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Using a Systematic Approach to Outperform in Markets Dominated by Retail Investors

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In this Q&A, Doug Gratz, CFA, Rayliant’s Director of Institutional Services, spoke to Phillip Wool, Ph.D., Rayliant’s Director of Research, to find out more about retail investor behavior and why now could be such a good time to harvest alpha in emerging equity markets.





Q: Why are you so interested in retail investors’ mistakes?

If you’re trying to outperform the market, you have to remember that outperformance has to come from somewhere. If you think you can persistently win, who’s persistently losing? It’s harder to imagine that it’s other professional investors who are subsidizing all of your outperformance than it is to picture untrained individual investors as a persistent source of alpha. If you take this view, it’s clearly worthwhile to study individual investors’ behavioral biases to determine whether we can avoid the same mistakes and even position ourselves to take advantage of them.


“Studying individual investors’ behavioral biases can help us avoid the same mistakes – and even turn those into a position of advantage.”


Q: What do we mean when we speak of behavioral biases in the way investors trade?

In most cases if you look at what’s behind a decision you make you’ll realize that instead of performing a bunch of conscious calculations, there’s actually just some rule of thumb you’re using to quickly decide what to do. Psychologists call these rules of thumb heuristics. They work really well in most contexts, but when you apply them to complicated financial decisions they often fail pretty miserably. So, when we refer to a behavioral bias, we’re effectively talking about the heuristics that failed.


Unfortunately, when it comes to studying biased trading, economists are almost never able to see individual investors’ biases directly. We can’t get the investors into a laboratory and study them. Instead, we’re usually just seeing their preferences reflected, for example, in their portfolio holdings or their trading activity. From there, we can try to look for some pattern in those preferences and then try to connect these patterns with the full range of biases that psychologists have been able to study more directly.

Q: Are retail investors more biased than professionals?

In most cases, looking at retail investors’ trading behavior and performance, we’d conclude that they exhibit much more pronounced biases than professional investors. That’s fairly intuitive. Most professional investors have had formal training, and they’re being paid good money to recognize when the rules of thumb they’d typically apply are going to fail and to suppress these bad behaviors.


In the U.S., it turns out that individual traders underperform the market by an average of about 8.5% per year—so, they’re essentially giving up a majority of their expected returns as a result of behavioral mistakes. That includes the effects of trading too much—which generates huge commissions and other costs—and poorly timing the market based on greed and fear.

Q: Aren’t professional investors subject to biases too?

Yes, absolutely. No one’s perfect, so professional investors still make mistakes. In terms of performance, the average professional investor also loses, but not by nearly as much as the individual traders we just discussed. Pension funds underperform by about 70 basis points per year, on average. Professionally managed high net worth accounts do a bit worse, losing around 3.5% per year versus the benchmark. So, while it’s true that retail investors do have a harder time earning alpha, even among institutional investors there are plenty of underperformers. Actually, one benefit of quantitative investment strategies is that a systematic approach like the one we take at Rayliant is explicitly designed to strip out bias from our decision-making process. That can be a big advantage in trying to buck the underperformance we’ve been talking about.


“Our systematic quantitative approach is explicitly designed to strip out bias from our decision making process.”


Q: Can you give some examples of individual investors’ behavioral biases?

Psychologists have identified a range of biases, such as the availability bias, representativeness, the disposition effect—and the list goes on and on. But it might be better to think about this from a different perspective that I alluded to before: What can we actually observe in terms of the outcome of individual investors’ decisions by looking at their portfolios? From there, we can think about how we could position ourselves to benefit from these errors of judgment.


China is a great country to consider because we know the market is full of individual investors, and when we look at those investors’ holdings, some clear patterns emerge. For instance, Chinese retail traders tend to load up on growth stocks – they buy sexy, glamorous companies that don’t have any profits but do have exciting stories about where future profits are going to come from. Investors buy these growth stories without any consideration of how high the stock’s valuation is.


We also see that Chinese individual investors prefer stocks with low prices and high volatility. I like to think of these as something like a “financial lottery ticket”—cheap to buy and they give investors the dream of some massive payout, even if these stocks generally aren’t good investments.


Often, you get the sense individual investors are taking some pleasure in the act of investing, itself—that it’s a form of entertainment rather than solely about making well-researched, profit-maximizing decisions. In China, investing is a very social activity, with people hanging out at the brokerage hall and blogging about their trades. You can imagine that if investing is in part about recreation, there might be less concern about avoiding biases.

Q: Is retail investors’ underperformance solely a result of these biases you’ve just mentioned?

Behavioral biases have a major impact, so I don’t want to understate the importance of that, but it’s also clear retail investors typically lack formal training and aren’t always doing as much research as we probably imagine they should. So, it shouldn’t be surprising that retail traders tend to misprice companies in terms of more nuanced concepts, such as earnings quality or precise estimation of a company’s default risk, for example. If we consider the biases we’ve been discussing alongside retail traders’ more general shortcomings, such as insufficient research and limited attention, we get a more complete picture of where they tend to stumble when they trade.

Q: Do retail investors learn from these mistakes over time?

Psychologists have long been concerned with how people learn, and they’ve shown that a big impediment to learning is overconfidence. People tend to have a much higher opinion of their skills than is warranted by the evidence. When it comes to trading, individual investors tend to think they know more than everyone else, and this often leads them to ignore information that might help them make better investment decisions.


So, investors trade a few stocks, they make a few mistakes, but do they learn from those experiences? The short answer is, no. Overconfidence is at play again, this time in the form of what psychologists call biased self-attribution. This is a technical term for the notion that if I won, it must be due to my skill, but if I lost, it’s just bad luck. And if people blame all their losses on chance, they’re probably going to make the same mistakes over again.


Something else to consider is that learning can be pretty hard to observe in practice. When it does happen, learning can take a long time. That’s especially true when stock returns are volatile and it’s hard to really know for sure if you’ve made a mistake or not. In some emerging markets like China, you’ve also got a flood of new individual investors coming into the market, and they’re all presumably starting at square one in terms of financial knowledge. So even if learning is going on, it can be hard to observe because new generations of investors are constantly entering
the scene.


“If people blame all their losses on chance, they’re probably going to make the same mistakes over again.”


Q: So, are there no good examples of learning in action?

Actually, there are some examples of investors learning from past experience—but even then, they don’t necessarily learn optimally, so learning could be hurting them, too. That sounds counterintuitive, but I think I can make it clearer by describing some research that’s been done on this.


There’s this great study in Taiwan, where researchers looked at the way investors learn from their past participation in IPO auctions.1 What they found is that retail investors did seem to learn from their experience: If they made money in the first IPO that they bought into, they were more likely to participate in future IPOs, while if they lost the first time, they were more likely to avoid these deals in the future. That seems good, but it turns out there’s more to the story.


When they looked closer, these researchers found the learning was ultimately harming investors: the ones who made money with their first IPO auction became much less choosy when it came to decide which were the good and bad IPOs to invest in going forward. They would effectively pursue all IPOs regardless of their quality, and they also became more aggressive in their bidding, so in future deals they were more likely to overpay for the stock.


Now, the study actually gets even more interesting, because they didn’t just look at individual investors—they also had data on professional investors. They found that the pros were also more likely to participate in future deals if they succeeded early on, but that over time they would actually improve in terms of picking the right IPOs and optimizing their bidding strategy to avoid overpaying.

Q: You’ve mentioned China and Taiwan—both emerging markets. How does individual investors’ trading behavior in these markets compare with that in developed markets?

When you look at these types of studies in different countries, it turns out that retail investors across the world have remarkably similar preferences in terms of which stocks they tend to buy. Chinese investors tend to go for high-risk, lottery-ticket stocks, and we see exactly the same in the U.S. There are plenty of other examples. You get the sense that human beings are roughly wired in the same way for decision making wherever they come from, and that means they’re likely to make the same mistakes when it comes to trading stocks.


That’s actually convenient for us because it means that when we start thinking about how we’re going to take advantage of the mispricing resulting from these biases, we don’t have to go back to the drawing board in terms of figuring out what investor behavior looks like in each country, how it affects stock prices and the trading strategies we should implement to profit from it.

Q: How do you take bias like this into account when constructing your portfolios?

We know that individual investors tend to have an irrationally strong desire to invest, for example, in growth companies or high-risk stocks, which results in their prices being bid up far too much. In response, knowing that growth stocks are likely to be too pricy, on average, we would look to underweight these stocks.


And if retail investors shun boring value stocks, that lack of demand is going to lead to underpricing, so we might want to add those stocks to our portfolios. This means part of our strategy is effectively to do the opposite of what psychologically biased individual investors are doing.


The other aspect is to think about what retail investors aren’t doing in terms of their research. For example, they don’t pay attention to the quality of a firm’s reported earnings, which makes it less likely that this kind of information will be baked into that firm’s stock price. Again, this leads to mispricing that we can exploit if we’re paying attention to the quality of earnings or statistical measures of the likelihood that a company is engaged in accounting manipulation or fraud.


“Human beings are wired the same way wherever they come from, and that means they’re likely to make the same mistakes when it comes to trading stocks.”


Q: Trading against the grain in markets where retail investors dominate sounds like it could lead to underperformance. Is that a risk to the strategy you’ve described?

Over short timeframes, that can certainly be a risk for single-factor strategies. Take value investing in China, for example. We know from our research that it produces strong returns over the long term, but individual investors can be highly irrational. They can sometimes get things wrong for a long time before there’s some kind of revaluation. Normally this happens at the market level, so if there’s a stock market crash like in 2015 in China, that’s when these high-flying growth stocks are likely to plummet. But it can be quite painful to be a value investor in the meantime.

Q: Are there ways to mitigate that risk?

The scenario I’ve just described is exactly why multi-factor investing is such a critical development. One factor might underperform for years on end, but retail investors’ mistakes aren’t perfectly correlated with one another. So, while value is underperforming, you’ll find momentum might produce strong returns. Diversifying through a multi-factor portfolio means you should earn the expected alpha of each strategy over the long run, but with a much smoother, much less stressful ride than the single-factor investor experiences.

Q: Do some markets provide more scope to exploit behavioral biases than others?

Based on what I’ve described before, it stands to reason we want to be trading in places where there are more retail traders relative to professional investors as that’s where you’ll find the biggest inefficiencies. When you trade in the U.S., at least 90% of trades are made by sophisticated institutional investors, so when you submit a trade it’s highly likely the counterparty knows more about the correct price of the stock than you do. We know that outperformance is a zero-sum game, so if everyone’s a professional it’s much harder to see where your profits are coming from.

But in emerging markets, the balance tips almost completely in the other direction. Take China, for example, where individual investors still make up 80–90% of trading volume. Emerging markets simply offer a much larger pool of alpha for us to tap through our rational approach to exploit mispricing.


“We want to be trading in places with a high proportion of retail traders, as that’s where you’ll find the biggest inefficiencies.”


Q: Are institutional investors likely to take over emerging markets, as they have developed markets?

Yes, I think so. If there’s huge alpha in a market, that’s going to act like a magnet, drawing sophisticated investors in to trade away the mispricing that created that alpha.


The data back this up. In the early 1960s in the U.S., almost 70% of trading volume was by individual investors, but that’s fallen to around 5% today. In 2000 in South Korea individual investors accounted for 75% of all trading, today it’s closer to 45%. These trends are all gradual, of course, and sometimes we see blips that go against the trend. For example, changes in wealth, looser regulation and new technology giving people access to trading on their mobile phones have resulted in the number of individual trading accounts in China tripling over the past decade.


On top of that, the markets themselves change over time as they grow. Regulations improve, so everyone is better informed at the same time. Accounting practices advance, as we’ve seen in the case of China’s convergence to International Financial Reporting Standards in the last two decades. This maturation can also lead to reduced alpha opportunities.

Q: Does that mean in a few years there will be no scope for active managers to outperform in emerging markets?

No, I wouldn’t go quite that far. Even in markets with limited retail trading, quantitative strategies still tend to produce alpha. Like I mentioned before, professional investors aren’t perfect either. Sometimes rational behavior leads to mispricing. For example, suppose mutual fund managers who aren’t allowed to use leverage buy high-beta shares to boost their returns. They’re going to bid up the price of risky stocks, and that makes it a profitable strategy to buy low-beta stocks, which are cheaper than they should be. Then, of course, when low-volatility strategies become popular—as they have in recent years—we find that low-vol stocks themselves become overpriced. Again, a multi-factor approach comes into play.


This is all to say that there’s plenty to keep quant investors busy even in developed markets—although I would still probably argue it’s preferable to be an early mover in emerging markets, given the relative size of the alpha opportunity there.

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This document is for information purposes only. It is not a recommendation to buy or sell any financial instrument and should not be construed as an investment advice. Any securities, sectors or countries mentioned herein are for illustration purposes only. Investments involves risk. The value of your investments may fall as well as rise and you may not get back your initial investment. Performance data quoted represents past performance and is not indicative of future results. While reasonable care has been taken to ensure the accuracy of the information, Rayliant does not give any warranty or representation, expressed or implied, and expressly disclaims liability for any errors and omissions. Information and opinions may be subject to change without notice. Rayliant accepts no liability for any loss, indirect or consequential damages, arising from the use of or reliance on this document.


Hypothetical, back-tested performance results have many inherent limitations. Unlike the results shown in an actual performance record, hypothetical results do not represent actual trading. Also, because these trades have not actually been executed, these results may have under- or over- compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical results in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to those shown. In fact, there are frequently sharp differences between hypothetical performance results and the actual results subsequently achieved by any investment manager.