What the research says about retail and institutional sentiment

TL;DR: Decades of finance research show that sentiment matters, but whose sentiment matters depends on the setting: retail chatter chases attention and often reverses, professional and long-form commentary carries information that lasts longer, and the two camps frequently disagree.

Investor sentiment got its foothold in mainstream finance with Baker and Wurgler (2006). They built a market-wide sentiment index and found that when sentiment runs low, small, young, volatile and hard-to-value stocks earn relatively high returns afterward. That fit an older intuition. Mood moves prices most where valuations are subjective and arbitrage is hard. Text then gave researchers a way to measure the mood directly. Tetlock (2007) scored a daily Wall Street Journal column and found that pessimistic language pushed the market down the next day before prices reversed, and that extreme pessimism also lifted trading volume. Loughran and McDonald (2011) found a problem with the measuring tools themselves. General-purpose word lists misread financial language: almost three-quarters of the words the Harvard dictionary flags as negative are not negative in a financial context. The finance-specific lexicons they published are what most studies now rely on.

On the retail side, the research keeps coming back to attention. Barber and Odean (2008) found that individual investors are net purchasers of attention-grabbing stocks, meaning names in the news, names with unusual volume and names with extreme one-day moves. Kumar and Lee (2006) showed that retail trades move together across stocks, which looks like a shared sentiment factor at work rather than independent views. Then the forums came online and the data got much bigger. Antweiler and Frank (2004) went through 1.5 million Yahoo! Finance and Raging Bull messages. Message volume helped predict volatility, and its effect on returns was statistically real but economically small. Cookson, Engelberg and Mullins (2023) found echo chambers on StockTwits. Self-described bulls were five times more likely than bears to follow other bulls on the same stock, and beliefs formed that way were associated with lower subsequent returns and heavier trading. Robinhood brought its own evidence. Barber, Huang, Odean and Schwarz (2022) found that the stocks Robinhood users piled into most heavily on a given day posted average 20-day abnormal returns of −4.7%. Small investors don't come off badly in every study, though. Kaniel, Saar and Titman (2008) found that stocks individuals accumulated after price declines went on to beat the stocks they unloaded, consistent with retail traders supplying liquidity to institutions. And Boehmer, Jones, Zhang and Zhang (2021) found that stocks with net retail purchases beat stocks with net retail sales by about 10 basis points the following week.

Professional and long-form commentary looks different. Its information arrives more slowly and lasts longer. Chen, De, Hu and Hwang (2014) analyzed roughly 100,000 Seeking Alpha articles and reader comments from 2005 to 2012, and the views in both predicted future returns and earnings surprises. DeVault, Sias and Starks (2019) asked who actually drives the standard sentiment indices. Their answer was institutional rather than individual demand shocks, tied to common institutional styles such as risk management and momentum trading. Taken as a whole, this work makes it hard to treat "market sentiment" as one number. Retail, news and analyst voices react to different things on different clocks, and the useful signal often turns up where they split apart, not in any one group's mood. That gap is what morningowl reports on each morning, as information rather than advice.

References

• Antweiler, W., and Frank, M. Z. (2004). Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards. Journal of Finance, 59(3), 1259–1294.
• Baker, M., and Wurgler, J. (2006). Investor Sentiment and the Cross-Section of Stock Returns. Journal of Finance, 61(4), 1645–1680.
• Barber, B. M., and Odean, T. (2008). All That Glitters. Review of Financial Studies, 21(2), 785–818.
• Barber, B. M., Huang, X., Odean, T., and Schwarz, C. (2022). Attention-Induced Trading and Returns: Evidence from Robinhood Users. Journal of Finance, 77(6), 3141–3190.
• Boehmer, E., Jones, C. M., Zhang, X., and Zhang, X. (2021). Tracking Retail Investor Activity. Journal of Finance, 76(5), 2249–2305.
• Chen, H., De, P., Hu, Y., and Hwang, B.-H. (2014). Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media. Review of Financial Studies, 27(5), 1367–1403.
• Cookson, J. A., Engelberg, J., and Mullins, W. (2023). Echo Chambers. Review of Financial Studies, 36(2), 450–500.
• DeVault, L., Sias, R., and Starks, L. (2019). Sentiment Metrics and Investor Demand. Journal of Finance, 74(2), 985–1024.
• Kaniel, R., Saar, G., and Titman, S. (2008). Individual Investor Trading and Stock Returns. Journal of Finance, 63(1), 273–310.
• Kumar, A., and Lee, C. M. C. (2006). Retail Investor Sentiment and Return Comovements. Journal of Finance, 61(5), 2451–2486.
• Loughran, T., and McDonald, B. (2011). When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks. Journal of Finance, 66(1), 35–65.
• Tetlock, P. C. (2007). Giving Content to Investor Sentiment: The Role of Media in the Stock Market. Journal of Finance, 62(3), 1139–1168.