Does investor sentiment predict market movements?

Posted by Robin Powell on February 12, 2021

Does investor sentiment predict market movements?




Investor sentiment refers to the general mood investors exhibit toward a particular market or asset. Sentiment can be an important determinant of investment performance because investors who exhibit relatively high sentiment tend to be overconfident and engage in excessive trading, resulting in subpar investment performance because they are trading on “noise” and emotions. Such activity can lead to mispricing. Eventually, any mispricing would be corrected when the fundamentals are revealed, making investor sentiment a contrarian predictor of stock market returns.

Examples of times when investor sentiment ran high are the 1968-69 electronics bubble, the biotech bubble of the early 1980s, and the dot-com bubble of the late 1990s. Sentiment fell sharply, however, after the 1961 crash of growth stocks, in the mid-1970s during the oil embargo, and in the crash of 2008.

Malcolm Baker and Jeffrey Wurgler constructed an investor sentiment index based on five metrics: the value-weighted dividend premium (the difference between the average market-to-book ratio of dividend payers and non-payers), the first-day returns on initial public offerings (IPOs), IPO volume, the closed-end fund discount, and the equity share in new issues. Originally, the Baker-Wurgler index included a sixth metric; however, the NYSE turnover ratio was dropped in the newest update. (Data is available at Wurgler’s New York University web page.)

Does investor sentiment (the psychology of crowds) affect stock prices, leading to mispricings? The argument against the belief that investor sentiment affects stock prices is that any effects caused by sentiment should, in theory, be eliminated by rational traders seeking to exploit the profit opportunities created by mispricings. However, if there are limits to arbitrage, rational traders cannot fully exploit such opportunities—and sentiment effects become more likely.

The question, then, is: Does investor sentiment predict overpriced or underpriced stock markets? Let’s review the evidence.


The evidence

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 also studied whether sentiment spreads across markets. The study covered the period 1980 through 2005 and six stock markets: Canada, France, Germany, Japan, the United Kingdom and the United States. As mentioned above, they compiled the first global sentiment index. Following is a summary of their findings:

  • Investor sentiment plays a significant role in international market volatility and generates return predictability of a form consistent with the correction of investor overreaction.
  • Total sentiment, particularly its global component, is a contrarian predictor of country-level market returns—high investor sentiment predicts low future returns and vice versa. Results were similar for both value-weighted and equal-weighted market returns and for non-U.S. markets.
  • The economic significance of the effect is nontrivial. A one-standard-deviation increase in a country’s total investor sentiment index was associated with 3.5 percent per year lower value-weighted market returns and 4.3 percent per year lower equal-weighted returns.
  • Global sentiment is the main driver of country-level results. A one-standard-deviation increase in the global sentiment index was associated with 5.4 percent per year lower value-weighted market returns and 5.6 percent per year lower equal-weighted market returns.
  • Broad waves of sentiment have greater effects on hard-to-arbitrage (due to greater costs and greater risks) and hard-to-value stocks (small-cap, high return volatility, growth and distressed stocks). These stocks will exhibit high “sentiment beta.” 
  • After sorting stocks across years according to whether the level of their total sentiment index was positive or negative, top-volatility-decile stocks earned 16.1 percent per year lower returns when the year started in a high-sentiment state—consistent with a correction of sentiment-driven overpricing. High-sentiment periods also portended 1 percent per month lower returns on the smallest capitalisation portfolio, another economically large effect. The effect of sentiment was much smaller on low-volatility stocks or large stocks, as they are relatively easy to arbitrage and value.
  • Not only do local and global sentiment predict the cross-section of a country’s returns, but investor sentiment also is contagious. For instance, U.S. sentiment affects returns for countries linked with the United States by significant capital flows. This conclusion doesn’t  depend on including the United States in the sample.

The authors concluded: “Global sentiment is a statistically and economically significant contrarian predictor of market returns. Both global and local components of sentiment help to predict the time series of the cross-section; namely, they predict the returns on high sentiment-beta portfolios such as those including high volatility stocks or stocks of small, distressed, and growth companies.”

Robert Stambaugh, Jianfeng Yu and Yu Yuan, authors of the study The Short of It: Investor Sentiment and Anomalies, which also appeared in the May 2012 issue of the Journal of Financial Economics, investigated the presence of sentiment effects by combining two concepts prominent in the academic literature:

  • Investor sentiment contains a market-wide component with the potential to influence prices on many securities in the same direction at the same time.
  • Impediments to short selling play a significant role in limiting the ability of rational traders to exploit overpricing.

The authors explored whether sentiment-related overpricing is at least a partial explanation for 11 asset-pricing anomalies. These anomalies reflect sorts on measures that include: financial distress (firms with high failure probability have lower, not higher, subsequent returns), net stock issuance (issuers underperform non-issuers), accruals (firms with high accruals earn abnormally lower returns, on average, than firms with low accruals), net operating assets (defined as the difference on a company’s balance sheet between all operating assets and all operating liabilities scaled by total assets, it is a strong negative predictor of long-run stock returns), momentum (high past recent returns forecast high future returns), the gross profitability premium (more profitable firms have higher returns than less profitable ones), asset growth (companies that grow their total assets more earn lower subsequent returns), return on assets (more profitable firms have higher expected returns than less profitable firms) and investment-to-assets (higher past investment predicts abnormally lower future returns).

The authors hypothesised that the most optimistic investors are more likely to be too optimistic when investor sentiment is high than when it is low. As the measure of sentiment, they used a composite index that included the six (initially) Baker-Wurgler metrics. For each of the 11 anomalies, the authors analyzed the strategy that goes long stocks in the highest-performing decile and short those in the lowest-performing decile. Following is a summary of their findings:

  • Each anomaly was stronger following higher-than-median levels of investor sentiment. Ten of the 11 results were statistically significant at the 5 percent level (there was a 5 percent or less chance the results were random).
  • When averaged across anomalies, 70 percent of the benchmark-adjusted profits from a long/short strategy occurred in months following levels of investor sentiment above its median value.
  • When averaged across anomalies, 78 percent of the benchmark-adjusted profits from shorting that leg occurred in months following high sentiment.
  • As expected, there was little evidence of overpricing in the long leg of the portfolio. The reason is that even though stocks in the long leg could be overpriced when market wide sentiment is high, it should contain the least degree of overpricing. None of the long legs in the 11 anomaly portfolios exhibited a significant difference (an average of just 0.04 percent per month) between high-sentiment and low-sentiment periods.

Stambaugh, Yu and Yuan cited other works that support their findings: 

  • The 2006 study Investor Sentiment and the Cross-Section of Stock Returns found that market-wide sentiment exerted stronger effects on difficult-to-value and hard-to-arbitrage stocks.
  • The 2011 study Investor Sentiment and the Mean-Variance Relation found that the correlation between the market’s expected return and its conditional volatility is positive during low-sentiment periods and nearly flat during high-sentiment periods. In other words, the market is less rational during high-sentiment periods due to higher participation by “noise” traders in such periods.

Given that the anomalies the authors examined are well known (they present challenges to the efficient market hypothesis), why do they persist?


Limits to arbitrage

Anomalies can persist when there are limits to arbitrage: 

  • Many institutional investors, such as pension plans, endowments and mutual funds, are prohibited by their charters from taking short positions.
  • Shorting can be expensive—you must borrow a stock to go short, and many stocks are costly to borrow because the supply available from institutional investors is low.
  • Investors are unwilling to accept the risks of shorting because of the potential for unlimited losses. Traders who believe a stock’s price is too high know they can be correct (its price may eventually fall) but still face the risk the price will go up before it goes down. Such a price move, requiring additional capital, can force traders to liquidate at a loss. Long-only investors don’t face this risk. The risk aversion is so high that the study All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors found that only 0.29 percent of the positions held by individual investors were short positions.

Because of these impediments to short selling, overpricing becomes more difficult to eliminate. Stambaugh, Yu and Yuan concluded that, given these short-sale impediments, overpricing should be more prevalent than underpricing. 

They wrote: “Investors with the most optimistic views about a stock, relative to the views of other investors, exert the greatest effect on the stock’s price, because their views are not counterbalanced by the valuations of the relatively less optimistic investors. The latter investors are inclined to take no position if they view the stock as undervalued, rather than take a short position. When the most optimistic investors are too optimistic and overvalue the stock, overpricing results. In contrast, underpricing is less likely. As long as the cross section of views includes the view of rational investors, the most optimistic investors do not undervalue the stock.”

Muhammad Cheema, Yimei Man and Kenneth Szulczyk contribute to the investor sentiment literature with their May 2020 study Investor Sentiment: Predicting the Overvalued Stock Market. The study covered the period July 1965 to October 2015.

They 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 were significant.

The authors concluded: “These results are consistent with a setting such as high-sentiment periods where overpricing is more prevalent than underpricing since short-sale restrictions limit the ability of rational investors to exploit overpricing but not underpricing.”

We now turn to a new study on investor sentiment and mutual fund returns.


Investor sentiment and mutual fund returns

 Qiang Bu and Odd Stalebrink contribute to the literature with their study Can Fund Sentiment Beta Predict Future Performance?, which was published in the September 2020 issue of the Journal of Asset Management. Using both actual and bootstrapped fund samples, they examined whether fund sentiment beta (FSB) can be used to predict future fund performance, whether FSB exhibits persistence across time periods, and whether investors are able to earn abnormal returns through an FSB-based investment strategy. They measured fund performance against the Carhart four-factor (beta, size, value and momentum) model. There were 3,516 funds in both the actual and bootstrapped fund samples. The estimation period spanned the three years from 2013 to 2015. Results were then measured over the following three-year period, 2016 to 2018. Following is a summary of their findings:

  • A very small group of funds exhibited statistically significant FSBs, either positive (2.5 percent) or negative (4.6 percent). Both figures were less than the results from bootstrapping, making it difficult to determine if the exposures were deliberate.
  • FSB has no significant effect on either current or subsequent fund performance—no pattern was observed between the level of FSB and the value of alpha.  
  • FSB does not exhibit any persistence across time. 
  • FSB of actual funds fails to predict the future return on a risk-adjusted basis.
  • The actual funds underperformed compared to the random performance of the bootstrapped funds, suggesting that fund managers do not appear to exhibit superior skills and that the effect of investor sentiment on fund performance is very limited.

The authors thus concluded: “These findings suggest that an FSB-based strategy is unlikely to be a profitable strategy for fund managers.” 



The lesson 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.


Important Disclosure:  This article is for educational and informational purposes only and should not be construed as specific investment, accounting, legal or tax advice. By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party websites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them. The opinions expressed by featured authors are their own and may not accurately reflect those of Buckingham Strategic Wealth®. IRN-21-1536


LARRY SWEDROE is Chief Research Officer at Buckingham Strategic Wealth and the author of numerous books on investing.



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Robin Powell

Robin is a journalist and campaigner for positive change in global investing. He runs Regis Media, a niche provider of content marketing for financial advice firms with an evidence-based investment philosophy. He also works as a consultant to other disruptive firms in the investing sector.


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