With the rapid development of financial markets, various invest models have emerged to help investors make better decisions. There are mainly single-factor models like CAPM, multi-factor models like Fama-French three factor model, as well as models incorporating machine learning techniques. Each model has its own advantages and limitations. Understanding these mainstream invest models can help investors grasp the logic behind them, compare them, and ultimately make more informed investment choices. Some of the key models are CAPM, Fama-French three factor model, Carhart four factor model, Hou-Xue-Zhang four factor model etc.
CAPM explains expected returns through market risk
The capital asset pricing model (CAPM) is based on the theory that the expected return of an asset is determined by its sensitivity to the market risk, represented by the beta factor. CAPM shows the relationship between an asset’s expected return and its risk. It assumes that investors are risk averse and only expect to be compensated for the systematic risk of the asset measured by beta. However, CAPM only considers market risk and ignores risks associated with size, value, momentum etc. It is a single factor model with limitations.
Fama-French model incorporates size and value factors
Eugene Fama and Kenneth French proposed the three factor model in 1993 to improve upon the CAPM model. The Fama-French three factor model considers three risk factors – the market factor, the size factor (SMB) and the value factor (HML). The model shows that small-cap and value stocks tend to outperform markets on a risk-adjusted basis. The model thus better explains the cross-section of stock returns using these additional factors.
Carhart model adds momentum as a factor
The Carhart four factor model builds upon the Fama-French model by adding the momentum factor. It argues that stocks with stronger past performance tend to continue to outperform in the future. Adding the momentum factor helps explain returns better. However, the model still suffers from data mining bias and does not fully explain all anomalies.
Advanced models use more factors and machine learning
Some other advanced multi-factor models like the Hou-Xue-Zhang four factor model uses an investment factor instead of the size factor. The Fama-French five factor model incorporates profitability and investment factors. Overall, multi-factor models with 3-5 factors are most popular in academia. Incorporating machine learning techniques into modeling is also an emerging trend. While more factors can lead to better in-sample fit, parsimony is key.
There are a variety of mainstream invest models like CAPM, Fama-French, Carhart model etc. More advanced models incorporate more factors like momentum, investment, profitability etc. and machine learning techniques. Each model has its own merits and limitations. Understanding these models can help guide investment practices.