Title: Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals,
Abstract: On a periodic basis, publicly traded companies are required to report
fundamentals: financial data such as revenue, operating income, debt, among
others. These data points provide some insight into the financial health of a
company. Academic research has identified some factors, i.e. computed features
of the reported data, that are known through retrospective analysis to
outperform the market average. Two popular factors are the book value
normalized by market capitalization (book-to-market) and the operating income
normalized by the enterprise value (EBIT/EV). In this paper: we first show
through simulation that if we could (clairvoyantly) select stocks using factors
calculated on future fundamentals (via oracle), then our portfolios would far
outperform a standard factor approach. Motivated by this analysis, we train
deep neural networks to forecast future fundamentals based on a trailing
5-years window. Quantitative analysis demonstrates a significant improvement in
MSE over a naive strategy. Moreover, in retrospective analysis using an
industry-grade stock portfolio simulator (backtester), we show an improvement
in compounded annual return to 17.1% (MLP) vs 14.4% for a standard factor
model.