lookahead factor models which plug these predicted future fundamentals into traditional factors. Finally, we incorporate uncertainty estimates from both neural heteroscedastic regression and a dropout-based heuristic, improving performance by adjusting our portfolios to avert risk. In retrospective analysis, we leverage an industry-grade portfolio simulator (backtester) to show simultaneous improvement in annualized return and Sharpe ratio. Specifically, the simulated annualized return for the uncertainty-aware model is 17.7% (vs 14.0% for a standard factor model) and the Sharpe ratio is 0.84 (vs 0.52).
