Institutional and individual sentiment: Smart money and noise trader risk?

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: Using a new data set on investor sentiment we show that institutional and individual sentiment proxy for smart money and noise trader risk, respectively. First, using bias-adjusted long-horizon regressions, we document that institutional sentiment forecasts stock market returns at intermediate horizons correctly, whereas individuals consistently get the direction wrong. Second, VEC models show that institutional sentiment forecasts mean-reversion whereas individuals forecast trend continuation. Finally, institutional investors take into account expected individual sentiment when forming their expectations in a way that higher (lower) expected sentiment of individuals lowers (increases) institutional return forecasts. Individuals neglect the information contained in institutional sentiment. JEL-Classification: G11, G12, G14 Keywords: Terms of use: Documents in EconStor mayinvestor sentiment, predictive regressions, noise trader, smart moneyWe would like to thank two anonymous referees and Lukas Menkhoff for very useful comments and especially Manfred Hübner for kindly providing the data.

Published Stock Recommendations as Institutional Investor Sentiment in the Near-Term Stock Market

“…Hengelbrock et al (2010) test the predictive power of sentiment for stock returns in the medium and long runs using U.S. survey data from the American Association of Individual Investors and German data from Sentix. Their findings replicate the results for the U.S. (Brown and Cliff, 2005) and for Germany (Schmeling, 2007) that sentiment proxies have medium-term forecasting power. In contrast, Lux (2010) uses animusX Investor Sentiment survey data over short and medium time horizons and finds evidence that this proxy has also near-term forecasting power.…”

“…They find that investors respond more strongly to negative than to positive news and that sentiment has only weak predictive power on future price movements. Schmeling (2007) uses Sentix, a weekly survey-based proxy for investor sentiment, which distinguishes between institutional and individual investors. He finds that institutional investors, but not individual investors, have correct expectations in the medium run.…”

Does Mood Explain the Monday Effect?

“…Third, this is the first paper that explores a social media factor to explore the Monday effect. Apart from a general link between sentiment/mood on returns, trading volume and volatility that has been previously reported (e.g., Joseph et al, 2011;Schmeling, 2007;Edmans et al, 2007), this study highlights the significance of mood in generating a price pattern in stock markets.…”

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“…Prominent surveys being used in US studies are the American association of individual investors (AAII) and investors Intelligence (II) as IS proxies to predict stock market movement (Brown and Cliff, 2004), (Zwergel and Klein, 2006), (Verma and Verma, 2008), (Verma and Soydemir, 2009) along with Michigan consumer confidence survey (Qiu and Welch, 2004). Another survey being widely used is sentix representing responses of German stock market participants (Schmeling, 2007), (Heiden et al, 2013) and (Bormann, 2013). However the survey approach is subjected of not being able to capture true behavior as the survey response and actual trading behavior might be different.…”