Using Fundamental Data in Quantitative Stock Strategies
Summary
The discussion asks how to build a quantitative stock strategy from prices and financial reports, with backtesting aimed at return and risk-adjusted performance. The responses explain that fundamental investing in quantitative form is often expressed through multifactor models that use company characteristics to predict returns or other investment variables. Examples mentioned include established academic factor frameworks and proprietary industry models.
A second response notes that professional strategies may use regulatory filings such as annual and quarterly reports, while cleaned historical fundamental data can be costly and is often supplied by commercial vendors. This helps explain why detailed examples and datasets may be less visible online. The post offers broad orientation rather than a working algorithm: it gives no factor definitions, accounting-data timing rules, portfolio construction method, or backtest results. In practice, access to reliable point-in-time data and careful treatment of reporting dates would be necessary to evaluate such a strategy.
Key ideas
- Quantitative fundamental strategies commonly use multifactor models to forecast returns or related variables.
- Academic and proprietary models are both used to organize fundamental signals.
- Professional strategies may incorporate information from company filings such as annual and quarterly reports.
- Commercial vendors provide cleaned fundamental data, which can limit public examples and research access.
- The discussion provides no specific factors, portfolio rules, or backtest evidence.
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Full text
# How to incorporate fundamental analysis in quantitative trading algorithm? # How to incorporate fundamental analysis in quantitative trading algorithm? I want to write a quantitative stock trading program based on fundamental analysis. It would crunch through prices, financial reports to look for value stocks. It can support backtesting of strategy with historical data to maximize annual return and sharpe ratio. All the studies, libraries or codes on quantitative trading that I can find are based on some form of technical analysis or arbitrage. Why isn't there any fundamental analysis trading algorithm? given that it is a cornerstone of investing. If you know any example of such algorithm, please share with me. ## Answer by Alexander Didenko (score 4) https://quant.stackexchange.com/a/10653 There are no "fundamental algos" analogous to "technical algos". Instead, quantitative useof fundamental data assumes applying multifactor models to predicting returns and other intrument parameters. That models vary from "academical" (like Fama-French 3-factor or Chen, Roll, Ross) to proprietary models of guys from industry: MSCI Barra, Bloomberg, CSFB, Morgan Stanley, Salomon Smith Barney are in the "open access", to some extent. ## Answer by Brian B (score 3) https://quant.stackexchange.com/a/10649 Most of what you see on the web is done by amateurs. Many professionals do use 10K and 10Q information in so-called "quant" strategies, but electronic stores of this information don't come free. As a consequence, you don't see a lot on the web about using fundamentals in model-driven investment strategies. Compustat/CapitalIQ is the most well-known fundamental data source. FactSet and Bloomberg clean and modify Compustat data before passing it on to their customers.
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