In the previous two chapters, we discovered **the Capital Asset Pricing Model**, which uses **a single factor**, and then transitioned to **the Fama-French Model**, which incorporates **three factors**.

However, there is still potential **to identify additional factors** that could enhance predictions.

One suggestion is **to expand the standard Fama-French Model** with three factors, by introducing two new factors:

* **Profitability factor** (**RMW** or **Robust Minus Weak**) - which distinguishes between companies with strong profitability and those with weak profitability;

* **Investment factor** (**CMA** or **Conservative Minus Aggressive**) - which highlights the relationship between investment strategies and stock returns.

**RMW** is computed **by taking the average return of robust companies** (companies with high profitability) and **subtracting the average return of weak companies** (companies with low profitability).

**CMA** is computed **by taking the average return of conservative companies** (companies that less tend to invest in growth opportunities and focus more on stable projects with lower risk) and **subtracting the average return of aggressive companies** (companies that invest heavily in growth and expansion, usually taking on more risk in hopes of higher returns).





As well as before, all necessary data could be found <a href="https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html"
target="_blank"
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onmouseover="this.style.color='#ff0000'"
onmouseout="this.style.color='#d27204'">by the following link</a>.





This course will help those who have knowledge of Python and Probability Theory and want to get some basic knowledge of Portfolio Management, including fundamental financial concepts like risk and return, basics of portfolio optimization and influence of factors on portfolio's behaviour.

To start, we'll explore some fundamental financial concepts that will be essential for the rest of the course. These include investing, diversification, portfolio, return and risk. We'll also learn how to use Python to work with them.

Now that we've covered the basics of portfolio analysis, it's time to explore methods for choosing optimal weights for our portfolio. Specifically, we'll discuss fundamental concepts in portfolio optimization, including Modern Portfolio Theory and its key component - the Efficient Frontier. Additionally, we'll learn how to balance high returns with low risk, and how to determine the weights for a portfolios that achieves the best possible return-risk trade-off or the lowest risk, all using Python.

Finally, after covering the fundamentals of portfolio analysis and optimization, we’re ready to dive into predicting our portfolio's behavior and understanding the impact of various factors on it. Practically, we'll explore the basics of Factor Analysis, including different types of factors and several models built around them.

The 5-factor Model

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