This post is part of a series on using Pandas datareader to retrieve financial data:
- Using Python and Pandas Datareader to retrieve financial data - part 1: Federal Reserve Data (FRED)
- Using Python and Pandas Datareader to retrieve financial data - part 2: Fama & French library
- Using Python and Pandas Datareader to retrieve financial data - part 3: Yahoo Finance
In this post we will look at the datasets made available by Eugene Fama and Kenneth French. Eugene Fama and Kenneth French did a lot of research on which factors drive security returns. In 1993, they published the Three Factor Model (see article "Common risk factors in returns of stocks and bonds", Journal of Financial Economics 33, 1993), which showed that their factors (size of the firm, book-to-market values and excess return) capture a statistically significant fraction of the variation of stock returns. In 2014, Fama and French adapted their model to include five factors. Fama won the Nobel Prize for Economics in 2013 for his research. Fama also published a number of papers on the Efficient Market Hypothesis and random walk theory.
Fama and French still publish the returns of various investment factors analyzed by them on their homepage on a regular basis. You can download this data using the pandas_datareader library - you can take a look at the official documentation, Fama-French Data (Ken French's Data library) to get started or take a look at the Jupyter notebook that I shared on Github https://github.com/jorisp/tradingnotebooks/blob/master/FAMA.ipynb
References:
- Fama/French forum ideas and observations from Fama and French
- How to use the Fama French model
- Fama-French 5-factor model: five major concerns
- In pursuit of the perfect portfolio: Eugene F. Fama (YouTube - MIT Laboratory for Financial Engineering )
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