dark side of the nudge

Month: April, 2013

“Just How Much Do Individual Investors Lose by Trading?” Barber, Lee, Liu & Odean (2006)

This paper uses a remarkably complete dataset on the Taiwanese stock market from 1995-1999 to examine the magnitude of investors’ losses from overtrading. Barber and Odean are the authors of the seminal paper on overtrading, showing the very clear negative relationship between portfolio turnover and investment returns. The advantage of the dataset in the 2006 paper is that it can be used to precisely measure these losses for individual investors in an entire country, and also show who these losses flow to.

Many of the papers discussed on this blog are about mutual funds. The Taiwanese market is peculiar in that only 1% of individuals’ investments are through mutual funds or other intermediaries; the rest is through direct investment in individual stocks. Additionally, turnover is incredibly high, at around 300%/year (the average stock is held for only around 4 months). These features make the market almost a perfect microcosm of the costs of individual stock overtrading.

The results are truly staggering: individuals lose 2.2% of GDP per year due to their overtrading.

Individual stock trading can be thought of as a zero-sum game. If one investor outperforms  relative to the market, then this must be mirrored by another investor’s underpeformance. So who wins from individual investors’ overtrading? Financial institutions. Much of this flows overseas to large institutional investors. From a social welfare perspective, it seems clear that limiting some of this large wealth transfer would be beneficial.

Investors’ losses are primarily through their most aggressive trades. Demanding immediate liquidity leads to excess returns for liquidity providers (market makers, and large institutional traders). Institutions, on the other hand, gain both with their aggressive and passive trades. The authors state that this is probably because of their superior market knowledge.

How can we reduce individual investors’ losses? My own feeling is that overtrading might partly be caused by the unusual representation of costs in investing. The bid-ask spread is the main cost for individual stock trading. For a round-trip transaction – buying one stock and purchasing another – this can easily amount to 5% of the amount bought and sold. Given that long-term expected stock returns are around 6%/year, this takes out a large chunk. And turning your portfolio over three times a year, as the average Taiwanese did in this study, could easily cost you 15% (before accounting for taxes and the direct cost of transacting).

But the bid-ask spread is not a salient cost. It is paid implicitly through selling your actual holdings for a low price and buying your new stocks at a high price. There are few implicit costs such as this in the real world. Buying and selling on Ebay, for example, leads to direct fees that are easily measured. And another issue with the bid-ask spread is that its constant corroding effects are masked by high stock volatility. A way of making the bid-ask spread salient would be to multiply half the spread by the number of units being bought or sold. If the spread is 5%, and an investor is trying to sell £10,000 of stock, then before confirming the transaction they might be informed that the total implicit cost of this trade is £250 (whereas the direct brokerage cost of this trade might only be £10). It would be fairly trivial to mandate this information disclosure on online trading platforms, and my feeling is that it would greatly reduce the incidence of overtrading – and could go some way towards reducing the massive wealth transfer towards financial institutions.

“Choice Bracketing” Read, Loewenstein & Rabin (1999)

This paper follows up on a paper by the first two authors with a more encompassing theoretical account. The idea is that preferences are susceptible to how broad or narrow the decision context is. People act differently when they consider the sum consequences of a number of decisions, compared to when they consider the merits and weaknesses of only a single decision at a time. For example, the authors say that although the individual decision to smoke each cigarette might seen inconsequential, bracketing over a year’s consumption would make the costs much more salient. When it comes to investing, I think that the common bias of insufficient diversification might be caused by choice brackets that are too narrow.

But for this post I’m going to stray slightly off-topic and talk about how choice bracketing might affect self-control issues surrounding potential addictions. My view is that in many cases not only are we unable to voluntarily choose our own brackets, but that outside forces prevent us from using brackets that are beneficially narrow.

The authors discuss how Alcohol Anonymous advises alcoholics to hand their problem “one day at a time”. The implication being that because a lifetime’s non-drinking might seem inconceivable, and hence doomed to fail, that a narrower and more achievable bracket might be helpful. Interestingly this deviates from the recommendations of hyperbolic discounting models, which recommend broad brackets for addictive substances.

My personal take is that with many addictive substances we are unable to choose our own brackets; instead, they are set by the product’s seller. It seems fairly common-sense to me that for many self-control issues we might lack the cognitive power to choose our own bracket size. Take smoking as an example. Cigarettes are most commonly purchased in packs of 10 or 20 (at least in the UK, some friends tell me that 10-packs are not sold in their country). The size of a purchased product then becomes the inevitable bracket. If I’ve bought 20 cigarettes then I’m not going to “waste” half of my purchase by throwing them away: I’m going to smoke them all (even if the last 10 cigarettes provide me a negative marginal utility).

Now consider the dilemma of a smoker trying to quit. We can think of their problem as lots of small decisions, spread out through time, not to smoke. If they at any time fail, then the number of cigarettes that they purchase will be bracketed together and inevitably smoked. Therefore, their decision problem is made much harder if cigarettes are sold in large default packets. They only have to fail on one occasion before they embark on a protracted bout of smoking. Now consider a hypothetical world where cigarettes can only be bought individually: now their resolve has to fail on 20 separate occasions before an equal amount of damage is done.

There are many instances when sellers of addictive products adopt large brackets. The brackets they push then become the consumer’s inevitable bracket and exacerbate self-control issues. Junk food sellers use sell large portions at a discount. My personal bracket problem is with Domino’s pizza: they offer “any-size” pizzas for a given price such as £9.99. Ordering a medium-size pizza at this price seems like a waste, but buying a large-size pizza leads to me eating far too much. Bulk-buying of alcohol at supermarkets seems like a great saving but leads to excessive drunkenness (OK – this is no longer about me). Gambling companies use incentive schemes to bracket many individual gambles together. And cigarettes are of course sold in fairly wide brackets.

My opinion is that many self-control issues could be helped if narrower brackets were encouraged by regulators, either with the price system, outright limitations on purchases, or hurdles to bulk-buying (such as the filling out of tedious forms). Some of these actions might seem a bit too interventionist for hardcore libertarian paternalists. But, given that sellers of these products are intentionally adopting broad brackets – my favourite “dark nudges” – I think such action would be justified.

“On Persistence in Mutual Fund Performance” Carhart (1997)

“Past performance is not indicative of future returns” is a familiar tag-line to investors, but is it true? Other papers covered in this blog have shown that past returns are given a high weight in investors’ decision making processes, and that past returns moderate diversification bias. This paper shows that good past performance does in fact not indicate high future returns, but that the opposite can in fact be the case (if the performance is based on loading up on specific risk factors).

As with any good finance study (and hence my gripe with Borges et al., 1999), returns have to be compared to the expected returns from an asset pricing model. Taking more risk than the market is an easy way to outperform without necessarily displaying any skill. This paper uses a four factor model, which includes the Fama French value and size factors, as well as a momentum factor. Depending on your view of finance these factors can either be rationally priced risk factors or behavioural anomalies, but they do help to explain and predict performance better than the CAPM. Returns on these additional factors tend to wax and wane. The value premium might be very negative for many years, such as in the dot com bubble, and then suddenly reappear. This is why investing based purely on high raw returns is a bad idea, as it might have been from loading up on asset classes with specific risk factor sensitivities which will then mean-revert.

This paper shows that after correcting for these risk factors, that there is very little persistence in fund performance. For example, a fund could outperform the market by overweighting small-cap value stocks, but this does not reflect skill since it can be mimicked by a passive strategy. And in fact the only real unexplainable persistence in returns in this study is that of the worst mutual funds. Now, obviously there is such a thing as investment skill. Perfectly efficient markets are not possible. But can your average retail access obtain access to skilled managers, and can they obtain their services for less than the cost differential? Investing with a manager who can produce 1% of alpha a year is no use if their marginal cost is 1.5% a year. My intuition, and the fact that high-performing hedge funds such as Renaissance Technologies do not accept any investor capital at all, suggests otherwise. Retail investors are much more likely to end up with the managers that are persistently bad.

This study also suggests that high-fee funds actually underperform by more than their cost differential. Increasing fees by 1% tends to decrease returns by 1.54%. Funds that charge a load (a sales charge to invest in the fund) also perform worse, even before accounting for the higher fees.

So why are investors so fixated on a piece of noisy information, while ignoring information – investment costs – which provide a true signal? It might be that the current presentation of fees is simply too abstract and too far removed from most investors’ experience of consumer decision making. Given that it is hard to evaluate fees, investors heuristically give up on evaluating them and “take the best” by focusing on past returns. My hypothesis is that changing the presentation of fees, by making them more salient, might help investors incorporate them into their decision making. Past returns are, after all, not indicative of future returns, so reducing this bias could do a lot of good.

“Reflections on the Efficient Markets Hypothesis: 30 Years Later” Malkiel (2005)

This paper is by the author of the most famous popular book in favour of the efficient markets hypothesis. Put briefly, this hypothesis states that it is very hard to outperform average market returns on a risk-adjusted basis. The only way to reliably outperform the market over time is by taking more risk (either via leverage or by buying stocks with above-average levels of undiversifiable risk). The efficient markets strategy is then to invest in index funds which provide risk exposure and diversification at a very low cost. It’s a controversial argument which is rejected by many practitioners and behavioural finance scholars. For example, Grossman/Stiglitz show that it can never be strictly true, since active investors need an incentive to collect and process costly information. The market cannot be efficient with nobody processing information to be reflected in stock prices.

When looking at the nudge/behaviour change argument central to this blog, however, this debate is not entirely relevant. All that we require is for naïve investors to make enough errors when picking high-cost actively managed funds, and individual stocks, that they’d perform better if nudged towards low-cost index funds. Malkiel gives us some firm data on actively managed mutual funds in support of this hypothesis.

First, past returns are not indicative of future returns. Funds with high past returns do not persist. In fact, there tends to be mean-reversion, where high performing mutual funds then underperform their benchmarks. And this is made worse by the pattern of money flows to these funds. Most high performing funds achieve their best returns when relatively small. The funds massively swell in size, and then tend to underperform. Although these funds overperform their benchmarks on a time-weighted basis, they often underperform on a money-weighted basis, because their low returns come when there is so much more invested in the fund. These funds, which are the best actively managed funds, actually underperform their average investor.

Second, Malkiel shows the average actively managed fund underperforming the S&P 500 by over 2% a year for both 10- and 20-year samples up to 2003. This is approximately equal to their average cost levels, indicating that choosing low-cost funds is the most important part of making good mutual fund choices (and not looking at past returns). Other papers examined on this blog show that expense ratios are given little weight in investors’ decision making process. I think this is an artifact of the way costs are disclosed, however, and I think this behaviour can be changed.

“Bargain Hunting or Star Gazing? Investors’ Preferences for Stock Mutual Funds” Wilcox (2002)

This paper compares the importance of management fees to other information that is important in fund choice, including company brand names and past returns. Somewhat unusually for a behavioural finance study this paper uses an experimental design (instead of being based on empirical data). It replicates two findings from the previous paper: that investors are responsive to front-end load fees (paid on purchase of the fund), but that ongoing management expenses are given little weight in the fund selection process.

Past returns are given the highest weight in this study, with their effect steadily increasing from one year to ten-years of data. An earlier study showed that diversification bias was much more prevalent when well-known assets have performed well. Could it be that a focus on past returns is driving the other biases? Diversification bias might be occurring when the first investment considered happens to have a high enough return that investors decide to “take the best” and make their decision based purely on that factor. Funds with especially high past returns are likely to also have high fees. This could be because of fund incubation, where fund families artificially inflate the average returns of their funds on offer by selectively picking funds that a high return before they were open for public investment. It could also be that actively managed funds have both higher average fees and a greater dispersion of returns than index funds. In that case a focus purely on past returns would move investors to high-fee funds.

Looking at front-end loads and ongoing management expenses, Wilcox finds that front-end loads are weighted with around twice the importance of ongoing fees. This is interesting, as from a rational perspective this would only make sense if investors’ expected holding periods are under two years! Although many investors do change their portfolios frequently, this is an extremely short period. The more likely explanation is that front-end loads are simply given far too high a weight, perhaps because they are more salient. Another explanation could of course be that investors place too much weight on the present relative to the future, as for example shown in hyperbolic discounting. The fact that ongoing expenses are underweighted in this controlled experimental study does at least suggest that this might be a separate bias to the chasing of high past returns.

The last curious relationship in this paper is an inverse relationship between financial experience and the weighting of these factors. More experienced investors in this experiment tend to place a greater weight on past returns relative to fees. This is interesting as it runs counter to the findings of a very large empirical study. One potential explanation is that the current study was fairly small, using only 50 participants. If that is the case, then it may fail to cover a full range of financial sophistication. A plausible hypothesis is that investors with a little bit, but not a great amount, of financial experience are the most likely to overweight past returns in their investment calculus. Of course the empirical study does have its own potential failings with the ever-present possibility of omitted variable bias. My tendency, however, would be to be in favour of those findings at least until the current study has been replicated.

“Out of Sight, Out of Mind: The Effects of Expenses on Mutual Fund Flows” Barber, Odean & Zheng (2003)

This paper presents some evidence toward my hypotheses for why investors overpay for mutual funds. Briefly, investors might be so prone to overpaying because fund charges are described in percentages – e.g. 1% of assets per year – and are subtracted from the fund’s gross return, so they are only indirectly “paid” for. These costs are not salient, unlike other fund expenses which this paper shows investors are sensitive towards . . .

The authors split fund fees into yearly management fees and front-end loads – paid at the point of purchase, and also described in a percentage of assets. The authors hypothesise that front-end loads are a more salient cost than the yearly fee, since their cost can be deduced on the first brokerage statement, while the yearly fee’s impact is swamped by fund volatility. Front-end fees are easier to mentally represent in terms of their actual costs.

Their results should that although investors are cost-sensitive to front-end loads, there is actually a positive relationship between the yearly fee and fund flows. It turns out that this relationship is driven by funds increasing their fees to finance marketing campaigns, with the net result being an increase of net flows to the fund. New investors attracted by the marketing campaign outweigh investors who leave because of the higher fee (and people not joining because the fee is now bigger). Furthermore, experienced investors are less likely to purchase funds with a front-end load but this does not translate to yearly fees.

An additional study on fund supermarkets, which offer a range of mutual funds from different companies, confirms the results on the differential impact of up-front and yearly fees. The funds without an up-front fee tend to have a higher yearly fee, and actually grow faster than the funds with an up-front fee. These fees are usually fairly small, so rational investors with long time horizons should place a much greater weight on the percentage fee, but they don’t.

A worrying conclusion from the paper’s conclusion is that mutual funds have gradually shifted away from using front-end loads and towards using higher yearly fees. They seem to be learning the way to frame their expenses that their customers will be least cost-sensitive to. A dark nudge if ever I’ve seen one.

“Diversification Bias: Explaining the Discrepancy in Variety Seeking Between Combined and Separated Choices” Read and Loewenstein (1995)

Although this is not a behavioural finance paper, I believe it can help explain the anomaly of investors underdiversifying their portfolios. The key finding is that participants seek a lot more variety while making simultaneous than sequential decisions. In the context of consumer decision making sequential choice actually leads to a bias. People seek more diversity than their consuming self at future points would actually desire.

From standard microeconomic theory, diminishing marginal utility is the reason to diversify consumption bundles. You might prefer 1 chocolates to 1 orange, but after consuming 5 chocolates you might now prefer an orange. Your marginal utility – the utility from consuming an extra unit – of chocolate has decreased. Diminishing marginal utility is a big factor over short timescales, but over longer timescales it’s impact is much less. If I’ve just eaten a chocolate I might prefer an orange, but if I last ate chocolate yesterday I might still prefer chocolate today.

In consumer decision making diversification bias arises when people have to make multiple purchases at one instant when the consumption is spread out over time – they go for more diversity than their future selves would prefer. The corollary of diversification bias in investing – where investors place their portfolios in too few assets – might be driven by the opposite mechanism: when investors make multiple asset allocation decisions at different time points. Framing individual investing decisions as part of a greater plan might ameliorate this bias. Two quotes from the paper point to this:

“the discrepancy in variety seeking occurs because simultaneous choices are presented together and are thus framed as a type of portfolio choice, whereas sequential choices are considered in isolation.”

“Simultaneous choices are presented to subjects in the form of a package, and perhaps the most straightforward choice heuristic applicable to such packages is diversification.”

The final experiment of this paper suggests that such a simple reframing can have an influence on choice. During Halloween children were asked to choose 2 chocolates from a tray of 3 alternatives. The manipulation was that some children made this choice simultaneously, while the others chose sequentially between two houses. All of the 13 children in the combined condition chose two different chocolates, while only half of the children in the separate condition did.

“Naive Diversification Strategies in Defined Contribution Saving Plans” Benartzi & Thaler (1999)

Diversification is the cornerstone of good investing. While previous papers (1, 2) have shown that investors tend to insufficiently diversify their portfolios, this paper intriguingly shows the opposite: in an experimental setup participants’ modal choices are to use maximum naïve diversification (splitting their portfolio equally between asset classes). Whereas, we might term sophisticated diversification the selection of weights to put an investor’s portfolio on the efficient frontier. Although insufficient diversification is without a doubt the bigger real-world sin, I think this paper helps to show the causal factors behind this error.

This paper found that participants’ allocation to stocks could be predictably influenced by changing the composition of the funds on offer to them. Give them a stock fund and a bond fund and they’ll usually end up at 50% stocks. Give them a stock fund and a balanced fund (which itself contains 50% stocks) and they’ll often end up at 75% stocks. This diversification is naïve, rather than reflecting innate risk preferences.

The most intriguing aspect is the experimental condition where the authors found that naïve diversification is much reduced. In this condition, participants have to invest all of their funds into one of five funds, which vary in constant increments from all bonds to all stocks. Naive diversification would predict that the 50:50 fund should be the modal choice. In fact, the 100% stock portfolio was chosen by 51% of participants, and the average portfolio was 75% stocks/25% bonds!

I believe that this last condition is most like real-world investing, and might explain why insufficient diversification is so commonly observed. Most people invest small amounts at regular intervals. Therefore, they only consider how best to invest this new bit of savings, rather than considering their entire portfolio (which would most likely lead to more diversification, such as in the naïve diversification experiments). My bet is that people who invest a lump-sum diversify to a much greater extent than those who invest periodically. Diversification is a much more intuitive concept when all the relevant parts can be thought about together. And in fact diversification can sometimes lead to a bias in simultaneous consumer choice.

“Excessive Extrapolation and the Allocation of 401(k) Accounts to Company Stock” Benartzi (2001)

 The overinvestment in familiar assets is a significant bias which has already been discussed on this blog. Using a US-based dataset it looks at employees’ voluntary contributions to company stock, this paper asks a slightly deeper question: is it just familiarity, or does the magnitude of returns matter too?

This paper finds that employee contributions to company stock are strongly linked to the stock’s performance over a 3 to 10 year window (although not shorter). Over 3 years employees contribute 13.64% more to high- than low-return stocks, while the difference increases to 29.33% over a 10 year window.

Naive extrapolation of returns is of course not limited to individual stocks; it is a common phenomenom across asset classes too, where it can lead to the formation of bubbles (Dot.com; property). Shiller’s Irrational Exuberance is the classic reference on this topic. Although people often place too great a weight on small samples of data, in most walks of life acquiring data should increase your confidence over random variables. Things that go up a lot in the past tend to also increase in the future. The stock market is different. Since stocks returns tend to mean-revert – periods of above-average returns are often followed by below-average periods and vice cersa – you should actually become wary if an asset increases very quickly!

This paper also presents evidence that many employees believe their company stock is less risky than the overall market, despite the fact that a large portfolio of stocks should lead to a large reduction in risk from diversification. Again, I think this is partly because diversification is an unintuitive concept with few equivalent processes in the real world, and partly this might be because investors make decisions in isolation without seeing how each should be part of a greater plan. So it’s little wonder than many investors would fail to appreciate the two unintuitive finance concepts of diversification and mean-reversion (the latter of which is stronger than regression to the mean).

“Why do Individuals Exhibit Investment Biases?” Cronqvist & Siegel (2012)

Throughout these blogs I’ve attempted to put my own story across over why many investment biases might occur. The underlying theme is that naïve investors, without access to any better tools, misapply heuristics that generally serve well in consumer behaviour. A mismatch between consumer decision making and the investment world then leads to these well-known biases. Therefore, the impact of these biases might be reduced by changing surface features of the investing environment (e.g. the representation of costs, the order of decision making).

This paper uses a twin-based study to see if financial experience, education, or genetic factors can predict investment biases. Genetic factors are analysed by comparing the differences between identical and non-identical twins. The authors find that genetic differences can explain up to 45% of the variation in their dataset, and then make some arguments about the evolutionary adaptiveness of some of these traits (which I’m a little bit skeptical about). More interestingly, they find that although financial experience reduces the genetic influence, general education does not. General education does not translate across to financial education, perhaps because of the unique problems that investing presents.

The authors also look at the relationship between investors with insufficiently diversified portfolios and preference for home location. They find that investors with a lot of domestic stocks in their portfolios also live a below-average distance from their birthplace. This behavioural consistency across domains does suggest to me that investors are applying heuristics from other areas to investing.