[MUSIC] Learning outcomes. After watching this video, you will be able to understand the concept of beta. [MUSIC] >> All right, now let's continue with our trading strategies. The strategy that I want to talk about right now is based on a concept known as beta. It's about risk. So before getting into the strategy, the paper is titled as Betting Against beta. But before we start the strategy, we have to have some understanding of this beta. Beta, beta, whatever, depending on which part of the world you are. In some part of the world, it's known as beta, some parts of the world it's known as beta, whichever, its meaning is the same. Now this whole concept of beta came from terms made by financial economist to measure risk, right? So an assumption made in academic finance is that since investors are sufficiently diversified, what really matters is the systemic risk, systematic risk, not individual stock specific risk or idiosyncratic risk. What do I mean by systematic risk and idiosyncratic risk? Let me give you an example. The risk of, say inflation. The risk of a recession. Now these are risks which are systematic. That means it is applicable to the entire market. This is not stock specific. Of course, different stocks respond differently to this. I'll come to it in a while. But these are not particular sector specific or stocks specific risk, currency depreciation for example. Whereas think of something happening to a company, some company loosing court case, for example, or CEO death of a company. Now, these are not risks which are applicable to the entire market. These are risks a company faces. These are known idiosyncratic risks. Now, if you think carefully, it's easy to see that idiosyncratic risk can be diversified away. Now, what I do mean by diversifying away idiosyncratic risk? Some companies will have negative shocks. Some companies will have positive shocks. In some companies, let's take this cold case, some companies may be losing cases, some companies may be winning cases right. Some industries may face positive price shock, some industries may face negative price shocks, right. So these are shocks, if you're sufficiently diversified, whenever idiosyncratic shocks hit, they cancel out each other. Therefore, these for a diversified investor, an investor who holds a diversified portfolio, these do not matter as much. Of course, there are some papers which show that these matter, because investors are not sufficiently diversified. But let's stick to this idea that for a diversified investor, these idiosyncratic risks that impact a particular company or a particular sector, they don't matter as much. What is more important is systematic risks. Now these systematic risks impact the entire market. Yes, they impact the entire market but the way they impact each company is differently. Now, sensitivity of each company, a company to the systematic risk is known as beta. That's all. So by definition, systematic risk of the market portfolio is one. Why? Because market portfolio represents a diversified portfolio of the entire market. So this is theory. Now how do we measure it? Very simple. All that you have to do is how sensitive is a particular stock return to market return. That's what is beta all about. For example, if when the market goes up by 1%, if a particular company's stock goes up on an average by 1.5%, then this particular company has a beta of 1.5. A company whose stock goes up or down with the market, say twice as much, has a beta of two. As simple as that. Now, some more points about beta. So companies which have high beta are known as aggressive stocks or growth stocks. Companies which have high growth, high uncertainty or high exposure to systemic risk have high beta. Companies which have low growth usually have a low beta and they are known as defensives. Now, can you think of an example? Pause for a while and think. Yes, utilities, utilities have very low betas. So because when the market moves, they don't move as much as an aggressive stock would move, as a high growth stock would move. So that is why they are lowest. So now how do you get your betas? It's very simple. Most of companies list their companies' betas are publicly available. You don't have to compute. Anyway, we have given the formula in the slides. But you need not compute. You'll get it publicly. So all that our strategy requires is, the strategy that I'm going to talk right now requires is calculation of this beta. And then you're to sort companies based on beta. Enough of the background now, let's get into the actual trading strategy. Like in the case of all other strategies what we are going to do is first we'll read out the abstract, understand what the strategy is all about. Where it works, where it will not work, and then we'll actually go through the strategy in detail and see what has been the return when our tests are calculated for the first time. And we'll produce some more results that we have calculated ourselves, right? So let's start with the paper. So the paper is titled as Betting Against Beta. So as I've told you, by now you are familiar with the concept of beta. This is a paper [COUGH] published, I'm sorry, published in Journal of International Economics. So, this is one of the top journals in finance. So far, we have been talking about strategies which are mostly published in accounting journals. So, even the the Sloan Circular are normally are even the uglier one Perdoski. I hope you guys still remember Perdoski? The value investing formula. All these are articles which were published in top accounting journals way back. And this is an article which is published in a finance journal, so JFE, Journal of Financial Economics. The title of the paper is Betting Against Beta. And the authors are Andrea Frazzini and Pedersen. Pedersen is from NYU. Now when I say this betting again beta, by now you must have some rough idea regarding what I'm going to do. So I think it's time that you pause for a while and absorb. So when I say betting against beta, what is likely to come in this strategy? Betting against beta, right? So let me go take you through the abstract. We present a model with leverage and margin constraints that vary across investors and time. Now, this is for, again, purely from a trading point of view, you need not worry about this. All that author is saying is they have built a theoretical model as to why their trading strategy works. So, [COUGH] that is not essential for us. Those of you who are interested in understanding the theory behind this can go through. If you have any question you can write to us. We'll proceed. We find evidence consistent with each of the model's five central predictions. So what they do is that, let me spend [COUGH] spend a few seconds here. They build a model where investors are leveraged, and they have very high constraints. So this model yields five predictions, so we are interested, as trainers, as students of training, we are interested in two of those five predictions. I'll just read out all the five. Again, as I've said, if you are interested you can go through them in detail, but we are interested in two of them. So what is the first? First, because constrained investors bid up high-beta assets. So their model says constrained investors bid up high-beta assets. Now why do they do it? I'll talk about the model a little bit because as I've said, here we are not going to mechanically implement reading strategies. This is not a data mining exercise. We are going to tell you economic rational behind strategies, I've been telling this repeatedly. In this paper, in this strategy, we will tell you, in reasonable detail, why this strategy works. So, he gives a hint here. Because constraint investors bid up high beta stock prices. Now, why they bid up is something that we'll see when we see the theory of this. But they bid up for time being assume that high constraint investors bid high beta stock prices up. High beta is associated with low alpha. Now, another term you should know is alpha. I should have told you about this when I described beta. So, alpha is nothing but the extraordinary return that you make. So, there is an expected return, right. So, suppose, let me give you an example. Suppose a stock has a beta of say two, right? Expected beta of two, in the past, using past data. Now the market return let's say is 5% for a period. And the risk-free return is, say, 3%. Just to give an example, we have already given you the formula, if you remember the formula what is that? Alpha plus, alpha is risk-free return plus market return minus risk free-return into beta, right, that's expected return. Suppose let;s take this example of four marks and expected return For 5%, risk free rate is 3%. So the excess return is 2%, beta is 2, that means you have 4% plus the risk free rate of 2%. So total expected return will be 6%. Now this is expected return but if the stock actually gives a return of 8% then the balance 2% is known as alpha. So, that is the alpha that they're talking about. So what they say that because constrained investors bid up stock prices significantly, high beta stocks have lower refund, that's the point. By now you should get hints of what is the trading strategy going to be. Some particular type of stocks, all that they're telling us is particular type of stocks are likely to be overvalued. That's all they are trying to say. Right? So if they're going to be overvalued what are you going to do? Pause for a while and think. Now, let's proceed. So they say that because constrained investors bid up high beta assets, high beta is associated with low alpha, as we find empirically for US equities, 20 international equity markets, treasury bonds, corporate bonds, and futures. See they test this strategy or they test this point that high beta stocks are excessively bid up, and hence they have low alphas on not just one asset class, they're tested on many asset class, they're tested on US equities, they're tested on bonds, they're tested on treasury bonds, corporate bonds and also futures, derivative instruments. So therefore, a betting against beta, BAB factor, which is long leveraged low beta assets and short high-beta assets, produces significantly positive risk-adjusted returns. This is the most important thing, entire strategy now is explaining these two lines. They create something known as BAB factor, betting-against-beta factor. What is this? Very simple. Long, low beta stocks, and short high beta stocks. Let me read this once again. Long leverage low beta assets, and short high beta assets. So they go long on low beta assets, and short high beta assets. Why? Because high beta assets had bid up, by these constraining investors and hence their future returns are expected to be low, or alphas are expected to be low. So long, high beta and short low beta. That's the strategy, is expected to lead to significantly positive risk adjusted data. That is their main finding. When funding constraints tighten, the return of the BAB factor is low. Now when does this strategy work? I've been telling you repeatedly that we are also going to tell you when does each of this strategy work. Here is the answer for this particular strategy. They say that when funding becomes very tight, when constraint tighten, then this strategy does not work. Increased funding liquidity risk compresses betas towards one. More constrained investors hold riskier assets. So this are their predictions from their reference. So we're going to see in detail, when we go further, why this strategy works. And we are also going to implement this strategy and tell you what has been the return in the past. Now, remember, the strategy is very simple. The idea is very simple, that levered investors, constrained investors bid up the stock price of high beta stocks beyond the expected stock price based on fundamentals. And therefore, the expected future returns, abnormal returns, alpha of these stocks are likely to be low. Now, as a trader, as a trader who is there to explore anomalies out there, what one should do is sell such stocks and buys stocks which are low beta. That's all the strategy's all about. We'll get into nuts and bolts of this strategy very soon.
