By LARRY SWEDROE
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. Studies have also found that individual investors are net buyers of attention-grabbing stocks, which consequently leads to contemporaneous positive price pressure and thus lower future returns. On the other hand, institutional investors are less susceptible to behavioral biases and are thus considered to be more sophisticated investors.
As an example, 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 11 stock market anomalies in U.S. markets and found that they were well explained by measures of investor attention—levels of investor attention are associated with the degree of mispricing, with anomalies being stronger following high rather than low attention periods. Their findings led them to conclude: “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.”
Jian Chen, Guohao Tang, Jiaquan Yao and Guofu Zhou contribute to the behavioral finance literature with their study Investor Attention and Stock Returns in which they examined whether an investor attention index based on 12 proxies could predict the stock market risk premium significantly: 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. They noted: “It is reasonable to assume that true investor attention is unobservable, and each individual measure is simply a proxy of it.” They used three information aggregating methods: partial least squares (APLS), scaled principal component analysis (ASPCA), and principal component analysis (APCA). Their sample period was 1980-2017. Following is a summary of their findings:
- Most individual attention proxies were positively correlated, though several had negligible negative values. The correlation coefficients ranged from -0.37 to 0.80, suggesting that the 12 attention proxies capture both common and different aspects of investor attention. Thus, using a specific proxy is unlikely to be complete in terms of the aggregate effect of investor attention on the stock market.
- Using the proxies collectively outperformed using them individually in terms of the return predictability—consistent with research showing that ensemble metrics outperform single metrics.
- Investor attention matters at the market level: It can strongly predict the aggregate stock market in and out of sample when individual proxies are used collectively, yielding sizeable gains to mean variance investors. Conversely, individual attention proxies had limited predictability.
- The economic magnitude of the benefit was large. For example, a one-standard-deviation increase in APLS led to a 0.64 percent decrease in the next month’s expected stock market return (7.68 percent annualized).
- The predictive power of the investor attention index stems primarily from the reversal of temporary price pressure and from the stronger forecasting ability for high-variance stocks—consistent with the findings of Nguyen and Pham.
- High investor attention increases the net buying, but the increase slows down at the subsequent month and diminishes in the long run.
- Aggregate investor attention negatively predicts excess returns of stock portfolios sorted on market beta and idiosyncratic volatility—consistent with research documenting that investors tend to be attracted to high-variance stocks (high-beta stocks and stocks with high idiosyncratic volatility), pushing their prices upward and thereby depressing their expected returns.
- Predictability existed up to two years, but the magnitude shrank with the increase in prediction horizons, indicating that the predictability effect weakens in the long run.
- The aggregate investor attention measure maintained strong predictability after controlling for economic variables and investor sentiment variables — aggregate investor attention contains unique forecasting information for the stock market, which cannot be explained by either economic fundamentals or investor sentiment.
- The investment portfolio based on aggregate investor attention generated remarkably large Sharpe ratios — the annualized Sharpe ratio was 0.74 for APLS at the monthly horizon, while the market had a Sharpe ratio of 0.50. After deducting 50 basis points for transaction costs, it was 0.67—the investor attention APLS strategy outperformed the naive buy-and-hold strategy. Similar results were found for AsPCA and APCA.
Their findings led Chen, Tang, Yao and Zhou to conclude: “There are potentially large investment profits in the asset allocation based on aggregate investor attention, suggesting substantial economic values for mean-variance investors. This analysis then emphasises the important role of investor attention on the aggregate stock market from an asset allocation perspective. … The predictive power of aggregate investor attention for stock market is likely derived from the reversal of temporary price pressure caused by net buying and from the stronger power for high-variance stocks.”
Chen, Tang, Yao and Zhou’s findings are consistent with those of studies on investor sentiment. Malcolm Baker and Jeffrey Wurgler have constructed an investor sentiment index based on six metrics: trading volume as measured by NYSE turnover, the dividend premium (the difference between the average market-to-book ratio of dividend payers and non-payers), the closed-end fund discount, number of IPOs, first-day returns on IPOs and the equity share in new issues. (Data is available at Wurgler’s New York University webpage.) Baker, Wurgler and Yu Yuan, authors of the study Global, Local, and Contagious Investor Sentiment, which appeared in the May 2012 issue of the Journal of Financial Economics, investigated the effect of investor sentiment’s global and local components on major stock markets, both at the country average level and as they affect the time series of the cross-section of stock returns. They found that investor sentiment plays a significant role in international market volatility and generates return predictability of a form consistent with the correction of investor overreaction—it is a contrarian indicator of country-level market returns.
Muhammad Cheema, Yimei Man and Kenneth Szulczyk also examined the impact of investor sentiment in their March 2018 study State of Investor Sentiment and Aggregate Stock Market Returns and found that the Baker-Wurgler investor sentiment index is a reliable contrarian predictor of subsequent monthly, six-month and 12-month market returns, but only during high-sentiment periods. For example, they found that during high-sentiment periods, the return was -0.9 percent over the subsequent month, -0.8 percent over the subsequent six months and -0.5 percent over the subsequent year. Each result was significant at the 1 percent confidence level. On the other hand, in periods of low sentiment, none of the data was significant.
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. The takeaway for investors is to avoid being a noise trader. Don’t get caught up in following the herd over the investment cliff. Stop paying attention to prognostications in the financial media. Most of all, have a well-developed, written investment plan. Develop the discipline to stick to it, rebalancing when needed and harvesting losses as opportunities present themselves.
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LARRY SWEDROE is Chief Research Officer at Buckingham Strategic Wealth and the author of numerous books on investing.
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