Managerial Beliefs and Banking Behavior

Several authors have pointed out the relevance of managerial sentiments in shaping banking behavior. Keynes (1936), and more recently Akerlof and Shiller (2009), have stressed the role of “animal spirits” in managerial behavior; and Minsky (1982) has explained how during normal times success spurs confidence that can set the stage for a subsequent financial crisis. Along the same line, Bordalo et al. (2018) points out that good news causes excessive optimism and bad news causes excessive pessimism among bank managers; and Geanakoplos (2009) explains how an endogenous increase in optimism or pessimism generates a leverage cycle. Danielsson et al. (2011) and Thakor (2015) provide models in which over a long period of sustained profitability, banks invest in increasingly riskier assets since all agents become more tolerant of risk-taking. This strand of literature relates excessive risk-taking in the upswing of a business cycle to the spread of optimism, and a credit crunch to the prevailing pessimism that occurs during a crisis.

Despite theoretical models and historical evidence (e.g., Kindleberger 2005; Reinhart and Rogoff 2009) that have pointed out the role of confidence in banking, the literature offers surprisingly little evidence on the effect of either managerial optimism or pessimism on the behavior of banks. One reason is the difficulty in detecting managerial beliefs. Bank managers may differ in their expectations because they hold private information about the nature of their customers and loans. In addition, the over- or undervaluation of future loan losses may be due to the subjective feeling of the bank managers, and determining whether expectations are due to private information or managerial beliefs is difficult. Therefore, bank managers may react in different ways to the same news, and consequently they may set aside lower or higher reserves for the same amount of loans (Plosser and Santos 2018).

The first goal of our paper is to provide an indicator of managerial beliefs (i.e., managerial optimism and pessimism). We measure managerial beliefs about the future performance of the bank with the difference between the actual and estimated values of the loan loss reserves.

Banks set aside loan loss reserves to prepare for future loan losses. In this paper, we divide the total allowances for loan losses of each bank into two components. The first (the estimated value of loan loss reserves) is the amount explained by public information, that is, balance-sheet items, macroeconomic data (includes some public outlooks and macroeconomic uncertainty), and indicators obtained by combining some balance-sheet data that represent bank and managerial characteristics (ability and risk tolerance). This component is obtained with a pool regression of the allowances on those variables. If bank managers hold better or worse expectations about future loan losses than those indicated by current news and balance-sheet conditions, then the actual reserves will differ from the estimated loan loss reserves. Further, the residuals of the estimated loan loss reserves – the second component of allowances – are of a particular relevance to future loan losses since they reflect either managerial optimism or pessimism. The reasoning behind the connection between residuals and future charge-offs is that optimism and pessimism are mainly grounded in the bank’s private information about the riskiness of its customers and projects, and their future economic situations.

Even if residuals are connected to future loan losses, the latter are far from perfectly consistent with the amount of reserves. Therefore, we posit that if optimism and pessimism are more reflective of the subjective feelings of the bank managers, then their forecasts of future loan losses will be characterized by errors; that is, the realized charge-offs at t+h will differ from the expected charge-offs relative to t+h. This difference means that bank managers often find themselves too optimistic or too pessimistic ex post in forecasting their losses, and we posit that these errors in evaluating future losses are persistent over time. On the other hand, forecasting errors may also be due to unexpected events, but they are less likely to be systematic.

Indeed, we show that managerial beliefs are significant determinants of future charge-offs, and they change during the business cycle. On average, optimism in banking prevailed before 2007. The 2007-2008 financial crisis turned managerial optimism into pessimism. However, as soon as the economy moved away from the crisis, a new wave of optimism began. Moreover, during the 2007-2008 financial crisis the forecasting errors of future loan losses spiked that indicated the financial crisis came as a surprise to bank managers. Our proxy for managerial beliefs is highly correlated to the Federal Reserve’s Senior Loan Officer Opinion Survey on Bank Lending Practices that has estimates on the managerial expectations about the future performance of loans. In addition, we argue that not only does the unexplained component of loan loss reserves have a significant impact on future loan losses of the bank but is particularly relevant in determining the future paths of other important variables (such as future loans and leverage) that are sensitive to bank managers’ optimism or pessimism.

Ho et al. (2016) show that the persistence of overconfidence (banks with overconfident CEOs before the Russian crisis of 1998 had significantly overconfident CEOs in 2006), and Fahlenbrach et al. (2012) find that a culture of risk is persistent (a bank that performed poorly in 1998 also performed poorly in the 2007-2008 crisis). By contrast, we provide evidence that the persistence of optimism is very low, and the bank’s optimism before 2007 does not have any explanatory power on the resurrection of optimism after 2011.

Our results support Keynes (1936) and Akerlof and Shiller’s (2009) arguments about the instability of animal spirits. In addition, we find that managerial beliefs play an important role in lending, leverage, and risk-taking. Managerial optimism (pessimism) leads to increased (decreased) lending and leverage as well as higher (lower) risk-taking. We provide evidence of increased optimism in lending, leverage, and risk-taking before the 2007-2008 financial crisis but pessimism in these activities during the crisis. Our findings support the view that widespread changes in managerial beliefs are more relevant than the behavioral biases of a relatively small number of CEOs in determining the lending and leverage cycles.

The rest of our paper is organized as follows: Section 2 has a survey of the recent literature on the determinants of discretionary provisioning. Section 3 has descriptions of the data and the method used in the econometric analysis. Section 4 presents our indicator of managerial optimism or pessimism, and in Section 5 we study the relationship between managerial beliefs and the forecasting errors in future charge-offs. Section 6 provides some robustness checks and a comparison of our indicator of managerial beliefs with a Fed survey addressing similar issues. Section 7 gives the results of several exercises that estimate the effects of managerial beliefs on bank lending and risk-taking that uses the method of local projections. Section 8 concludes the paper.

The starting point of the paper is that despite the regulatory requirements, bank managers have discretionary power to determine the allowances for loan losses. During the sample period, regulators required US banks to provision for loan losses under the incurred loss method. No. 5 and No. 114 of the Statement of Financial Accounting Standards (SFAS) require firms to provision for an estimated credit loss from a loss contingency when an asset becomes impaired or a liability is incurred by the date of the financial statements and when they can reasonably estimate the amount of the loss. In 2001, the SEC issued new requirements for the provisioning process in which banks should estimate loan losses for groups of loans through the application of loss rates to the aggregate loan balances of each group. Such loss rates reflect the bank's historical loan loss experience for each group of loans, and the bank can adjust the loss for relevant environmental factors over a specific period. However, the imprecise wording in the FASB standards and interpretations that the estimated loss is “probable” and “can be reasonably estimated” induces discretion in accounting for loan loss provisions. In addition, even after the issuance of the SEC Staff Accounting Bulletin No. 102 (2001) there are factors that magnify the discretion inherent in the provisioning process. The first factor is how banks decide to group and assess loans for collectability based on their type, past due status, and degree of risk. Second, it is how bank managers establish the appropriate time frames over which to evaluate the loss experience. Finally, there are qualitative factors (e.g. industry, geographical, economic, and political factors) that bank managers use to assess loss rates (see SAB, No. 102). Indeed, Collins, et al. (1995), Liu and Ryan (2006), Kanagaretnam et al. (2003, 2004), El Sood (2012), Hegde and Kozlowski (2015), among others, have shown the use of loan loss provisions for income smoothing or for tax reasons; and BIS (2015), Wall and Koch (2000), Ahmed et al. (1999), have pointed to the regulatory capital incentive to manage provisions. In this paper, we propose another reason for discretional provisioning related to managerial beliefs.

In principle, bank managers should set up loan loss reserves equal to the forecast of future charge-offs because if they provision too much, they forgo profitable lending opportunities; and if they provision too little, they risk having to cut back dividend distributions or having to bolster capital to cover the loan losses. However, a great deal of evidence (Balboa et al. 2013, Laeven and Majnoni 2003; Fonseca and Gonzales, 2008; Black and Gallemore 2013) exists that bank managers also use loan loss reserves for income smoothing; that is, they may also increase reserves when the expected loan losses are low, or they may delay recognition of future loan losses. Therefore, loan loss reserves may have objectives other than just covering the expected loan losses (Ozili and Outa 2017; Beatty and Liao 2014; Wall and Koch 2000).

Even though the principles that underlie US banks’ loan loss accounting emphasize the importance of maintaining “prudent” reserves that are sufficient to offset expected losses, the lack of definitive standards on what “prudent” means leaves managers with substantial discretion in determining provisioning. In addition, bank managers possess private information regarding the true health of their loan portfolio (Wahlen 1994; Collins et al. 1995; Kanagaretnam et al. 2004), and they may hide that information at any time if it is in their own interests to do so (Plosser and Santos 2018). Indeed, there is a great deal of evidence that provisioning is largely discretionary (Hasan and Wall 2004; El Sood 2012; Huizinga and Lauven 2012).

The baseline model that studies often use to investigate bank provisioning (e.g., Ahmed et al. 1999; Hasan and Wall 2004; Laeven and Majnoni 2003; Wahlen 1994) distinguishes among nondiscretionary and discretionary determinants, relevant bank-specific factors, institutional factors, and country or regional factors.

As an example, Hasan and Wall (2004) distinguish between the nondiscretionary (i.e., nonperforming loans and total loans) and the discretionary determinants (earnings) of loan loss reserves for a large number of countries. They find evidence that the discretionary determinants are generally significant: the coefficients for the earnings ratio have a positive sign with lower values for US banks than for non-US banks. El Sood (2012) uses a sample of 878 US bank holding companies over the period from 2001–2009 and finds strong evidence of the use of provisioning for income smoothing. Banks smooth income when they (1) hit the regulatory minimum target, (2) are in non-recessionary periods, and (3) are more profitable. Further, Balboa et al. (2013) examine 15,268 US banks during the period from 1996–2011 and find that US banks use loan loss provisions to smooth reported earnings when they are positive and substantial. On the other hand, Huizinga and Lauven (2012) find that weak US banks manipulated their loan loss provisioning during the 2007-2008 financial crisis to manage their regulatory capital.

However, the bulk of the literature does not consider the determinants of allowances for loan losses related to managerial traits. Indeed, bank managers may differ in ability, risk aversion, and degree of confidence; all of which affects the provisioning and risk-taking in banking. There is evidence that the managerial propensity for risk played an important role in the excessive risk-taking that instigated the 2008-2009 financial crisis (International Monetary Fund 2014; Fahlenbrach et al. 2012; Ho et al. 2016; Beltratti and Stulz 2012; Andreou et al. 2015). In addition, Demerjian et al. (2012) provide evidence that more capable bank managers are more likely to intentionally smooth earnings. Further, Black and Gallemore (2013) and Ahmed and Duellman (2012) find that overconfident bank managers recognize lower loan loss provisions and delay recognition of expected loan losses. Indeed, managerial traits are likely to also affect the expectations about future loan losses. High-ability managers may possess superior skills in estimating accruals (Demerjian et al. 2012) and in forecasting earnings (Baik et al. 2011). In addition, Hribar and Yang (2016) provide evidence that overly optimistic managers overestimate future outcomes and underestimate uncertainty when predicting uncertain events (miscalibration). Therefore, managers who are overly optimistic or who overvalue their capabilities are more likely to make forecasting errors. However, forecasting errors may occur not only for reasons related to a manager’s personality but also to their feelings about the future. In turn, the latter may change during the business cycle. The aim of this paper is to address these issues.

We use quarterly data on US banks from the Federal Deposit Insurance Corporation (FDIC).¹ The dataset contains more than 11,000 US depository institutions that the FDIC insured over the period from the first quarter of 2000 to the third quarter of 2019. These banks fall into the following categories: national banks, state-chartered banks, trust companies, savings banks, national or state-chartered commercial banks, and other financial institutions that operate under general banking codes or are specifically authorized by law to accept deposits. However, in our analysis we use only those banks which provide loans and set aside reserves to face future loan losses. The common feature of these banks is that they are subject to the same supervisory rules for operational safety and soundness.

After eliminating banks with missing data and outliers (see below), we have 8,890 banks and 471,734 observations. For the econometric analysis, we deleted banks with insufficient lags to perform the econometric analysis, and in the estimations that use fixed effects we only included banks with at least 12 quarters of observations.

Table 1 presents the summary statistics of the data, and Table 6 in the appendix has descriptions of the variables used in the study.

From now on, ln indicates the natural logarithm of the variable, and Δ indicates the absolute change in the value of the variable. Tables 7 and 8 in the appendix present respectively the correlation matrix and the persistency of the variables. We also used rank correlations in addition to the traditional Pearson r-correlations because of the high kurtosis value of all the balance-sheet data (see Table 1), but the results were similar. The persistence of the absolute values of the variables is quite high, while the persistence of the variation is low. In addition, there is seasonality in the data (the correlation is higher for the period of four lags). Hence, to reduce collinearity among the variables, we perform the estimations using the level of the regressors at t-1 and their variation in the other lags.

We estimated the optimal number of lags and leads of the variables. The most appropriate number of lags is eight, while the future values of charge-offs are significant up to 12 leads. Further, we estimated the loan loss reserves with an ordinary least squares, a quantile regression, and the GMM model.

We assume that the total allowances for the loan losses (T_ALLOWANCES_i) of each bank are the sum of the estimated values of loan loss reserves (E[ALLOWANCES_i]) and the residuals of the estimated loan loss reserves (u[ALLOWANCES_i]). Allowances for loan losses are estimated with a pool equation that uses an OLS. We use a GMM for the lagged dependent variable to avoid a biased coefficient only when we estimate fixed effects (see Nickell 1981). For every regression we drop all observations corresponding to the outlier residuals. We use Cook‘s distance "D(i)" for observation i (i=1, .., n) to detect them. As a cutoff for D(i), we use 15; while this is a very high level, the residual kurtosis remains particularly high even when outliers are eliminated.² Its advantage with respect to winsorizing is that it facilitates the finding of highly influential data for each variable, while winsorizing consigns outliers on the base of the same quantiles (e.g. 1%) for each variable. A comparison of Cook’s distance and winsorizing while using equation 1 shows that Cook’s distance generates more observations with a better shaped residual distribution.

We also add lagged dependent and independent variables to the regressors. Lagged variables may be important in determining the allowances for loan losses; because in determining their amount, banks may also account for the recent history of the reserves and of the explanatory variables. This is confirmed by the usual tests of the optimal number of lags, among them the Schwarz criterion, and the fact that many of the coefficients for the lagged variable are significant.

In addition, the residuals of the estimations of loan loss reserves that used different specifications and estimation methods are all strongly correlated; therefore, the choice between those various alternatives is not so crucial when we describe the behavior of managers with respect to the allowances for loan losses.

Our aim is to understand the effect of either managerial optimism or pessimism on the risk-taking of banks. First, we provide a proxy for managerial beliefs. Optimism or pessimism is likely to affect the way bank managers react to current news when setting their loan loss reserves. Optimistic (pessimistic) managers have better (worse) expectations about the future, and they set aside lower (higher) loan loss reserves than warranted by current news and bank and managerial characteristics. Hence, we represent managerial optimism or pessimism with the deviation in actual reserves from their estimated value based on the current news as well as bank and managerial characteristics.³ Following the literature (Ozili and Outa 2017; Beatty and Liao 2014; Hasan and Wall 2004), we first assume that managers determine “normal” loan loss reserves according to the current news about the performance of the bank while considering the economy, bank characteristics, and institutional factors as well as their attitudes and abilities. CEOs are more likely to make decisions on loan loss allowances in small banks, and most of the processes surrounding loan loss accounting are likely to be made by CFOs in large banks. However, Chava and Purnanandam (2010) study the managerial risk-taking incentives for the CEOs and CFOs of the corporation, and they find common driving forces behind the managerial decision-making as well as similar effects on leverage, cash balances, and earnings-smoothing. On the other hand, Baker et al. (2019) examine the effect of the CEO’s or CFO’s power on both accruals (AEM) and real earnings management (REM), and they show that this power mitigates the effect of one another on the AEM and REM. Similar results are obtained by Florackis and Sainani (2021). In the same vein, we can infer that managerial behavior in banking may reflect the CEO’s or CFO’s managerial beliefs depending more on their relative knowledge and power.

Using this set of regressors, we estimate the amount of loan loss reserves with the following equation:

The y_it is bank i‘s allowances for loans at time t when expecting loan losses; X is the vector of the bank’s performance indicators, and managerial and bank characteristics; Z is the vector of macroeconomic variables at the state level (X and Z also have lags of the regressors and of the dependent variable y_it); and D indicates a set of dummy variables. For equation 1, we use up to date information to establish the amount of allowances to set aside at time t for loan losses expected at time t + h for 1 ≤ ℎ ≤ 4. Supplementary Table A4 in the online appendix ⁴ has a summary of the regression results from the equation (1) estimations for h=1. Similar results hold for h>1.

If the above factors are the only determinants of loan loss reserves, the estimated reserves should be equal to the actual reserves, and no residual can have any explanatory power for future loan losses. By contrast, optimistic (pessimistic) bank managers are likely to set aside less (more) reserves for future loan losses than those warranted by the current news or by managerial and bank characteristics.

On the other hand, the residuals of equation 1 may be determined by private information about the riskiness of their loans to clients or managerial beliefs about future loan losses. However, disentangling optimism and pessimism is very difficult. We argue that if either managerial optimism or pessimism reflect only private information about the riskiness of the loans, and the riskiness is correctly estimated, our indicator of optimism or pessimism should have a high predictive power for future loan losses. By contrast, if managerial expectations are grounded in the subjective feelings of bank managers about future loan losses, they are likely to be characterized by persistent forecast errors. Hence we claim that forecast errors are an ex post measure of the subjective feelings of bank managers. Indeed, we show that bank managers make significant forecasting errors when determining the amount of reserves for future loan losses, and these errors are persistent over time (see Section 5). However, managerial subjective beliefs are not the only determinants of the forecast errors. As an example, bank managers may differ in risk aversion and their ability to predict loan losses or to assess the riskiness of the loans (see Baik et al. 2011, Demerjian et al. 2013, Choi et al. 2014). In addition, small and large banks may differ in their tools and capacities to assess the riskiness of the loans. Indeed, we show that more capable and less risk-averse bank managers face lower loan losses, and larger banks face more loan losses in the future. On the other hand, forecasting errors on future loan losses are likely to be higher when there is greater information asymmetry between borrowers and lenders because banks more randomly select borrowers. However, residuals may reflect other managerial or bank characteristics as well as unobservable omitted variables that affect both the loan loss reserves and their determinants. Therefore, we estimate loan loss reserves with and without bank fixed effects and with and without dummy variables for each quarter. The degree of correlation among the residuals in the case of the Pearson correlations is 80%. In addition, the effect of the determinants of the loan loss reserves is similar in the two cases. These results indicate that the problem of omitted variables may not be relevant.

Next we estimate the predictive power of our indicator of managerial beliefs on future loan losses and forecast errors. Indeed, if idiosyncratic managerial optimism and pessimism are significant determinants of the expectations about the future, the residuals of the estimated loan loss reserves should have predictive power for future loan losses.

To test this hypothesis, we use the following model:where \({u[ALLOWANCES]}\) are the residuals of equation 1, and \(E{[ALLOWANCES]}\) are the estimated values of allowances using equation 1; h= quarters 1,2,3, and 4 that are the forecasting horizons. i refers to the bank and s to the state of the United States of America.

A summary of the results from the estimations of equation 2 are reported in Table 2.

The results reported in Table 2 indicate that the residuals of equation (1) have a highly significant effect on future loan losses for h=1,…,4, and similar results hold for a longer time horizon.⁵

As mentioned in footnote 4, a problem with equations of Table 2 is that a couple of regressors, as u[ΔALLOWANCES], are the output of another regression, and this might create a so-called problem of “error in variable”, leading to bias OLS estimated parameters. Actually, one of the OLS assumption is that E[ε|X]=0, for each regressor X and the residual ε: in other terms, the correlation between any independent variable and the equation residuals must be zero, otherwise coefficients are biased. Unfortunately, these correlations cannot be measured by employing OLS residuals, since, in OLS estimation, the measured correlation is always zero even if the true correlation is not. In the case of Table 2, the problem related to u[ΔALLOWANCES] involves also E[ΔALLOWANCES], since E[ΔALLOWANCES] ≡ ΔT_ALLOWANCES - u[ΔALLOWANCES]. A solution is to use the estimated residuals coming from an estimator method not suffering from this problem, e.g. GMM. We used as instruments for GMM estimator the rank u[ΔALLOWANCES] and ΔT_ALLOWANCES, which is a balance sheet item and not an estimated variable. The correlation between GMM residuals and the explanatory variable u[ΔALLOWANCES] was found to be very low (-0.016), and also the GMM estimation of the coefficients of u[ΔALLOWANCES] is similar to the correspondent OLS estimation (OLS = 15.06, GMM = 17.74 in case of one-quarter forecasts): the bias is therefore small and OLS results are acceptable. Hence on average u[ΔALLOWANCES] is an unbiased measure of correct managerial expectations.

More pessimistic (optimistic) bank managers have more (less) loan losses in the future that supports the view that managerial beliefs are to some extent grounded in the managers’ private information about the nature of the loans and their customers. However, public information is more relevant than managerial beliefs in determining future loan losses. Further, more capable and more risk-averse bank managers face lower loan losses in the future. Moreover, managerial beliefs determine future loan losses even when we add the VIX, the Chicago Board Options Exchange's volatility index that is a measure of the stock market's expectation of volatility based on the S&P 500’s index options, to the regressors.⁶ Moreover, the correlations of the residuals from estimating equation 1 without using the VIX as a regressor, using the values of the VIX on the last day of the previous quarter (VIX_F) and with the average values of the previous quarter VIX (VIX_M) are close to one (see Supplementary Table A8 in the online appendix). Overall, the results reported in Table 2 indicate that the VIX improves the forecasting of future charge-offs but that the VIX only marginally changes the explanatory power of the residuals as well as that of the estimated loan loss reserves. Specifically, the values of the coefficients for \({u[\Delta ALLOWANCES]}\) decrease between 2% and 6% and those of E \({[\Delta ALLOWANCES]}\) between 10% and 12%, but the values of the coefficients for \({T\_ALLOWANCES}\) increase between 15% and 50% relative to the estimates of equation 2 without the VIX among the regressors (see Supplementary Table A15 in the online appendix).⁷ In addition, by adding the VIX to the regressors in equation 2, the adjusted R-squared is very similar to those without this regressor (see Supplementary Tables A11,A12,A13 in the online appendix). Finally, we recall that among the regressors in equation 1 there is the consensus forecast provided by the FRED. So, we can conclude that our indicator of managerial beliefs does not reflect either consensus forecasts or macroeconomic uncertainty.

With the following figure, we show the impulse responses of charge-offs in the next h=1,2, and 4 quarters on managerial beliefs, and the average effects of the latter through the entire period. The confidence bands are 2σ and 3σ (Fig. 1).

In this section, we address whether bank managers’ forecasts of future loan losses are characterized by errors, and if the latter persist over time. First, we summarize the evolution of managerial believes through time.

Figure 2 shows the mean and median values of the residuals from equation 1 as well as the 5% and 25% highest and lowest values over time.

The median values of the residuals indicate that before the 2007-2008 financial crisis, bank managers were on average optimistic, and they became pessimistic during the 2007-2008 financial crash. Starting in 2012, optimism among American banks again prevailed.

We measure forecasting errors by the difference between future charge-offs at t+h (CHARGE_OFFS_i,t+h) and the expected value of future loan losses estimated using equation 2 (E[CHARGE_OFFS_i,t+h]). Theoretically, we expect that all things being equal, forecasting errors on future loan losses are more likely to occur if expectations better reflect the subjective feelings of the bank managers, which induces them to systematically undervalue or overvalue future loan losses, than private information. It follows that when the residuals of the estimated value of the loan loss reserves of the bank wrongly reflect subjective managerial optimism or pessimism that forecast errors on future loan losses will be persistent and highly correlated over time. On the other hand, if forecasting errors are due to unexpected events, they are likely to be uncorrelated between subsequent periods.⁸

Figure 3 shows the forecasting errors made by the banks at time t related to the next t+h quarter, h=1,2,3,4

Overall, the average value of the forecasting errors is around zero before 2007, and it increases during the financial crisis of 2007-2008. That increase indicates that the crisis came as a surprise to many bank managers. In addition, the crisis induced more bank managers to become pessimistic about the future (the median value increased). However, during the crisis forecasting errors increased for both optimistic and pessimistic managers, and the distribution of the residuals became skewed toward pessimism. The results in Figure 3 (see the last panel) also indicate that forecasting errors are higher the longer the forecasting horizon.

A comparison of Figures 2 and 3 shows that the forecasting errors on average are higher in periods when bank managers are more pessimistic. Specifically, the period of average moderate optimism before the crisis had few forecasting errors, while the very pessimistic mood during the 2007-2008 financial crisis saw a spike in the levels of forecasting errors. The latter declined as soon as pessimism turned into a new wave of optimism in 2012.

We posit that the wrong expectations result in an inappropriate amount of reserves that are likely to persist for several quarters. To test this hypothesis, we compute the coefficient correlation among subsequent forecasting errors at the same bank. The average value of the coefficient for forecasting errors correlations are reported in Table 3.

Specifically, the correlation between forecasting errors of future loan losses in t+1 and t+2 is 43%, and that between t+1 and t+3 is 27%. In addition, the correlation coefficient between t+2 and t+3 is 52%, and that between t+3 and t+4 is 58%. Overall, the correlations between forecasting errors in subsequent periods are quite high that support the hypothesis that optimism and pessimism are not random events but persist over time.

On the other hand, the correlation between our indicator of optimism or pessimism and forecasting errors on loan losses at t+h, h=1,2,3,4 at the bank level is very low that reflects the fact that our indicator is a good predictor of future loan losses (see Table 2). That correlation confirms the hypothesis that managerial beliefs are grounded more in the private information about the quality of their clients than on subjective feelings of bank managers about future loan losses. However, if this is true for the banks included in Table 2, that is, belonging to the sample free of outliers, then the conclusions are different for banks not included in that sample. When we apply the coefficients from our OLS or GMM estimations to the banks belonging to outlier residuals obtained by estimating equation 1 with the ΔALLOWANCES, then the results show that the most optimistic and pessimistic bank managers strongly underestimate their future loan losses (see Table 4) (the hypothesis of equality between means is rejected at zero probability based on Welch F-test, and the hypothesis of equality between the medians is rejected at zero probability based on Wilcoxon/Mann-Whitney (tie-adj.), Med. Chi-square, Adj. Med. Chi-square, Kruskal-Wallis, Kruskal-Wallis (tie-adj.), and van der Waerden tests).

Specifically, the most pessimistic (optimistic) managers on average underestimate their future loan losses by about 27% (24%) in the OLS estimation and 26% (25%) when we use GMM estimations. Similar results hold with two, three and four quarters ahead forecast errors.

To assess the validity of our indicator, first we compare the aggregate weighted average values of managerial optimism and pessimism over time to the FED Senior Loan Officer Opinion Survey on Bank Lending Practices. The Federal Reserve conducts the survey quarterly with up to 80 large domestic banks and 24 US branches and agencies of foreign banks. Among other things, the survey asks senior loan officers whether the quality of future loans is likely to improve or deteriorate substantially or somewhat, or to stabilize around its current level. Panel 4.a of Figure 4 displays the net percentage of respondents that indicates the tightening standards for C&I loans (lower percentage= higher optimism). Their pessimism spiked in 2008, and forecasts again became optimistic in 2010.

Despite the different sources of data, our aggregate indicator of managerial optimism or pessimism (Panel 4.b) is highly correlated with the Federal Reserve’s indicator (Panel 4.a) that denotes how this indicator captures similar phenomena. However, our indicator of optimism or pessimism is based on banks’ balance-sheet data not survey data. In addition, comparing Panels 4.a and 4.b, managers of large banks seem to be more worried than other managers during the financial crisis.

We assume that optimism and pessimism are characteristics of the bank managers that affect the amount of allowances they set aside for future loan losses as well as other balance-sheet variables. An event in which managerial beliefs are likely to change is managerial turnover. So, we construct a sample of 50 banks which changed CEOs in the period from the first quarter of 2000 to the third quarter of 2019 to check the impact of turnovers on managerial beliefs.⁹

We report the evidence separately for large and small US banks in Figure 5. Changes in managerial beliefs refer to the difference in \({u[ALLOWANCES]}\) for the same bank between the quarter before and after the turnover.

Panels 5.a and 5.b show respectively for large and small banks whether the new CEOs were more optimistic or pessimistic relative to the old one. First, the turnover has a greater impact on the change in managerial beliefs for the bulk of the banks in our sample. In addition, even though new CEOs overall are more pessimistic than the old ones, there are more optimistic new CEOs among large than small banks.

Panels 5.c and 5.d in Figure 5 show the absolute values of changes in our index, respectively, for large and small banks between the quarter before and after the turnover as well as the median value of \({u[ALLOWANCES]}\) of the banks which did not change their CEO in the same year of the turnover. The absolute variation in the value of the residuals shows that overall managerial beliefs change more for banks which experienced a managerial turnover than those that did not. In addition, managerial turnover has a greater impact on large than small banks. These results provide additional evidence in favour of our hypothesis that the residuals from estimated loan loss reserves are likely to capture managerial beliefs in banking.

In this section, we use our measures of managerial sentiments to estimate the effects of managerial optimism and pessimism on lending, leverage, and risk-taking.

Thus, we propose the following hypothesis:

Although optimism and pessimism are normal aspects of managerial and non-managerial behavior, the evidence on bank managers refers only to the existence of behavioral biases (i.e., overconfidence).

In this study, we provide a proxy for optimism or pessimism, and we analyze its ability to forecast certain balance-sheet items. Our proxy is related to the determinants of allowances for loan losses. As explanatory variables of allowances, we have used financial statement data, some macroeconomic variables, and two indicators of managerial efficiency and risk aversion. The residuals of the regressions are attributable to the whole set of variables not included in the regressions and, in particular, the managers' private information on the quality of loans. These components are certainly relevant because the residuals are very much connected to the future performance of the uncollectable loans of the corresponding banks. We therefore interpret these residuals as a proxy for managers' optimism or pessimism regarding future losses coming from their confidential information.

In other words, while one component essentially represents the expectations that come from public information, the residual represents a proxy for the subjective component that is linked to the private information that managers have on their banks. Therefore, it is a proxy for their optimism or pessimism about the future that is not detectable in public information. Further, optimistic or pessimistic bank managers are more likely to make forecasting errors about future charge-offs. Indeed, the most optimistic bank managers also have the largest loan losses in the future as well as the most pessimistic ones that means overly optimistic bank managers are likely to base their decisions more on subjective beliefs than private information about their clients.

We have estimated the allowances for loan losses with different econometric methods and with different specifications, and the correlation of the corresponding residuals is always very high. Therefore, our results are robust. They indicate that banks were more optimistic before the 2007-2008 financial crisis and after 2011, while they were pessimistic during the crisis.

Furthermore, our aggregate measure of managerial beliefs is highly correlated with the Federal Reserve’s indicator of managerial expectation on the quality of future loans. Further, for a sample of large and small banks we show that our indicator of managerial beliefs changes more when there is managerial turnovers in banks.

Our clear cut results on the effect of managerial beliefs show that an increase in optimism leads to expanded lending, leveraging, and risk-taking, and the opposite effect occurs when pessimism prevails. The effect of managerial beliefs on lending, leveraging, and risk-taking was particularly strong during and after the crisis up to 2013. Indeed, widespread pessimism among bank managers during the crisis determined the contraction of the loans in subsequent periods.

Our results provide evidence that widespread optimism occurred before the 2007-2008 financial crisis, which led to increasing lending, leverage, and risk-taking, followed by pessimism during the crisis. By contrast, overconfidence and risk aversion are persistent personality traits that generate behavioral biases. Ho et al. (2016) find that overconfident CEOs bore excessive risk before the 2007-2008 financial crisis and were subject to more losses during it, and Fahlenbrach et al. (2012) show that the persistence of the risk culture is a determinant of that crisis. Our empirical evidence shows that the leverage and lending cycles documented in the literature are determined by the waves of optimism and pessimism that occur during the business cycle. From a policy perspective, our work indicates that to prevent excessive lending, leverage, and risk-taking in the upswing, and the credit crunch in the downswing of the cycle, a deep understanding of what fuels the broad-based spread of optimism and pessimism is important. Finally, we argue that our measure of managerial beliefs opens up new avenues for future research, such us the analysis of the impacts of optimism and pessimism on the business cycle as well as on the allocation of loans between sectors and firms that may differ in managerial beliefs as well or in the risk-return combination.