 Hello, and welcome to the session. This is Professor Farhad in which we would look at multi-factor model and CAPM. If you don't know what CAPM is, please look at the previous recording. You really need to understand CAPM in order to appreciate the multi-factor model. The multi-factor model is just one step further. Hello, and welcome to the session. This is Professor Farhad in which we would look at the multi-factor model and CAPM. So basically what we're going to do, we're going to take CAPM and add more factors to that model. That's all that's to it. So if you don't know what CAPM is, please look in the course essential of investments and look for the CAPM explanation. This topic is covered in essentials of investments. It's also covered on the CFA exam, also covered on the CPA exam. Simply put, on the CPA exam, you might see it in a multi-factor model or on the CPA exam. They might ask you to read or understand how multi-regression work. That's all we are doing here. We are taking a single regression and we're turning that single regression into a multiple model or a multiple regression. Very straightforward concept once you see the formula. As always, I'm going to remind you to connect with me on LinkedIn. If you haven't done so, YouTube is where you would need to subscribe. I have 1,800 plus accounting, auditing, tax, finance, as well as Excel tutorial. If you like my lectures, please like them, share them, put them in playlists. Thank you very much in advance. Connect with me on Instagram. On my website, farhatlectures.com, you will find additional resources to supplement and complement. This course, as well as your other courses, CFA, CPA or CMA exam, I strongly suggest you check out my website. So let's take a look at the multi-factor model. CAPM estimate the expected return on a security and it's the most popular way by looking at beta. So basically what we do is we'll take the risk premium of the security scaled by a beta. So if your beta is 1, you'll take the risk premium times 1. Beta of 1, remember, is the same as the market. Or your beta could be 1.5. So if the risk premium is 10, you'll take 10 times 1.5 and you add to it the risk-free rate. And this is how you come up with your expected return. So simply put, we are using only one factor. Remember, the risk-free is the intercept. If we remember when we draw this line or when we show CAPM visually, the risk-free rate is only Y-access. So this is the risk-free rate. Then this is the market portfolio. This is the beta. And this is the return. So if the beta is 1, it means we are dealing with the market portfolio or the market return. OK, so all what we're saying is is your return. And this is your expected return on the Y-access, your expected return. So your expected return is a function of your beta, basically one factor, one factor. So we're looking at the beta and based on the beta, so if your beta changes to 2, to 2, then your expected return will change. So we're looking at beta, looking at beta. So it's assuming only one factor and that factor beta is called systematic risk. It's the risk that we cannot diversify. And if you really think about it, systematic risk includes everything. The market risk would include stuff like inflation, expectation about the economy, the GDP, macroeconomic factors. It's a lot of information in beta because it's representing all the systematic risk. But nevertheless, it's only one factor, which is beta. We can break down the factors. If we want to have, we could have two factors. In a two factor, let's look at a two factor. The expected return of the security would be the sum of three terms. Why three terms? One is the risk-free. So what we're going to do, we're going to keep the risk-free as one factor. We're going to look at this, plus we're going to add the beta for something else. We're going to add the beta for something else. Therefore, we have risk-free. This is one factor, two factor, three factor. So we're only looking at basically two changes because risk-free rate is the same. The risk-free rate, the security sensitivity to the market index, its market data times the risk-premium of the index, whatever that risk-premium is, which is the risk-premium is this part here. Then the security sensitivity to, let's assume interest rate. Or instead of interest rate, we could use some other factor. We could use inflation. Let's assume t-bond beta represent the interest rate times the risk-premium of the t-bond portfolio. So now what's happening is the expected return is a factor of the market beta and the t-bond beta. It's two factor rather than one factor. So this formula right here is a one factor regression. Basically, it's a single regression. Here when we have two, now we have two factors. This is the market and this is the t-bond. So now we have multiple, multiple regression simply put. If you really want to look at it that way, this is basically one factor. And here we are talking about one and two. The best way to illustrate this is let's take a look at an example, just to see how the numbers would flow through the formula. Let's assume Southeast A-line has a market beta of 1.2. So that's one beta, one factor, market beta and t-bond beta, which is the interest rate of 0.7. Suppose the risk-premium of the market is 6%. This is the risk-premium while the t-bond portfolio is 3%. Well, what does that mean? It means the market beta is, the beta is 1.2 and the risk-premium for the market is 6. So we're going to take 1.2 times 6% plus. So this is the market beta plus. Plus we're going to take the beta for the t-bond 0.7 times 3%. So that's exactly what we are doing here. Now, obviously to complete the formula, we have to add to it, obviously the risk-free, but we're not talking about the risk-free yet. We're not giving the risk-free, but basically this is the complete formula. So if we compute the formula, what we find out is, let me just do it step by step. Let's take 1.2 times 6%. So if we take 1.2 times 0.06, that's 0.072 plus 0.021, that's 0.21, that's 9.3. Therefore, these two factors are 9.3 and this is what I'm going to show you. So it's 9.3, 7.2 plus 2.1. Now, what we need to do is find out what's the risk-free rate. Assuming the risk-free rate is 4%. So the expected return on the portfolio will be something like 4%. This is the risk-free rate plus 7.2. The 7.2, which is 6 times 1.2 plus the 0.7 times 3%. We'll give us total expected return of 13.3 in the formula. In the regression, it would look something like this. This is the y-intercept, the risk-free rate, and those are the two factors. Now, let's change the example here. Let's assume the market has a risk-premium of 4% and the T-bond has a risk-premium of 2%. What will be the expected return on Southeast or Southeast airline? Well, simply put, we'll change the numbers. The risk-free rate stays the same, 4%. And what's going to happen is this, we're not changing the beta here. We're changing the risk-premium plus the beta is 1.2. Times 0.04 plus the other beta is 0.7, 70% times 0.02%. And if we perform the computation, we're going to come up with 10.2%. So notice here, beta stayed the same. What was reduced is we assume a different risk-premium. Obviously, if your risk-premium goes down, your return will go down. Now, this is a multi or let's assume two-factor model. Now, can we have more factors? Of course we can. This is what we're going to look at in the next session. We're going to look at the Fama-French three-factor model. The point is, beta is not the only thing that could explain the return. Let's look at other factors. So in the next session, we'll look at three factors. As always, I'm going to ask you to like this recording if you like it. Share it, put it in playlist. As always, also, I'm going to invite you to visit my website, forhatlectures.com for additional resources. Study hard, good luck and stay safe.