In our previous video, we discussed the benchmark model for assessing an investment managers performance. Just to refresh, the benchmark model was specified as follows; saying that the return on the manager's portfolio, beyond that of a risk-free asset, could be broken into three components. One called an Alpha. Two, the Beta of the portfolio, multiplied by the return on the benchmark above the risk-free rate. The third, representing risk beyond the benchmark that an investment manager takes on. We discussed how investors can get cheap access to the benchmark and therefore, earn the return on the benchmark. We also discussed the fact that with ETFs, they can get levered access to the benchmark and therefore get the exposure, Beta. As a result, we believe that active managers should deliver Alpha in order to earn fees. We usually think of this Alpha as characterizing stock selection skill. A typical mutual fund manager will hold a portfolio that's often quite similar to the benchmark. What she will do is overweight or underweight some selected stocks that she believes are going to perform better or worse than the benchmark as a whole. In order to think about how a manager is going to do this, I'm going to ask you to read the principal investment strategies portion of the prospectus for the Fidelity Blue Chip Growth Fund as of 09/29/2018, and point out the fact that there are references within that language to investing in companies with above-average growth potential, and fundamental analysis of financial condition and industry position. These are components of what we refer to as fundamental analysis, which is the basis for most investment professionals stock picking skills. Fundamental analysis deals with evaluation equation that is central to finance theory. It suggests that a firm's value at time t, which we'll refer to as V, is the sum of discounted future cash flows. We represent this discounted future cash flow as follows: In this equation, the terms, the expectation of the cash flow at time t plus j, is the cash flow that the analyst expects are going to be generated by the firm at a time t plus j. R represents the discount rate that's appropriate to discount back the riskiness of the firm's cash flows. What a manager is doing in fundamental analysis is making estimates of these future cash flows, applying discount rates, and attempting to see if the value of the firm is different than those implied discounted cashflows. The question is, does this approach work? This particular graph shows a histogram of the performance of all of the funds that were in existence at the beginning of 2018, that were equity only mutual funds. I have ruled out, from this particular sample, all ETFs or index funds, and so these are all mutual funds that purport to be active managers managing equity portfolios. The red line represents the return on the S&P 500 index in 2018, which was negative 4.38 per cent. Firms to the left of the line did worse than the S&P 500, and firms to the right of the line did better than the S&P 500. On the X-axis, you can see the actual returns ranging from negative 60 percent to positive 40 percent. Of the roughly 6,000 funds, that existed at the beginning of 2018, 4,230 did more poorly than the S&P 500, and 1,799 did better than the S&P 500. These performance numbers suggests that only about 20 percent of the funds, did better than one could have done simply investing in the S&P 500. Now, a few caveats are in order here. The S&P 500 may not be the appropriate benchmark for all of these funds and additionally, some of these funds may have taken Beta position that were lower or higher than 1.0 on the S&P 500. Nonetheless, what this particular graph is suggesting is that it's relatively difficult to beat the S&P 500, and that the vast majority of funds failed to do so in 2018. If we consider a longer term window, looking at five years, we see that even fewer funds seem to perform better. Again, on the X-axis, what I'm showing you is the return on these funds over a five-year period ending at the end of 2018. The blue histogram is showing us the distribution of returns of all funds, and the red line is telling us about the return on the S&P 500 over those five years. Over the five years ending in 2018, the S&P 500 returned approximately 50 percent. Of the roughly 8,000 funds that were in existence at the beginning of that period, only 531 were able to outperform the S&P 500. This means that less than 10 percent of the funds, were able to outperform the S&P 500 on a five-year basis. Comparing this graph and the previous graph suggests that it's difficult to outperform the S&P 500 in one year, much less for five years in a row. Despite this evidence, there still remains considerable debate in academic circles about whether active fund managers deliver value. These two examples, we can cite Burton Malkiel's, 1995, Journal of Finance article, which suggests that in the aggregate, funds have underperformed benchmark portfolios both after management expenses and even gross of expenses. This encapsulates the negative view of mutual fund manager performance. In contrast, a recent article in the Journal of Financial Economics, suggests that the average mutual fund has used their skill to generate about $3.2 million for their investors per year. Investment manager's goal is to work to deliver funds beyond that of the benchmarks, because benchmarks are easily achieved by investors passively. They do so typically by performing fundamental analyses to select stocks in different weights than the benchmark holds them. The evidence for their success is mixed with some researchers suggesting that mutual funds offer little to no value, with other researchers suggesting that there is value to active management. To proceed into the world of fintech, we want to ask what happens if we remove some of the human element of investing? Instead of asking individual people to select stocks, ask technology to select stocks.
