All right, so in this video, we're going to look at what are some of the defenses for the kind of lackluster performance if you will, of the capital asset pricing model predicted returns at least over the kind of the long history from the 1920s till kind of the most recent century here. When you look at performance from 1931 to 1965, capital asset pricing model was performing extremely well predicting returns in a way that it should, okay? If you look at kind of the more long-time series, beta seems to only weakly predict stock returns and less so kind of since the mid-1960s. So, what's going on here? Advocates of the capital asset pricing model, I think their main defense would go something like this. The model maybe isn't performing as well as we'd like because the market is mis-measured, okay? Theoretically, when we talk about this kind of market, it should include all assets, not just the S&P 500. Real estate should be in there like a key asset, young folks would be there, human capital. We just simply proxy for the market portfolio. We aren't putting in all these kind of assets that really are in kind of a broad portfolio that people have. We just proxy with the stock market because we have good data on that. We have daily data on the stock market but by using a proxy for the real market, this is inducing measurement error into the regression and could bias against the CAPM performing as well as we would kind of expect it to. So, mis-measurement of what the market portfolio really is by using this proxy of kind of the stock market. Using this proxy, the stock market maybe could explain somewhat why the capital asset price model doesn't perform as well as we would like it to. Another defense of the capital asset pricing model. Just in a recently published paper in the journal of financial economics back, I think in 2014, that the capital asset pricing model actually does perform pretty well on days when an important macroeconomic systematic news is released, okay? So, the authors of this are Savor and Wilson 2014, Journal of Financial Economics piece. They basically, do an analysis in spirit to those of Black, Jensen and Sholes and Fama and French that we've kind of talked about already in this module. They used daily data over the last 12 months to estimate betas and then they form beta sort of portfolios over the next month. So, they're kind of using a little different horizon for when they kind of form the betas. But basically, the same idea, use past data to estimate betas for stock, rank stocks by their estimated beta, and then look ahead and see to the stocks that have the higher beta typically have higher returns. But the rub that they're adding is they're looking does the CAPM predict well on days where there's a big news announcement about some macro kind of economic factor. That's what they're really bringing to the table, okay? What do we mean by macroeconomic news? Well, could be news about inflation, employment, Federal Reserve, Federal Open Market Committee interest rate decisions, okay? So, they're looking at days when those announcements occur. Now, if you look at the trading days, these macro announcements are occurring about 13% of the time, roughly kind of one out of eight. So, once every kind of two weeks or so. So, it's 13% of the trading days. But when you look at the market risk premium, the performance of stocks these days, when the macroeconomics news has announced, they count for about 60% of the equity risk premium during the year. So, these are important days, 13% of the days, but 60% basically of the stock market return occurring on these macro-news announcements. Okay, so let's look here and this is kind of a nice analysis to get things started here, where they look to show how important these announcement days are. Now remember these announcement days here showing kind of the dark bar here, only are 13% of the total trading days, but most of the return of stock and here we have the excess return. This is in basis points, is occurring during days where you have this macroeconomic news. So, macroeconomic news comes out, average excess return is a little over ten basis points compared to about one basis point on non-announcement days. And Savor and Wilson compare this to kind of other turn of the month effect. So, the first day of the month versus kind of not the first day. And look at returns during the month of January. The dark bar versus not the month of January, the light bar here. So, pretty big difference in the performance of stocks, whether there's macroeconomic news coming out versus not. kind of the difference here, much larger than if we're looking for example, returns in the month of January versus non-January, which is also a well-documented result. So, let's look at some of the empirical results in their paper. Let's look at macro announcement days, which they call A days, A for announcement. What's the average excess return for 50? Forget looking at ten, let's look at 50 beta-sorted portfolios over the period 1964 to 2011, okay? So, in our x-axis, we have the capital asset pricing model beta, okay? And on our y-axis, we have the average excess return in basis points here. And what do we see? An interesting distinction, the diamonds here, these are the returns of these 50 portfolios from the highest beta to the lowest beta here on announcement days. Here, you have the relation, the highest beta to the lowest beta on non-announcement days. So, on non-announcement days, the capital asset pricing model isn't kind of doing very well. There isn't really any relationship if anything is slightly negative, but I won't make too much. That's basically no relationship between beta and the return. But when you look at the announcement days, when this macroeconomic news comes out, which should affect kind of stocks with systematic risk. You have a very clear relationship here, firms that have higher beta, higher exposure to macro or systematic risk have bigger changes in their returns. Those with lower betas have lower returns on these announcement days, okay? And kind of very, very, very clear relationship. So, when we look at the regression here of daily excess returns on kind of going back to just focusing on ten beta-sorted stocks over the ten beta-sorted portfolios or the period 1964 to 2011, let's recall the capital asset pricing model. So here, Savor and Wilson are basically doing an analysis. Pretty much similar to those done kind of more recently by Fama and French where we have ten beta-sorted portfolios. And we're just going to see if portfolio ten, which has the highest beta has higher subsequent returns on average, relative to portfolio one for example, which has the lowest betas. So, remember the capital asset pricing model here. Okay, predictions for the regression coefficients. The intercept term should be zero, okay? There's no intercept term predicted, there's no constant as part of this equation. So, that's another way saying when you do a regression, the prediction is the intercept term should be zero. The coefficient on beta when we're regressing the return of the beta-sorted portfolio on its average beta. The average beta the stocks in the portfolio. The prediction is, the coefficient on this regression should be just whatever the average excess return of the market was during that period, which in this case is 1964 to 2011, okay? And it's useful in terms of kind of daily average equity market premium, it's 10.5 basis points. So, there's a clear prediction when we run this regression of the excess return of the beta-sorted portfolio on its beta. This called the coefficient on the beta should be 10.5 basis points if returns are being predicted by the capital asset pricing model in the way the capital asset pricing model says they should. Okay, let's get to the results. First, let's put up our regression equation for the beta-sorted portfolios. We're regressing their excess return. Okay, we're doing this on a daily rate on the average beta, that portfolio prediction is higher beta. Higher should be the return. Okay, intercept of this regression should be zero, there's no constant term here. Coefficient on beta should be this average excess market return during the period. They're using daily data, so the average excess market return during this period was 10.5 basis points. What's the prediction? What are the results? Excuse me. When we have just focused on announcement days. So, these are days where macro announcements is occurring. There's US Federal government is announcing, employment numbers is announcing what inflation was. Federal Reserve, Federal Open Market Committee is announcing a change in interest rates are just focusing on these announcement dates. Remember there are about 13% of all trading days. The intercept in this regression 1.3 basis points but not statistically significant different from zero, okay? What's the beta? What's the coefficient on beta? Usually we talk about when we say beta we mean the coefficient. But in this case, what's the coefficient on beta? It's 9.2 basis point, 9.2 statistically significant results. So, it means higher beta, higher returns earned by the portfolio. And the key is this 9.2 estimate is not statistically different from the capital asset pricing model prediction, 10.5. So, in other words on these macroeconomic news days, you can't reject the hypothesis that the capital asset pricing model predicts returns, as it says, it should. Okay, so capital asset pricing model works basically very well in predicting which stocks go up the most on news when macroeconomic news concerning macroeconomic conditions. Now, when we go to the non-announcement days, the end days, then the capital asset pricing model does not work well. And in fact, there's no relationship between the beta of the portfolio and the return of the portfolio on these no-announcement days. These end days coefficients here is not statistically different from zero. So, another way to think about this, If you're like trading stocks. If you think you have some insight about what's going to happen to kind of the macro economy, what the news is going to be. If you think the news is going to be very good about the future economic development, you want to invest in stocks that have the highest beta. Because these regression results suggest the stocks with the highest beta are going to have the biggest kind of movement in returns when this macroeconomics news is released. So, if you think good news is going to come out about the future economy, you want to be holding that day stocks that have the highest beta because they will have the biggest jump in price according to this regression analysis. So, conclusion from a Tale of Two Days days when macroeconomic news is released versus days where it's not, beta strongly related to average returns, as the captain would predict on the days when macroeconomic news is released. kind of makes sense, right? Beta is a measure of the systematic risk or the macroeconomic risk of the stock. So, in the days when this macro-news comes, that's when the CAPM performs pretty well. But it is a puzzle, why no relation between the stock beta and returns on non-announcement days? Okay, so the authors of this study, a Tale of Two Days speculate maybe the announcement days provide a clear signal of systematic risk and expected future stock market returns to investors. Therefore by getting this clear signal, they kind of trade accordingly, and then that's kind of reflected in the prices. So bottom line, there is kind of a tale of two days here when macroeconomic news is being released capital asset pricing model is pretty good at predicting returns on days outside of that doesn't seem to work very well at all.
