Quantitative value investing refers to utilizing data science and coding to implement fundamental analysis principles for identifying undervalued stocks. For beginners interested in learning this approach, there are many free code repositories and tutorials on GitHub that demonstrate basic quantitative value investing techniques. These resources cover value factors like low P/E and P/B ratios, dividend yields, net-net stocks, and insider transactions that can be backtested on historical data. Beginners can use these GitHub tools to screen stocks, build valuation models, backtest value strategies, and get started with basic algo trading. Key benefits include leveraging data at scale, systematic rules-based investing, and gaining coding skills.
Valuation factor backtesting with free GitHub libraries
One of the most popular ways to employ quantitative value investing for beginners is by using GitHub libraries like Pandas, NumPy, Matplotlib etc. to backtest the historical performance of valuation factors. For example, the repository ‘value_investment_strategy’ implements a basic P/E and P/B screen along with price momentum on US stocks over 10 years. By cloning this repository, beginners can study the code, experiment with factor tweaks, evaluate outperformance vs benchmarks, and get hands-on practice in systematic value investing techniques.
Implementing net-net and insider transaction screens
Beyond simple value ratios, more advanced GitHub repositories allow beginners to screen for niche quantitative value strategies. The pythalesians library has methods for finding net-net stocks with market caps below their net current asset value. And the library stock_analysis implements event-driven screens for insider transactions and buyback announcements that signal potential undervaluation. Studying these resources allows beginners to move beyond textbook value approaches to real world implementation.
Resources for dividend stock analysis
In addition to asset-based valuation metrics, income-oriented value investors also focus heavily on dividends and buybacks. Quantative analysis of these cash distribution factors can also be implemented for free through GitHub libraries. The repository ‘dividend_stock_screener’ allows filtering stocks by dividend yields, payout growth rates, earnings coverage of dividends and more. Other tools like SimFinExcel help integrate fundamental data into Excel for further modeling and analysis of dividend stocks.
In summary, GitHub offers excellent free resources for beginners to learn quantitative value investing techniques through coding factor screens, valuation models, backtesting and more using real stock market data.