the impact of overconfident customers on suppliers’...

i

The Impact of Overconfident Customers on Suppliers’ Firm

Risk

Yiwei Fang

Illinois Institute of Technology

565 W Adams St., Chicago, IL 60661

Email: [email protected]

Iftekhar Hasan

Fordham University, Bank of Finland and University of Sydney

45 Columbus Avenue, 5th Floor

New York, NY 10023

E-mail: [email protected]

Chih-Yung Lin

College of Management

Yuan Ze University

No. 135, Yuandong Rd, Zhongli District, Taoyuan City, Taiwan 320

Email: [email protected]

Abstract

Research has shown that firms with overconfident CEOs tend to make aggressive decisions

because of an unrealistically optimistic estimate of their firms’ future performance. We

extend this literature by examining how the presence of an overconfident CEO in a major

customer firm impacts upstream suppliers’ investment policy and risk taking. We find that

serving an overconfident customer could lead to a higher level of firm risk due to

overinvestment. The evidence is stronger for idiosyncratic risk and cash-flow volatility

than market risk. Moreover, the effect is more pronounced when the major customer

possesses greater market power or has higher information friction.

JEL: G32, G33, G34, L14

Key words: CEO overconfidence; firm risk; investment intensity; supply chains; market

power.

mailto:[email protected]

mailto:[email protected]

ii

The Impact of Overconfident Customers on Suppliers’ Firm

Risk

Abstract

Research has shown that firms with overconfident CEOs tend to make aggressive decisions

because of an unrealistically optimistic estimate of their firms’ future performance. We

extend this literature by examining how the presence of an overconfident CEO in a major

customer firm impacts upstream suppliers’ investment policy and risk taking. We find that

serving an overconfident customer could lead to a higher level of firm risk due to

overinvestment. The evidence is stronger for idiosyncratic risk and cash-flow volatility

than market risk. Moreover, the effect is more pronounced when the major customer

possesses greater market power or has higher information friction.

JEL: G32, G33, G34, L14

Key words: CEO overconfidence; investment intensity; firm risk; supply chains; market

power.

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1. Introduction

The literature in psychology and behavioral economics has long demonstrated that

human inference and estimation are subject to systemic biases, which may lead to sub-

optimal decision making. For instance, a strand of behavioral finance research has shown

that managerial overconfidence affects a firm’s investment policy and consequently its risk

and return profile (Malmendier and Tate, 2005, 2008; Goel and Thakor, 2008; Gervais et

al., 2011; Malmendier et al., 2011; Hirshleifer et al., 2012). Complementing prior studies,

this paper investigates how the managerial biases of a firm’s top decision maker affect its

upstream supplier’s decision making. Specifically, we examine the risk impact on a

supplier of having a major customer whose CEO is overconfident.

CEO overconfidence provides a valid measurement to gauge the influence of

managerial bias on firms’ future performance. The experimental literature on

overconfidence provides massive evidence that, in general, individuals frequently exhibit

overconfidence and that CEOs are particularly likely to possess an optimistic bias (Camerer

and Lovallo, 1999). Specifically, CEOs are defined as overconfident if they postpone

exercising stock options that are more than 100 percent in the money at least twice during

their tenure, and we classify them as overconfident CEOs the first time the exercise is

postponed (Campbell et al., 2011).1 The rationale is that a CEO who chooses to keep

holding deep-in-the-money stock options after the vesting period is likely to be

overconfident about the firm’s prospects. While the nature of overconfidence can be

desirable when there are valuable but risky projects to be taken, e.g. R&D (Galasso and

1 The options-based CEO overconfidence measure has become widely used in recent empirical research

(Malmendier and Tate, 2005 and 2008; Malmendier et al., 2011; Hirshleifer et al., 2012).

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Simcoe, 2011, Hirshleifer et al., 2012), the downside is that overconfidence can lead

managers to overestimate returns and underestimate risk, as reflected by making

overinvestment and suboptimal decisions (Malmendier and Tate, 2005; 2008; Ben-David

et al., 2013). Research thus far, however, has mainly focused on the impact of managers’

overconfidence on decision-making in their own firms. Little is known regarding the

impact, if any, on upstream suppliers.

Supplier-customer relationships are economically important. A customer is

considered as a major customer if it provides at least ten percent of sales to the supplier(s).

To secure a lasting business relationship, supplier(s) often invest substantially in

relationship-specific assets (such as R&D) to tailor to the specific needs of a customer

(Joskow, 1988, Tiróle, 1988, Crawford, 1990). Due to such economic dependence,

downstream disruptions (e.g., M&As, bankruptcy) from the customer’s end can exert

considerable influence on upstream suppliers’ performance (Hertzel et al., 2008, Fee et al.,

2006). It can also cause strong asset value correlations between the two firms (Titman,

1984; Cohen and Frazzini, 2008; Campello and Gao, 2017; Cen et al., 2017). From the

both aspects, understanding the characteristics of major customers is crucial for the

suppliers.

Motivated by above two lines of research, this paper investigates whether and how

CEO trait of a major customer, specifically overconfidence, could impact its supplier firm.

We argue that major supplier-customer relationships are a source of firm risk when

customers’ estimates of their future performance are prone to managerial bias. Customer

CEOs’ personal investment behavior is observable by their suppliers. When these

individuals are overconfident, they tend to be excessively optimistic about future growth

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opportunities and exert aggressive corporate decisions. Both the personal investment

behavior and corporate decisions made by the overconfident CEOs are likely to transmit to

the supplier and affect the supplier’s anticipation of their own future sales growth (e.g.,

from that particular customer), leading to a bias in their decisions pertaining to investment.

These investments (including investment in or upgrading of productive assets such as plant,

machinery and equipment, and vehicles) are often capital-intensive as well as irreversible,

which consequently results in higher risk.

Our study finds strong evidence that an overconfidence customer can have a

significant impact on the supplier’s risk. Specifically, our estimation suggests that, on

average, firms serving overconfident customers experience 22.45 percent (=3.54/15.767)

higher idiosyncratic risks, compared with those working with non-overconfident

customers.2 The results are statistically significant at the p<1 percent levels, and are robust

using OLS regressions and firm fixed effects with controls of various firm-specific,

industry-wide, and relationship-specific characteristics. Secondly we find that supplier

firms serving overconfident customers experience higher capital investment intensity

(about 0.4 percent higher) than those with non-overconfident customers. This is equivalent

to a 7.4 percent increase for an average sample firm. These results support our argument

that suppliers take cues from customer CEOs’ overconfident behavior and overestimate

growth opportunities of future business with these customers. As a result, they take more

aggressive capital investment and higher firm risk. Our further tests suggest that the risk

exposure of serving overconfident customers is mainly caused by firm-specific risk factors,

as reflected by stock idiosyncratic risk and volatile cash flows, but not from market risk.

2 See Section 4.3 for more details.

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We further explore the contingency effects of market power and the information

asymmetry of the supply-chain relationship. First, customers with higher market power

often negotiate favorable contract terms and play a dominant role in a supply-chain

relationship. Suppliers serving such customers are more likely to be affected by their

managerial bias, leading to even greater firm risk. Second, firms would not be affected by

their major customers’ managerial biases if they rely less on their customers to estimate

downstream industries’ future performance. Information asymmetry occurs widely in

many industries (Lee et al., 1997). Consequently, we hypothesize that information

asymmetry is an important contingency factor, i.e., the higher the level of information

asymmetry faced by the suppliers, the stronger the impact of customer CEO

overconfidence on supplier firm risk. Taken together, we expect that the risk effect is more

pronounced for suppliers who serve customers with high market power and customers

whose prospects are less transparent to their suppliers. Furthermore, suppliers are less

dependent and thus less affected by the overconfident customer when their relationship

with that overconfident customer is longer or when they also serve major customers that

are not overconfident. Our empirical results confirm these hypotheses.

We conduct several tests and robustness checks to corroborate our main hypothesis.

First, we find that firms’ managerial biases can migrate further upstream along the supply

chain and can exert strong economic effects. Specifically, supplier firms are exposed not

only to risk incurred by their direct customers’ overconfidence, but also by that of their

ultimate customers. Second, our results are robust when we consider the situation of

multiple major customers by sales-weighted measure for CEO overconfidence. However,

under the subsample of multiple customers with partly OC customers, we find that the OC

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effect become weak and insignificant, which suggests that multiple customers help a

supplier to better adjust for individual customer CEOs’ overconfidence biases. Third, we

further control the role of customers’ characteristics and the results remains consistent with

our hypothesis. Finally, we further control the role of longer-lived relationships. We find

that a longer relationship makes the supplier learn more about the customer, which

mitigates the influence of managerial bias from an overconfident CEO.

Our paper contributes to the literature on behavioral economics and finance.

Behavioral economics explores the implications of limits of rationality (see Ho et al., 2006

and DellaVigna, 2009 for reviews). Studies in this area find evidence that decision-makers’

behavior might impact corporate performance. 3 In addition, it suggests that CEO

overconfidence influences investment/cash-flow sensitivity, mergers and acquisitions

(M&As), financing policy, corporate innovation, accounting conservatism, and bank

lending and leverage (Malmendier and Tate, 2005, 2008; Malmendier et al., 2011; Gallasso

and Timothy, 2011; Hirshleifer et al., 2012; Ferris et al., 2013; Ahmed and Duellman, 2013;

Banerjee et al., 2015; Ho et al., 2016). While the research is fruitful in this area, the main

focus has been on the firm itself. Our paper adds to these studies by examining if the

consequence of managerial biases has a spillover effect on supplier partners. Research

studying the influence of managerial biases across supply-chain partners is limited (see a

related review by Meyer et al., 2010). We show that managerial biases of customer firms

(especially CEO overconfidence) do influence supplier firms’ decision making in terms of

3 For example, Galasso and Simcoe (2011) suggest that overconfident CEOs are more likely to take their

firms in a new technological direction. Lowe and Ziedonis (2006) find that entrepreneurial overoptimism

contributes to unsuccessful development efforts for longer periods of time than in established firms. Billett

and Qian (2008) find that self-attribution of past success leads to hubris in future decision making in the

context of mergers and acquisitions.

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investment intensity and risk. Furthermore, customers with higher market power and more

information asymmetry amplify such effects on suppliers.

Our paper also complements the growing literature on the financial linkage between

customers and suppliers. This literature has demonstrated that the financial market take

into account such supply-chain interdependencies in predicting firm performance (Titman,

1984; Cohen and Frazzini, 2008; Campello and Gao, 2017; Cen et al., 2017). Hence, events

such as a customer’s bankruptcy (Hertzel, 2008; Kolay et al., 2016; Houston et al., 2016),

M&As (Fee and Thomas, 2004; Shahrur, 2005) or CEO turnover (Intintoli et al., 2016)

produce significant uncertainty in the relationship, negatively affecting supplier

performance. Complementing to these studies, we examine how managerial behavioral

bias in customer firms affects suppliers’ investment strategies and risk-taking outcome.

Our findings provide strong evidence that a CEO’s personal investment behavior acts as a

signaling channel of private information along the supply chain. Major supplier-customer

relationships become another source of risk when customers’ estimates of their future

performance are prone to such managerial biases. Hence, it is important for firms serving

overconfident customers to assess their forecasts and investment plans with caution. To

manage the risk resulting from customers’ managerial biases, firms should focus on

reducing information asymmetry and on acquiring more objective knowledge of their

downstream industries.

The remainder of this paper is organized as follows: In Section 2 we develop our

hypotheses. Section 3 explains our CEO overconfidence measure and data. Section 4

discusses the empirical results, Section 5 provides additional supporting evidence, and Section

6 concludes the paper.

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2. Hypotheses development

2.1 CEO overconfidence and impacts on suppliers

CEO overconfidence offers a valid measurement of the level of managerial bias in a

firm. The literature describes two key features that drive an individual’s overconfidence:

overoptimism and overprecision (Libby and Rennekamp, 2012). In the case of

overoptimism, individuals are unrealistically optimistic about future outcomes, as they

believe that an uncertain outcome will be better than that predicted by an unbiased

expectation. Being overprecise is associated with individuals with narrow confidence

intervals when predicting uncertain events (Hackbarth, 2008), which often results in an

underestimation of the riskiness of earnings. Given these two characteristics, prior studies

have shown that overconfident managers act more decisively and aggressively when

making corporate decisions (Gervais and Odean, 2001).

In this paper, we argue that such CEO overconfidence is likely to affect trading

partners, in that customers’ aggressive corporate decisions and overestimation of their

future performance will influence their suppliers’ risk taking. First, CEOs’ biases will

transfer to suppliers through their observable corporate decisions. It has been shown both

theoretically and empirically that overconfident CEOs are more likely to pursue more

aggressive corporate decisions, including capacity investment, financing policies,

innovation, and M&As (Malmendier and Tate, 2005 and 2008; Campbell et al., 2011;

Galasso and Simcoe, 2011; Hirshleifer et al., 2012). For instance, Malmendier and Tate

(2005) show that overconfidence measures are systematically related to the likelihood for

such managers to overinvest internal funds because of their tendency to overestimate

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returns on their investment projects. 4 Moreover, overconfidence leads to investment in

unprofitable ventures (Zacharakis and Shepherd, 2001), introduction of overly risky

product innovations (Simon and Houghton, 2003), and ill-fated market entries (Astebro et

al., 2007). We argue that such customers’ aggressive corporate decisions will convey a

biased estimation of their future performance to upstream supplier firms.

Second, CEO overconfidence is reflected in their personal investment behavior. CEOs

are classified as overconfident when they persistently exercise options later than the

rational timing because they are overconfident in their ability to keep the company’s stock

price rising or are optimistic about their firms’ future performance (Malmendier and Tate,

2005). Such individual investment behavior is publicly observable to suppliers, who can

infer CEOs’ private information regarding positive prospects of their firms’ future

performance.

Lastly, investors tend to give higher valuation to a firm with an overconfident CEO

because of the optimistic forecast that CEO has sent to the market (Hirshleifer et al., 2012).

Meanwhile, studies also find evidence that CEO optimistic behavior is likely to mislead

valuation of financial market participants (Felleg et al., 2012). Firms managed by

overconfident CEOs incur higher financial risk, such as stock-return volatility, from

overinvestment (Malmendier and Tate 2005 and 2008; Hirshleifer et al. 2012). For instance,

Hirshleifer et al. (2012) show that overconfident firms invest more in innovative activities

and have greater return volatility. Thus, we expect that suppliers are likely to be misled by

customer CEOs’ biased views, and, consequently, may invest in more productive assets

4 Malmendier and Tate (2008) also find a systematic relationship between CEO overconfidence and value-

destroying mergers, showing that the odds of making an acquisition are 65 percent higher if a CEO is

overconfident.

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(such as buildings, machinery and equipment, and vehicles) to increase

capacity or efficiency to prepare for their major customers’ future growth. If such future

market growth is not realized, the cash flows of those suppliers who have made significant

investments become more volatile. For the reasons presented above, we argue that

suppliers are also likely to expect an optimistic outlook of future growth opportunity and

overoptimistically estimate their future demand when customer CEOs are overconfident

and make aggressive expansion moves, which, consequently, will result in suppliers’ firm

risk.

H1. Serving a major customer with an overconfident CEO leads to higher firm risk.

2.2 Contingency effects

We further investigate under what circumstances the risk effect is more pronounced

or can be mitigated. First of all, information asymmetry along the supply chain is prevalent

(Lee et al., 1997). Many suppliers have strong dependence on their major customers5, and

therefore when suppliers do not have sufficient knowledge of their customers’ industries

and markets, the former’s ability to forecast future sales is dependent upon on the

information received from their customers, such as CEO behavior. A positive sale forecast

by a major customer firm is likely to make its supplier feel optimistic about its future

performance. This suggests that when there is high information asymmetry about the

customers, suppliers’ estimations of future sales are more likely to be affected by customer

5 The dependence also comes from the fact that suppliers often invest highly in relationship-specific assets,

both tangible and intangible. Such assets are often tailored to the specific needs of a customer and offer little

value outside of that relationship (Crawford, 1990). Hence, supply-chain relationships are often ongoing and

mutually dependent, which increases suppliers’ reliance on customers not only for sales, but also for

predictions concerning their future markets.

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CEO overconfidence. By contrast, if suppliers have in-depth knowledge of their customers

(and customers’ industries), they do not need to rely on the latter’s perception or actions to

infer their own future market. Hence, with greater depth of objective information

concerning their customers (and customers’ industries), suppliers have a lower risk

exposure to customer CEOs’ managerial biases and can make accurate forecasts and plan

investment accordingly. This leads to our third hypothesis

H2. The effect of customer CEO overconfidence on suppliers’ firm risk is greater when the

information asymmetry between partners is stronger.

The second contingency factor is market power. When customers have higher market

power, suppliers have less control over the trading terms and become more dependent on

customers’ perception for their own future market prediction. In this situation, the biased

views of customer CEOs’ on their firms’ future performance will have a more significant

impact on suppliers’ investment policies. In contrast, if suppliers have stronger market

power, they do not need to rely on customers’ perception or actions to infer their own future

market. The bargaining power they possess allows the suppliers to suffer less from an

overconfident customer. Taken together, we hypothesize that the stronger the customers’

market power, the greater is the risk suppliers experience. Thus, our fourth hypothesis is

as follows:

H3. The effect of customer CEO overconfidence on supplier firm risk is greater when

customers have stronger market power.

3. Data and Variables

3.1 Sample construction

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The data source for our sample of supplier-customer relationships is Compustat

segment files. Pursuant to Financial Accounting Standard No. 14, a firm is required to

report names of customers whose share is greater than 10 percent of the firm’s total revenue.

Following previous studies (e.g., Fee et al., 2006; Hertzel et al., 2008), we adopt a

conservative approach to match names of customers.6 As argued in the literature, the

potential costs of misidentifying noncustomer firms as customers are greater than those of

failing to identify a limited number of actual customers. Because the periodicity of

disclosure is annual, we form supply firm/major customer dyads for each calendar year. In

cases where one supplier has multiple major customers in a given year, we keep the largest

customer in the main regression sample. We also use the multiple customer sample to run

some robustness test.

We then match financial characteristics of the suppliers and their customers using

Compustat annual fundamental files. All the firm-level financial variables are obtained one

year prior to the supplier-customer reporting year. We also match CEO overconfidence

data for the customers at one year prior to the supply-chain reporting year. CEO

overconfidence is constructed using Standard & Poor’s ExecuComp database. Customers

with no available CEO-overconfidence information are dropped from the sample.

6 Compustat segment files contain such disclosure information. However, the file format does not allow the

direct use of such information, because customer names are often abbreviated, and several different names

refer to the same firm. Furthermore, many major customers are subsidiaries of a large conglomerate.

Augmented by an automated text-matching algorithm, we visually inspect each firm’s major customer

information file one by one, and carefully match a reported customer name to a GVKEY in Compustat. This

process may involve some discretion when matching abbreviated names to GVKEYs. To avoid measurement

errors, we exclude a pair when it is not possible to confirm that the firm is a match by comparing the

abbreviation with previous years’ customer descriptions.

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3.2 Variable construction

The following are the important variables used in our analysis. Detailed definitions of

the complete set of variables are given in Appendix A.

Overconfidence: Overconfident CEOs are classified as those who delay the exercise

of deep-in-the-money options (Malmendier and Tate, 2005 and 2008). Following Campbell

et al. (2011), we use a modified measure by defining three levels of managerial

overconfidence: high, moderate, and low, using 100 percent and 30 percent moneyness as

the cutoff points.7 To identify highly overconfident CEOs, the authors require that CEOs

have postponed exercise of 100 percent in-the-money options at least twice during their

tenure. CEOs are assigned to the highly overconfident category when they first exhibit this

behavior. Both Goel and Thakor (2008) and Campbell et al. (2011) have theoretically and

empirically found that only highly overconfident CEOs exhibit overly aggressive

investment behavior. Following their approach, we also define highly overconfident CEOs

as our overconfident group. The low and moderately overconfident CEOs are defined as

the non-overconfident group. In our analysis, overconfidence is a dummy variable, which

equals 1 if the customer CEO is highly overconfident, and 0 otherwise.8

In the literature, stock volatility can measure the total risk of a firm, which can be

decomposed to firm-specific risk (idiosyncratic risk) and market risk (beta). Thus, to

7 We compute options moneyness as follows. Realizable values per option are estimated from the total

realizable value of exercisable options divided by the number of exercisable options. Then, the estimated

average exercise prices of the options are computed from the fiscal year-end stock price minus the realizable

value per option. Hence, the percentages of average moneyness are obtained from per-option realizable value

divided by the estimated average exercise price. We employ a similar methodology as above to measure the

percentage of moneyness of exercised options. 8 Because Campbell et al. (2011) find approximately 8.2 percent of CEO-years in ExecuComp are classified

as low optimism. So we also consider the role of low optimism of CEOs in the regression design in the

unreported results. However, we find that there is no significant result of low optimism of CEOs in the

regression.

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examine this issue, we first measure firm risk by idiosyncratic risk, stock volatility, and

beta. In a robustness check, we use cash flow volatility as an alternative measure of firm

risk.

Idiosyncratic risk: The standard deviation of the residuals is obtained from a market

model of daily returns in excess of three-month T-bills using previous two-year data, where

the market is represented by the value-weighted CRSP index.

Stock volatility: Stock volatility is measured as the firm’s daily stock volatility using

the previous two-year data.

Market risk (beta): A firm’s equity beta is obtained from a market model of daily

returns in excess of three-month T-bills using the previous two-years’ data, where the

market is represented by the value-weighted CRSP index.

Cash-flow volatility: We measure the cash-flow volatility using the volatility of four

quarterly cash-flow-to-assets ratios in a given year. Cash flows refer to the operating cash

flows before depreciation.

3.3 Summary statistics

For the data-cleaning process, we winsorize all the variables (except for CEO

overconfidence) at 1 percent and 99 percent in all the analyses to prevent outliers from

biasing the results. In addition, following the literature, we exclude regulated utilities and

financial firms (SIC 4000-4999 and SIC 6000-6999) from our sample. Our final sample

consists of 4,463 supplier-year observations from 1993 to 2012. It contains 1,225 unique

suppliers and 306 unique customers. Table 1 divides our final sample into two groups and

reports the distribution by year and industries of the suppliers. c_OC is the indicator, which

equals 1 if the firm’s major customer has an overconfident CEO, and 0 otherwise. Panel A

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reports the number of observations in each year. Our sample is quite evenly distributed

across years from 1993 to 2012. The sample of the overconfident customer CEO group is

relatively larger than the other group in each year. With regards to the industry distribution,

our sample is concentrated in industries with a 1-digit SIC of 2 and 3, which are

manufacturing industries.

<Insert Table 1>

Table 2 presents the descriptive statistics of the key variables in our study. Our main

risk measure is idiosyncratic risk. The sample average is 15.767 and the standard deviation

is 60.920. The 5th and 95th percentiles are 1.389 and 10.287 respectively, which suggests

sufficiently large variations across our sample firms. In addition, we also examine market

risk, stock volatility, and cash-flow volatility. Our analysis also uses total assets as a

measure for firm size; leverage, a measure for capital structure; sales growth, a proxy for

revenue growth; cash flow, measured by operating cash flow before depreciation, divided

by total assets, as a proxy for profitability. The detailed measurement and data sources of

these variables are given in Appendix A.

From the summary statistics, we find that our sample firms are of relatively small size

(the mean of total assets is $2,318 million) and have a low debt-to-asset ratio (the mean is

19.1 percent), which is expected because many are manufacturing firms and are identified

as suppliers of big firms. The average sales growth is 12.6 percent and cash flow is 14.8

percent. These values suggest that the firms perform well.

Panel B reports some financial characteristics of the customers. The main

characteristic we study for the customer is CEO overconfidence. Based on the descriptive

statistics, the mean of c_OC is 60.6 percent, which means that 60.6 percent of the firm-

15

year observations are associated with an overconfident customer CEO. The distribution of

overconfident customers in our sample is due to the fact that major customers reported by

compustat firms tend to be large firms. We find that the mean of total assets of customer

firms is $60,523 million, which is typically more than 20 times of the size of the suppliers.

This is expected because customers must be large firms whose purchases can account for

more than 10 percent of their suppliers’ total sales, and must also be S&P1500 firms in

order to construct the CEO overconfidence measure. In addition, their investment intensity,

c_capex, is 6.3 percent, which is higher than that of their suppliers.

<Insert Table 2 >

Lastly, Panel C reports two characteristics of the relationships. Duration is the number

of years of the supplier-customer relationship from the starting year through the testing

year, and ranges from one year at the 5th percentile to ten years at the 95th percentile. The

average duration is 4.410 years.9 The variable Salespct_sales measures the percentage of

supply-chain sales to the total sales of the suppliers. Since our analysis only examines

major customers, supply-chain sales must account for at least 10 percent of the supplier’s

total sales. The mean of Salespct_sales is 22.9 percent, because we keep only the largest

customer if one supplier has multiple major customers in a given year. This statistic also

indicates that the supplier-customer relationships in our sample are important business

partnerships.

4. Empirical analysis

4.1 Econometric models

9 We drop durations that only last for one year because such relationships might not be important. However,

our results still hold when we include such observations.

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Our first objective is to ascertain whether a firm whose largest customer has an

overconfident CEO experiences higher firm risk and greater investment intensity than other

firms. If so, what is the economic magnitude and statistical significance of the effect? We

start with ordinary-least-square (OLS) regressions to estimate the coefficient of c_OC,

which is the indicator for customer CEO overconfidence. The model specification is shown

in Equation (1)

𝑌𝑖𝑡 = 𝛼0 + 𝛼1 𝑐_𝑂𝐶𝑖,𝑡−1 + 𝛽𝑋𝑖,𝑡−1 + 𝜇𝑖,𝑡 + 𝜀𝑖,𝑡 (1)

where 𝑌𝑖𝑡 represents the dependent variable. In our tests, we first look at different firm risk

measures as dependent variables, and then we examine investment intensity as a dependent

variable. The dependent variables are measured at year t for firm i. The independent

variable of interest is 𝑐_𝑂𝐶𝑖,𝑡−1 , which equals 1 if a major customer firm’s CEO is

classified as overconfident in year t-1, and is 0 otherwise. 𝑋𝑖,𝑡−1 represents a vector of

control variables of firm and relationship characteristics, measured at year t-1. 𝜇𝑖,𝑡

represents dummy variables for years and industries of the firms as well as those of their

major customers. Industries are identified based on a four-digit SIC code. The inclusion of

industry dummies controls for industry characteristics, such as product types, competition

environment, growth opportunity, and risk taking. Year dummies are also used in the OLS

regression to control for time-varying economic factors that might influence firm risk. In

all regression models, we cluster standard errors by suppliers because suppliers may

repeatedly show up in the sample given different durations of the relationships. OLS

estimation can be biased if residuals are correlated across the same firm (Petersen 2009).

Our second objective is to test whether market power and information friction could

enhance or mitigated the risk effect (H2 and H3). To achieve this goal, we use Equation

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(1) with an interaction term of CEO overconfidence and proxies for market power or

information friction of customers. The model specification is shown in Equation (2).

𝑌𝑖𝑡 = 𝛼0 + 𝛼1 𝑐_𝑂𝐶𝑖,𝑡−1 + 𝛼2 𝑐_𝑂𝐶𝑖,𝑡−1 × 𝑅𝑖,𝑡−1 + 𝛽𝑋𝑖,𝑡−1 + 𝜇𝑖,𝑡 + 𝜀𝑖,𝑡 (2)

In this specification, 𝑅𝑖,𝑡−1 represents the measures for the contingency factors on

which CEO overconfidence exerts a stronger or weaker effect on firm risk. These factors

(𝑅𝑖,𝑡−1) enter the regressions not only as the interaction with c_OC, but also independently

to control for the first-order effect. The interaction term captures how overconfidence

affects firm risk, contingent on the level of market power and information asymmetry.

Based on our hypothesis, the interaction term is expected to be positive, indicating that

customer CEO overconfidence increases supplier firm risk more significantly when the

information friction is high and customer market power is high. The estimation technique

is OLS regressions controlling for firm characteristics and relationship-specific factors.

Year and industry dummies (defined based on the four-digit SIC) are also included to

control for the year effects on risk and industry fixed effects of both trading partners.

Standard errors are corrected at the supplier level for heteroskedasticity (t-statistics are in

parentheses).

4.2 Discussion of endogeneity

The central concern surrounding most empirical studies is endogeneity. Specifically,

corporate decisions are not made at random, but are usually deliberate decisions by firms

or their managers to self-select into their preferred choices. This is termed the self-selection

bias. Biased estimators could result if unobservable variables affecting the firm decision

were not incorporated in the regression. In our study, the omitted-variable-bias issue could

18

arise if certain firms (suppliers) were riskier than others because of some unobserved firm

characteristics associated with customer CEO overconfidence.

In our model, we control various firm financial characteristics including firm size,

financial leverage, growth opportunity, and profitability. To rule out the omitted-variable

concern, we take advantage of a panel data setup and use a fixed-effect technique across

all estimations. Our sample is panel data with firm-year observations. We have multiple

observations of the same firm over different years. If the unobservable attributes are fixed

over time, we can control for them by including firm fixed effects. We include year

dummies to control for time-varying, economy-wide effects. The model specification for

firm fixed effects is the same as Equation (1), except that 𝜇𝑖,𝑡 represents firm and year

dummies. Furthermore, we also concern about omitted variables on the customer side in

the Section 5.3.

Besides omitted variables, there could be other sources of endogeneity, such as

reverse causality. Reverse causality refers to the possibility that firm risk leads to

overconfident CEOs. Firms that invest aggressively are more likely to hire someone as

CEO who has the ambition to continue to invest and grow the firm aggressively (such

people are more likely to be overconfident). In our study, however, it is easy to disentangle

causality from correlation. Our focal supplier firms are much smaller and less powerful

than their major customers, and thus it is unlikely that they have any influence on either

the selection of their customers’ CEOs or those CEOs’ decisions once appointed. In our

estimation, all firm characteristics and relationship variables are lagged one year to the

testing year to reduce any reverse causality stemming from the focal firm.

19

4.3 Effects on firm risk

Table 3 reports the effects of customer CEO overconfidence on the idiosyncratic risk

of their suppliers. The independent variable of interest is c_OC, which equals 1 when the

customer CEO is overconfident, and 0 otherwise. Column (1) runs OLS regression

controlling for firm characteristics of the testing firms, including firm size, financial

leverage, sales growth rate, and cash flow for the previous year. 10 Column (2) adds

additional control variables related to the relationship, including the length of the

relationship and supply-chain sales between partners. Column (3) implements firm fixed

effects to control for potential omitted variables. 11 All the models include year dummies

to control for year effects. Industry dummies (defined based on their four-digit SICs) are

also included to control for industry fixed effects of both the testing firms and their

customers.

Our overall findings suggest that customer CEO overconfidence is associated with

higher idiosyncratic risk at the supplier end. For example, upstream suppliers on average

suffer from 3.540 more idiosyncratic risk when serving a major customer with an

overconfident CEO, which is economically meaningful given that the mean idiosyncratic

risk of our large sample is 15.767. Thus, on average, firms serving overconfident customers

experience 22.45 percent (=3.54/15.767) higher idiosyncratic risks, compared with those

working with non-overconfident customers. The coefficients remain highly significant at

least at the p<5% level, even controlling for individual firm fixed effects. The use of firm

10 In the unreported results, we also control other supplier characteristics such as Tobin’s Q, cash

holdings…etc. Our results still hold when we include such supplier characteristics. 11 We do not control the CEO overconfidence of the supplier firms due to the small sample size question. If

we control the CEO overconfidence of the supplier firms, the sample size will reduce to 1,844. Thus, we use

firm fixed effects of the supplier firms to control for this omitted variable concern.

20

fixed effects absorbs the significance of most of firm characteristics, however, as shown in

Column (3). This supports our hypothesis H1.

<Insert Table 3>

In Table 4, we also report the regression results using total stock return volatility and

beta as two alternative risk measures, and as dependent variables. The findings suggest that

customer CEO overconfidence has no significant impact on these two types of risk. Hence,

the risk increase is mainly driven by firm-specific volatilities.

<Insert Table 4>

The literature has shown that higher cash-flow volatility leads to a higher cost of

accessing the external capital market, which further reduces the levels of investment in

capital capacity, R&D, and advertising (Minton and Schrand 1999). When a firm’s cash

flow is more volatile, it is more likely that the firm could have a cash shortfall, which may

lead to financial distress and even default. Thus, cash-flow volatility can be seen as an

alternative indicator of firm operating risk. Table 5 reports regression results regarding how

a firm’s operating risk is affected by its customer CEO’s overconfidence level. The

dependent variable across three regression models is “cash-flow volatility,” which is the

standard deviation of quarterly cash-flow-to-assets ratios of a given year. Consistent with

our baseline regression on idiosyncratic risk, we show that customer CEO overconfidence

also has a significant and positive effect on operating risk. Taken together, it confirms our

previous conclusion that the risk increase is driven by firm-specific factors.

<Insert Table 5>

21

4.4 Possible mechanisms: Effects on investment intensity

Table 6 reports the regression results regarding how a firm’s investment intensity is

affected by its customer CEO’s overconfidence level. The dependent variable in the first

three regression models is capex, which is the ratio of capital expenditure to total assets.

The dependent variable in the Columns (4) to (6) is R&D, which is the ratio of R&D to

total assets. Using the same framework as Table 3, Columns (1) − (2) and (4) − (5) show

OLS regressions both with and without controlling for relationship characteristics. 12

Columns (3) and (6) implements firm fixed effects to control for potential omitted variables.

First, we find that the effect of c_OC on capex is quite consistent and robust across

the three different model specifications. The coefficient is positive and significant at the

five percent level, which suggests that supplier firms that work with an overconfident

customer firm CEO make more aggressive capital investment than do other firms, keeping

all else equal. The coefficient is four percent, which translates to an average increase of

0.004 in the capital-expenditure-to-assets ratio. Compared with the mean capital-

expenditure-to-assets of the sample (0.054), an increase of 0.004 is equivalent to an

increase by 7.4 percent, which is economically meaningful. The adjusted R-squared of

Column (1) is 51.6 percent, which suggests a good model fit. The inclusion of relationship

characteristics in Column (2) does not increase model fit, which suggests that the length of

relationship and amount of supply-chain sales do not affect investment intensity

significantly. The firm fixed effect in Column (3) absorbs the effects of some firm

characteristics such as leverage and cash flow. Second, we find that the effect of c_OC on

R&D is insignificant across the three different model specifications, which suggests that

12 Our results still hold when we additionally control other supplier characteristics such as Tobin’s Q, cash

holdings…etc.

22

supplier firms that work with an overconfident customer firm CEO do not enhance their

R&D investment than do other firms.

Overall, our results show that customer CEO’s overconfidence increases the capital

expenditure intensity of the suppliers, which is the possible mechanism of our hypothesis

H1.

<Insert Table 6>

4.5 Contingency effects of information friction

Information friction refers to the level of information asymmetry faced by suppliers

regarding major customers. Customers whose future orders from suppliers are hard to

forecast are considered as having high information asymmetry. Our first proxy is high

cash-flow volatility, which is a dummy variable that equals 1 if the cash flow volatility of

a customer is greater than the sample median, and 0 otherwise.13 In the literature, cash-

flow volatility is considered closely related to operating risk (Minton and Schrand 1999).

Higher operating risk makes it harder for suppliers to forecast future demands of their

customers, and hence it is used as an indicator of information asymmetry. Our second

proxy is high capex, which equals 1 if a customer’s capital-expenditure-to-asset ratio is

greater than the sample median, and 0 otherwise. Capital intensity is often linked to the

speed of growth. Higher capital investment can imply an aggressive investment plan, which

makes it harder for suppliers to forecast future demands, and we use it as an indicator of

information asymmetry. The higher the customers’ capital investment, the greater is the

information asymmetry faced by their suppliers.

13 We also use the median of all compustat firms as an alternative cut-off to define high cash-flow. Our results

remain robust.

23

Table 7 reports the contingency effects of our baseline results on information friction.

Based on hypothesis H2, we expect that the interaction terms between CEO overconfidence

and information friction will be positive and significant, meaning that overconfidence of

the customer CEO increases firm risk more when the firm faces greater information

asymmetry. Examining Column (1) first, the interaction of c_OC with high cash-flow

volatility is 4.462 and is statistically significant at the p<5 percent level. Translating its

economic impact, when the customer is riskier and its CEO is overconfident, suppliers

would experience a 4.462 increase in idiosyncratic risk. Column (2) reports the interaction

between high capex and c_OC. We find that the interaction term is positive and significant

at the p<5 percent level, which means that customer CEO overconfidence has a larger

impact on supplier risk when customer firms have more information opacity for their

suppliers. Overall, the results of the two models consistently support H2.

<Insert Table 7>

4.6 Contingency effects of market power

We measure customers’ market power using firm size and industry competition

intensity. Our first indicator is larger firm, which is a dummy variable that equals 1 if the

total assets of a customer are greater than the sample median, and 0 otherwise. Intuitively,

larger firm size indicates stronger market power. Our second proxy is low competition of

the customer industry, which equals 1 if the customer industry has a higher Herfindahl

index (HHI) compared with the sample median, and 0 otherwise. HHI is computed as the

sum of squares of market shares (of sales) of all firms in the same industry (defined at 4-

digit SIC level). When the customer operates in a less competitive industry (sales HHI is

above the sample median), it is considered to have stronger market power.

24

Table 8 reports the contingency effects of our baseline results on customer market

power. Based on hypothesis H3, we expect that customer CEO overconfidence increases

firm risk more when customers have stronger market power. The interaction of c_OC with

larger firm is 5.623 and is statistically significant at the p<5 percent level. Translating its

economic impact, when the customer is a large firm and its CEO is overconfident, suppliers

experience a 5.623 increase in idiosyncratic risk. Compared with the baseline results in

Table 3, the economic magnitude of the CEO overconfidence of larger customers is greater

than the average effect of the overall sample. In Column (2), the coefficient of the

interaction term of low competition and c_OC is 5.360 and is statistically significant at the

p<5 percent level. It indicates that customer CEO overconfidence increases the risk to the

supplier more when the customer firm faces lower competition intensity in the industry.

Overall, the results of the two models consistently support H3.

<Insert Table 8 >

5. Additional Supporting Evidence

5.1 Risk spillover in a supply-chain network

Thus far we have shown that customer CEO overconfidence affects their suppliers’

level of firm specific risk. In this section, we extend this test to supply-chain networks by

examining whether the increase in risk migrates further upstream along the supply chain.

For convenience, we refer to the upstream suppliers as Tier 1 firms, to their immediate

customers as Tier 2 firms, and to the ultimate customers as Tier 3 firms. Similar to our

initial sample, Tier 1 firms’ sales to Tier 2 firms account for more than 10 percent of Tier

1 firms’ total sales, and Tier 2 firms’ sales to Tier 3 firms account for more than 10 percent

of Tier 2 firms’ total sales.

25

Table 9 reports the OLS regressions relating how ultimate customers’ CEO

overconfidence (Tier3_OC) affects Tier 1 firms’ risk, measured by idiosyncratic risk.

Tier3_OC is the indicator for the ultimate downstream customer firms’ OC, which equals

1 when the CEO is overconfident, and 0 otherwise. Model (1) runs an OLS regression

controlling for firm characteristics of Tier 1 suppliers, including firm size, financial

leverage, and cash flow of the previous year. Model (2) adds additional control variables

of relationship duration as well as transaction sales between the partners. All models

include year dummies to control for year effects. Industry dummies (defined based on a 4-

digit SIC) are also included for suppliers and Tier 3 customers.

As shown in Column (1), we find that ultimate customer CEO overconfidence,

Tier3_OC, has a positive and significant coefficient on the operating risk of Tier 1 firms.

The coefficient is 11.137, which is statistically significant at the p<5 percent level. It

suggests that firms’ managerial biases do migrate further upstream along the supply chain.

The control variables in Column (1) include only the financial characteristics of Tier 1

firms. Column (2) adds additional control variables for the characteristics of the

relationship between Tier 1 and Tier 2 firms. Our results remain robust with the controls

on relationship characteristics. The findings strengthen our baseline conclusion that CEO

overconfidence in major customers can lead to a significant increase in firm risk to their

suppliers. Thus, the impact of customer CEO overconfidence does migrate further

upstream in the supply-chain network and leads to an increase in risk to upstream suppliers.

<Insert Table 9 >

26

5.2 Using a sales-weighted measure for CEO overconfidence

It is possible, however, that a supplier firm has multiple major customers. In Table 10,

we use sales-weighted CEO overconfidence, where the weight is the percentage of

transaction sales to a single major customer, in relation to the total sales to all major

customers. For example, consider that a firm has two major customers. The first customer

has an overconfident CEO and sales to this customer account for 40 percent of total sales.

The second customer does not have an overconfident CEO and sales to this customer

account for 60 percent of total sales. In this case, the sales-weighted measure for CEO

overconfidence is 0.4×1 + 0.6×0. We label this sales-weighted measure as c_OC_w.

Table 10 reports the regressions results relating this measure to idiosyncratic risk

based on two samples, whole sample and the subsample of multiple customers with partly

OC customers. We use the subsample of multiple customers with partly OC customers to

investigate whether multiple customers help a supplier to better adjust for individual

customer CEOs’ biases, i.e., use the information from multiple CEOs to triangulate a true

estimate in forecasting future demand.

First, consistent with our primary findings, the results in Column (1) show that having

an overconfident CEO increases firm-specific risk. Hence, our results are robust when we

consider the situation of multiple major customers. Second, when we use the subsample of

multiple customers with partly OC customers. We find that the coefficients of c_OC_w are

insignificant across the two different model specifications, which suggests that multiple

customers help a supplier to better adjust for individual customer CEOs’ overconfidence

biases.

<Insert Table 10>

27

5.3 The role of customers’ characteristics

Our findings on the effect of overconfident customer on supplier firm risk might suffer

from omitted variables on the customer side. First, characteristics of customer that are not

in our regressions might also generate a positive relation between overconfident customer

on supplier firm risk. Second, there are several papers investigate the returns of stocks

connected via significant trading relationships (e.g., Cohen and Frazzini, 2008; Cen,

Hertzel, and Schiller, 2017). Third, nearly 61% of supplier-year observations are classified

as having an overconfident customer-firm CEO in this study, which is very high relative to

other papers investigating CEO overconfidence. For instance, Campbell et al. (2011)

reports approximately 35% of CEO-year observations for the ExecuComp universe are

optimistic using a similar definition of overconfidence as the present paper. Therefore, to

control the almost doubling of the frequency of overconfident CEO years for the customer

firms in the sample of supplier-customer relationships, we further control the customer

fixed effect.

To address these potential concern, we further controlling the characteristics of

customer, the risk levels of customer, and the customer fixed effect in the regressions (1)

to (3). Table 11 reports the regression results after controlling the role of customers’

characteristics. Consistent with our primary findings, the results in all regression models

show that having an overconfident CEO increases firm-specific risk even if we control

more customers’ characteristics. Hence, our results are robust when we consider the role

of customers’ characteristics.

<Insert Table 11>

28

5.4 The role of longer-lived relationships

Given the observations are at the supplier-largest customer year level, longer-lived

relationships are weighted more heavily in the analysis than shorter-lived relationships.

However, it could be conjectured that in longer-lived relationships, supplier CEOs could

better infer and learn any biases on the part of overconfident customer-firm CEOs. Thus,

we further examine whether the relationship length between customer and supplier could

mitigate the effect of overconfident customer.

Table 12 reports the regression results after considering the relationship length

between customer and supplier. The results show that the effect of overconfident customer

on supplier risk only exist when the relationship length is less than the median value of our

sample. Specifically, a longer relationship makes the supplier learn more about the

customer, which mitigates the influence of managerial bias from an overconfident CEO.

With greater depth of objective information concerning their customers, suppliers have a

lower risk exposure to customer CEOs’ managerial biases and therefore can make accurate

forecasts and plan investment accordingly.

<Insert Table 12>

6. Conclusions

There is a growing interest, but to date limited empirical evidence, regarding how

managerial biases affect supply-chain partners’ decision making. The managerial

overconfidence literature provides massive evidence that CEO overconfidence influences

firm investment and financing policy (Malmendier and Tate, 2005, 2008; Malmendier et

al., 2011; Gallasso and Timothy, 2011; Hirshleifer et al., 2012). Hence, CEO

overconfidence provides a valid measure to understand how managerial biases on firms’

29

future performance affects supply-chain partners’ decision making. Therefore, this study

examines whether and how the presence of an overconfident CEO in a major customer firm

impacts upstream suppliers’ investment policy and risk-taking strategies.

Using a comprehensive data set of 4,463 supplier-year observations over a timespan

from 1993 to 2012, we find that an overconfident customer CEO increases upstream

supplier investment intensity as well as idiosyncratic risk. The impacts are not only

statistically significant, but are also economically meaningful. Compared to customers with

non-overconfident CEOs, the investment intensity of suppliers serving overconfident

customers increases by 7.4 percent and these firms experience 22.45 percent higher

idiosyncratic risk. The results are robust using different econometric techniques.

Exploring the contingency factors, our analysis suggests that upstream firms suffer

greater risk when the market power of the customer is higher or when information

asymmetry between the trading partners is stronger. Interestingly, we also show that the

risk effect of customer CEO overconfidence not only accrues to their direct suppliers but

also migrates further upstream along the supply-chain network. Finally, our results are

robust to the situation of multiple major customers by sales-weighted measure for CEO

overconfidence, control the role of customers’ characteristics, and control the role of

longer-lived relationships.

The implications of our findings should be of interest from a practical perspective,

especially for upstream suppliers. Suppliers are usually in a dependent position in the

supply-chain relationship. It is a critical that suppliers manage their operating risk well

because it is a key determinant of their financing cost (Minton and Schrand 1999). Our

findings suggest that upstream suppliers should pay attention to the managerial biases of

30

their major customers, because it is a risk source that can cause greater firm risk or cash

flow risk. Forecasts of future market growth and investment decisions should be made with

special caution when in a business relationship with a customer with an overconfident CEO

and, to reduce such inherent risk, suppliers should focus on decreasing information

asymmetry in their trading partnerships.

31

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http://amj.aom.org/content/46/2/139.short

https://scholar.google.com/citations?user=UT3_bnIAAAAJ&hl=en&oi=sra



http://www.sciencedirect.com/science/journal/08839026/16/4

35

Table 1 Sample distribution

Sample distribution is reported in Table 1. Panel A reports the distribution of suppliers by year. c_OC is the

indicator of whether or not the firm’s major customer has an overconfident CEO in a given year. Our sample

is evenly distributed over the sample years from 1993 to 2012. Panel B reports industry distribution of

suppliers. Our sample is more concentrated in industries with a 1-digit SIC code of 2 and 3, which are

manufacturing firms.

Panel A: by year

year c_OC=0 c_OC =1 Total Sample %

1993 91 22 113 2.53%

1994 79 138 217 4.86%

1995 128 132 260 5.83%

1996 107 165 272 6.09%

1997 94 196 290 6.50%

1998 75 169 244 5.47%

1999 41 169 210 4.71%

2000 36 147 183 4.10%

2001 43 152 195 4.37%

2002 73 157 230 5.15%

2003 81 156 237 5.31%

2004 98 157 255 5.71%

2005 93 167 260 5.83%

2006 121 160 281 6.30%

2007 104 163 267 5.98%

2008 85 143 228 5.11%

2009 71 125 196 4.39%

2010 117 74 191 4.28%

2011 120 75 195 4.37%

2012 102 37 139 3.11%

Total 1,759 2704 4,463 100.00%

Panel B: by industry

1-digit SIC c_OC=0 c_OC =1 Total Sample %

0 17 11 28 0.63%

1 116 102 218 4.88%

2 477 779 1,256 28.14%

3 911 1,482 2,393 53.62%

5 74 114 188 4.21%

7 127 156 283 6.34%

8 28 43 71 1.59%

9 9 17 26 0.58%

36

Table 2 Summary statistics

Table 2 reports the summary statistics of the key variables for the overall sample. It includes financial

characteristics of the sample firms, their major customers’ financial characteristics, and characteristics of

their trading relationships. All continuous variables are truncated at their 0.5 percentiles and 99.5 percentiles

each year to tease out the outliers. Detailed variable definitions are reported in Appendix A.

Variables mean sd p5 p50 p95

Panel A: firm characteristics

idiosyncratic volatility 15.767 60.920 1.389 3.467 10.287

stock volatility 16.581 9.998 6.196 14.288 33.151

beta 0.867 0.590 0.083 0.766 2.002

cash-flow volatility 0.016 0.024 0.002 0.009 0.048

total assets 2318 10372 17 240 7870

leverage 0.191 0.173 0.000 0.167 0.504

sales growth 0.126 0.280 -0.232 0.083 0.625

cash flow 0.148 0.130 -0.083 0.158 0.327

R&D 0.132 0.710 0.000 0.015 0.368

capex 0.054 0.064 0.006 0.034 0.176

Panel B: customer characteristics

c_OC 0.606 0.489 0.000 1.000 1.000

c_totalassets 60523 78327 1500 30011 217123

c_tangibility 0.499 0.257 0.140 0.439 0.894

c_capex 0.063 0.043 0.008 0.060 0.130

Panel C: relationship characteristics

duration 4.410 3.036 1.000 4.000 10.000

salespct_sales 0.229 0.141 0.107 0.180 0.513

37

Table 3 Effects on firm idiosyncratic risk

This table reports regression results regarding how a firm’s idiosyncratic risk is affected by its customer

CEO’s overconfidence level. The dependent variable across three regression models is “idiosyncratic

volatility,” which is the standard deviation of the residuals obtained from a market model of daily returns in

excess of three-month T-bills using previous two-year data. The independent variable of interest is “c_OC,”

which is the indicator equal to 1 when the customer CEO is overconfident, and is 0 otherwise. Model (1) runs

OLS regression controlling for firm characteristics of the testing firms, including firm size, financial leverage,

sale growth rate, and cash flow of the previous year. Model (2) adds additional control variables of

relationship duration as well as transaction sales between the partners. Model (3) implements firm fixed

effects to control for potential omitted variables that are not time-variant. All the models include year

dummies to control for year effects. Industry dummies (defined based on 4-digit SICs) are also included to

control for the industry fixed effects of both the testing firms as well as their customers. Detailed variable

definitions are reported in Appendix A. Standard errors are corrected for heteroskedasticity (t-statistics are

in parentheses). *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

Dependent variable: idiosyncratic volatility

(1) (2) (3)

OLS OLS FE

c_OC 3.366*** 3.540*** 3.138**

(2.60) (2.76) (2.01)

log_totalassets -3.159*** -3.020*** -4.396***

(-6.83) (-7.35) (-2.63)

leverage 3.018 3.500 -5.327

(1.25) (1.41) (-0.96)

sales growth 1.687 1.392 1.825

(1.04) (0.85) (1.07)

cash flow -3.990 -3.363 -5.088

(-1.00) (-0.88) (-0.78)

log_duration -1.622* -1.364

(-1.82) (-1.09)

salespct_sales 6.313 -3.211

(1.13) (-0.54)

Constant 12.548* 10.796 26.610***

(1.81) (1.43) (3.02)

Year FE Yes Yes Yes

Supplier industry FE Yes Yes No

Supplier firm FE No No Yes

Customer industry FE Yes Yes Yes

Observations 4,463 4,463 4,463

Adjusted R-squared 0.838 0.838 0.868

Number of suppliers 1,225

38

Table 4 Effects on firm stock volatility and beta

This table reports regression results regarding how a firm’s risk (stock volatility or beta) is affected by its customer CEO’s overconfidence level. The dependent

variable across regression models are stock volatility and beta. In here, “stock volatility,” which is measured as the firm’s stock daily volatility using previous two-year

data. “beta,” which is firm’s equity beta from a market model of daily returns in excess of three-month T-bills using previous two-year data. The independent variable of interest

is “c_OC,” which is the indicator equal to 1 when the customer CEO is overconfident, and is 0 otherwise. Model (1) runs OLS regression controlling for firm

characteristics of the testing firms, including firm size, financial leverage, sale growth rate, and cash flow of the previous year. Model (2) adds additional control

variables of relationship duration as well as transaction sales between the partners. Model (3) implements firm fixed effects to control for potential omitted variables

that are not time-variant. All the models include year dummies to control for year effects. Industry dummies (defined based on 4-digit SICs) are also included to

control for the industry fixed effects of both the testing firms as well as their customers. Detailed variable definitions are reported in Appendix A. Standard errors

are corrected for heteroskedasticity (t-statistics are in parentheses). *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

Dependent variable: stock volatility beta

(1) (2) (3) (4) (5) (6)

c_OC -0.979 -0.760 -0.523 -0.018 -0.008 -0.035

(-0.76) (-0.58) (-0.59) (-0.84) (-0.37) (-1.27)

log_totalassets -2.439*** -2.469*** 0.087 0.117*** 0.117*** 0.108***

(-4.60) (-4.39) (0.11) (12.41) (12.23) (3.33)

leverage 7.124* 7.158* -3.369 -0.239*** -0.233*** -0.292***

(1.78) (1.82) (-0.52) (-3.22) (-3.14) (-2.58)

sales growth 3.886** 3.687** 3.166 0.007 -0.003 -0.070**

(2.18) (2.05) (1.34) (0.23) (-0.12) (-1.98)

cash flow -7.108* -7.123* -5.256 -0.309*** -0.303*** -0.282**

(-1.95) (-1.92) (-0.60) (-3.31) (-3.26) (-2.06)

log_duration -1.320** -0.025 -0.066*** -0.059**

(-1.98) (-0.02) (-3.18) (-2.55)

salespct_sales -2.154 -12.755* -0.025 -0.106

(-0.63) (-1.74) (-0.32) (-0.77)

Constant 32.546*** 35.624*** 2.794 0.609*** 0.713*** 0.227

(5.60) (5.58) (0.19) (4.13) (4.59) (0.94)

Year FE Yes Yes Yes Yes Yes Yes

Supplier industry FE Yes Yes No Yes Yes No

Supplier firm FE No No Yes No No Yes

39

Customer industry FE Yes Yes Yes Yes Yes Yes

Observations 4,463 4,463 4,463 4,463 4,463 4,463

Adjusted R-squared 0.015 0.016 0.393 0.447 0.449 0.685

Number of suppliers 1,225 1,225

40

Table 5 Effects on cash flow volatility

This table reports regression results regarding how a firm’s risk (cash-flow volatility) is affected by its

customer CEO’s overconfidence level. The dependent variable here is “cash-flow volatility,” which is the

standard deviation of quarterly cash flow to assets ratios of a given year. The independent variable of interest

is “c_OC,” which is the indicator equal to 1 when the customer CEO is overconfident, and is 0 otherwise.

Model (1) runs OLS regression controlling for firm characteristics of the testing firms, including firm size,

financial leverage, sale growth rate, and cash flow of the previous year. Model (2) adds additional control

variables of relationship duration as well as transaction sales between the partners. Model (3) implements

firm fixed effects to control for potential omitted variables that are not time variant. All the models include

year dummies to control for year effects. Industry dummies (defined based on 4-digit SIC) are also included

to control for the industry fixed effects of both the testing firms as well as their customers. Detailed variable

definitions are reported in Appendix A. Standard errors are corrected for heteroskedasticity (t-statistics are

in parentheses). *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

Dependent variable: cash-flow volatility

(1) (2) (3)

OLS OLS FE

c_OC 0.003*** 0.003*** 0.002***

(2.92) (3.00) (3.00)

log_totalassets -0.003*** -0.003*** -0.001

(-9.29) (-8.17) (-1.12)

leverage -0.001 -0.001 0.002

(-0.36) (-0.23) (0.41)

sales growth 0.003 0.003 0.001

(1.40) (1.22) (0.17)

cash flow -0.017*** -0.016*** 0.001

(-3.42) (-3.35) (0.06)

log_duration -0.002** -0.002***

(-2.47) (-3.46)

salespct_sales 0.004 0.008

(0.72) (1.08)

Constant 0.025*** 0.024*** 0.044***

(3.48) (3.22) (3.02)

Year FE Yes Yes Yes



Customer industry FE Yes Yes Yes




41

Table 6 Possible mechanisms: Effects on investment intensity

This table reports regression results regarding how a firm’s investment intensity is affected by its customer CEO’s overconfidence level. The dependent variable

across first three regression models is “capex,” which is the ratio of capital expenditure to total assets. The dependent variable in the Columns (4) to (6) is R&D,

which is the ratio of R&D to total assets. The independent variable of interest is “c_OC,” which is the indicator equal to 1 when the customer CEO is overconfident,

and is 0 otherwise. Models (1) and (4) run OLS regression controlling for firm characteristics of the testing firms, including firm size, financial leverage, sale

growth rate, and cash flow of the previous year. Models (2) and (5) add additional control variables of relationship duration as well as transaction sales between

the partners. Model (3) and (6) implement firm fixed effects to control for potential omitted variables that are not time variant. All the models include year dummies

to control for year effects. Industry dummies (defined based on 4-digit SICs) are also included to control for the industry fixed effects of both the testing firms as

well as their customers. Detailed variable definitions are reported in Appendix A. Standard errors are corrected for heteroskedasticity (t-statistics are in parentheses).

*, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.

Dependent variable: capex R&D

(1) (2) (3) (4) (5) (6)

OLS OLS FE OLS OLS FE

c_OC 0.004** 0.004** 0.004** 0.003 0.003 0.001

(1.97) (1.99) (2.00) (1.16) (1.16) (0.39)

log_totalassets 0.001 0.001 0.002 -0.002** -0.002* -0.018***

(1.11) (1.30) (1.15) (-2.08) (-1.80) (-5.65)

leverage 0.012* 0.012** -0.006 -0.046*** -0.046*** -0.011

(1.89) (1.98) (-0.70) (-5.03) (-4.96) (-1.16)

sales growth 0.011*** 0.011*** 0.008** 0.005 0.005 -0.003

(3.02) (2.97) (2.42) (1.16) (1.11) (-0.81)

cash flow 0.065*** 0.066*** 0.017 -0.173*** -0.172*** -0.058***

(6.88) (6.98) (1.36) (-7.98) (-7.98) (-3.45)

log_duration -0.002 -0.003* -0.001 0.001

(-1.10) (-1.86) (-0.46) (0.37)

salespct_sales 0.008 -0.004 0.012 -0.004

(1.01) (-0.47) (0.93) (-0.33)

Constant 0.012 0.011 0.02 0.583*** 0.577*** 0.090***

(0.72) (0.66) (0.77) (10.16) (10.03) (5.00)

Year FE Yes Yes Yes Yes Yes Yes

Supplier industry FE Yes Yes No Yes Yes No

Supplier firm FE No No Yes No No Yes

42

Customer industry FE Yes Yes Yes Yes Yes Yes

Observations 4,463 4,463 4,463 4,463 4,463 4,463

Adjusted R-squared 0.516 0.516 0.767 0.576 0.577 0.886

Number of suppliers 1,225 1,225

43

Table 7 Exploring the contingency effects of information friction

This table investigates how customer CEO overconfidence affects firm risk depending on the information

friction existent between trading partners. The dependent variable across regression models is “idiosyncratic

volatility,” which is the standard deviation of the residuals obtained from a market model of daily returns in

excess of three-month T-bills using previous two-year data. To proxy the extent of information friction, we

use (1) cash-flow volatility and (2) capital intensity of the customers as the judging criteria. Customers with

higher cash-flow volatility and higher capital intensity than the sample median are considered as having high

information asymmetry. Model (1) tests the interaction between customer CEO overconfidence and higher

cash-flow volatility. Model (2) tests the interaction between customer CEO overconfidence and higher capital

intensity. All the models are OLS regressions with control variables of firm characteristics and relationship

characteristics. Year dummies are used to control for year effects. Industry dummies (defined based on 4-

digit SICs) are also included to control for the industry fixed effects of both the testing firms as well as their

customers. Detailed variable definitions are reported in Appendix A. Standard errors are corrected for

heteroskedasticity (t-statistics are in parentheses). *, **, and *** denote significance at the 10%, 5%, and 1%

levels, respectively.


(1) (2)

Interaction with

cash-flow volatility

Interaction with

capex

c_OC 1.475 1.070

(1.18) (0.78)

c_OC × high cash-flow volatility 4.462**

(2.11)

high cash-flow volatility -3.263

(-1.59)

c_OC × high capex 5.713**

(2.39)

high capex -3.753**

(-2.15)

log_totalassets -3.001*** -3.013***

(-7.35) (-7.37)

leverage 3.483 3.446

(1.40) (1.39)

sales growth 1.306 1.237

(0.78) (0.75)

cash flow -3.397 -3.559

(-0.90) (-0.94)

log_duration -1.666* -1.670*

(-1.91) (-1.88)


(1.16) (1.12)

Constant 11.009 12.471

(1.47) (1.65)

Year FE Yes Yes

Supplier industry FE Yes Yes

Customer industry FE Yes Yes

Observations 4,463 4,463

Adjusted R-squared 0.838 0.838

44

Table 8 Interaction with customers’ market power

This table investigates how customer CEO overconfidence affects firm risk depending on the market power

of the customers. The dependent variable across regression models is “idiosyncratic volatility,” which is the

standard deviation of the residuals obtained from a market model of daily returns in excess of three-month T-

bills using previous two-year data. To proxy the market power of the customers, we use (1) firm size and (2)

industry competition as the judging criteria. “Larger firm” in Column (1) is an indicator that equals 1 if the

customers’ total assets are higher than the sample median, and 0 otherwise. “low competition” in Column (2)

is an indicator, which equals 1 if the industry of the customer has a lower Herfindahl index (HHI) compared

with sample median, and 0 otherwise. The Herfindalh index is computed as the sum of squares of market

shares (sales) of all available firms in the same industry (defined at 4-digit SIC level). Larger firm size and

low competition proxy for stronger market power of the customer. All the models are OLS regressions with

control variables of firm and relationship characteristics. Year dummies are used to control for year effects.

Industry dummies (defined based on 4-digit SIC) are also included to control for the industry fixed effects of

both the testing firms as well as their customers. Detailed variable definitions are reported in Appendix A.

Standard errors are corrected for heteroskedasticity (t-statistics are in parentheses). *, **, and *** denote

significance at the 10%, 5%, and 1% levels, respectively.


(1) (2)

Interaction with

firm size

Interaction with

market competition

c_OC 0.876 1.341

(0.82) (0.94)

c_OC × larger firm 5.623**

(2.15)

larger firm 0.050

(0.03)

c_OC × low competition 5.360**

(2.06)

low competition -2.496

(-0.97)


(-7.31) (-7.42)


(1.48) (1.41)


(0.89) (0.82)

cash flow -3.729 -3.682

(-0.97) (-0.96)

log_duration -1.833** -1.788**

(-2.07) (-2.01)


(1.06) (1.18)

Constant 13.926* 13.913*

(1.86) (1.72)

Year FE Yes Yes





45

Table 9 Risk spillover effect along the supply chain

This table reports regression results regarding to the risk spillover effect along three tiers of supply chain

partners. We refer to the upstream suppliers as Tier 1 firms, to their immediate customers as Tier 2 firms,

and to the ultimate customers as Tier 3 firms. Similar to our initial sample, Tier 1 firms’ sales to Tier 2 firms

account for more than 10 percent of Tier 1 firms’ total sales, and Tier 2 firms’ sales to Tier 3 firms account

for more than 10 percent of Tier 2 firms’ total sales. The dependent variable across three regression models

is supplier firms’ (Tier 1 firms’) idiosyncratic volatility. Tier3_OC is the indicator for Tier 3 firms’ OC,

which equals 1 when the CEO of the Tier 3 firm (ultimate customer) is overconfident, and is 0 otherwise.

Model (1) runs OLS regression controlling for firm characteristics of Tier 1 suppliers, including firm size,

financial leverage, and cash flow of the previous year. Model (2) adds additional control variables of

relationship duration as well as transaction sales between the partners. All the models include year dummies

to control for year effects. Industry dummies (defined based on 4-digit SICs) are also included to for suppliers,

and for Tier 2 and Tier 3 customers. Detailed variable definitions are reported in Appendix A. Standard errors

are corrected for heteroskedasticity (t-statistics are in parentheses). *, **, and *** denote significance at the

10%, 5%, and 1% levels, respectively.


(1) (2)

Tier3_OC 11.137** 10.660**

(2.15) (2.06)

logtotalassets -8.873*** -8.410***

(-6.30) (-5.76)

leverage -11.569 -9.977

(-1.14) (-0.99)

cashflow -27.111** -22.381*

(-2.30) (-1.88)

logduration -2.320

(-0.73)

salespct_sales 26.454**

(2.55)

Constant -24.005 -29.687

(-0.44) (-0.55)

Year FE Yes Yes



Observations 908 908


46

Table 10 Using a sale-weighted measure for CEO overconfidence

This table reports robustness tests using a sales-weighted measure for CEO overconfidence based on two

samples, whole sample and the subsample of multiple customers with partly OC customers. The independent

variable of interest, “c_OC_w”, is the sales-weighted average of the CEO overconfidence measures across

all major customers. For each major customer, CEO overconfidence is measured as a dummy variable which

equals 1 when the customer CEO is overconfident, and 0 otherwise. The dependent variable for all Models

is “idiosyncratic volatility,” which is the standard deviation of quarterly cash-flow-to-assets ratios in a given

year. The control variables include firm characteristics of firm size, financial leverage, sale growth rate, and

cash flow of the previous year. Models (2) and (4) add additional control variables of relationship duration

as well as transaction sales between the partners. All models include year dummies to control for year effects.

Industry dummies (defined based on 4-digit SIC) are also included to control for the industry fixed effects of

both the testing firms as well as their customers. Detailed variable definitions are reported in Appendix A.

Standard errors are corrected for heteroskedasticity (t-statistics are in parentheses). *, **, and *** denote

significance at the 10%, 5%, and 1% levels, respectively.


(1) (2) (3) (4)

Whole sample Multiple customers with

partly OC customers

c_OC_w 4.370*** 4.564*** 0.5848 1.9920

(2.88) (3.01) (0.19) (0.62)

log_totalassets -3.245*** -3.101*** -4.3160*** -4.2121***

(-6.83) (-7.38) (-6.55) (-6.85)

leverage 3.137 3.606 3.8687 3.8680

(1.23) (1.38) (0.90) (0.91)

sales growth 1.826 1.519 -0.6736 -1.1409

(1.06) (0.88) (-0.38) (-0.62)

cash flow -4.213 -3.560 -1.8335 -1.0068

(-1.02) (-0.90) (-0.28) (-0.15)

log_duration -1.711* -3.7357**

(-1.73) (-2.27)


(1.10) (0.33)

Constant 12.831 11.456 27.3277*** 32.3666***

(1.60) (1.35) (2.68) (3.17)

Year FE Yes Yes Yes Yes

Supplier industry FE Yes Yes Yes Yes

Customer industry FE Yes Yes Yes Yes

Observations 4,463 4,463 2,248 2,248

Adjusted R-squared 0.837 0.837 0.8437 0.8439

47

Table 11 The role of customers’ characteristics

This table reports the regression results after controlling the role of customers’ characteristics. The dependent

variable across three regression models is “idiosyncratic volatility,” which is the standard deviation of the

residuals obtained from a market model of daily returns in excess of three-month T-bills using previous two-

year data. The independent variable of interest is “c_OC,” which is the indicator equal to 1 when the customer

CEO is overconfident, and is 0 otherwise. In the Models (1) to (3), we further controlling the characteristics

of customer, the risk levels of customer, and the customer fixed effect. All the models include year dummies

to control for year effects. Industry dummies (defined based on 4-digit SICs) are also included to control for

the industry fixed effects of both the testing firms as well as their customers. Detailed variable definitions are

reported in Appendix A. Standard errors are corrected for heteroskedasticity (t-statistics are in parentheses).

*, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively.


(1) (2) (3)

OLS OLS FE

c_OC 3.243** 3.887*** 3.020**

(2.56) (2.79) (2.14)

log_totalassets -3.120*** -3.133*** -4.268***

(-7.53) (-7.17) (-3.37)

leverage 3.488 3.071 -3.543

(1.40) (1.17) (-0.67)

sales growth 1.001 1.576 1.870

(0.59) (0.93) (0.97)

cash flow -4.182 -3.331 -3.707

(-1.09) (-0.83) (-0.55)

c_log_totalassets 2.136***

(3.63)

c_leverage -6.591

(-1.26)

c_sales growth 10.180**

(2.51)

c_cash flow -7.074

(-0.76)

c_beta 0.713

(0.87)

c_ idiosyncratic volatility -38.573

(-1.12)

log_duration -1.515* -1.677* -1.151

(-1.75) (-1.81) (-0.91)

salespct_sales 5.815 6.059 0.376

(1.04) (1.06) (0.06)

Constant -3.403 12.970 47.744

(-0.40) (1.55) (1.22)

Year FE Yes Yes Yes



Customer industry FE Yes Yes No

Customer firm FE No No Yes


48



49

Table 12 The role of longer-lived relationships

This table reports the regression results after considering the relationship length between customer and

supplier. The dependent variable across three regression models is “idiosyncratic volatility,” which is the

standard deviation of the residuals obtained from a market model of daily returns in excess of three-month

T-bills using previous two-year data. The independent variable of interest is “c_OC,” which is the indicator

equal to 1 when the customer CEO is overconfident, and is 0 otherwise. Model (1) and (2) show the

subsamples with high and low relationship length. All the models include year dummies to control for year

effects. Industry dummies (defined based on 4-digit SICs) are also included to control for the industry fixed

effects of both the testing firms as well as their customers. Detailed variable definitions are reported in

Appendix A. Standard errors are corrected for heteroskedasticity (t-statistics are in parentheses). *, **, and

*** denote significance at the 10%, 5%, and 1% levels, respectively.


(1) (2)

log_duration ≤ median log_duration > median

c_OC 3.710** 1.100

(2.16) (0.71)


(-4.56) (-4.24)


(0.62) (0.31)


(0.47) (1.60)

cash flow -3.533 -1.065

(-0.65) (-0.25)

log_duration -2.405 -0.903

(-1.44) (-0.90)

salespct_sales 17.090 -3.222

(1.61) (-0.73)

Constant 11.680 19.043***

(0.73) (4.24)

Year FE Yes Yes





50

Appendix A Variable definition

Variables Definitions Data sources

c_OC

A dummy variable, which equals 1 if a customer CEO is classified as

overconfident, and 0 otherwise. In overconfident customers, chief executive

officers (CEOs) postpone exercising highly in-the-money options at least

twice during their tenure. CEOs are considered highly overconfident CEOs

the first time they exhibit this behavior. Following Campbell et al. (2011)

and Ho et al. (2016), we choose 100% moneyness as the cutoff point to

identify CEOs as highly overconfident.

ExecuComp

idiosyncratic

volatility

Standard deviation of the residuals obtained from a market model of daily

returns in excess of three-month T-bills using previous two-year data, where

the market is represented by the value-weighted CRSP index.

CRSP

stock volatility Stock volatility is measured as the firm’s stock daily volatility using the

previous two-years’ data.

CRSP

beta

Firm’s equity beta from a market model of daily returns in excess of three-

month T-bills using the previous two-years’ data, where the market is

represented by the value-weighted CRSP index.

CRSP

cash-flow volatility standard deviation of quarterly cash-flow-to-assets ratios in a testing year

Compustat-

Quarterly

data

total assets total assets Compustat-

Annual data

leverage long-term debt plus short-term debt in current liability, divided by total

assets

Compustat-

Annual data

sales growth percentage increase of sales in a given year from the previous year Compustat-

Annual data

cash flow operating cash flow before depreciation, divided by total assets Compustat-

Annual data

capex capital expenditure divided by assets Compustat-

Annual data

R&D R&D expenditure divided by total sales Compustat-

Annual data

duration number of years of the partnership until a testing year Compustat-

Segment file

salespct_sales percentage of transaction sales between the partners to the total sales of the

supplier

Compustat-

Segment file

the impact of overconfident customers on suppliers’...

Documents