In this paper, we investigate the effect of customer satisfaction on the individual company's future stock crash risk. Thus, with lower information asymmetry, we would expect companies with higher customer satisfaction to be associated with a lower risk of stock crashes. Therefore, we could expect that companies with higher customer satisfaction tend to have a higher risk of failure.
Finally, from an investor perspective, the “differences of opinion” hypothesis could also point to a negative impact of customer satisfaction on the risk of a stock crash. From this perspective, we can expect companies with higher customer satisfaction to be at greater risk of crashing. Therefore, we empirically test the impact of customer satisfaction on crash rate to shed light on this issue.
Our baseline regressions show a significant and negative relationship between customer satisfaction and the risk of inventory conflict. However, this negative relationship between customer satisfaction and inventory clash risk may suffer potential endogeneity problems. A higher value of ACSI represents a higher level of customer satisfaction with the firm's products or services.
These correlations provide an informal suggestion that companies with higher customer satisfaction are less likely to experience a stock crash in the future.
Main Regression Analysis
The three crash risk measures are highly correlated and similar to previous studies, with a significant value of 0.62 (between CRASH and NCSKEW), 0.56 (between CRASH and DUVOL), and 0.81 (between NCSKEW and DUVOL). The measure of customer satisfaction, ACSI, is significantly and negatively related to three measures of crash risks. Hutton et al., 2009; J.-B. Kim et al., 2011a, 2011b), we control for a number of variables including detrended turnover (DTURN), mean and standard deviation of firm-specific weekly returns (RET and SIGMA), log of firm size (SIZE) , financial leverage (LEVERAGE), return on assets (ROA), market-to-book ratio (MB) and NCSKEW with a one-year lag.
We control for leverage (LEVERAGE), as higher leverage appears to be associated with higher default risk (Ross, 1977; Beaver et al., 2005). Previous studies suggest that companies with a higher market-to-book value ratio are more likely to experience bubbles and are therefore more likely to crash (Harvey and Siddique, 2000; Chen et al., 2001). Finally, we check a company's crash risk in the previous year, because the experience of a crash can increase investor aversion to future crash risk (Bates, 2000). We also include sector fixed securities (𝐴𝐴𝑛𝑛𝐼𝐼𝐼𝐼𝑅𝑅𝐶𝐶𝑟𝑟𝐼𝐼𝑖𝑖) and year fixed securities (𝑌𝑌𝑌𝑌𝑌𝑌𝑟𝑟 𝑡𝑡) to capture the unnoticed heterogeneity within the sector and the year.
In Table 3 we report the effects of customer satisfaction (ACSI) on these three accident indicators (CRASH, NCSKEW and DUVOL) from estimation equation (5). It should be noted that we use the Probit model to investigate the relationship between customer satisfaction and CRASH as it is a dummy variable. Consistent with the negative correlations from Table 2, ACSI has statistically significant negative coefficients for all three measures of crash rate.
Overall, our main regression results provide strong support for the view that firms with higher customer satisfaction are less at risk of future stock crashes. First of all, the negative relationship between customer satisfaction and stock crash risk may be driven by unobserved shocks and omitted variables, such as macroeconomic shocks, for which we cannot control. For example, shareholders of companies with lower corporate performance and higher risk of failure may put pressure on managers to improve their short-term performance by sacrificing product and service quality, leading to lower customer satisfaction.
We attempt to address these endogeneity concerns in the next section by using a fixed-effects model, an instrumental variable (IV) approach, and a natural experiment that acts as an exogenous shock to customer satisfaction. This table reports the regression results for the impact of customer satisfaction on stock crash risk. In each regression, we control for firm-level variables including DTURN, RET, SIGMA, NCSKEW, SIZE, LEVERAGE, ROA, and MB.
Endogeneity Concerns
On the other hand, a company's risk of crashing is unlikely to be directly affected by ad spend. Moreover, previous studies show that the quality of the company's service has a positive influence on customer satisfaction (Barger & Grandey, 2006; Olorunniwo, Hsu, & Udo, 2006). Meanwhile, personnel costs are also unlikely to be associated with a business crash occurring immediately in the future.
Similarly, both the Probit model and the OLS regression provide evidence that customer satisfaction leads to a higher crash rate, with the Probit model being significant at the 10% level and the OLS model significant at the 5% level. We find that advertising costs are significant when predicting ACSI, indicating that an increase in advertising will significantly increase customer satisfaction. We then replace ACSI with the predicted value of ACSI from the first stage regression and present IV estimates in columns 4 through 6 for our three crash measurements.
To account for the limitation of over-identification of the instrumental variables, we also perform a Hansen's J-statistic test with the null hypothesis that there is a zero correlation between the IV and the error term. The p-value of the three second-level models is 0.46, respectively, indicating that both IVs have no correlation with stock crash risk and meet the exclusion condition. To further confirm the causal link between customer satisfaction and future stock crash risk, we employ a difference-in-differences (DiD) framework using an exogenous customer service shock in the financial industry as a natural experiment.
Regarding the exclusion condition, the law is announced only to relax the restriction of the financial industry. So, if customer satisfaction could reduce the risk of a stock price crash, we would expect a negative coefficient for this interaction variable. Consistent with our expectations, the coefficients for all three crash risk proxies are significantly negative with a p-value below 10% or 5%.
The DiD approach thus supports the view that higher customer satisfaction leads to lower firm crash risk. This table reports the two-stage least squares (2SLS) regression results for the impact of customer satisfaction on inventory collapse risk. Staff_Exp is the firm's annual staff costs scaled by sales in a given financial year.
Exploring Channels of Reducing the Stock Crash Risk
CRASH_Risk is one of the three crash risk variables, including CRASH, NCSKEW and DUVOL. Our primary variable of interest, the interaction between ACSI and High_Sigma, is expected to have a significant negative coefficient (𝛽𝛽3), suggesting that higher ACSI will reduce the crash risk caused by higher stock volatility. The coefficient on the interaction term is significant across all three measures, indicating that the impact of customer satisfaction on inventory clash risk is significant when the contemporaneous inventory volatility is high.
Thus, we might expect that higher customer satisfaction is associated with a lower change of opinion and thus a lower level of stock crash risk. Furthermore, we can expect that the impact of customer satisfaction on crash risk should be more pronounced for firms that have a higher level of opinion differences. We then expect an interaction term between ACSI and High_Doo to have a negative effect on crash risk.
If customer satisfaction reduces the risk of a stock market crash by reducing disagreements, we can expect a negative coefficient of 𝛽𝛽3. Customer satisfaction promotes prediction of the company's future performance, which would reduce the level of bad news hoarding. To test this bad news hoarding channel, we conduct a series of empirical tests to examine whether higher customer satisfaction leads to less bad news hoarding and subsequently lower risk of a stock crash.
This subsection examines the relationship between customer satisfaction, financial report readability and the risk of stock crashes. If customer satisfaction reduces stock crash risk through the bad news hoarding channel, we would expect a negative coefficient for customer satisfaction on these four measures of financial report readability. The interaction term between ACSI and readability dummies may have a negative coefficient on collision risk.
However, the full model results with the interaction term columns 2 to 4 show that the coefficients on interaction terms are insignificant for all three measurements of the crash rate and the four readability measurements. powers of attorney. The results indicate that the impact of customer satisfaction on the risk of a stock market crash is unrelated to the readability of financial reports. If customer satisfaction lowers the risk of a stock crash through the bad news hoarding channel, we can expect a negative coefficient for ACSI on idiosyncratic risk.
Overall, the results do not provide strong support that customer satisfaction reduces crash risk through the information transparency channel. Overall, our tests suggest that customer satisfaction reduces the risk of stock price crashes through volatility feedbacks.
Conclusion
A solid week is defined as a crash (jump) week if the firm-specific weekly return is 3.09 standard deviations below (above) its annual mean. An ACSI score is from an annual survey of customers about company product, ranges from 0 to 100, with 100 being the highest level of customer satisfaction. Volatility Dummy High_sigma A dummy variable equal to one if the firm's Sigma is greater than the sample median, and.
Dummy High_idio A dummy variable equal to one if the firm's idio is greater than the sample median, and zero. Opinion Dummy High_doo A dummy variable equal to one if the company's Doo is greater than the sample median, and zero. Dummy High_negflesch Dummy equals one if negflesch is greater than the sample median, and zero.
Dummy High_kincaid A dummy variable equals one if kincaid is greater than the sample median, and zero. Dummy High_length A dummy variable equals one if its length is greater than the sample median, and zero. Cash flow and voting divergence, opacity and risk of stock price crashes: international evidence.
The information content of stock markets: why emerging markets have synchronous stock price movements.
