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Financial Markets and Portfolio Management

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03.04.2021

Analyst herding and firm-level investor sentiment

verfasst von: John Garcia

Erschienen in: Financial Markets and Portfolio Management | Ausgabe 4/2021

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Abstract

This study examines the effect of firm-level investor sentiment derived from news articles and Twitter media content on analyst herding. The results indicate improvements (deterioration) in investor sentiment derived from news and Twitter media content lead to an increase (decrease) in analyst herding. This effect is primarily driven by media content with positive sentiment, and the effect size is magnified when the news and Twitter media content share the same sentiment polarity. Finally, the effect of firm-level investor sentiment on analyst herding is most pronounced in firms with low valuation uncertainty. By establishing a link between firm-level investor sentiment derived from news and Twitter content and analyst herding, this paper shows that analyst herding is amplified by firm-level investor sentiment, and the effect is more pronounced for firms with low valuation uncertainty.

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See Bradshaw (2011), Brown (1993), Kothari (2001), Ramnath et al. (2008) for reviews of the literature on analysts’ earnings forecasts and their capital market effects.

From the first quarter of 2010 through the first quarter of 2019, Twitter has reported an 11-fold increase in monthly active users (from 30 to 330 million; Twitter 2019a), generating over 500 million daily tweets (Twitter 2019b).

The quarterly EPS dispersion measure is log transformed to make the data approximately normal. Since some observations have a zero standard deviation, I add a small constant (0.00268) to all the dispersion values before taking the logarithm. Consistent with Liu and Nararajan (2012) and Berry (1987), I add a constant that minimizes the sum of the absolute values of skewness and excess kurtosis. The resulting distribution of log (DISP + constant) is approximately normal. The sum of the absolute values of skewness and excess kurtosis is 1.45.

See Cui et al. (2016) for a summary of Bloomberg’s sentiment measures and how they are computed. In summary, machine-learning techniques are used to construct news & social sentiment measures, which use classification engines trained to mimic a human expert in processing textual information. The classification is based on the question, “If an investor having a long position in the security mentioned were to read this news or tweet, is he/she bullish, bearish or neutral on his/her holdings?” Bloomberg then compiles and publishes company-level average daily sentiment scores, which are the confidence-weighted average of the past day’s story-level sentiments for both news and Twitter.

The absolute value of the prior quarter return is log transformed to make the data approximately normal. Since some observations have a zero return, I added a small constant (0.6185) to all the values before taking the logarithm. I add a constant that minimizes the sum of the absolute values of skewness and excess kurtosis. The resulting distribution is approximately normal. The sum of the absolute values of skewness and excess kurtosis is 0.45.

The quarterly debt-to-capital ratio and average bid-ask spread is log transformed to make the data approximately normal.

The debt-to-capital ratio is log transformed to make the data approximately normal. Since some observations have zero debt, one is added to all debt-to-capital ratios before taking the logarithm.

The absolute value of the mean EPS forecast is log-transformed to make the data approximately normal. Since some observations have a zero earnings estimate, I added a small constant (0.0717) to all the dispersion values before taking the logarithm. I add a constant that minimizes the sum of the absolute values of skewness and excess kurtosis. The resulting distribution is approximately normal. The sum of the absolute values of skewness and excess kurtosis is 0.34.

Bloomberg provides estimates of current analyst ratings on a scale from 1 and 5, with 5 indicating the strongest ranking (buy or similar) and 1 indicating the weakest (sell or similar). The first quartile is 3.4, the mean is 3.9, the median is 4.0, and the third quartile is 4.4. Given the positively skewed distribution, the ratings groups are grouped into three groups: analyst rating of 3 or less (group 1), rating from 3 to 4 (group 2), and ratings higher than 4 (group 3).

Using the PE ratio relative to the PE ratio of the firm’s relevant benchmark index as designated by Bloomberg (e.g., Standard and Poor’s 500, Russell 1000, etc.), firms above the 3rd quartile (1.575) are coded to 1.Firms with no earnings or negative earnings are also coded to 1; all other observations are coded to 0.

To measure economic policy uncertainty, I used the Economic Policy Uncertainty Index as derived by Baker et al. (2016). The index measures were downloaded from https://www.policyuncertainty.com/.

To mitigate the effect of extreme outliers, the quarterly return (prior to taking the absolute value), book-to-market ratio, and 90-day share price volatility measures are winsorized at the 1st and 99th percentiles and the debt-to-capital ratio and EPS dispersion measures are winsorized at the 99th percentile.

Following Newey and West (1994), where the optimal lags L = floor (4*(N/100)^2/9), and N is the firm-level sample size, and given the maximum number of quarterly observations per firm is 20, two lags are used.

The controls include; log of mean analyst’ EPS, log of share price, log of the 90-day share price volatility, log of market capitalization, beta, log of total analysts covering the firm, log of the quarterly average bid-ask spread, the log of the debt-to-capital ratio, the absolute value of the quarterly return, the book-to-market ratio, the change in EPS, economic uncertainty index, dummy variables for whether the firm reported a loss, experienced a negative quarterly return, the firm incurred R&D expense, the firm has a high relative PE ratio, valuation uncertainty (where “low” is the reference), and analyst rating groups.

The covariance matrix of the regression coefficients are corrected for heteroskedasticity and autocorrelation. All subsequent Wald tests also corrected for hteroskedasticity and autocorrelation.

Although not reported for brevity, I also run regressions similar to those in Table 3, column 5 but that also includes the total counts of firm-level new articles and tweets as additional controls. Those results are largely similar to those in Table 3, column 5.

The correlation between news and Twitter sentiment is only 0.23, with 72% of the observations sharing the same sentiment valence.

Due to the perfect collinearity between the polarity indicator variables for news and Twitter sentiment when the subsample of observations with the same polarity is selected, I re-run the regressions individually for Twitter sentiment (columns 2 and 3) and news sentiment (columns 4 and 5).

The median rating is 4.0 (a buy rating) on a 1- 5 scale, and the 1st quartile is 3.41 (between a hold and buy rating).

The Baker and Wurgler market sentiment index was available through 2018 and was obtained from Dr. Wurgler’s website: http://people.stern.nyu.edu/jwurgler/. To arrive at quarterly measures, the quarterly average is derived from the monthly measures.

The Pearson correlation between the quarterly Baker & Wurgler index measure and the quarterly news and Twitter sentiment measures are only −0.0204, and 0.0051, respectively.

On a daily basis, Bloomberg compiles analyst ratings. A rating scale between 1 and 5 is used, with 5 being the strongest rating (buy or similar) and 1 being the weakest (sell or similar).

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Titel: Analyst herding and firm-level investor sentiment
verfasst von: John Garcia
Publikationsdatum: 03.04.2021
Verlag: Springer US
Erschienen in: Financial Markets and Portfolio Management / Ausgabe 4/2021
Print ISSN: 1934-4554
Elektronische ISSN: 2373-8529
DOI: https://doi.org/10.1007/s11408-021-00382-8

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