key: cord-0719454-9tw47wlh
authors: Mazumder, Sharif
title: How important is social trust during the COVID-19 crisis period? Evidence from the Fed announcements
date: 2020-08-26
journal: J Behav Exp Finance
DOI: 10.1016/j.jbef.2020.100387
sha: 3359d2c1de37cfef804f460f59c1565141cb1fbf
doc_id: 719454
cord_uid: 9tw47wlh

During the COVID-19 crisis period, firms headquartered in high social trust US states perform better than their counterparts from the low social trust states. Stock returns over the crisis period are 3 to 4 percentage points higher, on average, if social trust increases by one standard deviation. The association is stronger for firms of more affected industries (COVID-19 industries). More specifically, a one standard deviation increase of social trust associates with a 6.45% increase of [Formula: see text] if firms belong to the COVID-19 industries. Next, I analyze the stock market reactions to the Fed’s announcements on March 23, 2020. The results show that firms headquartered in the high trust states benefit less from the announcements because these firms can access to other external financings cheaply. The average three-day announcement [Formula: see text] and [Formula: see text] (FF 3-factor adjusted) are higher by 2.5% and 2.6% respectively if firms headquartered in low trust states.

Existing literature documents the impacts of social trust on a broad array of financial outcomes (Arrow, 1972; Coleman, 1990 ). In the macro-level, Putnam (1993) documents that higher social capital with a high level of trust fosters economic growth. More related to the capital market, the social trust allows for higher market participation (Georgarakos & Pasini, 2011; Guiso, Sapienza, & Zingales, 2004) , larger earnings announcement returns (Pevzner, Xie, & Xin; 2015) , higher firm-level performance during housing crisis (Lins, Servaes, & Tamayo (hereafter LST, 2017) ), and lower crash risk (Li, Wang, & Wang, 2017) .

Although the study of social trust in the stock market performance is substantial, the extent to which social trust impacts stock performance during the COVID-19 crisis period is relatively unexplored in the literature. The purpose of the paper is to address two important questions. First, to see if firms from high trust US states perform better during the COVID-19 crisis period. Second, whether firms' performances are more (less) sensitive with the Federal Reserve Board's (hereafter the Fed) announcements during the crisis when firms are headquartered in high (low) trust intensive society. 1 I tie the analysis from two different angles: from the perspective of investors and from the viewpoint of corporations. First, from stockholders' point of view, the decision of whether to invest is not only a matter of risk and return tradeoff, but depends on the reliability of the reported financial information and the fairness of the overall system (Guiso, Sapienza, & Zingales, 2008) . Importantly, this reliability becomes more vital when the macro-level trust in the country deteriorates due to the sudden emergence of a crisis. As Stiglitz (2008) and Reich (2008) state, social trust weakened when the housing crisis occurred, and the recent COVID-19 pandemic also wakens a mistrust in society (Wadhams, 2020). 2 In this situation, ex-ante social trust plays a more crucial role in the declining phase of social trust. More to the point, outside investors usually place more valuation premiums if firms report the financials on time and in more reliable ways, as Merton (1987) states, investors demand more returns if the information asymmetry between managers and investors is high. I argue that managers from the high social trust areas are less likely to withhold bad information, if any (Li, Wang, & Wang, 2017) , which reduces the likelihood of stock price crash risk. Further, these firms from high trust society tend to report financials more reliably (Berglund & Kang, 2013) , which increases investors' willingness to pay premiums for these firms. Based on the above discussion, I hypothesize that firms headquartered in high trust areas, ex-ante, perform 2017), public credit (Meng & Yin, 2019) , issuing equity (Gupta et al., 2018) , and so on. On the other hand, firms headquartered in low trust areas incur higher costs of accessing external funds; thus, these firms might need to take shelter to the Government policy resorts, such as the Fed facilities. Hence, I hypothesize that the impact of the facilities is more prominent for firms headquartered in low social trust states since these firms benefit more from the facilities by accessing affordable credit from the Fed. I test the hypotheses using 1,709 US firms that are constituent of the Russell 3000 index during the crisis period over January 02, 2020 to May 30, 2020. 5 I measure social trust using the survey data from the "General Social Survey (GSS)" that the National Opinion Research Center (NORC) conducts. Following LST (2017) , social trust is a proportion of respondents who trust most of the people in the society (see also Guiso et al., 2004; Meng & Yin, 2019; Pevzner, Xie, & Xin, 2015) . After calculating the abnormal returns during the COVID-19 period, I test the hypotheses at the firm-level using cross-sectional regressions. 6 I find that state-level social trust is positively related to abnormal returns over the crisis period. The results are both statistically and economically significant. More precisely, if social trust increases by one standard deviation the abnormal returns measured as , , , and increase by 3.95%, 3.957%, 3.20%, and 3.67% respectively. The association is much stronger for firms of the most affected industries, COVID-19 industries. A one standard deviation increase of social trust associates with 6.45% and 7.47% increase of and if firms belong to the COVID-19

industries. Testing the second hypothesis of the impacts of social trust on the announcement day returns, I find that firms headquartered in the low-trust regions benefit more from the Fed announcement. More specifically, firms headquartered in the low-trust states earn and (three days 5 Russell 3000 is the largest 3000 publicly traded firms incorporated in US represents almost 97% of the total market capitalization. 6 I compute four abnormal returns: CAPM-adjusted cumulative abnormal returns ( ), FF three-factor adjusted cumulative abnormal returns ( ), CAPM adjusted buy-hold abnormal returns ( ), and FF three-factor adjusted buy-hold abnormal returns ( ).

announcement returns) of 2.5% or 2.6%, respectively, higher than firms located in the high-trust states.

The results are both statistically and economically significant.

To my best knowledge, this study is the first to offer an analysis of how social trust influences firms' performance during the COVID-19 crisis period when the macro-level social trust deteriorates. The study by LST (2017) (2013), I argue that investors tend to believe more about the information provided by the managers of firms from the high trust society, and this reliability of information becomes more prominent during the crisis period.

The results complement the findings of LST (2017) that social trust is a crucial macro-level variable that can explain firms' performance during the crisis periods. Second, I contribute to the impact of the social trust on firms' performances that are severely affected by the COVID-19 pandemic. The reason for analyzing the affected industries is because the performance of these industries is more sensitive to the investors' confidence. Lastly, I contribute to event study literature by analyzing the announcement effects of the Fed facilities in the context of the easiness of access to corporate borrowings. Thus, the market reactions to the Fed announcements in light of how easily firms can access external financing is an exciting addition to the existing literature.

The paper is organized as follows. Section 2 presents the important events of the study. In Section 3, I briefly describe the extant literature and develop the testable hypotheses. The data and sample statistics are presented in Section 4, followed by the empirical findings with analysis in Section 5.

Finally, Section 6 presents discussion and conclusions.

J o u r n a l P r e -p r o o f On March 23, 2020, the Fed announced two facilities-"Primary Market Corporate Credit Facility (PMCCF)" and "Secondary Market Corporate Credit Facilities (SMCCF),"-to provide easy corporate credit and to increase the liquidity of the outstanding bonds 7 . The PMCCF allows companies to access to credit so that firms can better maintain the business operation and existing capacity during the pandemic period.

This facility is open to investment-grade companies and also extends the bridge financing for four years.

Borrowers can elect to defer interest and principal payment up to the first six months of taking the credits.

The other facility, SMCCF, purchases the secondary market investment-grade bonds of US companies, and the objective is to provide broad exposure to the market for investment-grade bonds. These two facilities are designed to extend credit to employers and to support the corporate bond market. The Fed announcement about purchasing the newly issued bonds and loans from the primary market supports firms to meet up the immediate cash requirement by the corporations. The other announcement to purchase the outstanding corporate bonds and ETFs from the secondary market facilitates firms' leverage.

Existing literature examines the impact of the financial crisis of 2008-09 on firms' performance (e.g., Almeida et al., 2009; Campello et al., 2010; LST, 2017) . The stock market performance of the recent pandemic, COVID-19, also examines in limited extents in the literature, such as stocks' performance of Chinese firms with confirmed COVID cases (Al-Awadhi et al., 2020), global stock market performances (Liu et al., 2020) , government interventions and global stock returns (Zaremba et al., 2020; Zhang et al., 2020) , market volatility of the announcement of COVID cases (Albulescu, 2020) , and so on. However, the extent of how social trust associates with firms' performance during the COVID-19 crisis period gets little attention.

According to Stiglitz (2008) , the crisis period of 2008-09 made an abrupt collapse of confidence, and the trust also eroded. In this similar vein, the Bloomberg Businessweek recently headlines about the growing mistrust in society during the recent COVID-19 pandemic (Wadhams,2020) . This lack of trust may create a fundamental problem in the overall market, as Reich (2008) states that the lack of trust may fold up Wall Street in its fancy tents. Investors make an investment decision based on the information they acquire about firms. Moreover, the investment decision is more than a risk-return tradeoff, rather how reliable firms' financial reporting as well as the fairness of the overall system (Guiso, Sapienza, & Zingales, 2008) . I argue that firms headquartered in high trust societies tend to report more reliably (Berglund & Kang, 2013) and less likely to hoard bad news (Li, Wang, & Wang, 2017) . Based on the preceding, since the overall trust level decreases during the crisis period, the reliability of information appears a more vital factor in investment decisions. Consistent with this view, I hypothesize that social trust explains the crisis period stock performance positively. I further expect that this association is even more vital for firms of the highly affected industries because the likelihood of earnings management is higher for distressed firms (Habib et al., 2013) . H1b. Social trust is more positively associated with the crisis-period stock returns for firms that belong to the affected industries than those of other industries.

A growing stream of research focuses on the role of social trust in the capital structure decision of firms. Hasan et al. (2017) find that firms headquartered in high social trust states can finance with lower credit spreads. In another study using global data, Meng and Yin (2019) find that social trust is negatively associated with the cost of debt. A study on equity financing also reveals a negative association between social trust and the cost of equity (Gupta, Raman, & Shang, 2018) . In line with these views, firms headquartered in the high social trust regions can finance from various sources at a lower cost than their peers from low trust regions; thus, benefit less from the Fed's facilities. Based on the above discussion, I hypothesize that firms from low social trust states benefit more from the Fed facilities; thus, the announcement day returns are more positive for firms from low social trust states.

H2. Announcement day returns are more positive for firms headquartered in the low trust society than those of high trust societies.

In this section, I explain how I construct the sample and measure the performance during the sample period. I consider the firms belong to the Russell 3000 index. I obtain daily stock price data from the COMPUSTAT Capital IQ North America Daily database. The prices are adjusted by the dividends adjustment factor (adjustment factors cumulative ex-ante), and the daily total return factor that are 

Journal Pre-proof "Generally speaking, would you say that most people can be trusted or that you can't be too careful in dealing with people?" I use the 2016 survey to construct the trust measure. 9 There are 2,867 respondents from the nine regions of US. First, I drop 77 responses that answer the question-"It depends." Then, I remove another 920 responses that answer "Don't know or N/A." After cleaning these responses, I find 1,867 responses, of which 33.4% report they trust most of the people in the society. Then, I assign social trust to each firm based on the headquarter location from the COMPUSTAT variable "state." , is -0.9%, when the median value is -6.2%. In Panel B, I report the abnormal returns of the crisis period segregated by the before and after the Fed intervention date. 10 I find that the mean abnormal returns, both CAR and BHAR, are negative in the pre-Fed Intervention period but turn positive in the post-Fed Intervention period. The mean is 6.2% and the median is 3.9% after the Fed intervention period, while the before-intervention period mean (median) is -4.8% (-4.4%).

Panel C provides descriptive statistics for the variables used in the regression. To capture the firmlevel heterogeneity, I control five variables following the existing literature (LST, 2017): size, book to market, total leverage ratio, cash to total asset, and profitability. Appendix A.1 reports variable descriptions in detail. The first row of Panel C reports the summary statistics of the size of the firms used in the sample. The mean size of the firms is 7,847 million when the fifty-percentile value is 1,427 million.

The second row shows the book to market ratio with a mean of 0.558 and the median of 0.552, meaning the sample firms are growth firms on average. The next row presents the leverage ratio, where the mean total leverage ratio is 0.304 and the median value is 0.279. The other two firm-level control variables are cash to asset and profitability with the mean (median) value of 0.239 (0.115), -0.021 (0.054) respectively.

Next, I control two stock price-related factors: the previous years' stock performance (momentum), and the idiosyncratic volatility (the residual standard error from the market model estimated over the last year using daily stock returns, Fu (2009)). I take these two controls because firms' previous years' returns can predict stock returns (Jegadeesh & Titman, 1993) , and Goyal and Santa-Clara (2003) of firms based on social trust of firms' location. The findings suggest that firms in lower social trust areas report more substantial abnormal returns after the Fed intervention than those of high social trust areas.

These findings motivate us to study the association of social trust on firms' performance during the COVID-19 crisis with an intervention from the Fed. Figure 1 [Insert Table 2 about here]

[Insert Figure 1 about here]

I estimate various regression models of stock returns during the COVID-19 crisis period as a function of the pre-COVID-19 period social trust. For the primary hypothesis, I regress the crisis period cumulative or buy and hold abnormal returns on social trust along with a number of control variables.

Precisely, I estimate the following regression model: is the proportion of the positive answers from the survey respondents, of state k, that they trust most of the people. , is a vector of control variables. Following LST (2017) and Ramelli and Wagner (2020) , I control firms' size, book to market, total leverage ratio, cash to total asset, profitability, momentum, idiosyncratic volatility, and Fama French three-factor loadings in the models. I also control three state-level variables to capture the state-level variation: unemployment rate, GDP per capita, and the median age of the state. is the Fama and French 49 industry dummies and , is the white noise when standard errors are clustered at a firm-level. 11 Table 3 contains the results of the baseline regressions. The variable of interest is the . Columns (1) to (4) report the regression results when I do not control any firm-level and state-level variables. The result shows that social trust is positively and significantly associated with the crisis period abnormal returns. The results are economically significant, meaning that a one standard deviation increase of social trust (0.063) is associated with 3.95%, 3.96%, 3.20%, and 3.67% increase of , , , and respectively during the COVID-19 crisis period. One big concern is whether the association is due to firm-level omitted variables that may be correlated with the , rather than due to the itself. To overcome the concern, I re-run the regressions controlling the firm-level and state-level control variables mentioned earlier. I find the results robust.

More specifically, the results from columns (5) to (8) confirm that firms' location in high social trust states matters to the higher stock returns during the COVID-19 crisis period. An economic interpretation of the coefficients is as follows: a one standard deviation increase of is associated with the increase in , , , and by 3.74%, 3.745%, 3.21%, and 3.61% consecutively.

11 I control industry dummies because some industry may affect differently from the other industries.

These results qualitatively confirm the hypothesis H1a that social trust associates with better performance during the crisis periods.

Lastly, I analyze whether the association is higher for firms that belong to the directly affected industries, COVID-19 industries. Following OECD (2020), I identify the following sectors are the most affected, from Fama and French 49 industries: Entertainment, Construction, Automobiles and trucks, Aircraft, Ships, Personal Services, Business Services, Transportation, Wholesale, Retail, and Restaurants, hotels, and motels. Columns 9 and 10 report the cross-sectional regression for the sub-sample of 425 firms from the COVID-19 industries. I find that the coefficients of and are higher than those of the full sample. 12 A one standard deviation increase of social trust associates with the higher and by 6.45% and 7.47% respectively, while the and of full sample increase by 3.74% and 3.61% consecutively. These results support the hypothesis H1b that firms from the affected industries perform better than those of other industries during the crisis if the affected industries' firms are headquartered in high trust states.

[Insert Table 3 about here]

The findings so far evidence that ex-ante positively affects the stock returns during the COVID-19 crisis period when the overall trust in the society deteriorates. In this section, I extend the investigation to whether the positive association is unique during the crisis periods or is common to the pre-crisis periods, perhaps due to some unobservable risk factors that correlate with the . 13

To address this issue, I adopt a difference in difference (DID) model with industry and time fixed effects. 14 12 I also analyze using and and find the result robust but do not report for brevity. 13 I cannot take the post-crisis sample as the impact of the COVID-19 does not finish yet when I complete the study. 14 I take month fixed effects in the regressions. The result is robust if I take day fixed effect instead of month fixed effect. Moreover, I take industry fixed effects rather firm fixed effects because the variable of interest, Where, , is the raw return, , or abnormal returns ( ). is a dummy variable set to one if the data lies between January 02, 2020 and May 30, 2020. is a dummy variable one if the data lies between January 02, 2019 and December 31, 2019. and are the industry and month fixed effects. I take the same control variables that I use in the baseline regression as well as the variable of interest, state-level social trust ). captures the differential impact of social trust on daily returns during the crisis period from January 02, 2020 to May 30, 2020. crisis period. In terms of economic significance for column 1, the interaction term of 0.009 on the suggests that a one standard deviation increase of (0.063) is associated with six basis points higher daily raw returns during the crisis period. For columns 2 and 3, the interaction terms are 0.009 and 0.003, which mean that the daily abnormal returns are higher by six basis points and two basis points respectively for and during the crisis periods with an increase of one standard deviation of . Columns (4) to (6) report the interaction coefficients when I include the firm-level and state-level control variables. The results are similar to columns (1) to (3). These results indicate that the association of social trust and abnormal returns is unique during the crisis period. The , does not have any time variation unless firms shift the location, which is a very rare event. Hence, I take industry fixed effect rather than firm level fixed effects.

bottom two rows report the difference of coefficients tests from the crisis period to the pre-crisis periods. I find that the difference of coefficient tests are 0.01 for columns (1), (2), (4), and (5), meaning that one standard deviation of increase in social trust enhances the net firms' performance by 6.3 basis points. The results offer a robust view that social trust explains the crisis period returns.

[Insert Table 4 about here]

In this section, I analyze the market reactions to the Fed policy announcements on March 23 rd , 2020. The purpose of the section is to examine how sensitive firms' performances are on policy announcements based on firms' location. I argue that firms headquartered in the higher social trust regions benefit less from the policy announcements to the crisis. Extant literature finds that firms headquartered in the higher level of social trust regions incur the lower credit spreads (Hasan et al., 2017; Meng & Yin, 2019) . In another study using global data, Mazumder and Rao (2020) find that firms headquartered in high trust countries use more long-term debt. As a result, firms headquartered in high trust states can finance from the other sources such as banks (Hasan et al., 2017) , private placement, public debt (Meng & Yin, 2019) , issuing equity (Gupta et al., 2018) , and so on, quite cheaply; thus, rely less on the Fed facilities. Table   5 reports the association of social trust on abnormal returns surrounding the Fed's policy announcement date. Both the coefficients are economically and statistically significant at 1% level. In columns (3) and (4), I find that the three-day abnormal returns are positively associated with low social trust. More specifically, firms' and increase by 2.5% or 2.6% if firms' locations are in low trust states. Both the statistics are statistically significant at 1% level. The results offer robust evidence in favor of the hypothesis H2 that firms from low trust states benefit more from the Fed announcements on March 23, 2020.

Panel C reports the regression results of social trust on and in the two subcrisis periods: before and after the Fed announcement date. I primarily expect that social trust plays a significant role during the crisis period without policy intervention, which helps to overcome the liquidity crisis of a firm. Consistent with the belief, I find that the pre-Fed intervention crisis period cumulative abnormal returns are positively associated with . Columns (1) and (2) in Panel C report that the increases by 3.9% and increases by 3.8% if social trust increases by one standard deviation. The results are both economically and statistically significant at 1% level. Analysis of the post-Fed intervention period shows that the and are statistically non-significant with the . Overall, the result offers robust evidence in support of the claim that the matters for firms' performance during the crisis periods, especially without the policy support to access the credit from the Fed.

[Insert Table 5 about here]

In this paper, I examine the value of social trust during the unexpected COVID-19 crisis period. I find that, everything else equal, firms headquartered in the high trust states, ex-ante, perform better during the crisis period. Investors value more to firms from high trust states because they believe in getting more transparent and timely information from these firms. The association is stronger for firms of the more affected sectors, COVID-19 industries. Moreover, the observed positive association is exclusive to the crisis period, meaning social trust plays a significant role in firms' performance when the aggregate mistrust becomes prominent. I also investigate how firms' stock prices react to the announcement of the macroeconomic measures designed to support the firms. I hypothesize that firms headquartered in the high trust states benefit less from the announcements because these firms can enjoy affordable financing from other sources along with the Fed facilities. Consistent with the hypothesis, I find that the announcement day returns are higher for firms from the low trust society. The results further show that social trust does not associate with firms' post-intervention performance when the overall market performs better. All the results provide robust evidence that social trust is important to explain firms' performance, especially when the overall market is in crisis. The study is the very preliminary analysis of the impact of the Fed facilities (PMCCF, SMCCF); thus, the long-term effects of the facilities and the sustainability of the event date abnormal returns are subject to future research. Other reasons (such as financial flexibility, credit accessibility, and so on) of event date performance differentials are worthwhile to examine.

J o u r n a l P r e -p r o o f Table 1 

The sample consists of 1,709 firms from the Russell 3000 index. Return data is from January 02, 2020, to May 30, 2020. is the cumulative abnormal return during the sample period with market model parameters estimated over the previous year's (January 01, 2019 to December 31, 2019) daily return. is the cumulative abnormal return with Fama French three factors model and parameters estimated over the previous year's daily return.

is the buy and hold abnormal return with market model parameters estimated over the previous daily return.

is the buy and hold abnormal return with Fama French three factors model parameters estimated over the previous year's daily return. Panel A reports the abnormal returns of the full sample. Panel B reports the abnormal returns segregated into before and after the Fed announcement date period. Panel C reports the descriptive statistics of control variables. Size is the natural log of total assets. Book to Market is the book value scaled by the market value of a firm. Leverage ratio is the sum of long-term debt and short-term debt scaled by total assets. Cash to Asset is the ratio of cash and short-term liabilities by total assets. The EBIT scaled by total assets measures profitability. Momentum is the buy and hold raw return for the daily return from January 02, 2019 to December 31, 2019. Idios. Volatility is calculated as the residual standard error from the market model estimated over the last year. Panel D reports the correlation matrix of the control variables. J o u r n a l P r e -p r o o f

The sample consists of 1,709 firms from the Russell 3000 index. Return data is from January 02, 2020 to May 30, 2020. (1)

(3) 

, is the daily raw return ( ) or Abnormal Return ( ). is the dividend-adjusted daily return.

is the CAPM adjusted daily return. is the Fama-French three-factor adjusted daily returns. Market model and Fama French 3 factors parameters are estimated over the previous year's daily return. Market return, size and value factors are from Kenneth French website. Social trust is the proportion of the response that respondents trust most of the people in the society. Size is the natural log of total assets. Book to Market is the book value scaled by the market value of a firm. Leverage ratio is the sum of long-term debt and short-term debt scaled by total assets. Cash to Asset is cash and short-term liabilities scaled by total assets. Profitability is the EBIT scaled by total assets. Momentum is the buy and hold raw return for the daily return over January 02, 2019 to December 31, 2019. Idiosyncratic volatility is calculated as the residual error deviation from the market model estimated over the last year. State-level controls are the unemployment rate, GDP per capita, and median age. Three factors loadings are Fama French Three factors loadings are calculated with the daily return over the last year. The industry is Fama and French 49 industry. Except when otherwise indicated, numbers in parentheses are heteroskedasticity-consistent standard error clustered at the firm level. ***, **,* indicate the parameter estimates are significant at 1%, 5%, and 10% level respectively.

(1)

(2) J o u r n a l P r e -p r o o f

This table reports the mean event date returns and cross-sectional regression results of social trust on abnormal return surrounding the event date. In Panel A, I report the mean value of abnormal return in the announcement day and 3-day abnormal returns. Panel B reports the cross-sectional regressions of announcement day abnormal return ( ) and 3-days window abnormal returns. is the cumulative abnormal return during the sample period with FF 3 factor model parameters estimated over the previous year's (January 01, 2019 to December 31, 2019) daily return.

is the buy and hold abnormal return with FF 3 factors model parameters estimated over the previous daily return. Panel C reports the regression results of the cross-sectional regression of social trust on the abnormal returns before and after the fed intervention. After-Fed Intervention period starts from March 24 th , 2020. Social trust is the proportion of the response that respondents trust most of the people in the society. The following control variables are taken in Panels B and C: Size is the natural log of total assets. Book to Market is the book value scaled by the market value of a firm. Leverage ratio is the sum of long-term debt and short-term debt scaled by total assets. Cash to Asset is cash and short-term liabilities scaled by total assets. Profitability is the EBIT scaled by total assets. Momentum is the buy and hold raw return for the daily return over January 02, 2019 to December 31, 2019. Idiosyncratic volatility is calculated as the residual standard deviation from the market model estimated over the last year. State-level controls are the unemployment rate, GDP per capita, and median age. Three factors loadings are Fama French three factors loadings are calculated with the daily return over the last year. The industry is Fama and French 49 industry. Except when otherwise indicated, numbers in parentheses are heteroskedasticity-consistent standard error clustered at the firm level. ***, **,* indicate the parameter estimates are significant at 1%, 5%, and 10% level respectively.

(1)

(2) J o u r n a l P r e -p r o o f

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Raw return= ((adj_-ret-l.adj_ret)/l.adj_ret)*(1+trfd/100). Where adj_ret= prccd / ajexdi. ajexdi is dividend adjustment factor. trfd is daily total return factor.COMPUSTAT North America Security Daily.

If respondents of the GSS survey answer yes to the following question: "Generally speaking, would you say that most people can be trusted."