dark side of the nudge

Month: May, 2013

“Why Does the Law of One Price Fail? An Experiment on Index Mutual Funds” Choi, Laibson & Madrian (2005)

In their decision making process investors give too much weight to past returns, a statistic with little predictive value, and too little weight to costs, a statistic with much predictive value. These issues are exacerbated when choosing between index funds. If past returns vary solely due to different fund start dates, and the two funds track the same index with little error, then only costs should be considered. Another paper covered on this blog showed that high-cost index funds persist in the market despite their guaranteed underperformance. This paper uses an experimental setup to test whether additional information disclosures can help investors make better decisions when choosing index funds.

The experiment had three between-participant conditions. All participants received four S&P500 fund prospectuses and had to allocate $10,000 between them. In one condition participants received additional fee information, including a translation of front-end loads and ongoing fees into their dollar cost. In another condition, participants received historical returns information which was negatively correlated with fee amounts (due to different start dates). Participants were incentivised by the chance of receiving one of their chosen funds’ returns from the next year. Additionally, the experiment removed the impact of fund services – a common justification for high-fee funds.

The experiment showed that clarifying fee information shifted participants’ choices towards low-fee funds compared to the control condition, although allocations to high-fee funds were still substantial. Given the high correlation between these funds this is a situation where diversification does no pay off. And giving additional returns information lead to a shift towards high-fee high-performance funds.

This latter result is perhaps not surprising, and demand effects could be cited for both of these findings, but why is clarifying cost structure having such a weak effect on participants’ behaviour? One thing the authors note, and that I readily agree with, is naïve diversification. When choosing between multiple funds investors tend to allocate their funds fairly uniformly, even if the funds are not different enough to provide any true diversification benefit. Redesigned the experiment, so that investors must allocate their funds to only one fund, or are choosing from only two funds, should increase the size of the effect. Evidence in support of this interpretation comes from the finding that high-fee subjects tend to me more doubtful about their portfolio allocations.

“Systematic Noise” Barber, Odean & Zhu (2003)

The “noise” in the title of this paper refers to fluctuations in assets prices that are not based on information. It originates from a paper by Fischer Black, which states that a certain level of noise is unavoidable, as a level of uninformed investor activity is needed to provide markets with liquidity. This is unlike the famous “no-trade theorem”, where rational traders agree on asset prices without trading against each other. Of course we see a lot of trading in actual stock and currency markets, and it can be argued that this trading provides socially useful liquidity – allowing investors to buy or sell without unduly affecting prices.

Early views of finance assumed that noise would not lead to distortions. Either investor mistakes would be uncorrelated and hence cancel out in aggregate, would diminish over time as these investors lost capital, or rational traders would arbitrage any abnormal profit opportunities away. A paper by De Long and colleagues showed that this need not be the case: “noise” traders create their own risk which arbitrageurs on limited time horizons cannot eliminate. Noise traders might even make above-average returns in the long run due to their overconfidence! My own view is that noise hurts investor returns and negatively affects the economy through the bursting of asset price bubbles.

This paper uses two large datasets to make the following observations. There is a high correlation for individual investor purchases, more so than when it comes to sales. When it comes to stocks that have risen there are two countervailing biases. The disposition effect dominates at short lags (the last three months), where individual investors tend to sell stocks that have risen recently. At slightly longer lags (1year+) return chasing dominates, where individual investors buy stocks that have a more sustained recent history of price rises.

Efforts to moderate the systematic trading decisions of individual investors should therefore focus primarily on their buying decisions. The authors of the current study list two major causes: attention and the representativeness heuristic. Although this is probably the case historically, it’s my view that investors are merely grabbing on to these noisy heuristics because more fundamentally informative information is hard for them to process. Reducing the levels of  noise in our markets would probably do a lot of good. First, professional investors cannot eliminate all pricing distortions on their own. And second, when they do reduce such effects it will always be through a zero-sum transfer away from individual investors. Reducing these mistakes in the first place would be a much more equitable solution.

“Trading is Hazardous to Your Wealth” Barber & Odean (2000)

This paper is the classic reference on the cost to investors of individual stock overtrading. While the last paper covered on this blog showed that these costs are extremely economically meaningful in the aggregate, this paper focuses on the immediate impact that overtrading has on investor returns.

The striking result of this paper is that although the before-cost returns to investors are nearly unaffected by overtrading, the net after-cost returns are incredibly divergent. Before-cost average returns are clustered around 18.5%-18.7%/year. But the investors who trade most frequently in this sample earn an average after-cost return of 11.4%/year, compared to 18.5%/year for those who trade the least (and a return of 17.9%/year for the market).

The average annual portfolio turnover in this sample is 70%, and small account holders tend to have a higher turnover than large account holders. The authors argue that the poor performance of high turnover investors is due to overconfidence. My reading is a little different. If investors were overconfident then shouldn’t this also be reflected in a before-cost deficit? But individual investors actually outperform the market before costs. The clear negative relationship between costs and returns seems to me that investors are simply not accounting for the deleterious effects of costs. Large account holders likely have more appreciation for the total costs of trading and hence turn their portfolios over less frequently.

As I’ve argued in the last blog post, I think this could be because a major cost of trading, the bid-ask spread, is not a cost that is cognitively accessible. Investors have to infer it from looking at the spread. Making the cost of the spread clear, by multiplying the number of units bought or sold on each transaction by half the spread, could be a useful nudge.

This could have a number of beneficial knock-on effects. Investors could begin to trade more patiently, supplying more liquidity to the market instead of demanding it. Furthermore, increased cost-sensitivity would incentivise market-makers to quote more competitive prices. Individual investors’ trades tend to be highly correlated. Could it be that getting them to trade less often would help decrease the formation of financial bubbles?

Consumers are liquidity demanders. Make them demand less and increase their supply. Incentives for market makers to quote more competitively.