Uncertainty-Aware Lookahead Factor Models for Quantitative Investing
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).
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Uncertainty-Aware Lookahead Factor Models for Improved Quantitative Investing
On a periodic basis, publicly traded companies are required to report fundamentals: financial data such as revenue, earnings, debt, etc., providing insight into the company’s financial health. Quantitative finance resear…
Thinking into the Future: Latent Lookahead Training for Transformers
Autoregressive language models trained with next-token prediction generate text by sampling one discrete token at a time. Although very scalable, this objective forces the model to commit at every step, preventing it fro…
The Role of Lookahead and Approximate Policy Evaluation in Reinforcement Learning with Linear Value Function Approximation
Function approximation is widely used in reinforcement learning to handle the computational difficulties associated with very large state spaces. However, function approximation introduces errors which may lead to instab…
Robust Stackelberg Equilibria in Extensive-Form Games and Extension to Limited Lookahead
Stackelberg equilibria have become increasingly important as a solution concept in computational game theory, largely inspired by practical problems such as security settings. In practice, however, there is typically unc…
FormE2EAI: End-to-End Deep Learning Framework for Active Investing
Active investing aims to construct a portfolio of assets that are believed to be relatively profitable in the markets, with one popular method being to construct a portfolio via factor-based strategies. In recent years, …
Deep Learning