Abstract
We examine how the culture of origin of sell-side financial analysts in the United States influences their forecasting. We find that analysts from individualistic cultures are more likely to issue bold earnings forecasts and stock recommendations than analysts from collectivist cultures. Individualistic (collectivist) analysts’ tendency to overweight (underweight) their private information at least partly explains the results. The effect of culture decreases with the analysts’ professional skills and their exposure to the U.S. culture but increases with task difficulty. For market consequences, short-window market reactions are stronger to bold reports by collectivist analysts than to those by individualistic analysts, consistent with the analysts’ differential weighting of their private information. On average, however, a higher level of analyst individualism is associated with lower stock price synchronicity of the firm covered, indicating that more firm-specific information is impounded in the stock price. Our study extends research on the effect of national culture in the capital market by demonstrating how individualism, a central element of culture, affects analysts’ information processing and the value of their work.
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Notes
Baddeley (2010, p. 282) defines “herding” as “the phenomenon of individuals deciding to follow others and imitating group behaviors rather than deciding independently and atomistically on the basis of their own, private information.” Studies of herding in capital markets include, among others, the work of Cai et al. (2019) on institutional investors, Wermers (1999) and Brown et al. (2014) on mutual fund managers, and Zhang and Liu (2012) on lenders.
A thorough examination of how each of the cultural dimensions is differentially related to distinct analyst forecast properties other than herding is beyond the capacity of a single paper. Thus our aim is to provide leading evidence of the potential impact of national culture on various aspects of analyst behavior.
Clement et al. (2003) find that experienced analysts in collectivist countries produce less accurate forecasts than less experienced analysts, presumably because employers in these countries are reluctant to fire senior analysts even when they underperform. We examine how individualism directly influences analysts’ forecasting.
We obtain the scores from https://hi.hofstede-insights.com/national-culture. Hofstede’s culture index has the advantage of representing a wide range of countries and many respondents (Kagitcibasi 1997) and is the most widely used measure of national culture across disciplines (Aggarwal et al. 2016).
Merkley et al. (2020) also use the Oxford Dictionary of American Family Names to identify name origins. However, the dictionary does not provide distribution of names across countries, making it difficult to identify the origin of a name if it relates to multiple countries.
Our results are robust to alternative thresholds such as 50% or 90%. For surnames that are unmatched, we link a surname to a country if at least half of the records with that surname are from the country in both databases. We require a minimum of 100 (1000) records in ancestry.com (forebears.io) for a surname to be included in our sample. We rely on ancestry.com if there is a discrepancy between the two databases. We link a surname to the United States if it has fewer than 100 records in Ancestry.com and is most populous in the United States.
Most reports in the U.S. files of I/B/E/S are issued by analysts working in the United States. Reports by overseas offices of a U.S. brokerage are few and are unlikely to significantly affect our results. In addition, a female analyst could change her last name to that of her husband. Our results are robust to the exclusion of all female analysts from the sample.
Logistic regressions with many fixed effects can pose issues for estimation. Therefore we also run ordinary least squares (OLS) regressions of Models (1) and (2) and find similar results.
Because logistic regressions cannot account for the large number of firm-year fixed effects, we use an OLS specification. In subsequent cross-sectional analyses, we report OLS regression results controlling for firm-year fixed effects, but all our results hold for logistic regressions controlling for firm and year fixed effects.
In our sample, 38,926 recommendations (30.43%) are made by analysts of brokerages that use a three-tier recommendation system. We follow the practice of I/B/E/S to assign a score of 4, 3, and 2, respectively, to the buy, hold, and sell recommendations from such a system. Our ability to identify boldness for analysts working under a three-tier system is reduced by the narrower variation in the recommendation scores, but this should work against us in finding our hypothesized results. Nevertheless, when we exclude all recommendations under a three-tier system, our conclusions hold. Assigning a score of 5, 3, and 1, respectively, to the three types of recommendations yields very similar results. We also use an alternative approach to measuring recommendation boldness following Jegadeesh and Kim (2010) and reach the same conclusion.
Our results are similar if we standardize the control variables, as do Clement and Tse (2005).
Although some of the control variables have opposite signs in the earnings forecast and stock recommendation regressions, these results are consistent with the respective prior studies. The inconsistency could reflect different incentives faced by analysts when issuing bold forecasts versus bold recommendations (Jegadeesh and Kim 2010, footnote 7, p. 904; Malmendier and Shanthikumar 2014). For example, Jegadeesh and Kim note that it is easier for investors to measure the accuracy of earnings forecasts than that of stock recommendations. As a result, high-reputation analysts who are more confident are likely to issue bolder earnings forecasts than less reputable analysts. In contrast, the difficulty of assessing the accuracy of stock recommendations may encourage boldness of less reputable/capable analysts. Our results support these conjectures. The consistent effects of individualism in both settings suggest that the effect of culture is likely to occur through a subconscious process rather than through economic incentives.
Intuitively, if an analyst gives efficient weight to private information, the deviation of that analyst’s forecast from the consensus should not be systematically associated with her forecast error. However, if the analyst regularly overweights private information, the deviation term will be positively associated with the subsequent forecast error, and the greater the overweighting, the larger the coefficient on the deviation term. The same argument applies to the case of underweighting. Chen and Jiang (2006) provide detailed explanation of the model.
Based on the logistic regression results, the odds of overweighting (underweighting) increase (decrease) by 8.3% (16.3%) when IDV changes from the minimum of our sample to the typical Anglo-Saxon level.
Jegadeesh and Kim (2010, p. 935) note that the price impact of boldness can be harder to observe in earnings forecasts than in stock recommendations because earnings forecasts reflect analysts’ assessment of “stale” earnings information, which may not generate a market reaction. This may explain why it can be difficult to observe the effect of individualism on the market reaction to analyst earnings forecasts. Altinkilic et al. (2013) also suggest that analysts’ earnings forecast revisions release little new information using analysis of intraday stock returns.
For brokerage mergers, we require the primary SIC codes of the acquirer and the target to be 6211 (primarily investment banks and brokerages) or 6282 (primarily independent research firms) because firms in these industries employ sell-side financial analysts. We only include deals in which 100% of the target is acquired. For brokerage closures, we identify cases where a brokerage disappears from I/B/E/S and then search the press releases to confirm its closure. We also supplement our sample with the closure cases from Kelly and Ljungqvist (2012).
Kim et al. (2019) use two or three as the threshold of low analyst coverage. Our results are robust to using two, three, or five as alternative thresholds. In addition, we match each case in the collectivist group with a case in the individualistic group by the number of analysts following to obtain a balanced sample.
We did not find a reliable measure for the difficulty of predicting the stock prices from the literature, so we do not conduct a similar test for the stock recommendations.
In robustness checks, we add interactions of Star, Experienced, or Easy with other analyst characteristics in the regressions. Our conclusions remain unchanged.
We also run all analyses in Table 8 for forecast revision boldness. The coefficients have the predicted signs but are not statistically significant.
We obtain analysts’ first name from Factiva, Capital IQ, and Thomson One databases and identify the origin of these names primarily from www.behindthename.com, a source used by other studies such as by Pan et al. (2020).
In another analysis, we find that the effect of individualism on analyst boldness does not differ significantly in the pre- and post-Regulation Fair Disclosure (Reg FD) periods. Reg FD prohibits selective disclosure of material, nonpublic firm information to certain parties, including market professionals. Thus the insignificant result suggests that individualistic analysts do not have a significant information advantage over collectivist analysts.
Specifically, we collect a sample of quarterly earnings forecasts issued in the [3,3] window surrounding firms’ earnings announcements. We run a logistic regression of the likelihood of a forecast revision before the earnings announcement on IDV and other control variables. The coefficient on IDV is 0.001 (p value of 0.51). In addition, we run an OLS regression of forecast accuracy on IDV and the control variables of Model (1) for a sample of earnings forecasts issued three, five, or seven days prior to firms’ earnings announcements. The coefficient on IDV varies between 0.005 and 0.008 and the p-values vary between 0.18 and 0.56.
Over our sample period 896 analysts (9.56% of the full sample) donated, as recorded by the Federal Election Commission. We thank Kelvin Law for helping us with the analyses of political conservatism.
Results in other cross-sectional analyses are robust to the control of the cultural factors. These cultural variables are generally insignificant in explaining boldness and have little impact on the effect of individualism. Our results are also robust to the control of the degree of trust of analysts’ culture of origin (Bhagwat and Liu 2020).
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Acknowledgements
We appreciate the invaluable comments and suggestions from the editor, Stephen H. Penman, the anonymous reviewer, Zhaoyang Gu, Bin Ke, Kelvin Law, Philip Shane, the participants at the 2018 Annual Congress of the European Accounting Association and the 2018 Annual Meeting of the American Accounting Association, and the workshop participants at the National University of Singapore and the Chinese University of Hong Kong. We thank Y. Pan, S. Siegel, and T. Y. Wang for sharing their surnames data.
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Appendix: Variable definitions
Appendix: Variable definitions
Variable | Definition | Data sources |
|---|---|---|
Ability | Negative of the average of sign indicators for all forecasts that an analyst makes for a firm. The sign indicator is set to 1, 0, or − 1 if the product of the analyst’s forecast error and the error of the corresponding consensus is positive, zero, or negative, respectively (Chen and Jiang 2006). | I/B/E/S-Detail history |
ABR | The buy-and-hold abnormal return surrounding the forecast revision or the recommendation revision, calculated as the raw return minus the value-weighted market return for the respective holding period. | CRSP |
Affiliation | Set to 1 if the analyst is employed by a brokerage that underwrites the covered firm’s initial public offering in past five years or its seasoned equity offering in the past three years, 0 otherwise. | Thomson One |
BoldEPS | Set to 1 for a positively bold EPS forecast, −1 for a negatively bold forecast, and 0 otherwise. An EPS forecast is positively (negatively) bold if the analyst’s current forecast is greater than (less than) both the consensus forecast and the analyst’s previous forecast. | I/B/E/S-Recommendations-Detail |
BoldStock | Set to 1 for a positively bold stock recommendation, −1 for a negatively bold recommendation, and 0 otherwise. A recommendation is positively (negatively) bold if the recommendation score is higher than (lower than) both the consensus score and the analyst’s prior recommendation score. | I/B/E/S Recommendations-Detail |
BrokerSize | The number of analysts in the analyst’s brokerage, minus the minimum number of analysts of all brokerages following the firm in the year, scaled by the range of the size of all brokerages following the firm in the year. | I/B/E/S-Detail History |
CulturalSimilarity | The absolute difference between the individualism score of the financial analyst and the average individualism score of the covered firm’s CEO and CFO. | |
DaysElapsed | The number of days between an analyst’s forecast and the most recent forecast for the firm by any other analyst, minus the minimum number of days between two adjacent forecasts for the firm by any two analysts, scaled by the range of days between any adjacent forecasts for the firm. | I/B/E/S-Detail History |
Early | An indicator set to 1 if the modal arrival year of immigrants with the analyst’s surname in the United States is earlier than the sample median and 0 otherwise. The modal arrival year is the year which has the largest number of arrivals of a particular surname. | |
Easy | An indicator set to 1 if the R2 from an AR(1) regression of annual earnings (using data of the past six years) is above the median of all firms in the year and 0 otherwise. | Compustat |
EPSBold ijt | An indicator of the boldness of the annual EPS forecast revision of analyst i for firm j in year t. It is set to 1 if the analyst’s current forecast is greater than the most recent consensus forecast and that analyst’s previous forecast (made within the past 12 months) or if it is less than both. It is set to 0 otherwise. The consensus is the mean EPS forecast by all other analysts covering the firm in the 90 days prior to analyst i’s forecast revision. We use each analyst’s last annual EPS forecast revision up to one month before the firm’s fiscal year-end. | I/B/E/S-Detail History |
EPSBoldContinuous | A continuous measure of the boldness of the annual EPS forecast revision. It is the average of the absolute difference between an analyst’s current forecast and (1) the most recent consensus forecast and (2) the analyst’s prior forecast, minus the minimum average absolute difference for all forecasts made for the firm year, scaled by the range of the average absolute difference, and plus 1. It is set to 0 if EPSBold = 0. | I/B/E/S-Detail History |
EPSRevision | The analyst’s current EPS forecast minus that analyst’s prior forecast, scaled by the firm’s stock price at the beginning of the fiscal year. | I/B/E/S-Detail History; CRSP |
Experienced | An indicator set to 1 if the analyst’s industry experience is in the top quartile of all analysts issuing reports for the firm-year and 0 otherwise. | I/B/E/S-Detail History |
Firm-specific information | 1– Rf2, where Rf2 is the R-squared obtained from a regression of a firm’s daily stock returns on the current and lagged daily market returns (value-weighted) and industry returns, estimated over six months before or after the brokerage’s merger or closure. | CRSP |
FirmExperience | The number of years that an analyst has followed the firm minus the minimum number of years that analysts in our sample have followed the firm, scaled by the range of the number of years of following by these analysts. | I/B/E/S-Detail History |
FirmsCovered | The number of firms covered by an analyst minus the minimum number of firms covered by analysts following the same firm, scaled by the range of the number of firms covered by these analysts. | I/B/E/S-Detail History |
FirmSize | Logarithm of the covered firm’s market capitalization. | Compustat |
ForecastHorizon | Logarithm of the number of days between the issuance date of an analyst’s quarterly earnings forecast and the firm’s quarter-end date (that is, forecast horizon). A larger value indicates greater timeliness. | I/B/E/S-Detail History |
ForFrequency | Forecast frequency, the number of forecasts by an analyst for a firm during the year minus the minimum number of forecasts by analysts following the firm during the year, scaled by the range of the number of forecasts by analysts for the firm year. | I/B/E/S-Detail History |
ForHorizon | Forecast horizon, the number of days between an analyst’s forecast and the firm’s fiscal year-end date (i.e., the raw horizon), minus the minimum raw horizon of all analysts following the firm, scaled by the range of the raw horizon of all analysts following the firm. | I/B/E/S-Detail History |
Gender | Set to 1 if the analyst is male and 0 if the analyst is female. | I/B/E/S-Detail History |
GenExperience | General experience, the number of years that an analyst has worked in the finance industry minus the minimum number of industry experience of all analysts following the firm, scaled by the range of industry experience of these analysts. | I/B/E/S-Detail History |
IDV | The individualism score of an analyst’s country of origin obtained from Hofstede’s culture index, scaled by 100. An analyst is identified with a particular country if that analyst’s surname originates from that country. The details are described in Section 3.1. | forebears.io; |
Indulgence | The indulgence score of an analyst’s country of origin from Hofstede’s culture index, scaled by 100. | |
IndustriesCovered | The number of two-digit SICs covered by an analyst minus the minimum number of two-digit SICs covered by analysts following the same firm, scaled by the range of the number of two-digit SICs covered by these analysts. | I/B/E/S-Detail History |
Industry-level information | R f 2 – Rm2, where Rf2 is the R-squared obtained from a regression of a firm’s daily stock returns on the current and lagged daily value-weighted market returns and industry returns, and Rm2 is the R-squared obtained from a regression of firm daily stock returns on the current and lagged daily value-weighted market returns. The R-squareds are estimated over six months before or after merger or closure of a brokerage. | CRSP |
InstitutionalOwnership | Percentage of shares of the covered firm held by institutional investors. | Thomson Reuters Institutional (13f) |
LagAccuracy | Forecast accuracy in the previous year, the maximum absolute forecast error of analysts that follow a firm during the year minus the absolute forecast error of the analyst of interest, scaled by the range of the absolute forecast errors for the firm year. | I/B/E/S-Detail History |
LeaderFollowerRatio (LFR) | The lead time divided by the follow time for each firm that the analyst follows in the year. The lead time is the number of days that has passed since peer analysts issued their forecasts till the concerned analyst publishes a forecast. The follow time is the number of days other analysts take to issue a forecast after the concerned analyst publishes a forecast. We use two forecasts before and two forecasts after the current forecast issuance to calculate the time interval. A higher level of the LFR suggests greater timeliness (Cooper et al. 2001). | I/B/E/S-Detail History |
LTO | The score of the long-term orientation of the analyst’s country of origin from Hofstede’s culture index, scaled by 100. | |
Market-level information | The R-squared (Rm2) obtained from a regression of firm daily stock returns on the current and lagged daily market returns (value-weighted), estimated over six months before or after merger or closure of a brokerage. | CRSP |
MAS | The score of masculinity of the analyst’s country of origin from Hofstede’s culture index, scaled by 100. | |
MB | Market-to-book ratio of the covered firm. | Compustat |
NameAlignment | An indicator set to 1 if the first name of an analyst comes from the same culture as her last name, and 0 otherwise. | Factiva, Capital IQ, ThomsonOne, and Google search |
Overweight | An indicator of whether an analyst overweights private information in making EPS forecasts (Chen and Jiang 2006). For each analyst-firm, we regress the forecast error of the analyst’s quarterly EPS forecast on the deviation of that analyst’s forecast from the consensus using data of the last 30 quarters. Overweight is set to 1 if the estimated coefficient on the deviation term is in the top quartile of the estimated coefficients for all analysts for the firm-quarter and 0 otherwise. | I/B/E/S-Detail history |
PD | The score of power distance of an analyst’s country of origin from Hofstede’s culture index, scaled by 100. | |
PolCons | Political conservatism, an indicator set to 1 if all of an analyst’s political contributions recorded in the Federal Election Commission’s database are made to the Republican Party over our sample period and 0 otherwise (Jiang et al. 2016). | Federal Election Commission |
RBold ijt | An indicator of the boldness of analyst i’s stock recommendation for firm j in year t. It is set to 1 if analyst i’s stock recommendation score is higher than both the consensus score and the analyst’s prior recommendation score or lower than both and it is set to 0 otherwise. The consensus is the mean score of recommendations by other analysts covering firm j in the 180 days prior to analyst i’s recommendation revision. We reverse the recommendation scores assigned by I/B/E/S such that 1 is for strong sell, 2 for sell, 3 for hold, 4 for buy, and 5 for strong buy. | I/B/E/S-Recommendations-Detail |
RBoldContinuous | A continuous measure of the boldness of stock recommendation revision. It is the average of the absolute difference between an analyst’s current recommendation score and (1) the most recent consensus score and (2) the analyst’s prior score, minus the minimum average absolute difference for all recommendations made for the firm year, scaled by the range of the average absolute difference, and plus 1. It is set to 0 if RBold = 0. | I/B/E/S-Recommendations-Detail |
RBrokerSize | Logarithm of the number of analysts in a brokerage. | I/B/E/S-Recommendations-Detail |
RDayselapsed | Logarithm of the number of days between an analyst’s recommendation for the stock and the most recent recommendation by any other analyst for the stock. | I/B/E/S-Recommendations-Detail |
Return12month | Market-adjusted buy-and-hold stock return of the covered firm in the 12 months prior to the forecast. | CRSP |
RFirmExperience | Logarithm of the number of years that the analyst has covered the firm. | I/B/E/S-Recommendations-Detail |
RFirmsCovered | Logarithm of the number of firms covered by the analyst during the year. | I/B/E/S-Recommendations-Detail |
RFrequency | Logarithm of the number of stock recommendations for a firm by the analyst in the previous fiscal year. | I/B/E/S-Recommendations-Detail |
RGenExperience | Logarithm of the number of years that the analyst has worked in the finance industry. | I/B/E/S-Recommendations-Detail |
RIndustriesCovered | Logarithm of the number of two-digit SIC industries covered by the analyst during the year. | I/B/E/S-Recommendations-Detail |
RRevision | The analyst’s current recommendation score minus that analyst’s prior recommendation score | I/B/E/S-Recommendations-Detail |
SameDay | Set to 1 if the analyst’s EPS forecast is issued on the day that the covered firm issues its quarterly earnings announcement and 0 if issued on a subsequent day. | I/B/E/S-Detail History |
Star | Set to 1 if the analyst is on Institutional Investor’s annual roster of All-America Research Team in the year and 0 otherwise. | Factiva |
Synchronicity | The logarithm of Rf2/(1 - Rf2), where Rf2 is the R-squared obtained from a regression of a firm’s daily stock returns on the current and lagged daily market returns (value-weighted) and industry returns, estimated over six months before or after the brokerage’s merger or closure. | CRSP |
UA | The score of uncertain avoidance of an analyst’s country of origin from Hofstede’s index, scaled by 100. | |
Underweight | An indicator of whether an analyst underweights private information in making EPS forecasts (Chen and Jiang 2006). For each analyst-firm, we regress the forecast error of the analyst’s quarterly EPS forecast on the deviation of the analyst’s forecast from the consensus using data of the last 30 quarters. Underweight is set to 1 if the estimated coefficient on the deviation term is in the bottom quartile of the estimated coefficients for all analysts for the firm-quarter and 0 otherwise. | I/B/E/S-Detail history |
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Cao, Y., Hao, R. & Yang, Y.G. National culture and analysts’ forecasting. Rev Account Stud 29, 1147–1191 (2024). https://doi.org/10.1007/s11142-022-09752-7
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DOI: https://doi.org/10.1007/s11142-022-09752-7