Value investing aims to identify undervalued stocks that have the potential to deliver high returns over the long term. While traditional value investors rely on fundamental analysis, quantitative value investing uses statistical models and algorithms to identify attractive value stocks. For beginners looking to get into algorithmic value investing, there are a few key steps to follow.
Understand the principles of value investing
The core premise of value investing is to buy quality stocks trading at a discount to their intrinsic value. Beginners should understand valuation metrics like P/E ratio, discounted cash flows, and margin of safety before developing quantitative models.
Get historical pricing data for analysis
Accurate and clean historical pricing data is crucial for testing value investing strategies. Beginners can use free datasets or subscribe to financial data platforms like Tiingo, IEX Cloud for daily OHLCV and fundamentals data.
Research factors that identify undervalued stocks
Key factors like low P/E, high dividend yield, low PB ratio can signify undervalued stocks with upside potential. Beginners should research academic papers and backtest different factor combinations that have worked.
Build and backtest the algorithmic models
Using the historical data, beginners can train and test different machine learning and statistical models like regression, random forests to identify undervalued stocks automatically. Tracking model performance is key.
By following these steps of understanding value investing, collecting data, researching factors and finally building models, beginners can develop profitable algorithmic value investing strategies.