The current set of videos, we want to talk about fintech and management of different assets in a portfolio. We usually refer to a manager’s active. If there is a portfolio manager that identifies stocks or situations such that she believes she can outperform some sort of a benchmark. That is to say, she believes that she can add value to our investing decisions, beyond that of just holding a simple index of securities. Since investors can hold indices or passive portfolios at relatively little cost, we tend to measure managers performance by; first, whether her portfolio performs better than the benchmark. Second, whether adjusted for passive risk that we can take on ourselves, she is able to increase expected returns.
What we want to talk about in this particular video is how specifically we measure this and assess whether a manager is a good or bad performer. To do so, I want to introduce the benchmark model, which is a way of thinking about how investment managers generate returns. The benchmark model is really just as a statistical description of the return on a manager’s portfolio. This particular equation contains several different elements. R_p,t is the return on the manager’s portfolio, which is the return that the manager’s actually delivering for managing our assets. R_f,t is the return on a treasury bill, which is what we expect to be able to earn without taking any risk whatsoever.
R_b,t is the return on some sort of a benchmark index that the manager is typically trying to bid. Beta is a measure of how risky the portfolio is because it moves with the index. Epsilon is the risk in the portfolio that doesn’t come from the index. Alpha tells us how much extra return we are getting, accounting for the benchmark return and the benchmark risk. What this particular equation is saying, is that the risk premium that a manager gets on a portfolio, that is the difference between the return on her portfolio and the risk-free asset can be decomposed into three values.
The amount that she gets simply from being exposed to the benchmark, which is the Beta_p times the return on the benchmark minus the risk-free rate, a piece that is just pure risk deviating from the benchmark Epsilon and then Alpha, that is the extra return that the manager gets from picking stocks. The benchmark model is important first, because that’s very easy and low cost to hold a benchmark position. You may recall that we discussed index ETFs, which are available to investors at a relatively low B. I am showing you here examples of ETFs that are available to investors to purchase on exchange.
The table illustrates that there are a number of different indices that investors could buy into most of which, you should have heard of. For example, the S&P 500 large cap index is often thought of as one of the most representative indices for the overall stock market and has an expense ratio of less than one-tenth of one percent per year. The S&P 400 Mid Cap Index is an index of 400 mid-cap stocks. The S&P 600 index an index of small-cap stocks. Again, there are ETFs that are designed specifically to replicate these indices. If one wanted to invest solely in tech stocks, one could buy an ETF or the NASDAQ 100 index.
If one wanted representation of all stocks in the universe, one could invest in the total stock index. Again, all of these different ETFs are available at relatively low expense ratios, which represent the fee that investors are paying for investing in these ETFs. In addition to getting simple benchmark exposure where we purchased the benchmark itself, it’s now easy to get additional levered exposure to the benchmark, which is measured through Beta. This table shows a number of different ETFs that have different exposures to different indices. The ones on the left are called geared ETFs that have a gearing ratio of 2.0, and the ones on the right are ones that have a gearing ratio of 3.0.
What this means is that the ETF is expected to perform roughly twice as well or twice as poorly as the benchmark does. So if the S&P 500 for example, would go up by ten percent, the S&P 500 SSO ETF would be expected to go up by 20 percent and the UPRO index would be expected to go up by 30 percent. This table illustrates that there are a number of different geared ETFs for different indices. One can buy a geared ETF on the S&P 500, the NASDAQ 100, the S&P 400, the S&P 600 or the Russell 2000 as examples.
The idea behind these ETFs are to pay up double or triple the return of the underlying index, and they do come with higher fees. Since these fees are not that different than those paid to typical active mutual fund managers and represent very simple passive strategies, we don’t believe that investors should be paying mutual fund managers simply to get beta exposures of two or three to a particular underlying index.
This video has illustrated the fact that it’s cheap and easy for an investor to hold a benchmark, whether that happens to be the S&P 500, the Mid Cap Index, the NSDAQ index, or some other index in which they’re interested. Additionally, innovations in ETFs have made it easy to increase exposure to benchmarks where investors can get a two times or three times return on the underlying benchmark. As a results, we believe that we should not be paying investment managers simply for benchmark exposure. Instead, we believe that managers should be compensated for going beyond the benchmarks, and in our next video, we’ll discuss the way in which managers tried to go beyond those benchmarks.