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In this video, we want to discuss a little bit about what robo-advisors add to our investing decision. As we’ve talked about in our previous videos, we suggested that a typical robo-advisor will perform an asset allocation from us, which is something that standard finance theory tells us we should do. We should hold a diversified portfolio of different assets in accordance with our risk tolerance. We’ve discussed how robo-advisors operate, investing funds and ETFs at a relatively low cost for our investors and rebalancing their portfolios. We’ve discussed the ETFs themselves, the publicly available investment vehicles behind the asset allocations.

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It would be barely fair to ask that if robo-advisors first, just follow finance theory prescription and second, invest in ETFs that are available to anyone. What value are they in fact adding? The purpose of this video is to talk about some areas in which robo-advisors are adding value to investors decisions. The first place that robo-advisors add value to investors is through the inputs to an investment allocation. As you may recall, in order to perform an asset allocation, we need to know for the different security classes, what their expected returns are, what their variances are and finally, what the correlations amongst those asset classes in fact are.

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These are three critical building blocks for thinking about how to find a portfolio that gives us the best risk return tradeoff. Robo-advisors actually use models to estimate these inputs and to the extent that we think that robo-advisors models may be superior to what we could do as a rule of thumb, they’re adding value to our investment allocation decision. It’s often suggested that you would just use an average over time as an expectation and that you would measure all of these quantities by just looking at historical figures. But in fact, expectations, variances and correlations among assets seem to change quite a bit over time.

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In order to illustrate this, let’s take a look at the average returns over 20 years for stocks and bonds. On the left-hand side of this particular plot, I have the expected or average returns of stocks over a 20-year period, where that 20-year period ends at the end of the year that is on the x-axis. The red line is telling us the 20-year average for bonds and the blue line is telling us the 20-year average for stocks. The dotted lines in this graph are telling us what the overall averages. So over this entire period, stocks averaged about an 11 percent annual return and bonds averaged about a six percent annual return.

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However, as you look at the blue line and the red line, you can see that there are extended periods of time in which stocks or bonds deviated significantly from these long-term averages. Stocks have done as well as 18 percent over a 20-year period and as poorly as six percent over a 20-year period with the actually the worst performance having happened over the most recent 20-year period. Similarly, bonds have done as poorly as about three percent per year and have done as well is nearly 11 percent per year.

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So having a model that tells us something about what an updated expected return on a stock or a bond would be, is going to help us immensely in terms of thinking about our asset allocation. Similarly, stocks and bonds volatility changes considerably over time. Again, the blue line is telling us about 20 years stock volatility and the red line is telling us about 20-year bond volatility, with the red dotted and the blue dotted lines being long-term averages.

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As you can see from this particular plot, there’s been considerable movement in the standard deviation of bonds over time and if we were to look at even higher frequency returns, we would see that there was even more variation than what we see in this graph. The point however, is that we can’t just simply take a quantity of risk as some sort of a fixed number. Instead, market conditions are telling us something about what the relative volatility of stocks and bonds are at any given point in time. Finally, the correlation between stocks and bonds has changed dramatically over time as well. Again, what I’m looking at here is a 20 year average of the correlation of the stock and bond returns.

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Here, because of the fact that we just have two asset classes that are showing you one line. Again, the dotted line is just an unconditional number for the correlation and as you can see, the correlation between stocks and bonds on average tends to be fairly low in the neighborhood of about 10 percent. However, that number has wandered well above and well below 10 percent over time. In particular, over the 1980s and the 1990s, stock and bond returns tended to corr very positively. Whereas in more recent years, they’ve had much more negative correlation.

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In more recent years, you may have heard quite a bit in the popular press about how when interest rates go down, stock prices go up, which is reflected in these correlations. The second major contribution that robo-advisors have to asset allocation and simply cost and convenience. As we’ve mentioned before, robo-advisors have relatively low fees. We were dealing with a human advisers, someone who’s offices we worked in, a traditional rule of thumb would be that they would charge you one percent of the assets under management and so if you had a one million dollar portfolio, they would charge you $10,000 per year for managing your money. In contrast, robo-advisors charge between zero percent and one-quarter of one percent for basic services.

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As a result, a robo-advisor is charging about one-quarter of what a typical adviser would charge in order to provide investment management services. Investors frequently can get personal human advice from robo-advisors as well, but typically have to pay higher fees and maintain higher minimum balances. Another big advantage to the robo-advisor is periodic rebalancing. One of the things that happens with typical investment portfolios is that if one asset class performs particularly well, it will become a big part of your portfolio. So for example, if stocks are doing very very well, they may go from what was originally an 80 percent contribution to your portfolio, to 90 percent of your portfolio.

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One of the things that robo-advisors will do is look at your planned allocation and see if as a result of performance, that your allocation has varied considerably from what the plan was and result in an overexposure to an individual asset class. Robo-advisors automatically perform this rebalancing function for an investor and in fact will rebalance with changes in age and life situation that may affect risk tolerance and therefore an optimal asset allocation. One of the additional services that a robo-advisor will perform in a taxable account is tax loss harvesting, this is probably best illustrated through an example. So let us assume that at the beginning of 2020, you have $100,000 to invest and you invest it with a robo-advisor.

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You place those funds in 1,000 shares of ETF A, with each share costing $100 a piece. Now suppose halfway through the year in June of 2020, the price of that ETF has fallen to $90. We have the choice of either holding onto our portfolio or selling those shares, and if we sold our shares, we would have a $10,000 short-term capital loss.

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Now suppose that there’s some other ETF, which we’ll conveniently call ETF B, that’s nearly 100 percent correlated with ETF A. This frequently happens, especially with large stock or bond portfolios that are tracking very similar indices. Because ETF B is nearly 100 percent correlated with ETF A, its price has also fallen by 10 percent, from $50-$45. We could re-invest the $9,000 that we earned from selling ETF A into ETF B by buying 2,000 shares. At the end of 2020, if the prices of ETF A and ETF B have recovered to $100 and $50 respectively, we would have had no net loss on this particular position. This however is where potential tax advantages come in.

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If at the end of 2020, you had sold your holdings of ETF B, you would have what is called a short-term capital gain of $10,000. Again, you would’ve had a loss from the sale of ETF A, but then having it reinvested in ETF B, you would have gained $10,000 back. This would wash with your $10,000 loss to provide absolutely no tax benefit. Because your $10,000 gain on ETF B offset the $10,000 loss on ETF A, you will get no tax benefit from this situation. You could potentially have some tax benefit however if you did not sell at the end of 2020, and continued to hold ETF B.

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Let’s us assume that you are an investor who has a 40 percent ordinary tax rate and a 25 percent capital gains tax rate. What tax loss harvesting is doing is deferring taxes to take advantage of the difference between those ordinary and capital gains tax rates.

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At the end of 2020, you would have a $10,000 short-term capital loss. Again, you’re holding on to ETF B, so there’s no offsetting capital gain. This would reduce your tax liability by $10,000, times 40 percent, or $4,000, and so you would have a reduction in the amount of taxes that you had to pay in ‘21 of $4,000. Now suppose you took that $4,000 and bought an additional 80 shares of ETF B at the end of 2020. Again, the 80 shares is coming from the fact that you have a $4,000 offset of your taxes, the price of ETF B is $50, and so you would be able to buy an additional 80 shares.

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If ETF A and B were to appreciate to $55 and $110 respectively, and you sold your shares, you would be able to realize a tax benefit on this particular strategy. This table illustrates the contrast in the two actions in a bit more detail. On the left-hand side of this table is what happens if we choose to do nothing. Again, our original investment, as shown in the top row, was $100,000. At June 2020, we had $90,000 due to the loss on ETF A. In June 2020, we do nothing, and therefore, we do not realize a capital loss. Without a capital loss, there is no tax benefit.

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The value of our portfolio with the tax reinvested is just $100,000 because we had had nothing further to invest in the portfolio, and with a 10 percent appreciation in 2021, the value of the portfolio in December of 2021 is $110,000. If we then chose to sell, this would represent a long-term capital gain of $10,000. Our original investment was $100,000, we sold for $110,000 two years later, and therefore, we book a long-term capital gain of $10,000. The tax on that particular position, or 25 percent, would be $2,500, and the net gain to doing nothing would be $7,500 over that two-year period. Let’s compare that to what happens if we opt to harvest the tax losses.

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Our original investment in ETF A is $100,000. As of June 2020, the value of that investment has fallen to $90,000. In June of 2020, we decide to realize that loss, selling the shares of ETF A and buying shares of ETF B to get a short-term capital loss of $10,000. When we’re thinking about tax planning for 2021, we know that we are going to have a tax benefit of $4,000 because of the short-term capital loss, which represents ordinary income for 2020. Therefore, we can take that $4,000 benefit, add it to our original $100,000, and have $104,000 in value in December 2020 with the tax benefit reinvested.

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With a 10 percent return, the value of our portfolio, which originated at $104,000 in December 2020, would be $114,400. If we sell that portfolio, our basis was our original investment of $100,000, and so therefore, we’ll have a long-term capital gain, in this case, of $14,400. The taxes on that long-term capital gain, 25 percent are $3,600, and so netting that from $14,400, we see that we get a net gain on this particular position of $10,800. Again, the benefit that is going on here is harvesting those short-term capital losses and reinvesting the proceeds, resulting in a gain of $10,800 after tax, as opposed to $7,500 if we did nothing. Tax loss harvesting is simply a tax deferral strategy.

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We’re deferring taxes until they’re paid at a lower rate. Robo-Advisors look for tax loss harvesting opportunities daily to minimize the tax liability of their investors. This can have a significant impact on net of tax returns, as illustrated in the previous example.