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Introduction to Portfolio Management with Python
Introduction to Portfolio Management with Python
The Fama-French Factor Model
Previously we've discussed the most famous factor model - the CAPM.
This model allows us to predict a portfolio's return using the market's excess return as a key factor.
However, there are many other factors that can impact the performance of our portfolio. Given this, we will now explore a broader version of this model.
What is Fama-French Model?
Created by Eugene Fama and Kenneth French in the early 1990s, this model has become a fundamental concept in finance, especially in portfolio management and asset valuation.
It can be expressed using the following formula:
As we can see, if we take only the first term from the right side, we receive the CAPM itself.
However, we are now considering two additional factors: SMB, which represents the size factor, and HML, which is the value factor.
Next, we will provide a more detailed explanation for each of these factors in practice.
SMB and HML
Research has shown that small-cap stocks often outperform large-cap stocks over the long term.
The primary reasons for this are that small companies typically have greater growth potential compared to established large firms, and smaller companies may be undervalued due to receiving less market attention, creating more opportunities for investors.
SMB is calculated by taking the average return of small-cap stocks over a specific period and subtracting the average return of large-cap stocks.
To begin with, let's clarify that value stocks have low stock prices relative to their earnings or low book value, while growth stocks have high stock prices relative to their earnings or high book value.
This book value offers insights into whether a stock is undervalued or overvalued in relation to its fundamental worth.
Here, value stocks tend to outperform growth stocks over time, because value stocks may be undervalued due to market overreactions to negative news and value stocks often carry a higher perceived risk, leading to higher expected returns to compensate investors.
HML is computed by taking the average return of the value stocks and subtracting the average return of the growth stocks.
Practically, you can get values of all this factors on this website.
Alpha
Another key feature of the Fama-French Model is Alpha.
Practically, Alpha is a measure of an investment's performance compared to the expected return predicted by the model, based on its exposure to various factors.
Specifically, positive alpha suggests that the asset has outperformed the expected return given its factor exposures, indicating that it may be a good investment. Conversely, negative alpha indicates underperformance relative to the expected return, suggesting that the investment may not be optimal.
For a more detailed explanation with code examples - watch the following video:
Here is a corresponding Google Colab.
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