Originality/value - To the authors best knowledge, this is the first study to identify the effect of time-varying firm-level political sentiment conveyed in conference calls on stock price crash.
Keywords Political sentiment, Earnings conference calls, Stock price crash risk, Signaling,
Misleading sentiment
Paper type Research paper

American business leaders increasingly have been pulled into-or in some cases have dived willingly into - the hot cauldron of today's political system.

Wall Street Journal July 23, 2021.
Companies are getting more political-make statements clarifying their position on political events [1]. According to FactSet, there are a significantly higher number of S&P 500 companies discussing the presidential election during their earnings conference calls in Q3 2020 relative to Q3 2016. Market participants perceive these calls as an increasingly important source of information about the firms (e.g. Frankel et al., 1999). In addition to "hard" data presented in these calls, sentiment ("soft information") reflected in the tone of conference calls conveys incremental information that is associated with contemporaneous and future stock returns (Price et al., 2012). Given the importance of firm-level political sentiment, can it predict firm stock crash risk?

In this paper, we focus on the political sentiment reflected in earnings conference calls and examine how it is related to future stock price crash risk. According to the misleading

The authors would like to thank the Editor and two anonymous referees for valuble comments.
Political sentiment and stock crash risk
139

Received 21 November 2021
Revised 20 December 2021
Accepted 23 December 2021

The Journal of Risk Finance
Vol. 23 No. 2, 2022
pp.
(C) Emerald Publishing Limited
sentiment hypothesis, managers, concerned about their career and personal financial benefits, either inflate or deflate sentiment to affect stock prices. First, they have incentives to downplay negative political impact on firm performance (Verrecchia, 2001; Kothari et al., 2009). The overly optimistic tone in earnings conference calls misleads investors, inducing inflated stock prices (Merkel-Davies and Brennan, 2007). However, the short-term accumulation of hidden bad news exposes firms to sudden and significant drops in stock price or stock crashes (Jin and Myers, 2006). Second, managers can display overly pessimistic sentiment while expecting less negative market reaction in the future. If more negative information is revealed later, the investors are already prepared by the negative sentiment conceded by managers earlier. Therefore, stock crash risk is reduced. Alternatively, if future information is less negative, investors, realizing that the actual situation is better than expected, would react positively, resulting in lower stock crash risk. Hence, this hypothesis predicts a positive association between political sentiment in calls and future stock crash risk.

In comparison, the signaling hypothesis suggests that managers can use their tone in earnings conference calls to signal their private information (Loughran and McDonald, 2016), which affects stock crash risk. Given the interactive and impromptu nature of earnings conference calls, it is easier to detect managers' intention to mislead through the calls than through written documents or pre-rehearsed announcements (Hobson et al., 2012; Larcker and Zakolyukina, 2012). In addition, to reduce potential litigation costs associated with earnings calls, managers have incentives to reveal information about political impact on firm's business, including intensified market competition, costs of doing business and restrictions on business opportunities (Skinner, 1997; Druz et al., 2020). Embedded in the calls, the information concerning the underlying asymmetries in future cash flow shocks drives the skewness in stock returns, which measures stock crash risk (Easterwood et al, 2021). Therefore, lower sentiment in calls is related to high stock price crash risk.

Using a sample of 40,254 US firm-year observations between 2002 and 2020, we investigate the relation between firm-level political sentiment and future stock price crash risk. We employ the political sentiment data constructed by Hassan et al. (2019) that quantify sentiment using textual analysis of earnings call transcripts. The sentiment captures the percentage of positive and negative words in the conversation. Following the literature on crash risk, we measure stock crash risk using two variables: the negative skewness of firmspecific daily returns over a fiscal year (NCSKEW) and the down-to-up volatility (DUVOL).

We document a strong and negative association between the level of political sentiment and future stock price crash risk. A one standard-deviation increase in political sentiment is associated with a decrease in crash risk measured by NCSKEW (DUVOL). The results remain strong after we control for various firm-specific determinants of crash risk, and firm-, industry- as well as time-fixed effects. In addition, we find that the predictive power reflected in political sentiment is independent of either non-political sentiment or political risk. Our analysis shows that political sentiment plays a significant predictive role during periods with both high and low market-wide economic policy uncertainty.

Furthermore, we observe that change in political sentiment is also informative: a positive shift in political sentiment is associated with a decrease in future crash risk. The finding is consistent with the signaling hypothesis, but not with the misleading sentiment hypothesis. The evidence suggests that managers can use their tone to signal future stock performance.

Finally, we conduct tests on how litigation risk influences the relation between political sentiment and crash risk. By interacting political sentiment with an indicator of high litigation risk, we document that higher litigation risk is associated with a greater predictive effect of political sentiment on crash risk. The result is consistent with the argument that litigation concerns incentivize managers to timely reveal their view on a firm's outlook.

We make three contributions to the literature. First, our study belongs to growing literature that examines the link between politics and the stock market (e.g. Santa-Clara and Valkanov,
2003; Addoum and Kumar, 2016). We extend the existing literature that focuses mainly on the impact of political environment. We examine the informational role of political sentiment in predicting stock crashes. Our paper is related to the study by Addoum and Kumar (2016) that examines the effect of market-wide political sentiment, proxied by political sensitivity of industries, on stock returns through the investor-demand channel. However, the measure of aggregate political sentiment mutes the rich variation in political sentiment across firms over time and thus provides limited understanding of the relation between political sentiment and firm-specific stock performance. In addition, we focus on stock crash risk rather than firm cash flows or asset prices in prior studies. To the best of our knowledge, we are the first to focus on time-varying firm-level political sentiment and identify its predictable power in stock crashes.

Second, this paper adds to the stock crash literature by examining the predictive role of political sentiment. Prior studies on stock crash risk mostly focus on market-wide attributes (Chen et al., 2001; Huang and Wang, 2009) and firm characteristics (Jin and Myers, 2006; Hutton et al., 2009). We add to this literature by highlighting the valuable qualitative information in earnings conference calls that predict crash risk.

Third, we contribute to growing literature on market participants' sentiment and its economic consequences. Existing studies focus primarily on the sentiment from investors' perspective, either market-wide (e.g. Baker and Wurgler, 2006; Stambaugh et al., 2012) or firm-specific investor sentiment (Aboody et al, 2018; Fu et al, 2021). Fu et al. (2021) investigate the impact of firm-specific investor sentiment, a composite index constructed from principal analysis of three sentiment proxies: price earnings ratio, average turnover rate and buy-sell imbalance. Our study is different from Fu et al. (2021) in two major ways. First, our measure of sentiment is based on the tone in earnings conference calls and mainly reflects the sentiment of firm managers and analysts rather than that of retail investors. Our results show that qualitative contents or emotive tone can provide predictable crash-relevant information. Second, we restrict our attention to sentiment associated with political topics. We find a strong link between political sentiment and stock crash risk, controlling for non-political sentiment. Our results suggest that political sentiment delivers firm-specific information and affects market perception of the firm and its stock performance. Our findings call for careful consideration of social costs, such as stock crash risk, associated with political policies. Ill-conceived policies may lead to market crashes, which can potentially outweigh the upsides of well-meaning political reforms.

The rest of the paper is organized as follows. Section 2 reviews literature and develops our hypotheses. Section 3 discusses data sources. Section 4 reports the empirical analysis. Finally, section 5 concludes.

Political policy and its uncertainty can affect financial and product market as well as individual firms (Pastor and Veronesi, 2013; Brogaard and Detzel, 2015). A proposal of new political policy brings a great deal uncertainty to the market, mainly regarding the effective date of the policy, the magnitude of policy change, and the extent and direction of the influence. Managers' beliefs about the potential political impact can lead to corresponding changes in firm financial policy, investment policy, payout policy and firm risk-taking behavior [2]. As a result, investors try to identify the "winners" and "losers" and revise their portfolios accordingly. One important source of information that investors rely on, through firm announcement and disclosure, is managers' view on the challenges and opportunities from the new political environments.

Compared with polished written financial documents, conference calls are more impromptu and interactive. A burgeoning literature suggests that both verbal (e.g. the content of communication and sentiment) and nonverbal (e.g. facial gestures, vocal expressions and body movements) components of conference calls contain useful information about firm fundamentals and security pricing [3]. For example, Mayew and Venkatachalam (2012) analyze one important type of nonverbal communication-the managerial vocal cues-in conference calls. They find
Political
sentiment and
stock crash
risk
Political sentiment and stock crash risk
that the positive (negative) dimension of a manager's affective or emotional state conveys good (bad) news about firm future performance. Other studies examine the verbal component in conference calls. For example, Frankel et al (1999) find that the verbal contents in conference calls convey significant information that are associated with unusual large stock return volatility, large trade size and high trading volume. In addition, Matsumoto et al (2011) examine the conference call transcripts. They document that the Q&A sections of conference calls are more informative than the introductory parts of the calls and that managers provide more disclosure when firms have poor performance.

In this study, we focus on the tone in the verbal component of conference calls using textual analysis. As one of the important soft signals in verbal contents, the tone in conference calls provides incremental information and therefore is predictive of asset returns. For example, Price et al. (2012) find that conference call linguistic tone explains future abnormal returns and trading volume, especially for non-dividend paying firms. By focusing on managers' tone, Jiang et al. (2019) show that managers' sentiment in conference calls negatively predicts stock returns both at the market level and in the cross-section. Druz et al. (2020) find that increase in the negativity of managerial word choice (managerial tone) in conference calls is a strong indicator of firms' low future earnings and great uncertainty.

Managers' sentiment, defined broadly, is their subjective opinions and beliefs about future cash flows and risks that are not justified by currently available public information. We argue that political sentiment in conference calls is associated with future stock crash risk. The crash risk, a third moment of stock returns, captures the negative skewness of return distribution or the extreme negative returns. Existing theoretical work suggests an important role of return skewness in asset prices (Xu, 2007; Theodossiou and Savva, 2016). There is large literature investigating the potential determinants of stock price crash risk, including firm size, profitability, firm-specific volatility, trading turnover and corporate disclosure quality (e.g. Chen et al, 2001; Jin and Myers, 2006; Hutton et al, 2009). However, we are not aware of any prior work that studies the link between political sentiment and crash risk. Previous literature offers several channels through which political sentiment can affect stock crash risk.

Managers have private information regarding a firm's future cash flows and risk. However, they can engage in strategic tone management to mislead investors about firm future fundamentals due to agency incentives (Huang et al, 2014). First, firm stakeholders react negatively to potentially inferior firm prospects, questioning the managers' capabilities, which can threaten managers' career on issues such as reputation, promotion, employment opportunities, potential termination and postretirement benefits such as directorships (Verrecchia, 2001; Kothari et al, 2009). In addition, negative information can cause stock price to decline, causing monetary loss to managers since executive compensation is linked to stock and firm performance. Finally, the impact of information can be short-lived. Therefore, managers can affect stock price through their sentiment with the hope that the firm's situation changes before they are required to release hard information (Graham et al, 2005). Previous literature shows that managers manipulate the tone in disclosure to mislead and inflate investors' perception of the firm (Merkel-Davies and Brennan, 2007). In addition, Rogers et al. (2011) document that firms involved in shareholder litigation use substantially more optimistic language in their earnings announcements than do non-sued firms.

There are two ways that misleading sentiment affects stock price. First, managers can inflate sentiment to downplay the impact of negative private information concerning firm prospects. Although the misleading positive sentiment in conference calls can inflate stock prices, managers have to reveal the accumulated bad news at a certain point when it becomes too costly or difficult for managers to bury the information (Kothari et al, 2009). When a stockpile of negative messages burst into the market all at once, stock prices drop abruptly
and significantly, causing stock crashes (Jin and Myers, 2006). As a result, strategic high sentiment is associated with high crash risk.

Second, managers can display overly negative sentiment toward potentially detrimental information. There are at least two reasons why managers would utilize such a strategy. They can use the sentiment as a preemptive measure against future negative information. If more negative information is revealed, the investors are already prepared by the negative sentiment conceded by managers earlier. Therefore, the market reaction tends to be less severe. Alternatively, if future information is less negative, investors, realizing that the previous sentiment is too negative, would react positively. Consequently, negative sentiment is associated with less crash risk. This leads to our first hypothesis:

H1. There is a positive relation between political sentiment in conference calls and future firm crash risk.

Prior literature suggests that managers have incentives to signal their private information to the market. Doing so helps firms reduce information asymmetry, resulting in lower cost of capital (Glosten and Milgrom, 1985; Diamond and Verrecchia, 1991) and litigation risk (Skinner, 1997; Druz et al, 2020).

Managers can use tone to signal their private information concerning firm future performance and risk (Loughran and McDonald, 2016). Several empirical studies on linguistic tone and vocal cues show that sentiment during earnings conference calls induces stock market reactions such as unusual large return volatility and high trading volume (e.g. Tetlock et al, 2008; Mayew and Venkatachalam, 2012; Price et al, 2012). These studies generally interpret their findings as consistent with the notion that managers use language to signal their expectations about firm future performance. In addition, Davis et al. (2012) and Demers and Vega (2014) examine tone in earnings press releases and find that tone is related to current and future performance. Similarly, Davis et al. (2015) study manager-specific optimism during earnings conference calls and find that manager-specific tone is positively linked with future operating performance. In a recent study, Druz et al. (2020) analyze the negativity of managerial word choice (managerial tone) on conference calls and show that increase in the negativity of tone strongly predicts lower future earnings and greater uncertainty.

Furthermore, the pessimistic tone in conference calls is indicative of negative future cash flow shocks and large downside return risk. Easterwood et al. (2021) examine the information content of stock return skewness. They show that stock return skewness can be driven by information concerning the underlying asymmetries in future cash flow shocks. Therefore, information embedded in low sentiment is related to a greater degree of left skewed return distribution (i.e. high price crash risk). Taken together, we have the following hypothesis:

H2. There is a negative association between political sentiment in conference calls and future stock crash risk.

It is worth noting that managers' tone is jointly determined by economic fundamentals and managerial incentives (Huang et al., 2014). In other words, both signaling and misleading sentiment exist. We examine which plays a dominant role: the signaling or the misleading sentiment.

3.1 Sample selection

Our sample includes publicly listed US firms for years 2002-2020. We collect financial data from Compustat and stock information from Center for Research in Security Prices (CRSP).
Political sentiment and stock crash risk

We exclude financial firms with Standard Industrial Classification (SIC) code between 6000 and 6999 because their financial statements tend to be influenced by factors unique to the financial industry [4]. The final sample contains 40,254 firm-year observations.

3.2.1 Stock price crash risk. We employ two measures of firm-specific stock price crash risk following Chen et al. (2001) and Callen and Fang (2013). We first estimate firm-specific daily returns for every year following the expanded market model:

where is the return on stock in day is the return on the CRSP value-weighted market index in day , and is the return on the value-weighted industry portfolio in day . The industry is defined based on the two-digit SIC code. The firm-specific daily return for stock in day is calculated as the natural logarithm of one plus the residual return from Equation 1.

Our first measure of crash risk is the negative skewness of firm-specific daily returns over the fiscal year, NCSKEW, calculated as in Equation 2:

where is firm-specific daily return, and is the number of daily returns during fiscal year . According to the definition, a higher value of NCSKEW indicates higher crash risk.

Our second measure of crash risk is the down-to-up volatility measure ( . For each firm over a fiscal-year period , we separate firm-specific daily returns into two groups: "down" trading days (i.e. days with returns below the annual mean) and "up" trading days, (i.e. days with returns above the annual mean). We calculate in the following way:

where and are the number of up and down trading days in year , respectively. A larger value of indicates greater crash risk.
3.2.2 Political sentiment. Our firm-level political sentiment measure is from Hassan et al's (2019) database, which is based on textual analysis of the transcripts of earnings calls [5]. The measure is defined as the number of political bigrams conditional on proximity to positive and negative words divided by the total number of bigrams. The treatment of positive or negative sentiment follows the sentiment dictionary of Loughran and McDonald (2011). Intuitively, frequently used positive words include "good", "strong" and "great", representing high sentiment; in contrast, the use of negative words, such as "loss", "decline", and "difficult", reflects low sentiment (Hassan et al, 2019). To have a better comparison among the sentiment across different firms, following Gad et al. (2021), we winsorize the sentiment measures at the tails and then normalize the values.
3.2.3 Control variables. We control for a list of variables that have been identified in the literature to explain stock crash risk. Following the literature on crash risk (e.g. Chen et al, 2001; Jin and Myers, 2006; Hutton et al, 2009; Callen and Fang, 2013), we include (1) stock price crash
risk over the previous fiscal year (NCSKEW ), (2) the kurtosis and the standard deviation (IDV) of firm-specific daily returns over the fiscal year, (3) firm size (AT), defined as the natural logarithm of total book assets, (4) the market-to-book ratio (MB), (5) firm leverage (LEV), calculated as total long-term debts divided by total book assets, (6) firm profitability -return on assets (ROA), (7) an indicator for industry-level litigation risk, LITIG, which is set to 1 for firms in the biotechnology, computer, electronics, and retail industries, and zero otherwise, (8) detrended stock trading volume (DTURN) and (9) opaqueness of financial reports (OPAQUE).

One concern is that the measure of political sentiment is correlated with non-political sentiment and political risk, which may confound our findings. In the robustness analyses, we control for non-political sentiment and political risk, both from Hassan et al. (2019). Nonpolitical sentiment is based on the use of positive and negative words associated with nonpolitical topics. Political risk is constructed by counting the number of occurrences of bigrams indicating discussions of a given political topic within a set of 10 words surrounding a synonym for "risk" or "uncertainty", scaled by the total number of bigrams in the transcript of earnings calls. Detailed variable definitions are in Appendix.

Table 1 reports summary statistics for the variables that we use in the analysis. The average NCSKEW is positive ( 0.06 ) and DUVOL is negative ( -0.03 ), indicating that the sample firms' returns are negatively skewed on average. The median values for both NCSKEW and are lower than the means, suggesting that some firms have extremely low returns.

As expected, the normalized sentiment measures have means close to zero and standard deviations of 1 . However, the median of political sentiment is -0.06 , which indicates that, of firm-year observations exhibit a political sentiment level lower than the average sentiment, about 0.06 standard deviations below it [6]. In contrast, the median of non-political sentiment is close to zero, suggesting that a firm's sentiment toward non-political topics is rather symmetric around its long-time mean.

The summary statistics of all control variables are within reasonable ranges and in line with the statistics reported in the literature (Chen et al., 2001; Hoberg et al., 2014; Callen and Fang, 2013).

Note(s): This table presents descriptive statistics for the sample of firms included in our study. Our sample consists of firm-year observations for non-financial firms in matched Compustat and CRSP datasets from 2003 to 2020. Following Gad et al. (2021), PSentiment and NPSentiment are first winsorized at the level and then standardized. All other continuous variables are winsorized at the level. The detailed variable definitions are provided in Appendix
Table 1.
Descriptive statistics

Table 2. Note(s): This table presents the results of OLS regressions of stock price crash risk on political sentiment over Political sentiment and the period from 2003 to 2020. The -values in parentheses are computed using robust standard errors. Firm, future stock price crash risk

We test the relation between political sentiment and firm-specific stock price crash risk using the following regression model:

where CrashRisk is the firm-specific crash risk proxied by NCSKEW and DUVOL. PSentiment is the political sentiment measure from Hassan et al. (2019). refers to a set of control variables for each firm in year is year-fixed effects, means firm-fixed effects, and represents industry-fixed effects. The variable of interest is , which captures the relation between stock price crash risk and political sentiment.

Table 2 reports the regression results of crash risk on political sentiment, a set of control variables, firm-, industry- and year-fixed effects. Following the literature that explains crash risk, we identify the following controls: return kurtosis, firm-specific risk, cumulative stock return, turnover, market-to-book ratio, book leverage, profitability, firm size, opaqueness of earnings reports and an indicator of high-litigation risk. Models 1 and 2 (Models 3 and 4) present the results using NCSKEW (DUVOL) as the measure of stock price crash risk. We adjust standard errors for heteroskedasticity.

The results are generally consistent with the evidence in the literature. For example, we observe that firms with large size, high growth, high profitability and opaque quality of reported earnings are more likely to experience stock price crashes. In addition, stocks with high turnover, high returns and high firm-specific volatility have high crash risk. Consistent with prior studies, we document a strong negative relation between current and previous period crash risk. Moreover, the results show that high-leveraged firms have low stock crash risk, everything else being equal. The evidence is consistent with the notion that firms deep in debts face high

year, and industry fixed effects are included. , and indicate significance at 10,5 , and levels, respectively. All variables are defined in Appendix
scrutiny from market participants, and therefore have less capacity for hiding bad news from the public.

Focusing on political sentiment, we observe that both NCSKEW and DUVOL are negatively associated with political sentiment as shown in models 1 and 3, respectively. In addition, such negative relation is statistically and economically significant. In Model 1, on average, one standard deviation increase in political sentiment decreases stock crash risk by with a -value of . Such negative relation is consistent with the signaling hypothesis but inconsistent with the misleading sentiment hypothesis. In other words, during earnings conference calls, managers are more inclined to provide signals about firm outlooks, which is indicative of stock price crashes.

It is possible that the tone in the conversations on political topics is intertwined with that on non-political issues. Therefore, the predictive power of political sentiment can be partially attributed to the signals from non-political sentiment in the calls. To purge out the effect of non-political sentiment, we re-estimate the regression model by controlling for non-political sentiment. The results are presented in Table 2 models 2 and 4. We find that non-political sentiment is also negatively linked with future stock price crashes. The evidence is consistent with the hypothesis that managers signal firm information through their tone in conference calls, irrespective of whether the topics are political or not. More importantly, we observe that the predictive effect of political sentiment remains strong statistically and economically.

Our results highlight the important role of political sentiment in predicting future stock crashes, which is independent of the effects of non-political sentiment. Overall, the results in Table 2 are supportive of the signaling hypothesis.

Prior literature documents that economic policy uncertainty (EPU), the risk arising from uncertainty in fiscal, regulatory, and monetary policies at the macroeconomic level, affects both corporate policies and firm behaviors (Gulen and Ion, 2016; Bonaime et al., 2018) as well as influences asset returns and volatility (Pastor and Veronesi, 2013; Boutchkova et al., 2012; Baker et al., 2016).

Political sentiment may reflect the market-wide economic policy uncertainty. Therefore, political sentiment can exhibit, at least partially, significant predictive power during the period with high economic policy uncertainty. In this section, we examine the influence of political sentiment in high- and low-EPU periods.

Baker et al. (2016) develop an EPU index based on the frequency of newspaper coverage to proxy for movements in policy-related economic uncertainty. We first calculate 12 -month moving average of the EPU index from 2002 to 2020. We then define a year as a high-EPU period if the average of the EPU index over the year is above the sample median. Next, we re-run the regressions conditional on whether the sample year is a high- or low-EPU year.

Table 3 provides our estimation results. Models 1 and 2 (models 3 and 4 ) are for periods with low (high) economic policy uncertainty. The coefficients for political sentiment are significantly negatively across all models, regardless of high- or low-EPU periods. For example, as shown in Model 1, during the low-EPU period, the coefficients of PSentiment is -value of -3.15 . In addition, we observe that, compared with that of low-EPU period, the economic impact of political sentiment is at similar levels when the economic policy uncertainty is high. Specifically, during the high-EPU period, the coefficient of is -0.058 ( -value of -3.87 ). The evidence suggests that the predictive effect of political sentiment is consistent and robust over both high- and low-EPU periods.

Securities litigation poses large costs to firms and managers. Litigation risk intensifies when firms experience large unexpectedly earnings disappointments. Threats of possible lawsuits

Political sentiment, economic policy uncertainty (EPU) and future stock price crash risk
Note(s): This table presents the results of OLS regressions of stock price crash risk on political sentiment over the period from 2003 to 2020 . We include in the regression the same control variables as the ones used in Table 2. The -values in parentheses are computed using robust standard errors. Firm, year and industry fixed effects are included. , and indicate significance at 10,5 and levels, respectively. All variables are defined in Appendix
incentivize managers to quickly disclose bad news (Francis et al., 1994; Bourveau et al., 2018). We expect that the predictive effect of political sentiment to be stronger for firms with higher litigation risk.

Following Francis et al. (1994) and Callen and Fang (2013), we define litigation risk based on industry legal exposure. Specifically, the high-litigation dummy equals one for firms within an industry with a high incidence of litigation, such as the biotechnology, computers, electronics, and retailing industry, and zero otherwise.

To further test the signaling hypothesis, we examine the joint effect of litigation risk and political sentiment on future stock crash risk. Specifically, we estimate the following regression model:

where CrashRisk is firm-specific crash risk proxied by NCSKEW and DUVOL. LITIG is a dummy that equals 1 if firm has a high industry litigation exposure. The coefficient of interest is , which captures the joint effect between firm litigation risk and political sentiment on future stock crash risk. The interaction term captures the difference in crash risk between high- and low-litigation risk firms, given the same firm-level political sentiment. If litigation risk incentivizes managers to provide signals about firms outlooks in conference calls, we expect that the informativeness of political sentiment for firms with high litigation exposure to be much higher than that for firms with low-litigation risk. Hence, we expect to be significantly negative.

Table 4 shows that the coefficients for political sentiment measures are significantly negative across all models. The evidence suggests that among low-litigation risk firms, political sentiment alone is negatively associated with stock crash risk. Our results remain robust with either crash risk measure, or .

Importantly, the coefficients for the interaction term, PSentiment LITIG , are statistically negative for all columns. Our evidence suggests that, given the same level of political sentiment, managers from high-litigation risk firms provide more informative signals than those from low-risk firms. As shown in Model 1, compared with the low litigation risk firms, a one standard deviation increase in the political sentiment leads to an additional decrease in crash risk (NCSKEW) by ( value of -2.18 ) for the high litigation risk firms.

Note(s): This table presents the results of OLS regressions of stock price crash risk on political sentiment over the period from 2003 to 2020 . We include in the regression the same control variables as the ones used in Table 2. The -values in parentheses are computed using robust standard errors. Firm, year, and industry fixed effects are included. , and indicate significance at 10,5 , and levels, respectively. All variables are defined in Appendix

Table 4.
Political sentiment, litigation risk and future stock price crash risk

Overall, our evidence is consistent with the signaling hypothesis. The negative relation between political sentiment and stock crash risk becomes much stronger when firms are exposed to larger litigation risk. The results suggest that litigation risk reinforces managers' incentives to provide signals regarding political impact on their business via the tone in conference calls.

It is possible that political sentiment is correlated with political risk, which may confound our previous estimates. To rule out the possibility, we control political risk in our regression analysis.

Table 5 shows that, consistent with the signaling hypothesis, the coefficients on political sentiment remain negative and significant, even after controlling for political risk and nonpolitical sentiment. We observe that the predictive power of political risk is statistically and economically insignificant. The evidence suggests that it is the political sentiment, rather than political risk, that forecasts the future stock price crashes.

Previous literature shows that new information drives a stock's return. To understand how information shocks in political sentiment impacts crash risk, we take the first difference in political sentiment to obtain changes in political sentiment. Doing so ensures that our result is robust to the concerns about potential stationary nature of political sentiment over time. We control for changes in non-political sentiment to tease out the effect of political from that of non-political information.

Table 6 shows that changes in political sentiment are all negatively correlated with crash risk, consistent with previous results. For example, in Model 2, the coefficient on change in political sentiment (NCSKEW) is -0.023 with a -value of -2.40 . The evidence suggests that a positive shift in political sentiment is associated with a decrease in stock crash risk in the future. We also show that there is a negative link between change in non-political sentiment and crash risk.

In summary, we document that change in political sentiment is informative and helps forecast future stock crashes.

Table 5.
Political sentiment, political risk and future stock price crash risk

Note(s): This table presents the results of OLS regressions of stock price crash risk on political sentiment over the period from 2003 to 2020 . We include in the regression the same control variables as the ones used in Table 2 . The -values in parentheses are computed using robust standard errors. Firm, year and industry fixed effects are included. *,**, and indicate significance at 10,5 and levels, respectively. All variables are defined in Appendix

Table 6.
Change in political sentiment and future stock price crash risk
Note(s): This table presents the results of OLS regressions of stock price crash risk on political sentiment over the period from 2003 to 2020 . We include in the regression the same control variables as the ones used in Table 2. The -values in parentheses are computed using robust standard errors. Firm, year, and industry fixed effects are included. *,**, and indicate significance at 10,5 , and levels, respectively. All variables are defined in Appendix

In this study, we provide unique evidence on the relation between firm-level political sentiment and future stock price crash risk. Specifically, we document a strong and negative association between political sentiment and future crash risk. Our additional analysis shows that change in political sentiment conveys valuable information: a positive shift in political sentiment is linked with a decrease in future crash risk. We observe that the predictive information reflected in political sentiment is independent of either non-political sentiment or political risk.

Our results stay robust even controlling for various determinants of crash risk, firm-, industry- and time-fixed effects. Moreover, we observe that the predictive effect of political sentiment remains significantly strong during either high or low market-wide economic policy uncertainty. The evidence is consistent with the signaling hypothesis that managers use their tone in conference calls to signal firms' outlooks. In addition, our finding is more pronounced for firms with high litigation risk. The results suggest that litigation concerns incentivize managers to timely signal their view on firm outlooks.

Our findings highlight the information role played by political sentiment in predicting stock crashes. Future research could extend this line of inquiry in following ways: (1) whether managers compensation structure affects the predictive power of political sentiment or sentiment in general; (2) whether the difference in the sentiment among managers, analysts and investors affects asset return and risk; and (3) whether and how the information embedded in the nonverbal aspects of conference calls, jointly with managerial sentiment, predicts future firm and security performance.

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NCSKEW: the negative coefficient of skewness of firm-specific daily returns over the fiscal year.

DUVOL: the log transformed ratio of the firm-specific standard deviation during the down times (i.e. when firm-specific daily returns above the average return during the fiscal year) to the firm-specific standard deviation during the up times.
(N)PSentiment: Standardized firm-level (non-) political sentiment in a conference call from Hassan et al. (2019), equal to the proportion of the use of (non-)political bigrams, conditioning on proximity to positive and negative tone words as defined by Loughran and McDonald (2011). Examples of positive (negative) tone include "good", "strong", and "great" ("loss", "decline" and "difficult").

DTURN: the average monthly share turnover over the current fiscal year, minus the average monthly share turnover over the previous year, where monthly share turnover is calculated as the monthly share trading volume divided by the number of shares outstanding over the month.
EPU: US economic policy uncertainty index developed by Baker et al. (2016).
IDV: the standard deviation of firm-specific daily returns over the fiscal year.
: the kurtosis of firm-specific daily returns over the fiscal year.
LEV: the leverage ratio, equal to long-term debt plus debt in current liabilities divided by total assets.
LITIG: the dummy of litigation risk, equal to 1 for firms within the following industries with high litigation risk (Francis et al., 1994) and zero otherwise: the biotechnology (4-digit SIC codes 28332836 and 8731-8734), computer (4-digit SIC codes 3570-3577 and 7370-7374), electronics (4-digit SIC codes 3600-3674) and retail (4-digit SIC codes 5200-5961).
: the log value of total book asset at the end of the fiscal year.
: the ratio of the market value of equity to the book value of equity.
the cumulative 12-month firm-specific daily returns over the fiscal year.
: Return on asset, equal to the income before extraordinary items divided by the book asset value.
OPAQUE: the three-year moving sum of the absolute value of annual discretionary accruals as in Hutton et al. (2009).
PRisk: Standardized firm-level political risk from Hassan et al. (2019), which equals the number of occurrences of bigrams indicating discussion of a given political topic within the set of 10 words surrounding a synonym for "risk" or "uncertainty", divided by the total number of bigrams in the transcript of conference calls.

Cathy Xuying Cao can be contacted at: caoc@seattleu.edu

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