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A comprehensive examination of the wealth effects of recent stock repurchase announcements

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Abstract

The main purpose of this paper is to examine the wealth effect of stock repurchase announcements using a sample of 11,862 repurchase programs announced during 1994–2007. The results of several recent industry surveys indicate that managerial motivations for repurchasing shares may have changed in recent years. To better understand the reasons for repurchasing shares we classify our sample in various ways—by year, by the method used for repurchasing shares, and by the stated purpose of the program. We find that the median size of firms repurchasing shares has increased dramatically recently, and concomitantly, the announcement returns have declined. Signaling undervaluation of share prices appears to become less important than previously assumed. While smaller firms signal undervaluation using open market repurchases, tender offers are chosen to enhance shareholder values by other means.

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Notes

  1. http://www2.standardandpoors.com/spf/pdf/index/121307_SP500_THREE_YEARS_OF_BUYBACKS.pdf.

  2. For example, Business Wire reported on August 28, 2006 that “Amazon.com, Inc. (the “Company”) announced today that its Board of Directors authorized the Company to repurchase up to $500 million of the Company's common stock within the next 24 months, through one or more open market transactions, privately negotiated transactions, transactions structured through investment banking institutions or a combination of the foregoing. The Company may do so if it believes its shares are undervalued.”

  3. Business Wire, December 18 2006.

  4. Business Wire, June 16 2006.

  5. All our conclusions remain the same when we use the Fama–French three-factor model instead of the market model.

  6. According to Cowan and Sergeant (1996), the Patell test standardizes the event-date prediction error for each stock by its standard deviation. The individual prediction errors are assumed to be cross-sectionally independent and distributed normally, so each standardized prediction error has a Student t distribution. By the Central Limit Theorem, the distribution of the sample average standardized prediction error is normal. The resulting test statistic is

    \( {\raise0.7ex\hbox{${Z = \sum\nolimits_{j = 1}^{N} {{\text{SR}}_{\text{jE}} } }$} \!\mathord{\left/ {\vphantom {{Z = \sum\nolimits_{j = 1}^{N} {{\text{SR}}_{\text{jE}} } } {\sqrt {\sum\nolimits_{j = 1}^{N} {{\frac{{{\text{T}}_{j} - 2}}{{{\text{T}}_{j} - 4}}}} } }}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{${\sqrt {\sum\nolimits_{j = 1}^{N} {{\frac{{{\text{T}}_{j} - 2}}{{{\text{T}}_{j} - 4}}}} } }$}}, \)where SRjE is a stock j’s estimated standard deviation of abnormal return during the estimation period; T is number of trading days in stock j’s estimation period, equal to 255 if there is no missing return.

     The rank test procedure treats the 255-day estimation period and the event day as a single 256-day time series, and assigns a rank to each daily return for each firm. K jt represent the rank of abnormal return AR jt in the time series of 256 daily abnormal returns of stock j. Rank one signifies the smallest abnormal return. We adjust for missing returns by dividing each rank by the number of non-missing returns in each firm’s time series plus one:

    U jt  = K jt /(M j  + 1),

    where M j is the number of non-missing abnormal returns for stock j. The rank test statistic is

    \( {\raise0.7ex\hbox{${Z = {\frac{1}{\sqrt N }}\sum\nolimits_{j = 1}^{N} {\left( {U_{jt} - 0.5} \right)} }$} \!\mathord{\left/ {\vphantom {{Z = {\frac{1}{\sqrt N }}\sum\nolimits_{j = 1}^{N} {\left( {U_{jt} - 0.5} \right)} } {S_{u} }}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{${S_{u} }$}}, \)where the standard deviation Su is

    \( S_{u} = \sqrt {\frac{1}{256}} \sum\nolimits_{t = - 255}^{0} {\left[ {\sqrt {{\frac{1}{{\sqrt {N_{t} } }}}\sum\nolimits_{j = 1}^{{N_{t} }} {\left( {U_{it} - 0.5} \right)} } } \right]} , \)

    where N t is nonmissing returns across the stocks in the sample on day t of the combined estimation and event period.

  7. For the pre-announcement returns, market model parameters are estimated by regressing each firm’s daily return on the CRSP value-weighted index return over 255 trading days, beginning on day −406 and ending 151 trading days before the announcement date.

  8. We also provide cross-sectional regression evidence later (in Table 9) that announcement-period CARs are significantly negatively correlated with pre-announcement CARs before 2003. The correlation is positive and statistically insignificant between 2003 and 2007. A significantly negative regression coefficient suggests that repurchase announcements signal undervalued share prices. The regression evidence presented in Table 9 strengthens our argument that recent repurchase announcements by relatively bigger firms are made for reasons that may not have much to do with signaling undervalued shares.

  9. Previous studies report abnormal returns of 3–5% for open market repurchases and 8–15% for fixed-price and Dutch-auction tender offers.

  10. Dutch-auction tender offers are also less expensive for the repurchasing firms because the purchase (winning) price in a Dutch-auction is likely to be lower than the purchase price of a fixed-price offer.

  11. Open market/negotiated repurchases are considered as open market repurchases.

  12. The GLMMOD procedure constructs the design matrix for a general linear model. When we use SAS's REG procedure to fit a model with a classification variable like the repurchase methods and the stated purpose of repurchases, we first need to compute the indicator (dummy) variables. The GLMMOD procedure creates the indicator variables automatically and uses them as input to other SAS procedures.

  13. According the SAS manual, if a CLASS variable has m levels, GLMMOD will generate m variables in the design matrix. This means that the model produced by GLMMOD is overparameterized. There are more columns for these effects than there are degrees of freedom for them. Therefore, we need to drop at least one column for each class variable when we fit the model. Among the four repurchase methods, we drop negotiated repurchases. Among the six stated purposes of repurchases, we drop repurchases that are coded as “General Corporate Purpose.”

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Authors and Affiliations

  1. The Johns Hopkins Carey Business School, 100 International Drive, Baltimore, MD, 21202, USA

    Ken C. Yook

  2. Finance, Insurance, and Real Estate, Herberger College of Business, St. Cloud State University, St. Cloud, MN, 56301, USA

    Partha Gangopadhyay

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  1. Ken C. Yook
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Correspondence to Ken C. Yook.

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Yook, K.C., Gangopadhyay, P. A comprehensive examination of the wealth effects of recent stock repurchase announcements. Rev Quant Finan Acc 37, 509–529 (2011). https://doi.org/10.1007/s11156-010-0215-y

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