Manager Sentiment and Stock Returns

Jiang, Fuwei; Lee, Joshua A.; Martin, Xiumin; Zhou, Guofu

doi:10.2139/ssrn.2662288

Cited by 49 publications

(70 citation statements)

References 56 publications

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“…Both of these treat the text as a collection of exchangeable tokens, a token being either a word or a phrase. The first approach is the so-called bag-of-words approach that requires the researcher to specify a dictionary of positive and negative tokens McDonald, 2011, 2014;Chen et al, 2014;Heston and Sinha, 2017;Renault, 2017;Jiang et al, 2019). The second approach, which we refer to as the tokenization approach, does not require the researcher to explicitly specify any prior beliefs regarding the positivity or negativity of individual tokens but rather uses manually labeled text to identify relevant tokens (Taddy, 2013b,a;Mitra and Gilbert, 2014;Ranco et al, 2015;Oliveira et al, 2016;Renault, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Chen et al (2014) find their investor sentiment index is able to predict stock returns up to three months ahead. Jiang et al (2019) utilize the Loughran and McDonald (2011) dictionary to create a manager sentiment index from annual and quarterly filing, and conference calls. Overall Jiang et al (2019) find that manager sentiment is able to predict stock returns using out-sample forecast evaluation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For robustness a forecasting model including lag returns is estimated and does not improve forecast accuracy over other models. Jiang et al (2019) determine if current manager sentiment is able to predict cumulative returns from 1, 3,6,9,12,24, and 26 months ahead. All sentiment measures perform relatively well with the biggest gain being 9 months ahead.…”

Section: Forecastingmentioning

confidence: 99%

“…Following the literature (Yang et al, 2015;Sun et al, 2016;Heston and Sinha, 2017;Renault, 2017;Jiang et al, 2019), we estimate the following forecasting model utilizing the unigram and bigram investor sentiment indexes: 20…”

Section: Forecastingmentioning

confidence: 99%

“…While much of the textual analysis literature has been able to account for the preceding critiques, the estimation methods used may be not be able to fully capture investor sentiment. A portion of the previous literature has relied on dictionary based methods in determining Investor Sentiment (Loughran and McDonald, 2011;Chen et al, 2014;Jiang et al, 2019). To estimate sentiment, these studies pre-define a dictionary of positive and negative finance words and determine overall investor sentiment as the net positive word counts.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Stock returns and investor sentiment: textual analysis and social media

2019

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The behavioral finance literature has found that investor sentiment has predictive ability for equity returns. This differs from standard finance theory, which provides no role for investor sentiment. We examine the relationship between investor sentiment and stock returns by employing textual analysis on social media posts. We find that our investor sentiment measure has a positive and significant effect on abnormal stock returns. These findings are consistent across a number of different models and specifications, providing further evidence against non-behavioral theories. JEL-Classification: G12, G13, G14

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Forecastingmentioning

confidence: 99%

Section: Forecastingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stock returns and investor sentiment: textual analysis and social media

2019

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Forecasting stock return volatility using a robust regression model

Hao

Zhang

et al. 2021

Journal of Forecasting

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This paper aims to accurately forecast stock return volatility based on a robust regression model. The robust regression model is developed by replacing the mean squared error (MSE) in the autoregressive (AR) model with the Huber loss function, and the resulting model is called the ARH model. The empirical results show that the ARH model displays significantly stronger predictive power than the AR benchmark model for different evaluation periods and forecasting horizons. From an asset allocation perspective, a mean–variance investor can obtain sizeable utility gains based on the volatility forecasts produced by the ARH model. Furthermore, we find that the superior performance of the ARH model comes from assigning small weights for the extreme values, which are mainly found during recessions and periods of high volatility. Finally, our results are robust to various settings.

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Fundamental index aligned and excess market return predictability

Ze-To

2021

Journal of Forecasting

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We document the significant predictive power of an aligned fundamental index for aggregate excess stock market returns. The index incorporates the major financial indicators used by Piotroski (2000) and eliminates the idiosyncratic error components of individual indicators using the partial least squares approach modified by Kelly and Pruitt (2015). Our proposed fundamental index outperforms the aggregate financial score index of Piotroski (2000) and the equally weighted financial index in predicting returns for both in‐sample and out‐of‐sample tests. The aligned financial index also provides significant forecasting power for future market returns after controlling for the major economic variables. The proposed index persistently exhibits strong return forecasting power for longer time horizons and generates higher certainty equivalent gain for risk‐averse investors. The positive relations between the proposed index and future returns of portfolios sorted by momentum, book‐to‐market ratio, and company size are all significant.

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Manager Sentiment and Stock Returns

Cited by 49 publications

References 56 publications

Stock returns and investor sentiment: textual analysis and social media

Stock returns and investor sentiment: textual analysis and social media

Forecasting stock return volatility using a robust regression model

Fundamental index aligned and excess market return predictability

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