Investor Attention and Stock Returns

Chen, Jian; Tang, Guohao; Yao, Jiaquan; Zhou, Guofu

doi:10.2139/ssrn.3194387

Cited by 12 publications

(14 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Third, the best performing model uses the term spread and the moving average term spread to predict recessions. The R 2 OS values are significant at the 5% level for each forecast horizon and monotonically increase in h. In terms of magnitude, the R 2 OS statistics of these simple models are comparable to or larger than those of other predictors for longer horizons but are somewhat smaller for shorter forecast horizons (Huang, Jiang, Tu, and Zhou, 2015;Chen et al, 2020). As noted by , the forecast performance heavily depends on the data set and on the state of economy.…”

Section: Forecasting the Equity Premium With Recession Probability Forecastsmentioning

confidence: 79%

“…Several articles show at the firm level that even though information is publicly available, it is often not fully incorporated into stock prices, resulting in delayed price reaction to news (see, e.g., Bernard and Thomas (1989); Huberman and Regev (2001); Hirshleifer, Lim, and Teoh (2009)). Chen et al (2020) find that aggregate investor attention -extracted from 12 different attention measures -has predictive power for the aggregate stock market that cannot be explained by macroeconomic fundamentals. Moreover, Gómez-Cram (2021) shows that news about impending recessions are incorporated into stock prices only with some delay.…”

Section: Dissecting the Sources Of Predictabilitymentioning

confidence: 88%

“…where μP and σ2 P are the sample mean and variance of the portfolio over the out-of-sample period. We multiply the CER by 12 to interpret it as the annual risk-free rate that an investor would be willing to accept to not hold the risky portfolio (Chen et al, 2020). The difference in CER of two models -also known as utility gain -can be interpreted as the annual portfolio management fee that an investor would be willing to pay to have access to the alternative forecasting model (Ferreira and Santa-Clara, 2011).…”

Section: Implications For Asset Allocationmentioning

confidence: 99%

See 2 more Smart Citations

Equity Premium Predictability Over the Business Cycle

Moench

Stein

2021

SSRN Journal

View full text Add to dashboard Cite

Research Question Nichttechnische Zusammenfassung Fragestellung Kann die Eigenkapitalrisikoprämie vorhergesagt werden? Diese Frage ist ein zentraler Bestandteil der finanzwirtschaftlichen Forschung. In diversen Studien wird berichtet, dass die Risikoprämie lediglich im zeitlichen Umfeld von Rezessionen prognostiziert werden kann. Aus anderen Studien geht zudem hervor, dass Informationen in der Zinsstrukturkurve geeignet sind, um Rezessionen vorherzusagen. In dieser Arbeit nutzen wir diese beiden Erkenntnisse, indem wir mit Hilfe der Zinsstrukturkurve zunächst Rezessionswahrscheinlichkeiten bestimmen, mit denen wir dann die Risikoprämie vorhersagen. Beitrag Die amerikanische Eigenkapitalrisikoprämie weist ein deutliches v-Muster zu Beginn von Rezessionen auf. Die Risikoprämie wird typischerweise kurz vor Beginn einer Rezession negativ und erholt sich dann im Laufe der Rezession. Wir verwenden ein Probit-Modell mit erklärenden Variablen aus der Zinsstrukturkurve und zeigen, dass der Beginn von Rezessionen gut vorhergesagt werden kann. Die Vorhersagegüte verbessert sich signifikant, wenn sowohl verzögerte als auch gemittelte Vergangenheitswerte des Zinsspreads berücksichtigt werden. In einem zweiten Schritt zeigen wir, dass die vorhergesagten Rezessionswahrscheinlichkeiten ebenfalls die Eigenkapitalrendite vorhersagen können. Ergebnisse Die 2-Schritt-Methode ist geeignet, um sowohl negative Risikoprämien zu Beginn einer Rezession als auch positive Risikoprämien während eines Aufschwungs abzubilden. Des Weiteren zeigt die Analyse, dass der durchschnittliche Zinsspread einen Strukturbruch im Jahr 1982 aufweist. Eine Berücksichtigung des Strukturbruchs im Probit-Modell verbessert die Prognosegüte sowohl für Rezessionen als auch für Risikoprämien. Grundsätzlich können wir feststellen, dass es einen ausgeprägten Zusammenhang zwischen Risikoprämie und Konjunkturzyklus gibt und dass die Zinsstrukturkurve daher wichtige Informationen über die zukünftige Entwicklung der Risikoprämie liefern kann.

show abstract

Section: Forecasting the Equity Premium With Recession Probability Forecastsmentioning

confidence: 79%

Section: Dissecting the Sources Of Predictabilitymentioning

confidence: 88%

Section: Implications For Asset Allocationmentioning

confidence: 99%

See 1 more Smart Citation

Equity Premium Predictability Over the Business Cycle

Moench

Stein

2021

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…The results show that portfolio sorting based on investor's attention levels and trading with long positions in high attention and short positions in low investor's attention leads to significant returns. Chen et al (2019) aggregated different attention proxies in order to predict future market returns. Thus, the Google search volume is a part of the whole picture, due to authors using the partial LS (least squares) method by combining seven major individual attention proxies on the NYSE.…”

Section: Related Researchmentioning

confidence: 99%

Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets

Škrinjarić¹

2019

IJFS

View full text Add to dashboard Cite

This research observes a time varying relationship between stock returns, volatilities and the online search volume in regard to selected CESEE (Central, Eastern and South-Eastern European) stock markets. The main hypothesis of the research assumes that a feedback relationship exists between stock returns, volatilities and the investor’s attention variable (captured by the online search volume). Moreover, the relationship is assumed to be time varying due to changing market conditions. Previous research does not deal with the time-varying multi-directional relationship. Thus, the contribution to existing research consists of estimating the aforementioned relationship between return, volatility and the search volume series for selected CESEE countries by using a novel approach of spillover indices within the VAR (Vector AutoRegression) model framework. The results indicate that the Google search volume affects the risk series more than the return series on the selected markets.

show abstract

“…At present, some scholars in China have paid much attention to the investors' attention based on the objective Internet data of Baidu index.Wang Zhen and Hao Gang (2013) found that investors' attention had a positive impact on stock returns, and the earlier concerns had negative effects by setting up panel data model; Ying Qianqwei, Luo Danglun, Kong Dongming's (2014) regression analysis shows that investor attention has a significant impact on the next week's earnings, and that the effect is reversed after a week, and that the impact will not be offset within a year; Liu Feng, Ye Qiang, Li Yijun (2014) found that the influence of media attention on stock returns was significantly weaker than the influence of investor attention on stock returns. Moreover, it is the investors' attention and investment behavior caused by media information transmission that lead to changes in stock returns; On the whole, the qualitative research of investor attention based on objective Internet data shows that stock change is closely related to investor attention.…”

Section: The Relationship Between Stock Returns and Investor Attentiomentioning

confidence: 99%