If managers have the ability to predict stock prices, they can choose the right time to make important decisions. Many empirical researches show the existence of long-run abnormal returns after important decisions. Managerial timing ability offers a rational explanation for the past year. In recent years, many authors doubt the results. However, pseudo market timing provides an alternative explanation. Based on a large sample of M&As in US between 1995 and 2006, we investigate whether the abnormal returns after mergers are due to the managerial timing ability or due to pseudo market timing.
The main results are as follows. First, according to growth firms’ and value firms’
characteristics, we find that the value firms’ abnormal returns are lower than the growth firms’ one year after the merger. The evidence allows the rejection of managerial timing ability.
Second, this thesis uses the negative binomial regression models with the M&A index level at t+1 point as the dependent variable based on the number of firms at the decision point of time and M&A index levels at the decision point of time. This result observed that it is not significantly negative between the numbers of M&As at the decision point of time and M&A index level at t+1 point of time. The results reveal that ex-post prices do not have significantly abnormal returns in calendar time. The evidence is in contrast with the managerial market timing and partly is consistent with pseudo market timing.
Finally, the correlation between the stock market and firms on stock mergers should be more intense, as a result, the abnormal returns should more significant compared to other
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firms. This thesis tests whether the abnormal return of acquiring firms on stock mergers in event time and calendar time fit in with managerial timing ability hypothesis. The abnormal returns of firms on stock mergers over one year after the merger are -14.49% in event time. The M&A index level at t+1 point on M&A activity is not significantly related to the number of firms on stock mergers on M&A activity on decision points in calendar time. The result is partly consistent with a pseudo market timing hypothesis but in contrast with managerial market timing.
This findings of this thesis indicate that firms simply make M&As for current market prices but not for the future market prices. The evidence proves that the pseudo market timing hypothesis better explains the abnormal returns of firms after mergers.
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Figure1:Pseudo Market Timing Hypothesis and Mergers and Acquisitions
Figure 2: Hot and Cold Months Classified by the Number of Transactions
Hot and Cold months classifications are based on quartile ranking of a three-month moving average of the aggregate number of mergers and acquisitions in US, obtained from SDC. Hot months are at least three contiguous months where the number of mergers and acquisitions exceeds the upper quartile.
Cold months are at least three contiguous months where the number of mergers and acquisitions are below the lower quartile.
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Table1: Sample data
The standard of criteria Number of sample
The announcement date is from 1995 to 2006 133452
The acquirer traded on the New York exchange , the American exchange and the
NASDAQ exchange 55422
Excluding financial and utility industries 40599
The deal value is more than one million US dollars 23619
Bidders acquire at least 50% of the targets 13685
Merger financed with stock 5044
Dropping those with missing daily stock returns 4358
Dropping those with missing B/M ratio and size 3330
Table2: The Descriptive Statistics of 4358 Sample Firms from1995 to 2006
Number of announcement
Number of Stock-financed mergers
Number of announcement
with fully stock return size($M)
Book-to-market ratio
1995 1058 399 74 $159,138.75 0.4622444
1996 1332 448 183 $406,091.64 0.37743
1997 1680 699 225 $993,420.17 3.1245392
1998 1650 708 344 $812,372.98 0.393979
1999 1456 638 353 $1,249,754.15 0.3643382
2000 1425 753 526 $1,663,386.69 0.3872473
2001 884 395 336 $1,442,218.75 0.4403033
2002 826 253 407 $663,514.07 0.5497321
2003 775 209 401 $535,318.04 0.518838
2004 861 178 489 $630,562.29 0.4426811
2005 884 209 573 $859,165.89 0.4384489
2006 854 155 447 $464,085.92 0.4436487
All 13685 5044 4358 $823,252.45 0.66
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Figure3: Monthly Number of M&A and Monthly M&A Index from 1995 to 2006
Table3: The Relation between Abnormal Returns and the Book-to-Market Ratio
The table presents the buy-and-hold abnormal returns of 4358 sample firms which is the difference between the buy-and-hold return of sample firms and the buy-and-hold return of control firms. I put t from 1 to 252 in order to calculate a year’s abnormal return. Then I used the equally weighted method to calculate average abnormal returns. Time lags of 1 month(s) are indicated by t-1 and time futures of 1month(s) are indicated by t+1 and so forth. According to the book-to-market ratio, I ranked them it to five levels. The largest level defines High B/M.
The smallest level defines Low B/M. The three middle levels define Mid B/M. The number in brackets is a p-value using the bootstrap method.
Time Total Low book-to-market Mid book-to-market High book-to-market
Number 4358 1457 1747 126
* 10% significance level **5% significance level ***1% significance level
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Table 4: Negative Binomial Regression Estimations of the M&A Index Level at t+1 point
The dependent variable in all negative binomial regression models is a M&A index level at a t+1 point.
Independent variables are the natural logarithm of the M&A index l (ln_ M&A index), the natural logarithm of the number of M&As (ln_NoM&As) and I define a hot period as at least three consecutive months where the number of M&A exceeds the upper quartile. The cold period is defined as at least three consecutive months where the number of M&As are below the lower quartile. The M&A index are set as 100 in January 1995. Time futures of 1 month(s) are indicated by t+1. In each model, the α is significantly different from 0, indicating that the negative binomial regression model fits the data better than the poisson regression model. N denotes the number of observations. Since we apply explanatory variables, we have 144 observations from July 1995 to December 2006. The theoretical measure for the models’ explanatory power is the Pearson Chi-Square. The chi-square statistic is calculated by finding the difference between each observed and theoretical frequency for each possible outcome, squaring them, dividing each by the theoretical frequency, and taking the sum of the results.
Pearson Chi-Square 0.9734 0.9844 0.9586 0.9537
* 10% significance level **5% significance level ***1% significance level
Table5: The Relation between Abnormal Returns and the Method of Payment
The table presents the buy-and-hold abnormal returns of 4358 sample firms that is the difference between the buy-and-hold return of sample firms and the buy-and-hold return of control firms. I put t from 1 to 252 in order to calculate a year’s abnormal return. Then I used the equally weighted method to calculate average abnormal return. Time lags of 1 month(s) are indicated by t-1 and time futures of 1month(s) are indicated by t+1 and so forth. The method of payment in cash presents the acquiring firm mergers the target of at least 50% in cash.
The method of payment in stock presents the acquiring firm mergers a target of at least 50% in stock. The number in brackets is a p-value using the bootstrap method.
Time Total Cash Stock
* 10% significance level **5% significance level ***1% significance level
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Table 6: Negative Binomial Regression Estimations of the M&A on a Stock Merger Index Level at the t+1 point
The dependent variable in all negative binomial regression models is a M&A index level at the t+1 point.
Independent variables are the natural logarithm of the M&A index (ln_ M&A index), the natural logarithm of the number of M&As on stock mergers (ln_NoM&As) and I define a hot period as at least three consecutive months where the number of M&As exceed the upper quartile. The cold period is defined as at least three consecutive months where the number of M&As below the lower quartile. The M&A index are set 100 in January 1995. Time futures of 1 month(s) are indicated by t+1. In each model, the α is significantly different from 0, indicating that the negative binomial regression model fits the data better than the poisson regression model. N denotes the number of observations. Since we apply explanatory variables, we have 144 observations from July 1995 to December 2006. The theoretical measure for the models’ explanatory power is the Pearson Chi-Square. The chi-square statistic is calculated by finding the difference between each observed and theoretical frequency for each possible outcome, squaring them, dividing each by the theoretical frequency, and taking the sum of the results.
Model I II III IV
Indep. Variables
M&A index t=0 0.0009 0.0009
(0.0001)*** (0.0001)***
Pearson Chi-Square 11.1188 1.0775 11.0765 1.0862
* 10% significance level **5% significance level ***1% significance level