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).

中文翻译：

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量化投资的不确定性前瞻因子模型

上市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，即报告数据的功能，这些因素在历史上与股票市场表现相关。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面计算的因子（通过 oracle）来选择股票，我们的投资组合将远远优于标准因子模型。受此见解的启发，我们训练深度网络以从过去 5 年的历史中预测未来的基本面。我们提出了先行因子模型，将这些预测的未来基本面插入到传统因子中。最后，我们结合了神经异方差回归和基于 dropout 的启发式方法的不确定性估计，通过调整我们的投资组合来规避风险来提高业绩。在回顾性分析中，我们利用行业级投资组合模拟器（回测器）来显示年化回报率和夏普比率的同步改善。具体而言，不确定性感知模型的模拟年化回报率为 17.7%（标准因子模型为 14.0%），夏普比率为 0.84（0.52）。