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The effect of customer concentration on stock sentiment risk


The impact of stock sentiment risk on their returns has been well documented in literature, but exploration into the determinants of stock sentiment risk is lacking. We theorize that concentrated customer bases help mitigate stock sentiment risk. Empirical results based on a large sample from the U.S. market strongly support this hypothesis. Specifically, the mitigating effect takes place through three channels. Companies with high customer concentration tend to have better performance and information quality and attract more long-horizon institutional investors. All these factors contribute to diminishing stock sentiment risk. The results are robust when the endogeneity concern is addressed by investigating the effect of an exogenous shock, or when they are examined with alternative measures of sentiment risk. The negative relationship between customer concentration and stock sentiment risk is ubiquitous but even stronger during the 2008 financial crisis.

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  1. The idea that stocks’ systematic risk can be explained by firm fundamentals is prevalent. For example, Campbell et al. (2010) argue that the betas of growth and value stocks are determined by the cash-flow fundamentals of these companies. Ng (2011) links liquidity risk to companies’ information quality, and Cao and Petrasek (2014) demonstrate that certain types of institutional ownership lower liquidity risk of stocks.

  2. We appreciate a reviewer making these suggestions.

  3. We appreciate the valuable comments by the reviewers that suggest explorations into the impact of data frequency, foreign sales, and firm age.

  4. To formally test the potential impact of thin trading, we follow Miller et al. (1994) to adjust stock returns for thin trading. Then we rerun the main regressions with the adjusted sentiment beta, whose results are consistent with those in the paper and are thus unreported.

  5. For a robustness test on this issue, we assign delisting-month returns following Shumway (1997) and Shumway and Warther (1999). We also employ the Heckman (1979) model to test the possible impact of delisting bias on our main results. The untabulated results are consistent with those reported in the paper and thus refute concerns for delisting and survivorship bias.


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We thank Ramesh Rao for helpful comments.


Wang and Huang acknowledge financial support from the National Natural Science Foundation of China (Grant Numbers 71571038 and 71971048), the Fundamental Research Funds for Central Universities in China (Grant Number N2006010) and LiaoNing Revitalization Talents Program in China (Grant Number XLYC1907015).

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Correspondence to Hongrui Feng.

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Appendix A: description of main variables

Appendix A: description of main variables

Variable name



Sentiment beta of stocks obtained from Carhart four-factor model, following Baker and Wurgler (2006)


Sentiment beta of stocks obtained from market single-factor model (CAPM)


Sentiment beta of stocks obtained from Fama–French there-factor model


Sentiment beta of stocks based on Consumer Confidence Index (CCI), following Keiber and Samyschew (2019)


HHI ratio of Customer Concentration as defined in Patatoukas (2012)


Institutional investor turnover as proposed by Gaspar et al. (2005)


Audit fees in $US thousands from Audit Analytics


The sum of audit and audit-related fees in $US thousands from Audit Analyticsa


An indicator variable for restatements of previously audited financial statementsb


The ratio of total debt divided by total asset


The ratio of the market value of equity to the book value of equity measured at the beginning of the fiscal year


The ratio of net PP&E to the total asset


The ratio of cash and all securities readily transferable to cash as listed in the Current Asset section to the total assets


The ratio of the Operating Income Before Depreciation (OIBDP) to non-current assets


The working capital minus cash and short-term investments, divided by the non-current assets


The ratio of capital expenditures to non-current assets


Research and development expenditure divided by total assets


The natural log of total assets


Institutional ownership, defined as the equity held by institutional investors at the end of the last quarter in fiscal year t


The natural log of the number of years since IPO


The return-on-assets calculated as the ratio of operating income after depreciation divided by total assets


The annual percentage of growth rate in sales


An indicator variable for negative operating income after depreciationc


The ratio of short-term debt to total debt


An indicator variable for foreign income or foreign income taxesd


The year-over-year growth in cash sales, where cash sales is calculated as sales minus the change in accounts receivables


The year-over-year change in return-on-assets


The ratio of total assets minus net PP&E minus cash and cash equivalents divided by total assets


The firm’s gross profitability as revenues minus cost of goods sold, scaled by total assets following Novy-Marx (2013)


Earnings before interest, tax, depreciation and amortization scaled by the market value of equity


Sales growth from year t to year t + 1


The annualized standard deviation of cash flow in 5 years

  1. aAudit-related fees are assurance and related services that traditionally are performed by the independent accountant and include employee benefit plan audits, due diligence related to mergers and acquisitions, accounting consultations and audits in connection with acquisitions, internal control reviews, attest services that are not required by statute or regulation, and consultation concerning financial accounting and reporting standards
  2. bIndicator variable = 1 for financial statement restatements not attributed to clericala errors identified by Audit Analytics, 0 otherwise
  3. cIndicator variable = 1 if operating income after depreciation is negative, 0 otherwise.
  4. dIndicator variable = 1 if the firm reports foreign income or foreign income taxes, 0 otherwise.

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Wang, J., Huang, Y., Feng, H. et al. The effect of customer concentration on stock sentiment risk. Rev Quant Finan Acc 60, 565–606 (2023).

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