Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Forecasting Company Fundamentals
Company fundamentals are key to assessing companies' financial and overall success and stability. Forecasting them is important in multiple fields, including investing and econometrics. While statistical and contemporary…
EconometricsUncertainty-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…
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 …
Stock Market Market Crash of 2008: an empirical study of the deviation of share prices from company fundamentals
The aim of this study is to investigate quantitatively whether share prices deviated from company fundamentals in the stock market crash of 2008. For this purpose, we use a large database containing the balance sheets an…
regressionFinTradeBench: A Financial Reasoning Benchmark for LLMs
Real-world financial decision-making is a challenging problem that requires reasoning over heterogeneous signals, including company fundamentals derived from regulatory filings and trading signals computed from price dyn…
Response Generation