CAPM, Fama-French Factors, and Efficient Frontier Portfolio Construction

Summary

This project report explains portfolio diversification through Modern Portfolio Theory and introduces measures including alpha, beta, standard deviation, R-squared, and the Sharpe ratio. It describes the Fama-French three-factor model as an extension of CAPM that adds size and value factors. The project applies these ideas to Indian equities, using Python to retrieve market data, select stocks across market-cap segments, and use an efficient frontier to compare portfolios by expected return, volatility, and Sharpe ratio.

The proposed model allocates most capital to a diversified group of large-, mid-, and small-cap stocks, with a smaller cash or liquid allocation for opportunities. Its selection method uses beta for large-cap names and volatility for mid- and small-cap names. The report is incomplete in the supplied text, and detailed factor-model specifications, selected holdings, backtest periods, transaction costs, and performance evidence are absent. Its portfolio results therefore cannot be independently evaluated, and Sharpe ratio limitations for non-normal returns and nonlinear risks are acknowledged.

Key ideas

• Modern Portfolio Theory uses diversification and asset correlations to seek improved risk-return tradeoffs.
• CAPM beta measures market-linked risk, while the Fama-French model adds size and value factors.
• The project uses Python data analysis and an efficient frontier to compare candidate Indian equity portfolios.
• Its proposed allocation combines stocks across market-cap categories with a cash or liquid reserve.
• The report omits enough implementation and performance detail that its portfolio findings cannot be verified.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.