The term noise traders is used to describe ordinary investors who make decisions to buy or sell based on information they believe to be helpful but in reality will give them no better returns than random choices. But what effect do noise traders have on stock market prices? Do they contribute to market anomalies, or mispricings? LARRY SWEDROE weighs up the academic evidence.
Generating alpha, before expenses, is a zero-sum game — if one group of active investors is generating alpha, another group of active investors must be generating negative alpha. Is there a group of sophisticated investors who persistently exploit more naive investors? The body of research has found that institutional investors are informed investors who do generate alpha, at least on a gross return basis. For example, in his study Mutual Fund Performance: An Empirical Decomposition into Stock-Picking Talent, Style, Transactions Costs, and Expenses, Russ Wermers found that the stocks active mutual fund managers selected outperformed by 0.7 percentage point per annum. Who is on the other side of institutional trades?
The field of behavioural finance has produced a large body of evidence on the poor performance of individual investors, resulting in their being referred to as naive or “dumb money”. For example, Brad Barber and Terrance Odean have performed a series of studies on retail investors, including Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors and Too Many Cooks Spoil The Profits:
Investment Club Performance, demonstrating that the stocks individual investors buy tend to underperform on average, and the ones they sell go on to outperform—on average, they are the proverbial suckers at the poker table who don’t know they are the suckers. There is also a large body of evidence demonstrating that retail investor sentiment can skew the demand for securities, which in turn causes prices to deviate from their fundamentals. Institutional investors are less susceptible to behavioural biases and are thus considered to be more sophisticated investors.
The behaviour of “dumb money” has led to the existence of many anomalies — at least for asset prices models such as the CAPM and the Fama-French three-factor (beta, size and value) and five-factor (adding investment and profitability) models that have persisted well after publication of the findings — with limits to arbitrage allowing the anomalies to persist post-publication. In their 2017 study Mispricing Factors, Robert Stambaugh and Yu Yuan identified 11 of them:
Failure Probability (Distress): Stocks with a high probability of failure have lower future returns.
O-score: Stocks with higher O-scores (a higher probability of bankruptcy) have lower future returns compared to stocks with lower scores.
Net Stock Issuances: The stocks of firms that issue equity underperform the stocks of non-issuers.
Composite Equity Issuance: Firms with higher equity issuance underperform firms with lower measures.
Accruals: Stocks with high accruals underperform stocks with low accruals.
Net Operating Assets: Stocks with higher net operating assets underperform stocks with lower net operating assets.
Momentum: Stocks with higher past performance outperform stocks with lower past performance.
Gross Profitability: Stocks with higher gross profitability have higher future returns.
Asset Growth: Stocks with higher asset growth have lower future returns.
Return on Assets: Stocks with higher return on assets have higher future returns.
Investment-to-Assets: Stocks with higher past investment (scaled by total assets) have lower future returns.
Hung Nguyen and Mia Pham, authors of the study Does Investor Attention Matter For Market Anomalies?, published in the March 2021 issue of the Journal of Behavioral and Experimental Finance, examined the impact of investor attention on the aforementioned 11 stock market anomalies in U.S. markets. For measures of individual investor attention, they selected 12 individual attention proxies identified in the literature: abnormal trading volume; extreme returns; past returns; nearness to 52-week high and nearness to historical high; analyst coverage; changes in advertising expenses; mutual fund inflow and outflow; media coverage; search traffic on the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system; and Google search volume. Their data sample covered the period 1980 (when attention measures became available) through 2016. Following is a summary of their findings:
Levels of investor attention are associated with the degree of mispricing—anomalies are stronger following high rather than low attention periods.
Nine of the 11 anomalies produced positive and significant long-short returns following high attention periods, while only two generated significant long-short returns following low attention months.
Consistent with the notion that high levels of attention can exacerbate investor overreaction to irrelevant information, returns on a long-short strategy based on a composite mispricing score during high attention months were 2.25 times higher than those during low attention periods (1.309 percent versus 0.581 percent).
Their findings led Nguyen and Pham to conclude: “Investor attention plays important roles in understanding capital market efficiency… The results are consistent with the conjecture that too much attention allocated to irrelevant information triggers investor overreaction to information. Once the mispricing is corrected, more anomaly returns are realized following high attention periods.”
Noise traders vs informed traders
The evidence demonstrates that the attention-induced behaviour of individual investors leads to anomalies (mispricings). That is why retail investors are referred to as “noise traders”, while sophisticated institutional investors are referred to as informed traders. Sadly, individual investor trading driven by attention leads to poor returns. On the other side of those trades are institutional investors who exploit their misbehaviour. Which side of those trades are you or your portfolio on?
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