What’s in a name? What leads a firm to change its name and what the new name foreshadows

Journal of Banking & Finance 34 (2010) 1344–1359

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What’s in a name? What leads a firm to change its name and what the new name foreshadows YiLin Wu * National Taiwan University, Taipei, Taiwan, ROC

a r t i c l e

i n f o

Article history: Received 14 July 2009 Accepted 28 November 2009 Available online 4 December 2009 JEL classification: G30 G32 K22 Keywords: Name change Reputation Brand name capital Intangible assets Corporate strategy Propensity score matching Preplay communication Investor sentiment

a b s t r a c t This paper examines corporate name changes. This paper connects different types of corporate name changes to the reputational concerns that precede them and to the important corporate events and performance changes that follow them. The empirical results suggest that a firm adopts a radically different name to disassociate from a poor reputation, the name of one of its well-recognized brands to associate with a good reputation, or a minor change in its name by adding or deleting a part of its name that identifies it with a particular product to accompany a narrower business focus or a broader business focus. The empirical results also suggest that, except for radical name changes, significant organizational upheaval follows most corporate name changes. The strength of the firm’s subsequent economic performance is tied to changes in the business direction that the type of name change foreshadows. Ó 2009 Elsevier B.V. All rights reserved.

1. Introduction Corporate name changes are a common practice; since 1925, over 30% of Center for Research in Securities Prices (CRSP)-listed firms changed their names at some point after going public.1 While certainly the bulk of these name changes is directly related to concrete organizational events, like mergers or acquisitions, many times the name change occurs in the absence of a closely preceding corporate control or restructuring activity. These ‘pure’ name changes are the focus of this paper. This paper examines corporate name change decisions, particularly the reputational aspects of such changes. It also links the types of name changes to economic performance and important corporate events following the change. In Act II, Scene 2 of Shakespeare’s Romeo and Juliet, Juliet Capulet, the tragic heroine, delivers the famous lines that form the null hypothesis for this paper: ‘‘What’s in a name? That which I call a rose/By any other name would smell as sweet”. In other words,

in the context of this paper, the null hypothesis is that the name of a firm is a meaningless veneer that needs not convey the firm’s reputation or any information of importance to investors. Shakespeare in the very same scene motivates an alternative hypothesis when Juliet implores Romeo Montague with ‘‘O, be some other name”. The competing hypothesis is that corporate names do capture the main attributes of a firm and firms change their names to rebuild reputation (Tadelis, 1999) or to capitalize on their good reputation (Cabral, 2000, 2007; Choi, 1998). In a similar vein, it is equally likely that new names serve as a preplay communication with stakeholders to coordinate subsequent business strategies.2 A long-term study on a broad cross-section of name changes has not been performed perhaps because the investigator is obliged to undertake an arduous task to categorize the types of name changes.3 Here, I identify 1965 corporate name changes between 1980 and 2000. The reputation concerns of name changes and the communication role of the proposed business strategies motivate the classification of the

* Tel.: +886 2 2351 9641; fax: +886 2 2351 1826. E-mail address: [email protected] 1 During the 1925–2000 period, on the CRSP tapes, there were 13,559 firms with no name changes and 5920 firms with name changes. Of the 5920 firms that made 7687 name changes, 4566 firms (77.1%) made one change, 1030 firms (17.4%) made two changes, and 324 firms (5.5%) made more than two changes.

2 Bhagat et al. (1990) argue that stakeholders are those with significant firmspecific investments. 3 Cooper et al. (2001) examine 95 dot.com name additions during the dot.com bubble. Cooper et al. (2005) examine 67 deletions of dot.com names during the dot.com decline. Cooper et al. (2005) examine 296 equity mutual funds name changes over the period April 1994 to July 2001.

0378-4266/$ - see front matter Ó 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jbankfin.2009.11.029

Y. Wu / Journal of Banking & Finance 34 (2010) 1344–1359

types of name changes. In my sample of name changes, the most common type of name change (35.5%) is changing the name of the firm to that of a prominent brand, such as when Consolidated Foods became Sara Lee Corporation in 1985. Also common are minor name changes in which firms keep their identities but either shed a part of their names to broaden their perceived business focus (broader focus name changes (31.9%)) or add terms to their names to narrow their perceived business focus (narrower focus name changes (16.3%)). Examples of broader focus name changes and narrower focus name changes are, respectively, when Snap-On Tools became Snap On in 1994 and when Morrison Inc. became Morrison Restaurants Inc. in 1992. Less frequent are radical name changes (9.8%) in which the former and the new name bear no apparent semantic relation and the new name lacks association with the firm’s history, such as when Lincoln Bancorp became C U Bancorp in 1990.4 I expect that some major name changes occur because the firm wishes to change the perceptions of stakeholders after the firm’s reputation has deteriorated. Radical name changes typically fall into this category. I expect that other major name changes occur because the firm has a good reputation in a certain market and the firm seeks to leverage this good reputation across the firm as a whole. Adopting a brand name as the company name typically falls into this category. Empirical results are consistent with my hypothesis. Compared with radical name changes, brand name adoption is generally associated with a relatively good reputation, as reflected in better prior stock performance, better prior accounting performance, more neutral and favorable media coverage, and larger numbers of well-recognized brands. I expect that minor name changes occur as a preplay communication with stakeholders of coordinated restructuring of business portfolios. I expect firms changing their names to a narrower focus to make related asset acquisitions (including related acquisition of divisions and subsidiaries) and/or unrelated asset divestitures (including unrelated spin-offs and divestiture) to reflect the focusing (refocusing) of business strategies subsequent to the name changes. In contrast, I expect firms changing their names to a broader focus to make unrelated asset acquisitions and/or related asset divestitures to pursue a diversifying strategy. A number of key results emerge from the analysis in this paper. First, I document that the types of name changes have a bearing on short-term and long-term stock performance. Overall, I document that most name changes have positive and significant short-term announcement effects, while firms with radical name changes have negative stock responses over both short-term and longer horizons. Second, I document significant alterations in the firm’s future course of action following most corporate name changes, except for the radical type of name changes. Relative to firms with focus broadening name changes, those with focus-narrowing name changes seem to sell related assets less frequently but to sell unrelated assets more frequently following their name changes, suggesting that firms making narrower focus name changes are refocusing (or focusing) the scope of their businesses. Furthermore, a small-scale unrelated asset disposal program usually precedes the decision to do a focus-narrowing name change. Finally, I document that subsequent performance is better for firms with activities that are consistent with the message transmitted by new names than it is for other name-changing firms. For example, Tobin’s q and long-run excess returns are greater for firms with narrower focus name changes that refocus (or focus) their scope of business than for other narrower focus name-changing firms without refocusing activities. 4

Broader focus name changes and narrower focus name changes are not as drastic as radical name changes because they maintain the old identity while radical name changes do not involve tying the firm’s identity to a particular brand name product (or subsidiary) as brand adoption name changes do.

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The remainder of this paper is organized as follows. Section 2 develops empirical predictions on what leads a firm to change its name and what the new name foreshows. Section 3 describes the dataset. Sections 4 through 6 present the empirical results, and Section 7 concludes the paper. 2. Hypotheses on the name change decision and on the following changes 2.1. Changing names as investments in brand name capital Tadelis (1999) proposes that, under information asymmetry, a corporate name summarizes a firm’s reputation. If a firm’s name carries a reputation component, then I expect that there are two situations under which a firm changes its name. The first is when the firm wishes to shed its bad reputation, to disassociate itself from something with a bad reputation, or both. In the second situation, a firm has a good reputation in a certain market (product, subsidiary, or division), and wishes to capitalize on that good reputation across the firm. Two examples illustrate the first situation: Blinder International Enterprises Inc. changed its name to Intercontinental Enterprises Inc. in 1989. At the time, both the firm and its founder, Meyer Blinder, were facing a series of federal and state fraud investigations. The fear that the public opinion of the company is connected to that of its controversial founder, Meyer Blinder, may have contributed to the name change. In the second example, because it may have seemed difficult for depositors to discern differences between the healthy Lincoln Bancorp and the failed Lincoln Savings and Loan Association headed by Charles Keating who was mired in scandal, Lincoln Bancorp, run by John Keating, changed its name to CU Bancorp in 1990. The next example illustrates the second situation of capitalizing on a good reputation: In 2000, Etinuum Inc. chose Digital Lighthouse Corp as its new name and stated that the new name came from its nationally recognized brand and that the new name would allow for cross-marketing of services. I track name changes that bear no semantic link to the firm’s prior name and that lack association with the firm’s history and call them radical name changes. The disassociation with a poor reputation paradigm builds on Tadelis’s (1999) history-dependent reputation model of a firm’s name. Specifically, under information asymmetry and assuming that stakeholders use the track record of the company’s name and of a mistaken identity to form beliefs about the reputation of the company, a firm may adopt a new name that is unrelated to its prior name when failures occur. I also track name changes when the firm adopts one of its wellrecognized brand name products, subsidiaries, or divisions as the company name and call these brand adoption name changes. The association with a brand name reputation paradigm draws parallels between the explanation of using an established brand name in introducing new products (Cabral, 2000, 2007; Choi, 1998) and the explanation of adopting an established brand name as the corporate name. In the minds of stakeholders who believe that the brand name matters, the prospect of the firm adopting the brand name is good as long as the performance history of the brand name is good. This type of belief concerning brand adoption name changes allows firms with optimistic profitability prospects to indicate their expected profitability prospects. The loss of brand capital may prevent firms with questionable prospects to adopt brand names (Klein and Leffler, 1981).5 5 Note that the implicit costs of the brand adoption type of name change could be tremendous. This is because after brand adoption, the brand name cannot be insulated from information revealed about the rest of the firm. Firms could lose brand capital by associating the firms’ brand names with poor performance instead of with profitable businesses, which is strong punishment for brand adoption.

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2.2. Changing names as a preplay communication of coordinated restructuring of business activities Cooper et al. (2001, 2005), and Cooper et al. (2005) argue for the cosmetic effect of name changes in the sense that corporate name changes do not align with future business directions. In contrast, the costly preplay communication paradigm suggests that the type of new name credibly communicates information about proposed business strategies.6 The importance of meaningful name changes prior to a proposed refocusing (diversifying) strategy is to improve on coordination of efforts (Cooper et al., 1992). The incentives to resolve coordination problems by means of effective preplay communication may stem from the dependence on stakeholders for resources critical to the success of corporate strategies. As the interests of the parties become more closely aligned, the new name can reveal finer and finer information in equilibrium and the extent of the separating equilibrium increases, in the sense of Dickhaut et al. (1995). I track minor name changes that add terms to identify a particular product (or business) to the firm’s old identity and call these narrower focus name changes. As an example, Vaughns Inc. lengthened its name to Vaughn Communications Inc. in 1987. Its thenchairman, David Willette, said that the new name better described the strategic thrust of the company into communications products (PR Newswire; July 1, 1987). Accordingly, the costly preplay communication argument implies that the narrower focus type of name change occurs as part of a coordinated plan to refocus business activities when the firm has a good match with the proposed business direction. Specifically, the firm tends to make related asset acquisitions and/or unrelated asset divestitures subsequent to a narrower focus name change. Note that the concept of relatedness and unrelatedness is relative to the proposed business direction. I also track the opposite type of minor name changes that deliberately shed a part of the company’s old name that identifies the firm with a particular product (or business) and call these broader focus name changes. For example, Candela Laser Corporation shortened its name to Candela Corporation in December, 1995. The company mentioned that the new name better represented Candela’s expansion mission (Business Wire; January 4, 1996). The costly preplay communication argument implies that a broader focus name change occurs when the growth opportunities of a firm’s existing business are limited and it is searching for better matches by pursuing a diversifying strategy. There is conflicting evidence that diversification destroys firm value (Dos Santos et al., 2008). Therefore, I empirically examine the economic performance of narrower focus and broader focus name changes.

and SEC filings (Proxy Statements, Annual Reports to Shareholders, Forms 8-K, 10-Q, and 10-K). I assign the announcement date of the name change as the earlier of the press date or the SEC filing date. From the reported reasons for name changes, I identify pure name changes that are not confounded by contemporaneous corporate control or restructuring events. Table 1 details the sample creation process. From the initial sample of 5685 name changes occurring between 1980 and 2000, I exclude 814 that arise due to mergers, acquisitions, and tender offers, 1256 that coincide with a corporate restructuring event, and 1650 that, in my judgment, include 1041 name changes for which no reason could be found as well as 609 name changes for which some other confounding event was present.7 This screening process yields a final sample of 1965 name change events, corresponding to 697 brand adoption name changes, 627 broader focus name changes, 321 narrower focus name changes, and 193 radical name changes. The remaining 127 name changes cannot easily be grouped into any of the above four categories, I call these miscellaneous name changes, for lack of a better term. To categorize the brand adoption name changes, I obtain information on the brand names from the Lexis–Nexis database (Directory of Corporate Affiliations file and the business description item in the S&P Corporate Descriptions file), the products item of the Hoover Profiles database, and brand data from the Global Market Information database. 3.2. Construction of key analysis variables For each firm, I use nine variables to capture its reputation linked to historical performance, publicity, and equity investors’ perceptions of the firm. In addition to the reputation measures, I also control for intangibles, physical capital, information asymmetry, industry competitiveness, and the leverage ratio. Appendix A provides details of the construction of key analysis variables.

To construct a large sample of publicly traded firms that experience name changes after going public, I first use the CRSP name change record date to identify name-changing firms during the 1980–2000 period. I then obtain name change announcement dates and reasons for name changes from company news files in the Lexis–Nexis database, the Dow Jones Interactive database,

 Reputation linked to performance: I use past stock-returns (market-adj returns), accounting measures of performance (operating margin, ROA, and sales growth), and Tobin’s q to capture a firm’s reputation linked to historical performance.  Reputation linked to publicity: Public opinion can be a powerful generator of reputation (Zingales, 2000; Tetlock, 2007). To measure the reputation linked to publicity, it is critical for my purposes to distinguish bad media coverage (Bad Coverage) from neutral and positive media coverage (Other Coverage).  Reputation linked to equity market perception: Since a ticker symbol could be the manifestation of a publicly traded firm’s identity in the stock market, I expect the ticker change decision (Ticker) to coincide with the name change decision. I also expect that the incentive to avoid ticker symbol confusion (Ticker3) motivate name changes.8  Intangible capital and physical capital: I measure intangibles captured in the accounting system (R&D and ADV) and in well-recognized brands.  Controls: Information asymmetry, industry competitiveness, and leverage: The role of reputation is linked inextricably to the information asymmetry that typically exists between firms and stakeholders. I measure asymmetric information using the firm’s Age Since IPO and trading activity, particularly the market-adjusted relative effective spread and the market-adjusted

6 Corporate name changes are likely to be quite expensive as the direct costs of name changes can range in the tens of millions of dollars as they did during the 1979– 1987 period (see Karpoff and Rankine (1994) and the references therein), not to mention the implicit costs of name changes. For example, International Harvestor spent upwards of $13 million on simply reprinting signs and stationery when it changed its name to Navistar. Esso spent a reported $200 million to become Exxon. More recent examples are no less extreme: Andersen Consulting spent a reported $100 million to change its name to Accenture.

7 These other confounding events include parent-subsidiary mergers, changes in organizational structures (e.g., changes to holding companies), re-incorporation, stock splits or reverse stock splits, changes in stock exchanges, or changes in legal status. For example, Harvard Securities Group PLC became Harvard Group PLC in June 1988 because the company changed to a holding company structure. 8 Rashes (2001) documents the co-movement of stocks for firms having little in common besides similar stock symbols and suggests that ticker confusion could affect stock prices significantly.

3. Data 3.1. Data on pure corporate name changes

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Table 1 Corporate name change classification. This table provides the details of the name change classification. I obtain reasons for name changes from company news files in the Lexis– Nexis database, the Dow Jones Interactive database, and SEC filings (Proxy Statements, Annual Reports to Shareholders, Forms 8-K, 10-Q, and 10-K). ‘Others’ includes 1041 name changes for which no reason could be found as well as 609 name changes for which some other confounding event was present. To categorize the brand adoption type, I obtain information on the brand names from the Lexis–Nexis database (Directory of Corporate Affiliations file and the business description item in the S&P Corporate Descriptions file), the products item of the Hoover Profiles database, and brand data from the Global Market Information database. Type

Number

Comments

Total Merger Restructure Others No confounding event present of which change is attributable to Brand adoption

5685 814 1256 1650 1965

Includes all CRSP-listed firms with change in CRSP NUMNAM field Omitted Omitted Omitted Comprises our total sample

697

Focus

627

Narrower focus

321

Radical change Miscellaneous

193 127

Company takes on name of major brand of product, service, divisions, or subsidiaries, e.g. Consolidated Foods changes to Sara Lee in 1985 Company deletes part of name associated with particular line of business, e.g. Snap-On Tools changes to Snap On Inc. Company adds terms to narrow perceived focus of operation, e.g. Birtcher Corp. changes to Birtcher Medical Systems, Inc. New name bears no semantic link to the former name, e.g. Western Union changes to New Valley in 1991 Includes name changes that cannot be grouped into any of the four categories above, typically because only part of name changes. For example, Acapulco y Los Arcos Restaurants changed its name to Acapulco Restaurants in March 1984; MLF Bancorp changed to ML Bancorp in August 1996; First Republic Bancorp Inc. changed its name to Republic First Bancorp Inc. in July 1997; I M H Commercial Holdings Inc. changed its name to Impac Commercial Holdings Inc. in December 1997

share turnover.9 A wider market-adjusted bid-ask spread, or a lower market-adjusted share turnover coincide with more asymmetric information (Rakowski and Wang, 2008). As industry competition increases, as measured by the Herfindale–Hirschman index, it becomes less likely that a firm will maintain a persistent brand reputation and profitability. I therefore expect that brand adoption name changes tend to occur in less competitive industries and that, compared with broader focus name changes, narrower focus name changes tend to occur in less competitive industries. 4. The determinants of the name change decision and the types of new names 4.1. Univariate analysis Given the work of Karpoff et al. (1996), I compare the namechanging firms with three-digit industry/size matched firms that do not experience name changes, where size is the prior year’s book value of assets. I also compare the name-changing firms with three-digit industry/price-matched non-name-changing firms, where price is measured during a two-week window around the name change announcement date. Finally, I compare brand adoption with radical name change firms and broader focus with narrower focus firms. For brevity, Table 2 reports only the comparisons between the full sample firms and their corresponding industry/size matching group, the comparisons between brand adoption and radical name change firms, and the comparisons between broader focus and narrower focus firms. To mitigate prob9 Measures of the random spread have been documented to be cross-sectionally related to expected returns (Amihud and Mendelson, 1986). Extending this argument, Chordia et al. (2000) argue that measures of the random spread co-vary across stocks, implying that a stock’s sensitivity to systematic spread randomness could potentially play the role of a priced risk factor. Since the bid-ask spread is determined by either inventory risks or by asymmetric information, Chordia et al. (2000) examine whether the spread co-movement is induced by inventory risk, asymmetric information, or both. They provide some suggestive evidence that both inventory risks and asymmetric information are sources of commonality in spreads. Since the individual firm spread co-moves with the market-wide spread, I measure the risk-adjusted spread using the market-adjusted (excess) spread. Not surprisingly, Hasbrouck and Seppi (2001) document commonality in share turnover. Since individual firm share turnover co-moves with the market-wide share turnover, I measure the risk-adjusted share turnover using market-adjusted (excess) share turnover.

lems with outliers, I winsorize the continuous variables at the smallest 2.5% and the largest 2.5% of the values. Columns 1 and 2 of Table 2 show that, compared with the industry/size matching group, notwithstanding better market-adjusted stock returns, the overall sample is associated with poor prior operating margins, poor ROA, and bad media coverage. More than half (55%) of the name-changing firms simultaneously change their ticker symbols. Around one-third (28%) of the name-changing firms simultaneously change ticker symbols that look similar to other ticker symbols. Furthermore, the name-changing firms are generally associated with smaller numbers of brands, smaller advertising/asset ratios, and smaller physical assets. Finally, notwithstanding a longer time since going public, name-changing firms have pervasive information problems as they are associated with larger market-adjusted relative effective spreads and smaller share turnover.10 In sum, the univariate analysis shows that poor reputation measures characterize name-changing firms. Columns 3 and 4 show comparisons between brand adoption and radical name-changing firms. The results appear broadly consistent with my conjecture that firms with brand capital tend to choose brand adoption name changes while firms with poor reputations tend to adopt radical name changes. Based on the Herfindale–Hirschman index, there is negligible evidence that brand adoption name changes tend to occur in less competitive industries. Columns 5 and 6 show comparisons between broader focus and narrower focus name-changing firms. On the one hand, broader focus name-changing firms have slower prior sales growth and a smaller Tobin’s q. On the other hand, broader focus name-changing firms are generally larger, older, better known, and have better prior stock and accounting performance. Based on the Herfindale–Hirschman index, there is no evidence that narrower focus name changes tend to occur in less competitive industries. 4.2. Logit regression analysis of the name change decision Table 3 examines the binary choice between changing a corporate name and otherwise keeping an old identity and reports six 10 As robustness checks, I measure the market-adjusted proportional quoted spread (ask price minus bid price and then divided by the quote midpoint) and the industryadjusted relative effective spread and industry-adjusted proportional quoted spread. The results are similar to those presented here.

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Table 2 Summary statistics. This table reports the 95% Winsorized mean values of continuous variables and counts for the discrete variables and (median) values. Grand total Sample vs.

Major name changes

Minor name changes

Match

Brand adoption vs.

Radical change

Broader focus vs.

Narrower focus

Panel A: Reputation linked to performance Market-adj returns (%) 3.70*** (34.42)*** Operating margins 0.50*** (0.04)*** ROA 0.10*** (0.03)*** Sales growth 0.23 (0.13) Tobin’s q 1.66 (0.93)

34.42 (42.96) 0.31 (0.01) 0.05*** (0.00)*** 0.25 (0.14) 1.76 (0.96)

1.69*** (36.53)*** 0.27*** (0.04) 0.09* (0.03)** 0.24** (0.15)** 1.68 (1.05)***

25.59 (47.14) 0.67 (0.05) 0.14 (0.04) 0.19 (0.08) 1.75 (0.70)

2.03 (29.35) 0.32*** (0.02)*** 0.07*** (0.01)*** 0.21* (0.12)* 1.63** (0.93)**

4.81 (37.08) 0.85 (0.05) 0.15 (0.06) 0.26 (0.17) 1.91 (1.12)

Panel B: Reputation linked to publicity Bad Coverage 3.44** (2.00)* Other Coverage 27.09* (18.00)***

2.11 (1.00) 29.05 (25.00)

3.82 (2.00)*** 29.70*** (22.00)***

3.88 (4.00) 25.00 (16.00)

3.31* (2.00) 27.0 (19.00)

2.82 (1.00) 24.5 (19.00)

Panel C: Reputation linked to equity market’s perception Count (%) of ticker 55.42*** 1.17 0.61 Count (%) of ticker3 28.14***

63.41*** 33.29

79.27 34.72

42.90*** 22.17***

56.39 31.46

Panel D: Intangible capital and physical capital Number of Brands 1.13*** (0.00)** R&D 0.073 (0.022) ADV 0.028** (0.010) Physical capital 2.20*** (2.00)***

1.24*** (0.00)** 0.075 (0.030)* 0.033 (0.014)* 2.26** (1.97)

0.65 (0.00) 0.075 (0.004) 0.028 (0.009) 1.87 (1.78)

1.34** (0.00)* 0.068 (0.018) 0.023 (0.009) 2.41*** (2.28)***

0.93 (0.00) 0.086 (0.026) 0.028 (0.008) 1.49 (1.26)

8.31 (5.00) 0.025 (0.011) 0.026 (0.003) 0.21 (0.15) 0.31 (0.25)

9.55*** (6.00)*** 0.018** (0.008)** 0.032 (0.009) 0.20 (0.15) 0.25* (0.21)

7.81 (5.00) 0.025 (0.011) 0.027 (0.006) 0.20 (0.15) 0.28 (0.22)

2.67 (0.00) 0.075 (0.024) 0.033 (0.012) 2.43 (2.22)

Panel E: Controls: information asymmetry, industry competitiveness, and leverage 8.47 9.75** Age Since IPO 9.27*** (6.00)*** (6.00)* (5.00) 0.016 0.014*** Bid-ask spread 0.018** (0.007)* (0.004)*** (0.006) 0.035 0.038* Share turnover 0.031 (0.013)** (0.009) (0.006)** Herfindahl 0.20 0.19 0.18* (0.14) (0.14) (0.14) Leverage 0.27 0.26 0.26* (0.22)* (0.22) (0.22) *

Indicate that the Winsorized mean (median) and counts between: (1) the full sample firms and their corresponding industry/size matching group, (2) brand adoption and radical change, and (3) broader focus and narrower focus differ significantly at the 10% level using two-tailed t-test (Kruskal–Wallis test) and a chi-squared independence test. ** Indicate that the Winsorized mean (median) and counts between: (1) the full sample firms and their corresponding industry/size matching group, (2) brand adoption and radical change, and (3) broader focus and narrower focus differ significantly at the 5% level using two-tailed t-test (Kruskal–Wallis test) and a chi-squared independence test. *** Indicate that the Winsorized mean (median) and counts between: (1) the full sample firms and their corresponding industry/size matching group, (2) brand adoption and radical change, and (3) broader focus and narrower focus differ significantly at the 1% level using two-tailed t-test (Kruskal–Wallis test) and a chi-squared independence test.

cross-sectional multivariate logit regressions. In these models, the dummy dependent variable equals one for a name change firm and zero for an industry/size matched firm. All continuous explanatory variables are Winsorized at 2.5% and 97.5% to mitigate problems with outliers. To allow for a possible non-linear effect, I employ natural logarithmic transformations of one plus Bad Coverage, Other Coverage, Number of Brands, and Age Since IPO. Model (1) estimates the impact of firm reputation linked to performance. Model (2) replaces past performance with media coverage. Model (3) estimates both the media coverage effect and the performance effect. Model (4) adds a measure of the equity market’s perception (Ticker) to model (3). Model (5) replaces Ticker with Ticker3 to examine whether the incentive to avoid ticker symbol confusion (Ticker3) motivate name changes. To check if my results are sensitive to the dot.com clustering effect (Cooper et al., 2001, 2005), model (6) excludes firms that add (or delete) dot.com names. Each model controls for intangible capital, physical capital, information asymmetry, industry competitiveness, and leverage. The microstructure literature suggests that the share turnover is highly correlated with the bid-ask spread (Chordia et al., 2001). Since the

spread measure from the TAQ database is unavailable before 1993, to increase the sample size, each regression measures information asymmetry using share turnover instead of bid-ask spread. In addition, each regression includes the year dummies and a dummy that equals one for firms with multiple name changes in the sample. Remarkably, in all the six regressions, the measures of reputation have the expected sign. Model (1) shows that as past ROA and Sales Growth deteriorate, the probability of a name change increases. These findings are broadly parallel to those in Cooper et al. (2005) that mutual fund name changes are more likely when past benchmark-adjusted performance has been poor. Model (2) shows that bad media coverage raises the probability of a name change, whereas other media coverage lowers it. This finding complements the observation of the importance of the media to stock prices (Tetlock, 2007). Model (3) includes both historical performance and public opinion. The magnitude of the coefficients on ln(1 + Bad Coverage) and ln(1 + Other Coverage) is comparable to that of model (2). Wald tests show that, compared with the five measures of historical performance, the

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Table 3 Multivariate logit models on the name change decision. This table presents estimates of six logit regressions of a name change dummy on the explanatory variables. Regressions (1)–(5) estimate the name change decisions for the full sample. Regression (6) excludes firms that add (or delete) dot.com names. All continuous explanatory variables are Winsorized at 2.5% and 97.5% to mitigate problems with outliers. P-values (in parentheses) are based on White-corrected standard errors. Coefficients with P-values of .10 or lower are highlighted in bold face type. In each regression, the P-value for the significance of the regression equation is less than 0.000. (1)

(2)

(3)

(4)

(5)

(6)

Logit model: dependent variable = 1 for name change and 0 otherwise Intercept Panel A: Reputation linked to performance Market-adj returns (%) Operating margins ROA Sales growth

Tobin’s q

0.632 (0.000)

0.727 (0.000)

1.639 (0.000)

1.128 (0.000)

1.704 (0.000)

0.117 (0.000) 0.000 (0.699) 1.517 (0.000) 0.149 (0.088)

0.116 (0.000) 0.000 (0.794) 1.513 (0.000) 0.145 (0.100)

0.135 (0.000) 0.000 (0.890) 1.181 (0.000) 0.245 (0.094)

0.123 (0.000) 0.000 (0.794) 1.478 (0.000) 0.089 (0.093)

0.135 (0.000) 0.000 (0.831) 1.053 (0.001) 0.246 (0.037)

0.012 (0.322)

0.014 (0.325)

0.001 (0.692)

0.012 (0.559)

0.004 (0.844)

0.012 (0.079) 0.038 (0.092)

0.075 (0.086) 0.027 (0.097)

0.018 (0.074) 0.008 (0.085)

0.072 (0.093) 0.029 (0.064)

Panel B: Reputation linked to publicity ln(1 + Bad Coverage)

0.675 (0.000)

0.004 (0.034) 0.066 (0.074)

ln(1 + Other Coverage) Panel C: Reputation linked to equity market’s perception Ticker

5.063 (0.000)

Ticker3 Panel D: Intangible capital and physical capital ln(1 + Number of Brands)

5.065 (0.000) 4.368 (0.000)

0.133 (0.002) 0.077 (0.000)

0.121 (0.007) 0.028 (0.091)

0.096 (0.068) 0.023 (0.348)

0.118 (0.016) 0.001 (0.670)

0.105 (0.062) 0.024 (0.348)

Panel E: Controls: Information asymmetry, industry competitiveness, and leverage ln(1 + Age Since IPO) 0.261 0.257 (0.000) (0.000) Share turnover 0.372 0.903 (0.239) (0.052) Herfindahl 0.409 0.393 (0.154) (0.142) Leverage 0.242 0.321 (0.149) (0.131) Dummy for multiple name changes in sample Yes Yes Year fixed effect Yes Yes Correct prediction rate (%) (CPR) 57.75 56.01 0.028 0.013 Pseudo R2 Sample size 2618 2834

0.266 (0.000) 0.305 (0.243) 0.426 (0.138) 0.253 (0.133) Yes Yes 58.78 0.030 2617

0.257 (0.000) 0.438 (0.485) 0.792 (0.031) 0.079 (0.711) Yes Yes 77.65 0.376 2617

0.275 (0.000) 0.317 (0.551) 0.742 (0.017) 0.294 (0.102) Yes Yes 66.32 0.356 2617

0.279 (0.000) 0.027 (0.967) 0.817 (0.027) 0.060 (0.778) Yes Yes 77.96 0.357 2573

Physical capital

0.117 (0.008) 0.023 (0.094)

two measures of public opinion have a larger marginal impact on the name change decision. Model (4) shows a positive and significant coefficient on the Ticker dummy, suggesting that the name change decision tends to coincide with the ticker change decision. Model (5) shows a positive and significant coefficient on Ticker3, suggesting that the name change decision tends to coincide with the ticker change decision in the presence of analogous ticker symbols. Note that the impacts of past performance and media coverage remain significant. Model (6) excludes firms that add or delete dot.com names and discern few, if any, identifiable differences. This suggests that the dot.com effect is not the key factor affecting name change decisions during our sample period. The signs of the control variables are plausibly estimated. Compared with the matching firms, the name-changing firms tend to have a smaller number of brands, smaller physical capital, a longer time since going public, and greater information asymmetry measured by share turnover (although insignificant in some specifications). As checks on the results obtained, I perform several

robustness tests. The results are qualitatively very similar to those in Table 3.11 4.3. Multinomial logit (MNL) analysis on the choices of types of new names There seems to be no clear ordering in choosing the types of new names, for example, between brand adoption and focus

11 I match name-changing firms by three-digit SIC codes and prices (Cooper et al., 2001), I measure the explanatory variables as two-year averages (or one-year) before the name change, and I replace the market-adjusted share turnover with the industry-adjusted share turnover to consider the industry component commonality in share turnover (Chordia et al., 2000; Bailey et al., 2009). The results are qualitatively very similar to those in Table 3. To test if the impact of media reporting may be contingent upon the level of information asymmetry, I add interaction terms between the share turnover variable and the two measures of media coverage. The unreported result shows modest evidence that the name change decision is more likely to be affected by media reporting when there is more asymmetric information in the capital market.

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Table 4 Multinomial logit (MNL) analysis for the choices of new names. This table presents regression coefficients and their P-values from the six MNL models. The five categories of the dependent variables are: radical change = 0, brand adoption change = 1, focus narrowing change = 2, focus broadening change = 3 and miscellaneous change = 4. Models (1)–(3) report the comparison of brand adoption to radical name changes (reference category). Models (4) and (5) report the comparison of broader focus to narrower focus name changes (reference category). Regression (5) excludes firms that add (or delete) dot.com names. Model (6) reports the comparison of brand adoption to narrower focus name changes (reference category). The regression results on the alternative comparisons are omitted for brevity. All continuous explanatory variables are Winsorized at 2.5% and 97.5% to mitigate problems with outliers. P-values (in parentheses) are based on White-corrected standard errors. Coefficients with P-values of .10 or lower are highlighted in bold face type. In each regression, the P-value for the significance of the regression equation is less than 0.000. (1) Brand adoption vs. radical change Intercept

1.419 (0.000) Panel A: Reputation linked to performance Market-adj 0.088 returns (%) (0.061) Operating 0.017 margins (0.051) ROA 0.599 (0.075) Sales growth 0.373 (0.079) Tobin’s q 0.022 (0.590)

(2) Brand adoption vs. radical change

(3) Brand adoption vs. radical change

(4) Broader focus vs. narrower focus

(5) Broader focus vs. narrower focus

(6) Brand adoption vs. narrower focus

2.007 (0.000)

1.757 (0.001)

0.452 (0.236)

0.513 (0.191)

0.087 (0.816)

0.067 (0.082) 0.019 (0.045) 0.616 (0.072) 0.340 (0.098) 0.025 (0.534)

0.066 (0.089) 0.018 (0.063) 0.612 (0.084) 0.333 (0.108) 0.030 (0.451)

0.084 (0.055) 0.001 (0.098) 0.714 (0.071) 0.090 (0.565) 0.025 (0.051)

0.075 (0.090) 0.001 (0.095) 0.666 (0.165) 0.095 (0.553) 0.034 (0.033)

0.043 (0.283) 0.001 (0.054) 0.441 (0.083) 0.120 (0.441) 0.014 (0.066)

0.040 (0.748) 0.114 (0.083)

0.036 (0.719) 0.053 (0.535)

0.031 (0.762) 0.072 (0.414)

0.055 (0.157) 0.019 (0.082)

0.769 (0.004)

0.511 (0.003)

0.467 (0.008)

0.422 (0.013)

0.157 (0.104) 0.030 (0.094)

0.098 (0.062) 0.082 (0.057)

0.110 (0.038) 0.075 (0.091)

0.042 (0.106) 0.088 (0.038)

0.124 (0.033) 0.288 (0.801) 0.386 (0.504) 0.499 (0.120) Yes 1773.0712 0.050 1338

0.107 (0.086) 0.861 (0.508) 0.227 (0.698) 0.555 (0.091) Yes 1714.496 0.050 1294

0.125 (0.029) 1.622 (0.072) 0.020 (0.972) 0.383 (0.204) Yes 1773.0712 0.050 1338

Panel B: Reputation linked to publicity ln(1 + Bad Coverage) ln(1 + Other Coverage) Panel C: Reputation linked to equity market’s perception Ticker 0.759 (0.005) Ticker3 0.304 (0.162) Panel D: Intangible ln(1 + Number of Brands) Physical capital

capital and physical capital 0.172 0.162 (0.059) (0.092) 0.050 0.041 (0.054) (0.079)

Panel E: Controls for information asymmetry, ln(1 + Age Since 0.073 IPO) (0.086) Share turnover 0.286 (0.081) Herfindahl 0.330 (0.643) Leverage 0.537 (0.160) Year fixed effect Yes Log-likelihood 1808.0617 0.031 Pseudo R2 Sample size 1338

industry competitiveness, 0.084 (0.058) 0.216 (0.086) 0.379 (0.585) 0.533 (0.170) Yes 1781.8122 0.045 1338

and leverage 0.080 (0.060) 0.071 (0.095) 0.334 (0.633) 0.488 (0.213) Yes 1773.0712 0.050 1338

broadening name changes. Table 4 therefore employs multinomial logit models to analyze the fivefold choice of new names. I define the five-way MNL model as follows: radical change = 0, brand adoption change = 1, focus narrowing change = 2, focus broadening change = 3, and miscellaneous change = 4. Compared with the bivariate logit model, the MNL model uses the universe of data rather than subsets for the pair-wise estimation, so the estimates are more efficient.12 I am interested in the relative probabilities of types of new name choices and could estimate various relative probabilities of selecting new name type i relative to new name type j, depending on the category of the reference (that is, the one to which

12 A model specification issue arises on whether a five-way multinomial logit model is justified or whether a more parsimonious specification is adequate. I test for several possible groupings of choices and find that these pooled models are rejected in favor of the full model. In addition, I perform nested logit model analysis. The process of choosing a new corporate name can be addressed by using a two-level nested sequence of the logit model consisting of (1) the choice between whether or not to change the corporate name; (2) the choice among types of new names.

all the other choices are compared). I choose either the radical type of name change or the focus narrowing change as the reference dependent variable. Since I am mainly interested in the odds ratio between two major name changes (brand adoption versus radical change) and between two minor name changes (focus broadening change versus focus narrowing change), models (1)–(3) estimate the probabilities of selecting brand adoption relative to radical name changes, and models (4) and (5) estimate the probabilities of selecting broader focus relative to narrower focus name changes. Model (5) excludes broader focus name changes that delete dot.com from the company name and exclude narrower focus name changes that add dot.com to the company name. To account for the possibility that a firm may adopt a brand name to reflect its refocusing to brand name products, subsidiaries, or divisions, model (6) reports the comparison of brand adoption and narrower focus name changes (reference category). Our models in Table 4 include the explanatory variables in Table 3. The regression coefficients of the measures of reputation in models (1)–(3) suggest that the propensity to choose the brand

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Table 5 Abnormal returns surrounding the name change decision. This table presents abnormal returns associated with the name change decision. The group labeled ‘market model’ is the excess returns from a market model regression estimated over the (260, 11) day window prior to the name change. Peer-matched is the difference in buy-and-hold returns between a name change firm and an industry/price-matched peer firm. The group labeled ‘propensity score matched’ is the difference in buy-and-hold returns between the name change firm and a matched firm generated from the nearest propensity score from the logit regression (4) of Table 3. P-values (in parentheses) are for t-test that the mean abnormal returns are equal to zero. Mean abnormal returns with P-values of .10 or lower are highlighted in bold face type. Mean excess returns (%)

Grand total

(P-value)

Misc. reason

Broader focus vs.

Narrower focus

2.279*** (0.000) 2.721** (0.000) 7.145* (0.006) 14.008** (0.002) 24.729* (0.001) 688

0.007 (0.992) 0.747 (0.350) 20.778 (0.006) 39.530 (0.004) 50.912 (0.015) 188

0.895** (0.014) 1.213* (0.008) 5.070 (0.049) 11.498 (0.016) 21.702 (0.006) 621

2.257 (0.000) 2.770 (0.000) 7.787 (0.048) 17.250 (0.010) 31.416 (0.008) 320

0.211 (0.716) 0.037 (0.964) 10.348 (0.138) 20.808 (0.117) 43.979 (0.106) 115

Panel B: Industry/price peer-matched CAR_IP days (1,0) 0.888 (0.108) CAR_IP days (3,0) 1.699 (0.000) CAR_IP months (0,6) 4.020 (0.061) CAR_IP months (0,12) 5.969 (0.068) CAR_IP months (0,24) 0.613 (0.882) Sample size 1932

2.754** (0.004) 2.418 (0.000) 3.265* (0.319) 9.031* (0.174) 2.904 (0.699) 688

1.683 (0.371) 0.312 (0.801) 9.420 (0.130) 7.092 (0.348) 14.141 (0.079) 188

0.763 (0.426) 0.901* (0.138) 6.109 (0.130) 4.214 (0.409) 2.991 (0.711) 621

1.446 (0.264) 2.913 (0.001) 11.060 (0.067) 11.120 (0.136) 15.035 (0.093) 320

1.052 (0.590) 0.512 (0.655) 1.438 (0.780) 4.100 (0.692) 7.227 (0.518) 115

Panel C: Propensity score matched CAR_PS days (1,0) 2.295 (0.003) CAR_PS days (3,0) 2.075 (0.001) CAR_PS months (0,6) 7.749 (0.031) CAR_PS months (0,12) 6.380 (0.198) CAR_PS months (0,24) 10.221 (0.196) Sample size 765

4.409* (0.000) 3.570* (0.000) 8.560 (0.132) 15.696 (0.212) 5.681 (0.636) 288

0.614 (0.798) 0.056 (0.973) 3.248 (0.604) 7.832 (0.479) 4.872 (0.740) 70

0.904 (0.541) 0.907 (0.421) 1.828 (0.771) 1.070 (0.875) 33.050* (0.057) 234

3.654 (0.091) 2.975 (0.053) 20.554 (0.090) 11.871 (0.369) 8.962 (0.610) 127

3.040 (0.287) 0.695 (0.798) 4.181 (0.622) 9.516 (0.475) 20.568 (0.203) 46

CAR_MM days (3,0) CAR_MM months (0,6) CAR_MM months (0,12) CAR_MM months (0,24) Sample size

**

Minor name changes Radical change

Panel A: Market model CAR_MM days (1,0)

*

Major name changes Brand adoption vs.

1.451 (0.000) 1.878 (0.000) 8.110 (0.000) 16.638 (0.000) 28.632 (0.000) 1932

(1) Brand adoption and radical change and (2) broader focus and narrower focus at the 10% level. (1) Brand adoption and radical change and (2) broader focus and narrower focus at the 5% level. (1) Brand adoption and radical change and (2) broader focus and narrower focus at the 1% level.

***

adoption type of name change relative to the radical type increases as past stock returns, accounting performance, neutral and good media coverage, and the number of reported brands increase. The significant negative coefficient on Ticker suggests that, relative to radical name changes, brand adoption name changes tend to leave their ticker symbols unchanged. The unreported estimated elasticity associated with Ticker is larger relative to the elasticities of the other reputation variables. It is difficult to determine which type of name change has a benign reputation, based on the regression coefficients of the measures of reputation in models (4) and (5). On the one hand, relative to narrower focus name changes, broader focus name changes are associated with higher past market-adjusted stock returns, higher past operating margins, greater ROA, and a greater number of brands. On the other hand, broader focus name changes are associated with fewer growth opportunities to the extent that Tobin’s q measures investment opportunity. The estimated elasticity associated with Tobin’s q in models (4) and (5) suggests that a 1% increase in a firm’s Tobin’s q results in a 0.023–0.030% decrease in the probability of a broader focus name change. The negative coefficient on Tobin’s q in model (6) suggests brand adoption name changes may have fewer growth opportunities relative to narrower focus name changes. As for the other parameters, in general, firms adopting brand names tend to be

associated with better past performance, good media coverage, larger size, and being better known relative to narrower focus name changes.

5. The stock market’s reaction to the name change announcements I now examine the market’s reaction to corporate name change announcements. I first use the market model to calculate abnormal returns and estimate its parameters using returns from t = 260 through t = 11, with date 0 being the announcement of the name change and the CRSP equally weighted return index as the market returns. I also generate two sets of excess returns based on a comparison between a name change firm and an appropriately chosen benchmark. Following Cooper et al. (2001), I calculate the difference in buy-and-hold returns between a name-changing firm and an industry/price-matched peer firm. These are labeled ‘peermatched’ in Table 5.13 Imbens (2000) suggests that matching on 13 We should be cautious that the peer-matched returns might not be meaningful, since the name-changing firms may change their industry concentration. For example, the firm adopting a broader focus name change may seek to enter new product sectors or new industries.

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the propensity score removes all bias associated with differences in the pre-name change variables. To control for the endogeneity of the name change decision, I match each name-changing firm with a firm that does not undergo a name change and has the nearest or exact probability of a name change (propensity score). These are labeled ‘propensity score matched’ in Table 5. I use the regression in column 4 of Table 3 for propensity score matching. Table 5 shows that name-changing firms, overall, enjoy significantly positive announcement period (announcement windows day 1 to day 0 and day 3 to day 0) cumulative abnormal returns. The results are insensitive to the benchmarking technique. Among the five types of name changes, brand adoption, narrower focus, and broader focus name changes are associated with significantly positive abnormal returns, while radical and miscellaneous name changes are associated with negligible abnormal returns. Taken together, the results suggest that the type of name change differs in relation to firm value according to the announcement period returns. The early literature shows that the positive name change announcement effect is transitory (Karpoff and Rankine, 1994). In contrast, behavioral finance studies document that the name change announcement effect is large and permanent. To examine whether the name change announcement effect is transitory or permanent, I measure the short-term (approximately 6 months), medium-term (approximately 12 months), and long-term (approximately 24 months) stock performance of name-changing firms. The results in Table 5 seem to be sensitive to the matching methods. For the overall sample firms, Panel A uses the market model and shows average excess returns of 8.11% for the first six months, 16.64% for the first 12 months, and 28.63% for the first 24 months. In contrast, Panel B uses the peer-matched method and shows average abnormal returns of 4.02% for the first six months and 5.97% for the first 12 months. The positive results are mainly due to narrower focus name changes, which exhibit significant average abnormal returns of 11.06% for the first six months and 15.04% for the first 24 months. The patterns of results in Panel C using the propensity score matching method are qualitatively similar to those in Panel B. I caution, however, that the long-horizon abnormal returns are sensitive to the test methodology. Kothari and Warner (1997) document that long-horizon cumulative abnormal returns based on the market model could yield negatively biased test statistics. Lyon et al. (1999) advocate the use of buyand-hold abnormal returns over cumulative abnormal returns.14 6. Follow-up changes 6.1. Changes in courses of action following corporate name changes I now examine important events following name changes. The notion of viewing brand name adoption name changes as investments in brand name capital suggests that I may observe subsequent important changes that complement an increase in the brand name capital, to the extent that types of capital are complementary. Once again, the notion of viewing focus narrowing and focus broadening name changes as preludes to subsequent changes in business policies suggests a significant alteration of the firm’s courses of action following name changes. Table 6 tracks important corporate events from the company news file in the Lexis–Nexis database, the Dow Jones Interactive database, and SEC filings, including CEO turnover and proxy fights, major corporate restructurings, and major asset purchases and 14 Furthermore, the time-varying sensitivity to priced risks holds particularly for our sample firms that may chart a new course for themselves. Nevertheless, an in-depth exploration of the dynamic of name-changing firms’ sensitivity to priced risk is beyond the scope of this paper.

sales. CEO turnover includes all CEO departures, except those due to death, illness or where the outgoing and incoming CEOs are members of the same family. Corporate restructurings include asset or debt restructurings, reorganizations, and large-scale employee layoffs. Asset purchases include acquisition of divisions and subsidiaries that exceed $1 million. Asset sales include spin-offs and divestitures that exceed $1 million. To examine whether the narrower focus (broader focus) type of name change conveys the refocusing (diversifying) strategy, I further partition the asset purchases and sales into related and unrelated. Note that the concept of relatedness and unrelatedness is relative to the proposed business direction implied by the corporate name (either by the old or new names depending on whether the events occur before or after the name changes). If I am unable to judge based on the corporate name, I define the asset purchases and sales as unrelated if there is no match with the firm’s primary three-digit SIC code. The data on primary SIC code are available from Thomson Financial’s SDC Platinum databases. Panel A of Table 6 presents the frequency of important corporate events, and Panel B of Table 6 presents the corresponding propensity score matched-adjusted frequency. Panel A shows that, compared with the number of corporate events over the three years prior to the name changes, except for asset purchases, firms seem to experience major changes over the three years following their name changes. In particular, firms are associated with more unrelated asset sales than related asset sales following name changes. It appears that name-changing firms increase their degree of business-relatedness by either divestitures or restructuring programs. In a comparison of follow-up changes across types of name changes, Panel A shows that brand adoption name changes seem to have the highest frequency of asset acquisitions, both related and unrelated, and the highest frequency of asset unbundlings through divestitures or restructuring activities. Firms with radical name changes, notwithstanding small-scale unrelated asset disposal programs over the three years prior to the name changes, seem to experience the least follow-up changes. The results persist after double-checking with accounting data, which show a significant increase in capital expenditures and acquisitions for the brand adoption name-changing firms, whereas little, if any, change in such investment for the radical name changes. Relative to broader focus name changes, narrower focus name changes seem to sell related assets less frequently but to sell unrelated assets more frequently and there is a small-scale unrelated asset disposal program over the three years prior to the name changes, suggesting that narrower focus name changes are refocusing (or focusing) the scopes of their businesses. Panel B of Table 6 presents the propensity score matched-adjusted frequencies of important corporate events and, in general, are similar to those in Panel A. 6.2. The relation between name changes and subsequent performance The notion of viewing a name change as a mechanism to facilitate subsequent coordinated activities suggests that subsequent performance will be better for firms whose activities are consistent with the message transmitted by new names than it will be for other name-changing firms. Furthermore, I expect that the name change announcement effect will be greater for firms whose name changes are consistent with subsequent events than for the rest, to the extent that the market is able to disentangle consistent from the other name changes. It may take time for the market to observe performance to disentangle the economic consequences; I would expect that the post-announcement stock performance will be greater for firms with consistent policies than for the rest. I measure accounting performance using 95% Winsorized operating margins, ROA, Tobin’s q in year t + 4, where year t equals zero for the year of the name change, sales growth (the difference in the

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Table 6 Changes in important corporate events/activities around name changes. This table presents the frequency of announced important corporate events in the three years before and following a name change and relative to a propensity score matched control group. CEO turnover refers to all CEO departures, except those due to death, illness or where the outgoing and incoming CEOs are members of the same family. Asset purchases include acquisitions of divisions and subsidiaries. Asset sales include spin-offs and divestitures. Asset purchases and asset sales are limited to those greater than $1M. Corporate restructuring related activities include asset or debt restructuring, reorganization, and large-scale employee layoffs. The assets are defined as unrelated if they are unrelated to the proposed business direction as implied by the corporate names or there is no match with the firm’s three-digit primary SIC code. The data on primary SIC code are available from SDC. P-values (in parentheses) are for paired t-test comparing corporate events before and following a name change. Frequency (%)

Grand total

(P-value) Before

* **

After

Major name changes

Minor name changes

Misc. reason

Brand adoption vs.

Radical change

Broader focus vs.

Narrower focus

Before

After

Before

After

Before

After

Before

After

29.53 (0.011) 29.02 (0.554) 6.74 (0.842) 4.66 (0.077) 15.54 (0.001) 9.84 (0.000)

44.56

23.44 (0.000) 46.41 (0.803) 9.25 (0.247) 3.35 (0.000) 8.93* (0.000) 5.90 (0.000)

45.45

23.05 (0.000) 42.99 (1.000) 9.97 (0.924) 2.80 (0.000) 14.33 (0.000) 6.85 (0.000)

50.47

16.54 (0.000) 36.2 (0.507) 4.72 (0.797) 7.09 (0.117) 4.72 (0.000) 4.72 (0.000)

41.73

10.41* (0.144) 45.10 (0.000) 10.41 (0.047) 3.47 (0.174) 10.82 (0.000) 9.18 (0.413)

17.14**

0.85 (0.000) 49.58 (0.000) 12.29 (0.231) 2.54 (0.504) 16.10 (0.000) 8.90 (0.498)

32.20

7.87 (0.004) 41.60 (0.055) 4.49 (0.741) 10.11 (0.140) 6.74 (0.000) 8.99 (1.000)

25.84

Before

After

Panel A: Raw frequency (%) of important corporate events CEO turnover and proxy contests 25.70 46.82 (0.000) Asset purchases (related) 41.32 44.17 (0.318) Asset purchases (unrelated) 8.60 9.57 (0.386) Asset sales (related) 4.28 13.74 (0.000) Asset sales (unrelated) 11.09 38.73 (0.000) Corporate restructurings 6.26 62.24 (0.000)

29.56 (0.000) 40.32* (0.184) 8.61 (0.013) 5.16 (0.000) 11.48 (0.000) 5.60 (0.000)

47.92

Panel B: Excess frequency (%) of important corporate events CEO turnover and Proxy contests 9.52 21.18 (0.000) Asset purchases (related) 42.63 21.78 (0.000) Asset purchases (unrelated) 9.85 7.24 (0.066) Asset sales (related) 4.49 0.27 (0.006) Asset sales (unrelated) 12.60 42.63 (0.000) Corporate restructurings 9.38 1.47 (0.001)

relative to propensity score matched 13.28 19.37 17.78 20.74 (0.173) (0.759) ** 24.40 8.15 42.07 26.38 (0.018) (0.072) 9.41 10.33*** 8.89 2.22 (0.703) (0.072) 4.98 1.29 5.93 5.19 (0.036) (0.025) ** 18.52 34.81 12.18 49.26 (0.000) [0.027) *** 4.44 20.74 11.07 16.24 (0.000) (0.128)

47.35

***

13.49*** 14.06 46.05

***

82.07***

32.64 6.22 9.84 32.64 46.63

45.14 7.02 18.07 29.91 53.11

21.63 5.51 0.61 14.61 4.69

42.99 9.66 12.44* 37.96

*

54.21

22.46 7.20 5.51 35.71 2.97

42.52 5.51 13.39 33.86 42.52

13.48 5.62 0 44.94 8.99

Denote statistically significant differences between: (1) brand adoption and radical change, and (2) broader focus and narrower focus, at the 10% level. Denote statistically significant differences between: (1) brand adoption and radical change, and (2) broader focus and narrower focus, at the 5% level. Denote statistically significant differences between: (1) brand adoption and radical change, and (2) broader focus and narrower focus, at the 1% level.

***

natural log of three-digit industry-adjusted sales in year t + 4 and t  1), and the percentage of firms that are not delisted from the stock exchange due to poor performance until year t + 4. I measure stock performance using the announcement period return, medium-term return (approximately 12 months), and long-term return (approximately 24 or 36 months). I classify name-changing firms with at least one of the following courses of action over the three years following name changes as consistent: brand adoption name changes with unrelated asset purchases. The marketing literature documents that firms with wellestablished brands tend to use the brand adoption practice to introduce new products in totally different areas (Choi, 1998). Analogous with the marketing literature, I expect that firms making brand adoption name changes may purchase unrelated assets including the acquisition of unrelated divisions and subsidiaries in order to enter into new businesses. Second, radical name changes with corporate restructuring. In order to be plausible, a radical name change shows the firm’s commitment to improving its performance. Corporate restructuring is often viewed as a strategic investment decision in response to distressed performance (Jostarndt and Sautner, 2008). I therefore classify a radical name change with subsequent corporate restructuring events as a consistent type of radical name change. Third, broader focus name changes with unrelated asset purchases and/or related asset sales. To be plausible, a consistent broader focus name change reflects a broadened business portfolio. Finally, narrower focus name changes with related asset purchases and/or unrelated asset sales.

To be plausible, a consistent narrower focus name change reflects the refocusing (or focusing) of a firm’s business scope. In support of the idea that the sample firms change their names to accompany a change in business direction, the binomial sign test shows that a majority of name-changing firms experience consistent subsequent activities. For example, Panel A of Table 7 shows that there are 87% (604/697) brand adoption, 65% (125/193) radical name changes, 84% (527/627) broader focus, and 55% (176/321) narrower focus name changes with consistent subsequent activities. Panel A of Table 7 compares the economic performance of the name-changing firms with consistent activities with that of the other name-changing firms. Panel A shows that brand adoption name-changing firms with consistent activities are associated with significantly better operating margins, ROA, sales growth, and survival rates, but lower Tobin’s q in year t + 4, compared with the other brand adoption name-changing firms. There appears to be few discernable differences between radical name-changing firms with consistent activities and the other radical name-changing firms. The broader focus name-changing firms with consistent activities tend to be associated with higher ROA, name change announcement effects, and long-term stock performance. The narrower focus name-changing firms with consistent activities tend to be associated with better operating margins, ROA, sales growth, Tobin’s q, and long-term stock performance, compared with the other narrower focus name-changing firms. In general, Panel B of Table 7 compares the propensity score matched-adjusted economic performance of the consistent type of name-changing firms

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Y. Wu / Journal of Banking & Finance 34 (2010) 1344–1359

Table 7 The Performance comparisons between consistent type of name changes and the other name changes. This Table reports the year (t + 4) 95% Winsorized mean accounting performance. Survival rate is the percentage of sample firms that are not delisted from the stock exchange due to poor performance (with the first digit of the three-digit CRSP delisting code being 5) in the t + 4 year. I classify name-changing firms with at least one of the following courses of action as consistent: brand adoption with subsequent unrelated asset purchases, radical change with subsequent corporate restructuring events, broader focus with subsequent unrelated asset purchases and/or related asset sales, and narrower focus with subsequent related asset purchases and/or unrelated asset sales. The other name changes are classified as ‘Others’. Major name changes

Minor name changes

Brand adoption Consistent type vs. Panel A: Sample firm performances 0.12*** Operating marginst+4 0.02** ROAt+4 0.31** Sales growtht+4 0.08** Tobin’s qt+4 Survival ratet+4 0.88*** CAR_MM days (3,0) (%) 1.30 CAR_MM months (0,12) (%) 4.88 CAR_MM months (0,24) (%) 18.40 CAR_MM months (0,36) (%) 35.60 Sample size 604

Radical change

Broader focus

Narrower focus

Others

Consistent type vs.

Others

Consistent type vs.

Others

Consistent type vs.

Others

0.52 0.04 0.01 0.36 0.78 1.97 15.40 25.70 34.70 93

0.82 0.06 0.15 0.01 0.61* 0.40 30.80 46.10 44.60 125

0.49 0.02 0.29 0.35 0.73 1.25 44.20 53.50 53.90 68

0.39 0.01** 0.09 0.22 0.77 2.50* 7.70** 16.33* 23.90* 527

0.54 0.05 0.05 0.21 0.78 0.81 31.00 49.30 70.30 100

0.40* 0.03*** 0.40* 0.69*** 0.76 2.2 11.49 9.47** 14.20** 176

0.79 0.11 0.11 0.15 0.76 2.2 24.23 58.00 83.60 145

0.61 0.04 0.10 0.01 0.09 2.70 13.80 11.00 46.60 149

0.25 0.02 0.09 0.03 0.15 1.70 2.40 2.00 106.00 44

0.02** 0.14** 0.41* 0.13 0.01 1.4 0.60 30.00 19.70** 544

0.35 0.01 0.03 0.62 0.04 0.9 4.70 15.00 148.0 83

0.31 0.00 0.17 0.14 0.03 1.50 15.30 0.00 3.86* 206

0.17 0.09 0.25 0.39 0.06 2.90 2.0 8.0 88.20 115

Panel B: Propensity score matched-adjusted performances Operating marginst+4 0.22*** 0.25 0.01** 0.07 ROAt+4 1.48** 0.16 Sales growtht+4 0.57 0.08 Tobin’s qt+4 0.15* 0.02 Survival ratet+4 CAR_PS days (3,0) (%) 0.30 3.30 CAR_PS months (0,12) (%) 6.90 1.00 CAR_PS months (0,24) (%) 6.00 7.00 CAR_PS months (0,36) (%) 57.30 102.10 Sample size 608 89 *

Denote statistically significant differences between the consistent types and the other name changes within each category of name change at the 10% level. Denote statistically significant differences between the consistent types and the other name changes within each category of name change at the 5% level. *** Denote statistically significant differences between the consistent types and the other name changes within each category of name change at the 1% level. **

with that of the other name-changing firms and shows a very similar pattern as that in Panel A. In sum, the evidence is more in line with the observation that name-changing firms with consistent subsequent activities outperform the other name-changing firms. The results in Table 7 persist after a number of robustness checks.15

þ

j¼6 X

dj

t¼1 X

! Eventi;j;t

þ ei ; . . .

ð1Þ

t¼3

j¼1

Eventi;j;t ¼ a0 þ w1 Herfindahli þ w2 Leveragei þ w3 Liquidityi

t¼1

þ I now examine the information content of the name change announcement returns (i.e., CAR_MM days (3,0)). I expect a positive relation between the type of name change and name change announcement returns. A positive relation could indicate that the new name per se has intrinsic value, or the expected value of important subsequent courses of action, or both. The causality could be reversed here. There could be a delayed stock price reaction to previous important events (Lo and MacKinlay, 1990). Firms that previously initiated small-scale courses of action display positive excess returns on the name change announcement because the market agrees that managers are on the right track. Motivated by the positive returns, managers scale up certain courses of action. Thus, it is instructive to examine the quadripartite association among name change dummies, past corporate events, subsequent important corporate events, and excess stock returns. Accordingly, I develop the following system of equations:

bn Namei;n þ b5 Tickeri

n¼1

t¼3 X

6.3. The information content of corporate name change announcements

n¼4 X

CAR MM days ð3; 0Þi ¼ a0 þ

n¼4 X

ki;n þ k5 CAR MM days ð3; 0Þi þ gi;j ; . . .

ð2Þ

n¼1

Tobin’s qi;tþ4 ¼ a0 þ

j¼6 X

gj

t¼3 X

! Eventi;j;t

þ g7 Herfindahli;tþ3

t¼1

j¼1

þ g8 Tobin’s qi;t1 þ

n¼4 X

cn Namei;n

n¼1

þ c5 CAR MM days ð3; 0Þi þ mi;tþ4; . . .

ð3 Þ

or

Operating Marginsi;tþ4 ¼ a0 þ

j¼6 X j¼1

gj

t¼3 X

! Eventi;j;t

t¼1

þ g7 Herfindahli;tþ3 þ g8 Physical Capitali;tþ3

15 These checks include measuring changes in performance in year t + 4 relative to the level in year t  1 and different classification schemes of the consistent activities for radical name-changing firms. Note that I do not rule out the possibility that managers are not obligated to make announcements about corporate events, and hence those announcements that they do make are likely to be better than average. Therefore, I may observe that name-changing firms with consistent subsequent activities are associated with better performance than the other name-changing firms including firms with no subsequent announced events.

þ

n¼4 X

cn Namei;n

n¼1

þ c5 CAR MM days ð3; 0Þi þ mi;tþ4 ; . . . ð3 Þ or

Table 8 The information content of corporate name change announcements. I employ the three-stage least squares approach (3SLS). I first estimate a model of CAR_MM days (3,0) using exogenous variables. Then, I estimate six equations of the number of subsequent events using predicted CAR_MM days (3,0) as one of the explanatory variables. The final step is to simultaneously estimate the Winsorized Tobin’s q (Winsorized Operating Margins or CAR_MM months (0,36)) model using predicted CAR_MM days (3,0) and predicted events as two sets of the explanatory variables, and re-estimate CAR_MM days (3,0) and subsequent events using seemingly unrelated regression. P-values (in parentheses) are based on White-corrected standard errors. Coefficients with P-values of .10 or lower are highlighted in bold face type. In each regression, the P-value for the significance of the regression equation is less than 0.000. Simultaneous equations

Constant Panel A: Types of new names Brand adoption Radical change Broader focus Narrower focus

Panel B: Previous events Pre CEO Turnovers/Proxy Fights Pre related asset purchases Pre unrelated asset purchases Pre related asset sales Pre unrelated asset sales Pre corporate restructuring

(3) CAR_MM days (3,0) (%)

(4) Operating marginst+4

(5) CAR_MM days (3,0) (%)

(6) CAR_MM months (0,36)

1.155 (0.497)

2.295 (0.001)

1.490 (0.360)

0.317 (0.649)

0.437 (0.707)

0.415 (0.748)

1.052 (0.058) 2.912 (0.186) 0.544 (0.764) 0.980 (0.312) 0.639 (0.304)

0.144 (0.073) 1.126 (0.028) 0.679 (0.094) 0.271 (0.545)

1.300 (0.051) 1.804 (0.397) 0.079 (0.964) 2.122 (0.252) 0.050 (0.924)

0.058 (0.087) 0.569 (0.165) 0.056 (0.857) 0.327 (0.390)

2.266 (0.071) 0.160 (0.195) 1.066 (0.394) 2.374 (0.079) 1.493 (0.014)

2.390 (0.091) 0.001 (0.997) 0.297 (0.297) 1.319 (0.044)

0.415 (0.455) 0.624 (0.271) 3.589 (0.002) 0.386 (0.753) 1.907 (0.029) 1.207 (0.241)

Panel C: Subsequent events Post CEO Turnovers/Proxy Fights (predicted)

0.164 (0.753) 0.297 (0.567) 3.656 (0.001) 0.777 (0.493) 0.210 (0.796) 0.728 (0.457) 1.646 (0.087) 1.963 (0.012) 6.881 (0.000) 0.642 (0.546) 0.289 (0.558) 0.558 (0.421)

Post related asset purchases (predicted) Post unrelated asset purchases (predicted) Post related asset sales (predicted) Post unrelated asset sales (predicted) Post corporate restructuring (predicted) Panel D: Others Winsorized Tobin’s qt1

0.245 (0.000) 0.220 (0.000) 0.019 (0.970)

CAR_MM days (3,0)% (predicted) Herfindahlt+3 Physical capitalt+3 Yes 1.46% 1114

Yes 6.34% 1114

Yes 0.94% 1163

0.075 (0.889) 0.162 (0.539) 2.817 (0.000) 2.246 (0.011) 0.607 (0.365) 2.518 (0.025) 1.641 (0.126) 1.614 (0.096) 3.513 (0.125) 2.434 (0.063) 0.580 (0.376) 2.721 (0.001)

0.390 (0.189) 0.792 (0.525) 4.969 (0.064) 0.147 (0.321) 1.281 (0.081) 0.181 (0.000)

0.122 (0.069) 0.419 (0.353) 0.192 (0.000) Yes 3.09% 1163

0.005 (0.923)

Yes 0.48% 1013

Yes 2.47% 1013

1355

Year fixed effects R2 Sample size

Simultaneous equations

(2) Tobin’s qt+4

Y. Wu / Journal of Banking & Finance 34 (2010) 1344–1359

Ticker

Simultaneous equations

(1) CAR_MM days (3,0) (%)

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Y. Wu / Journal of Banking & Finance 34 (2010) 1344–1359

CAR MM months ð0; 36Þi ¼ a0 þ

j¼6 X j¼1

þ

n¼4 X

gj

t¼3 X

! Eventi;j;t

t¼1

cn Namei;n

n¼1

þ c5 CAR MM days ð3; 0Þi þ mi;tþ3 ; . . . ð3 Þ Eq. (1) specifies that the announcement period returns for firm i (CAR_MM days (3,0)) are a function of Namen which equals one of n-type name change, where n = 1, 2, 3, and 4 for brand adoption, radical change, broader focus, narrower focus, respectively and zero otherwise, Ticker equals one for firm i that experiences ticker symP bol changes and zero otherwise, and the sum of t¼3 t¼1 Eventi;j;t equals the number of j-type events, where j = 1, 2, 3, 4, 5, and 6 for CEO Turnovers/Proxy Fights, related asset purchases, unrelated asset purchases, related asset sales, unrelated asset sales, and corporate restructurings, respectively, for firm i over the three years prior to the name change.Eq. (2) specifies that the number of important corporate events, j, for firm i over the three years following the name P change, t¼3 t¼1 Eventi;j;t , is a function of industry competitiveness, the leverage ratio, liquidity (the availability of internal funds: cash and marketable securities/assets) at the beginning of the period, name change dummies, and CAR_MM days(3,0). Eqs. 3* (3** or 3***) specifies that Tobin’s qt+4 (operating marginst+4, or CAR_MM months (0,36)) is a function of the number of important corporate events over the three years following the name change, industry competitiveness (physical assets) at time t + 3, Tobin’s q in previous periods, name change dummies, and CAR_MM days (3,0). The error terms, ei, gi,j, and mi,t+4(mi,t+3), are assumed to be independently and identically distributed. I employ the three-stage least squares (3SLS) estimation technique to examine the information content of the announcement returns. This is because, as explained above, CAR_MM days (3,0), subsequent important corporate events, and Tobin’s qt+4 (operating marginst+4, or CAR_MM months (0,36)) may be endogenously determined. The 3SLS approach first estimates a model of CAR_MM days (3,0) using exogenous variables. Then, it estimates six equations of the number of subsequent important events using predicted CAR_MM days (3,0) as one of the explanatory variables. The final step is to simultaneously estimate the Winsorized Tobin’s qt+4 (operating marginst+4, or CAR_MM months (0,36)) regression using predicted CAR_MM days (3,0) and predicted subsequent events as two sets of the explanatory variables, and re-estimate the CAR_MM days (3,0) and subsequent events using a seemingly unrelated regression. For brevity, the odd numbered columns [(1), (3), and (5)] of Table 8 report the 3SLS estimates on CAR_MM days (3,0) and the even numbered columns [(2), (4), and (6)] of Table 8 report 3SLS estimates on subsequent economic performance and omit the estimates on subsequent event regressions.16 Column (1) of Table 8 shows significant coefficients on the previous events. Firms with unrelated asset sales display positive stock price reactions while those with unrelated asset purchases experience negative stock price reactions. The positive loading on brand adoption in column (1) is consistent with the interpretation that brand name adoption either signals positive information about the future firm prospects or carries brand name capital per se. Columns (3) and (5) show sim16 Turning to the subsequent important corporate events regressions, the unreported results show that, indeed, CAR_MM days (3,0) is significantly related to subsequent events, including the post unrelated asset sales regression, the post related asset sales regression, the post unrelated asset purchases regression, and the post corporate restructuring activities. These results suggest that the frequencies of subsequentimportant corporate events are related to thename change announcement returns.

ilar patterns of results as those in Column (1). We note that the intercepts in columns (1), (3) and (5) are not significantly different from zero. Thus, the name change dummies we have included in our analysis fully explain the CAR_MM days (3,0). Column (2) shows a significant (though modest) positive effect of brand adoption name changes on Winsorized Tobin’s qt+4, while it indicates a significant negative effect of radical name changes and broader focus name changes. Besides, column (2) shows that the Winsorized Tobin’s qt+4 is significantly related to predicted CEO Turnovers/ Proxy Fights, predicted related asset purchases, and predicted unrelated asset purchases. Column (4) shows a significant (though modest) positive effect of brand adoption name changes on operating marginst+4. Column (6) shows a significant (though modest) positive effect of brand adoption name changes and narrower focus name changes on CAR_MM months (0,36). Finally, we note that the intercepts in columns (4) and (6) are not significantly different from zero. Thus, the name change dummies and future important corporate events we have included in our analysis fully explain operating marginst+4 and CAR_MM months (0,36). To examine if the information content of the announcement returns of name changes is consistent with subsequent activities, I use the following system of equations:

CAR MM days ð3; 0Þi ¼ a0 þ

n¼4 X

bn Namei;n þ b5 Tickeri

n¼1

þ

j¼6 X

dj

t¼1 X

! Eventi;j;t

þ i ; . . .

ð4Þ

t¼3

j¼1

Consistent Eventi;n ¼ a0 þ w1 Herfindahli þ w2 Leveragei þ w3 Liquidityi þ Namei;n þ k5 CAR MM days ð3; 0Þi þ gi;n ; . . .

ð5Þ

Tobin’sqi;tþ4 ¼ a0 þ g1 Herfindahli;tþ3 þ g2 Tobin’sqi;t1 þ

n¼4 X

cn Consistent Eventi;n

n¼1

þ c5 CAR MM days ð3; 0Þi þ mi;tþ4 ; . . .

ð6 Þ

or

Operating Marginsi;tþ4 ¼ a0 þ g1 Herfindahli;tþ3 þ g2 Physical Capitali;tþ3 þ

n¼4 X

cn Consistent Eventi;n

n¼1

þ c5 CAR MM days ð3; 0Þi þ mi;tþ4 ; . . . ð6 Þ or

CAR MM months ð0; 36Þi ¼ a0 þ

n¼4 X

cn Consistent Eventi;n

n¼1

þ c5 CAR MM days ð3; 0Þi þ mi;tþ3 ; . . . ð6 Þ The notation in Eqs. (4)–(6***) is the same as the notation in Eqs. (1)–(3***) except that I replace the vector of six subsequent important events with the vector of four consistent event dummies, defined by Consistent Eventn = 1 with activities occurring within three years following name changes that are consistent with the message implied by name change type n (n = 1, 2, 3, 4 for brand adoption, radical change, broader focus, narrower focus, respectively) and zero otherwise.

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Y. Wu / Journal of Banking & Finance 34 (2010) 1344–1359

Table 9 The value of consistent type of name changes. I employ the three-stage least squares approach (3SLS). I first estimate CAR_MM days (-3,0) using exogenous variables. Then, I estimate four equations of the consistent subsequent events using predicted CAR_MM days (3,0) as one of the explanatory variables. The final step is to simultaneously estimate the Winsorized Tobin’s q (Winsorized Operating Margins or CAR_MM months (0,36)) using predicted CAR_MM days (3,0) and predicted consistent events as two sets of the explanatory variables, and re-estimate CAR_MM days (3,0) and consistent subsequent events using seemingly unrelated regression. P-values (in parentheses) are based on White-corrected standard errors. Coefficients with P-values of .10 or lower are highlighted in bold face type. In each regression, the P-value for the significance of the regression equation is less than 0.000. Simultaneous equations

Constant Panel A: Types of new names Brand adoption Radical change Broader focus Narrower focus Ticker Panel B: Previous events Pre CEO Turnovers/Proxy Fights Pre related asset purchases Pre unrelated asset purchases Pre related asset sales Pre unrelated asset sales Pre corporate restructuring

Simultaneous equations (2) Tobin’s qt+4

(3) CAR_MM days (3,0) (%)

(4) Operating marginst+4

(5) CAR_MM days (3,0) (%)

(6) CAR_MM months (0,36)

1.845 (0.254)

1.185 (0.000)

0.409 (0.788)

0.362 (0.000)

0.356 (0.759)

0.304 (0.005)

0.486 (0.074) 3.783 (0.071) 1.693 (0.319) 0.536 (0.769) 2.094 (0.006)

1.090 (0.497) 2.263 (0.052) 0.011 (0.995) 2.473 (0.053) 1.599 (0.027)

2.102 (0.092) 0.096 (0.949) 0.822 (0.510) 2.134 (0.091) 1.434 (0.018)

0.213 (0.805) 0.539 (0.540) 2.871 (0.065) 1.673 (0.361) 1.128 (0.384) 1.545 (0.297)

0.011 (0.989) 0.128 (0.882) 3.160 (0.039) 1.261 (0.491) 1.207 (0.358) 2.356 (0.117)

0.260 (0.720) 0.299 (0.682) 2.539 (0.045) 1.717 (0.268) 0.601 (0.584) 2.437 (0.058)

Panel C: Consistent subsequent events Consistent brand Adoption (predicted) Consistent radical change (predicted) Consistent broader focus (predicted) Consistent narrower focus (predicted)

0.202 (0.254) 0.079 (0.751) 0.088 (0.598) 0.426 (0.005)

Panel D: Others Winsorized Tobin’s qt1

0.323 (0.000) 0.044 (0.088) 0.542 (0.030)

CAR_MM days (3,0)% (predicted) Herfindahl Physical capital R2 Sample size

Simultaneous equations

(1) CAR_MM days (3,0) (%)

1.65% 1127

9.10% 1127

1.90% 1266

For brevity, the odd numbered columns [(1), (3), and (5)] of Table 9 report the 3SLS estimates on CAR_MM days (3,0) and the even numbered columns [(2), (4), and (6)] of Table 9 report the 3SLS estimates on subsequent economic performance and omit the estimates on the four consistent subsequent events. Column (2) of Table 9 shows a positive effect of narrower focus name changes with consistent subsequent activities on the Winsorized Tobin’s qt+4, suggesting that refocusing on the core business is associated with greater Tobin’s qt+4. Column (4) shows a positive effect of brand adoption name changes with consistent subsequent activities on operating marginst+4, suggesting that penetration into new businesses for firms with brand name adoption is associated with higher operating margins. Column (6) shows that long-horizon excess returns from the market model

0.352 (0.060) 0.443 (0.081) 0.023 (0.905) 0.134 (0.424)

0.023 (0.938) 0.116 (0.751) 0.418 (0.093) 0.541 (0.032)

0.012 (0.675) 0.331 (0.230) 0.174 (0.000) 6.68% 1266

0.004 (0.929)

1.33% 1013

13.40% 1013

(CAR_MM months (0,36)) relate positively with consistent broader focus name changes and consistent narrower focus name changes.

7. Conclusions This paper provides answers to two key questions about corporate name changes. First, what leads a firm to change its name? Second, do firms change their courses of action following name changes and, if so, how? The analysis of this paper suggests that poor reputations characterize name-changing firms. Nevertheless, reputation affects the type of name change in significantly different ways. Firms with superior past stock performance, accounting

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Y. Wu / Journal of Banking & Finance 34 (2010) 1344–1359

performance, neutral and favorable media coverage, and a greater number of brands tend to change names to associate with those brand names. Firms with poor stock returns, accounting performance, or unfavorable media coverage, tend to make radical name changes. The results indicate that the announcement period returns are negligible and the post-announcement period returns are negative for firms with radical changes in their names and they tend to experience infrequent follow-up corporate events. The announcement period stock price reaction is positive for firms adopting brand names and they seem to experience the highest frequency of asset acquisitions and asset unbundlings. The peer-matched-adjusted long-run excess returns are positive for firms changing to a name that implies a narrower business focus and those that indeed refocus (or focus) their scope of business enjoy higher Tobin’s q than the other name-changing firms. The announcement period stock price reaction is positive for firms changing to a name that implies a broader business focus and those that indeed diversify their activities enjoy higher announcement period stock returns than the other name-changing firms. The above findings cast doubt on the cosmetic effects of name changes (Cooper et al., 2001, 2005). This paper documents that corporate name changes usually foreshadow a change in business direction. It is also difficult to explain why brand adoption, narrower focus, and broader focus types of name changes fool investors, whereas radical name changes fail to fool investors.

Acknowledgments The author would like to thank seminar participants at Washington University in St. Louis, National Taiwan University, National Cheng Kung University; National Chengchi University, National Central University, National Chiao Tung University, and National Tsing Hua University. This paper was presented at the 2008 Annual Meeting of the Financial Management Association, and the 2009 Conference on Quantitative Finance and Risk Management. This paper has benefited from comments from Alex Butler, Joseph Fan, Jonathan Karpoff, Per Olsson, and Henri Servaes. The author also thanks Ike Mathur (Managing Editor), and an anonymous reviewer for helpful comments and suggestions. Finally, the author is grateful to Virginia Unkefer for excellent editorial assistance. The Hong Kong Research Grants Council and the National Science Council from Taiwan, ROC provided funding for this project.

Other Coverage: The number of news reports other than the number of bad news items defined immediately above within three years prior to name changes. Ticker: Equals one for firms that experience ticker symbol changes. Ticker3: Equals one for firms that experience ticker symbol changes and there are other ticker symbols with the same first three letters. R&D: The average of R&D expenses/Asset over the 3-years preceding name change. ADV: The average of Advertising expenses/Asset over the three years prior to the name change. Number of Brands: The number of reported brand names. Physical capital: The natural logarithm of net property, plant and equipment. Age Since IPO: The number of years between IPO and name change announcement. Bid-ask spread (i.e., market-adj relative effective spread) (%): I calculate the spread on a transaction basis using the TAQ (Trade and Automated Quotations) database, which is unavailable before 1993. The effective spread (ESPR) is twice the absolute difference between the current trade price and the mid-quote. The relative effective spread (RESPR) is the effective spread expressed as a percentage of the mid-quote. Market-adj relative effective Spread is the firm’s three-year market-adjusted (excess) monthly relative Rt¼1 RESPR Rt¼1 RESPR

m;t i;t t¼36 , where RESPRi,t is effective spread, namely, t¼36 36 the RESPR of firm i of month t and RESPRm,t is the market RESPR of month t. I calculate RESPRi,t for every transaction for every sample firm. I then average the transaction observations within each day and then within each month. I calculate RESPRm,t for every transaction using all stocks in the corresponding sample firm stock exchange. I then average the transaction observations within each day and then within each month. Share turnover (i.e., market-adj share turnover): I calculate the share turnover on a monthly basis, which is the total monthly trading volume to the number of shares outstanding. The annual share turnover is the cumulative monthly share turnover for the calendar year. The firm’s three-year market-adjusted (excess) annual share Pt¼1 Pt¼1 Turnoveri;t  Turnoverm;t t¼36 t¼36 , where Turnoveri,t turnover, namely, 3 is the share turnover of firm i of month t and Turnoverm,t, is the market share turnover of month t. Herfindahl: The three-year average of Herfindale–Hirschman index. Leverage: The average of (short- and long-term debt)/assets over the 3-years prior to the name change.

Appendix A. Construction of key analysis variables Market-adj returns: The firm’s three-year market-adjusted comt¼1 pounded monthly stock return. Namely, Pt¼1 t¼36 ð1 þ Ri;t Þ  Pt¼36 ð1 þ Rm;t Þ, where Ri,t is the return of firm i of month t and Rm,t is the CRSP value weighted market return of month t. Operating margin: The average three-digit industry median-adjusted EBITDA (earnings before interest, taxes, depreciation, and amortization) scaled by net sales over the three years prior to the name change. ROA: The average three-digit industry median-adjusted EBITDA over total assets over the three years prior to the name change. Sales growth: The difference in the log of three-digit industry median-adjusted sales in years (t  1) and (t  4). Tobin’s q: The average (Market value of equity + Book value of debt)/Assets over the three years prior to the name change. Bad Coverage: The number of news reports on fraud, unreliable acts, litigation, and poor performance in the company news file of the Lexis–Nexis database and the Dow Jones Interactive database within three years prior to name changes.

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