((11))

Some Recent Trends in Some Recent Trends in Empirical Corporate Finance ResearchEmpirical Corporate Finance Research

Sheng-Syan ChenSheng-Syan Chen

Department of FinanceDepartment of Finance

College of ManagementCollege of Management

National Taiwan UniversityNational Taiwan University

((22))

• Overconfident managers tend to think they are better than they really are in terms of ability, judgment, or prospects for successful life outcomes (“optimism”).

Overconfident managers are likely to overestimate the net discounted expected payoffs from uncertain endeavors, either because of a general tendency to expect good outcomes, or because they overestimate their own efficacy in bringing about success.

Overconfident managers tend to overestimate the precision of their own forecasts or underestimate the volatility of random events, resulting in subjective probability distributions that are too narrow.

Managerial OverconfidenceManagerial Overconfidence

((33))

Empirical evidence

((44))

Are overconfident CEOs better innovators? (Hirshleifer, Low & Teoh (HLT), 2012, JF)

• People tend to be more overconfident about their performance on hard rather than easy tasks (Griffin & Tversky 1992).

Thus, relatively overconfident CEOs are expected to be especially enthusiastic about risky, challenging, and talent- and vision-sensitive enterprises.

Innovative projects—which apply new business methods, develop new technologies, or offer new products or services—are risky and challenging.

Firms with overconfident managers are expected to accept greater risk, invest more heavily in innovative projects, and achieve greater innovation.

((55))

• Measuring CEO overconfidence

Options-based measure:

It is typically optimal for risk-averse, undiversified executives to exercise their own-firm stock options early if the option is sufficiently in the money (Hall & Murphy 2002).

Confident CEO (Options) takes a value one if a CEO postpones the exercise of vested options that are at least 67% in the money, and zero otherwise (Malmendier & Tate (2005a, 2008)).

As there are no detailed data on a CEO’s options holdings and exercise prices for each option grant, HLT calculate the average moneyness of the CEO’s option portfolio for each year (Campbell et al. (2011)).

((66))6

First, for each CEO-year, HLT calculate the average realizable value per option by dividing the total realizable value of the options by the number of options held by the CEO.

The average exercise price of the options is calculated as the fiscal year-end stock price minus the average realizable value per option.

The average percent moneyness of the options equals the per-option realizable value divided by the estimated average exercise price.

As they are only interested in options that the CEO can exercise, HLT include only the vested options held by the CEO.

((77))

Press-based measure:

Following Malmendier & Tate (2005b, 2008) and Hribar & Yang (2011), HLT search for articles referring to the CEO.

For each CEO and year, HLT record:

(1) total number of articles;

(2) number of articles containing the words confident, confidence, or variants (overconfidence and over-confident);

(3) number of articles containing the words optimistic, optimism, or variants (overoptimistic and over-optimism);

(4) number of articles using pessimistic, pessimism, or variants (over-pessimistic);

(5) number of articles using reliable, steady, practical, conservative, frugal, cautious, or gloomy.

For each year, HLT compare the number of articles that use the “Confident” terms (categories 2 & 3) and the number of articles that use the “Cautious” terms (categories 4 & 5).

((88))

HLT measure CEO overconfidence for each CEO i in year t as

where ais is the number of articles using the Confident terms and bis is the number of articles using the Cautious terms.

Following Malmendier & Tate (2008), HLT also control for the total number of press mentions over the same period (TotalMention).

The press may be biased toward positive stories and this would imply a higher number of mentions as confident or optimistic when there is more attention in the press.

((99))

Measuring innovation

HLT measure resource input into innovation with R&D scaled by book assets.

Their output-oriented measures of innovation are based on patent counts and patent citations.

Owing to the finite length of the sample, citations suffer from a time truncation bias. HLT follow Hall et al. (2001, 2005) and adjust the citation count of each patent in 2 different ways:

Qcitation count: Each patent’s citation count is multiplied by the weighting index from Hall et al. (2001, 2005).

TTcitation count: Each patent’s citation count is scaled by the average citation count of all patents in the same technology class and year.

((1010))

Sample

Since CEO overconfidence measures are lagged by one year, HLT require that the CEO be the same one in the prior year to ensure that the characteristics of the CEO in place are observed at the time the innovation is being measured.

Financial firms and utilities are excluded.

The final sample consists of 2,577 CEOs from 9,807 firm-year observations between 1993 and 2003.

Of these observations, 8,939 firm-years have information on the options-based measure, while 7,762 firm-years have information on the press-based measure of overconfidence.

((1111))

Steve Jobs of Apple Computers turns out to be overconfident using both measures of overconfidence.

((1212))

((1313))

((1414))

((1515))

((1616))

Note: An industry is defined as innovative if the average Qcitation count per patent for the industry is greater than the sample median.

((1717))

((1818))

• The greater innovative output for given R&D input achieved by overconfident CEOs does not necessarily translate into higher firm value.

Hall et al. (2005) show that patent citations are positively correlated with firm value, but overconfident CEOs could be overpaying to achieve increased citation counts (possibly using resources other than R&D expenditures), reducing firm value.

A possible way to address this issue is to regress firm value on CEO overconfidence or the innovation that results from it. However, such a test is subject to endogeneity problems.

HLT hence examine a more specific issue, whether overconfidence allows firms to translate growth opportunities into realized firm value (measured by Tobin’s Q).

((1919))

• Following Bekaert et al. (2007), HLT use the industry price to earnings (PE) ratio as an exogenous instrument for firm growth.

HLT calculate the monthly industry PE ratio as the logarithmic transformation of the ratio of the industry’s total market capitalization to the industry’s total earnings.

PE is affected by risk as well as growth opportunities, so their tests are weakened by noise to the extent that the PE ratio is influenced by discount rate changes.

As in Bekaert et al. (2007), HLT hence subtract the 60-month moving average of the PE ratio, motivated by the idea that discount rates are more persistent than growth opportunities.

Finally, HLT average the difference over the fiscal year to form our measure of exogenous growth opportunities.

((2020))

Overconfident CEOs are more effective at exploiting growth opportunities and translating them into firm value, especially among innovative industries.

((2121))

Supply ChainSupply Chain

• Wealth effects of corporate decisions on announcers: Short-term market reactions Long-run performance

• Wealth effects of corporate decisions on industry rivals: M&As: Eckbo (1983, JFE), Stillman (1983, JFE), Song & Walkling (2000,

JFE), Cai, Song & Walkling (2011, RFS) IPOs: Hsu, Reed & Rocholl (2010, JF) SEOs: Szewczyk (1992, JF), Slovin, Sushka & Polonchek (1992, JFE) Share repurchases: Hertzel (1991, JF) Bankruptcy: Lang & Stulz (1992, JFE), Benmelech & Bergman (2011, JF) Going private: Slovin, Sushka & Bendeck (1991, JF) Privatization: Eckel, Eckel & Singal (1997, JFE)

((2222))

• Linkage between corporate decisions and suppliers and customers:

((2323))

Investigate the wealth effects of distress and bankruptcy for filing firms’ suppliers and customers.

Lang and Stulz (1992)Lang and Stulz (1992)

• Inter-firm linkages and the wealth effects of financial distress along the supply chain:

(Hertzel, Li, Officer & Rodgers, 2008, JFE)

((2424))

• Identifying suppliers and customers

Hertzel et al. use the approach of Fee & Thomas (2004) by relying on various financial accounting standards, which require firms to disclose the identity of any customer representing more that 10% of the firm’s total sales.

Because the bankruptcy filing firms account for more than 10% of each supplier’s total sales, this procedure yields a set of suppliers that is reliant on the filing firm.

Hertzel et al. use the same procedure to form the sample of filing-firm customers, but these customers may not be reliant on the filing firms. Thus, they also examine a subset of reliant customers defined as those for which purchases from the filing firm scaled by total cost of goods sold is greater than 1%.

((2525))

• Sample

A preliminary sample of 1,695 bankruptcy filings between 1978 and 2004 is obtained from Bankruptcy DataSource Index.

The final sample includes a total of 250 filing firms that have at least one customer or one supplier.

For the full sample of 250 filing firms, a total of 311 customers and 275 suppliers are identified.

The analysis of supply-chain contagion effects is based on separate portfolios of customers and suppliers for each bankruptcy filing and for each pre-filing distress event.

The average numbers of customers and suppliers per bankruptcy are 2.0 and 2.2 .

((2626))

((2727))

Identifying pre-filing distress

Bankruptcy Filing DatePre-filing Distress date

Distress Period Filing Period

Compute abnormal return by using the

Market-adjusted return model (Brown and Warner(1985))

-5 +5 -5 +5

search the CRSP database over

the year prior to the Chapter 11

filing date and find the day on

which the filing firm has the most

negative dollar abnormal return.

Bankruptcy Filing DatePre-filing Distress date

Distress Period Filing Period

Compute abnormal return by using the

Market-adjusted return model (Brown and Warner(1985))

-5 +5 -5 +5

search the CRSP database over

the year prior to the Chapter 11

filing date and find the day on

which the filing firm has the most

negative dollar abnormal return.

The average filing-period abnormal percent return for bankrupt firms is 19%.

The average distress-day abnormal percent return (dollar abnormal return) for filing firms is 26% ($118M).

((2828))

((2929))

Hertzel et al. also use the Fama & French (1993) three-factor model and the Carhart (1997) four-factor model to examine post-filing returns to suppliers and customers, and find no evidence of investor underreaction.

((3030))

((3131))

Empirical predictions:

Cross-sectional determinants of the returns to suppliers & customers

Supplier & customer leverage:

To the extent that leverage reduces the flexibility of suppliers & suppliers to respond to the distress of the filing firm, contagion effects are expected to be larger for suppliers or customers with greater leverage.

Product specificity:

Suppliers that sell specialized products to the filing firm (or customers that purchase specialized products from the filing firm) are more likely to experience contagion because of the likelihood of greater contractual ties to the filing firm and higher costs of rerouting outputs (or inputs). Use R&D intensity as a proxy for the specialization of a firm’s product.

((3232))

Filing firm industry concentration:

Suppliers & customers of filing firms in concentrated industries are expected to have fewer switching alternatives for re-routing supply or demand, respectively, and thus are more likely to suffer greater contagion effects.

Outcome of the bankruptcy process:

The contagion effects to be greater when the filing firm does not successfully emerge from the bankruptcy process as an independent entity.

Empirical results:

None of multivariate regressions delivers statistically significant results as anticipated above.

This may be due to the lack of statistical significance of tests to small sample sizes, the considerable cross-sectional variation in supplier & customer returns, and the coarseness of the proxies used to capture the characteristics.