Stock return predictability and investor sentiment: A high-frequency perspective

Sun, Licheng; Najand, Mohammad; Shen, Jiancheng

doi:10.1016/j.jbankfin.2016.09.010

Cited by 146 publications

(34 citation statements)

References 36 publications

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“…Second, to avoid diluting the effect of shock on low frequency data [18,22,32,66] and the subjectivity of the surveys (see [40]) an investor sentiment index had to be applied. As a result, the literature on fashioning sentiment indexes has proliferated, even inciting financial information providers, such as Bloomberg and Reuters, to build and publish their own.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, there is no such consensus on how to measure investor sentiment (see [40] for an in-depth literature review and [15] for an analysis related to the financial sector). A first approach to investor sentiment is through building indexes that incorporate market variables (among others, [4,8,41]).…”

Section: Introductionmentioning

confidence: 99%

“…In general, these empirical studies using surveys find relationships between the sentiment indexes and market variables, but, like the first of the approaches, it is not without its drawbacks, like its low observation frequency (monthly or quarterly) and, like [40] point out, these rates are less reliable when the non-response rate in surveys is high or the incentive to answer honestly is low.…”

Section: Introductionmentioning

confidence: 99%

“…As the use of investor sentiment indexes has become more widespread, empirical studies on this issue have begun to use high-frequency data, that is, daily and intraday data [20,40,63], as opposed to less frequent data, such as monthly or weekly data from surveys [64][65][66]. The frequency effect of the data is relevant as indicated by [67] since while the relationship between short-term sentiment and portfolio returns is positive, in the long term the relationship is the opposite.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Influence of Bloomberg’s Investor Sentiment Index: Evidence from European Union Financial Sector

Sánchez

Vega

2021

Mathematics

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A part of the financial literature has attempted to explain idiosyncratic asset shocks through investor behavior in response to company news and events. As a result, there has been an increase in the development of different investor sentiment measurements. This paper analyses whether the Bloomberg investor sentiment index has a causal relationship with the abnormal returns and volume shocks of major European Union (EU) financial companies through a sample of 85 financial institutions over 4 years (2014–2018) on a daily basis. The i.i.d. shocks are obtained from a factorial asset pricing model and ARMA-GARCH-type process; then we checked whether there is both individual and joint causality between the standardized residuals. The results show that the explanatory capacity of the shocks of the firm Bloomberg sentiment index is low, although there is empirical evidence that the effects correspond more to the situation of the financial subsector (banks, real estate, financial services and insurance) than to the company itself, with which we conclude that the sentiment index analyzed reflects a sectorial effect more than individual one.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Influence of Bloomberg’s Investor Sentiment Index: Evidence from European Union Financial Sector

Sánchez

Vega

2021

Mathematics

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“…Stambaugh et al (2012) and Renault (2017) found that, in the months following a high-mood period, the long-term anomaly strategy was more profitable than following a low-mood period. Sun et al (2016) found that, during a recession, investor sentiment had little ability to predict stock returns. Investor sentiment can also change the perception of investors, affect their investment decisions, and lead to changes in stock prices (Stambaugh et al, 2012;Sun et al, 2016).…”

Section: Investor Sentiment and Stock Yieldmentioning

confidence: 99%

Using E-GARCH to Analyze the Impact of Investor Sentiment on Stock Returns Near Stock Market Crashes

Chen

Haga

2021

Front. Psychol.

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Purpose: Investor sentiment, the willingness of market participants to invest, is a difficult concept to measure. Exploring the relationship between investor sentiment and stock returns can reveal how investor sentiment affects the operation of the stock market. Such an understanding can assist market participants in making more rational investment decisions based on market laws. Such an understanding can also assist regulators in their roles of supervision and policy making.Methodology: Although the E-GARCH model has the advantage of considering volatility clustering, it has not previously been used to investigate the impact of investor sentiment changes on the Shanghai Composite Index's market return. This research therefore applies the E-GARCH approach to data from 2015 to 2018, to explore the influence of investor sentiment on the return rate of the Shanghai Composite Index.Main Findings: There are three main findings. First, when the investor sentiment is increased by the same amount, the rate of return before a stock market crash will have a smaller increase than the rate of change after the crash, which is a new finding. Second, the rate of return on stocks is susceptible to emotional sentiment, rather than simply depending on stock price. Third, the tendency of retail investors to follow the crowd is less in periods of pessimism than it is in periods of optimism, which, in turn, can push up stock yields.Application: Based on these research results, this article can provide insights to understand how investors' subjective judgments on future earnings affect their investment behavior and how great the impact is on the market. At the same time, it can help investors make more rational investment decisions based on an understanding of market laws, and help regulators with guidance for their supervision and policy making.Originality/Value: This paper contributes to the theory of the investor sentiment index, improving the index construction method by adding two sentiment proxy indicators: investor activity ACT and stock market leverage level. After constructing the sentiment index and comparing it with the stock market index (Shanghai Composite Index), the fit is found to be improved.

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Forecasting volatility with investor pessimism index: Exploring the predictive power of search queries

Chu,

2023

Journal of Forecasting

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This paper discusses whether investor sentiment contains useful information to predict volatility by constructing investor pessimism index based on Baidu, which is the largest search engine in China. We discover that the investor pessimism index is significantly negatively correlated with contemporaneous returns but predicts following days' returns reversals. Furthermore, the heterogeneous autoregressive model of realized volatility (HAR‐RV) with investor pessimism index performs better than other HAR‐RV‐type benchmark models. We perform a series of robustness tests, which include alternative construction of investor pessimism index, crisis subsample, alternative predictive model, alternative measures of volatility, and considering the role of institutional investor. Our results remain robust. Taken together, investor pessimism index constructed in this paper seem to be a suitable proxy to measure investor sentiment in China and can enhance the accuracy of volatility forecasting in the Chinese stock market.

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Stock return predictability and investor sentiment: A high-frequency perspective

Cited by 146 publications

References 36 publications

Influence of Bloomberg’s Investor Sentiment Index: Evidence from European Union Financial Sector

Influence of Bloomberg’s Investor Sentiment Index: Evidence from European Union Financial Sector

Using E-GARCH to Analyze the Impact of Investor Sentiment on Stock Returns Near Stock Market Crashes

Forecasting volatility with investor pessimism index: Exploring the predictive power of search queries

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