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Uncertainty-Aware Lookahead Factor Models for Quantitative Investing
Lakshay Chauhan · John Alberg · Zachary Lipton

Tue Jul 14 08:00 AM -- 08:45 AM & Tue Jul 14 08:00 PM -- 08:45 PM (PDT) @ Virtual #None

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).

Author Information

Lakshay Chauhan (Euclidean Technologies)
John Alberg (Euclidean Technologies)
Zachary Lipton (Carnegie Mellon University)

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