  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).
