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Realized volatility and acquisitions

Realized volatility and acquisitions. Sean Puneky 25 February 2009. A New Focus. I’ve decided to focus more on acquisition analysis as narrowing down which dates to study for an ad campaign involves too much guesswork Have been reading papers on event study methodology.

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Realized volatility and acquisitions

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  1. Realized volatility and acquisitions Sean Puneky 25 February 2009

  2. A New Focus • I’ve decided to focus more on acquisition analysis as narrowing down which dates to study for an ad campaign involves too much guesswork • Have been reading papers on event study methodology

  3. What I’ve been reading • Event-study methodology under conditions of event-induced variance (Boehmer, Musumeci, Poulsen, 1991) • Volatility Clustering and Event-induced Volatility: Evidence from UK M&A (Balaban, Constantinou, 2006) • Divergence of Opinion and Post-Acquisition Performance (Alexandridis, Antoniou, Petmezas, 2007)

  4. Theory • Announcement of a merger should have a significant effect on the Realized Volatility of a stock • Realized Volatility might be effected differently if the merger is announced over the weekend vs. during the week vs. during the trading day

  5. The Puneky Index® • Complied from twenty-seven stocks in the S&P 100, three stocks from each sector of the economy • Attempted to choose the three stocks in each sector from three different industries but that wasn’t always possible • Available data runs from: April 4th, 1997 through December 31st, 2008

  6. The Puneky Index®

  7. Returns: The Puneky Index®

  8. S&P 500 Returns

  9. Pindex: Annualized RV (8min)

  10. Statistical Comparison: Pindex and S&P500 Conclusion: The Pindex is a somewhat valid proxy for the S&P500

  11. Microsoft Analysis • Test whether or not announcement of acquisitions by Microsoft in the year 2007 had a significant impact on realized volatility • Methodology: Regress “MSFT log(RV) – Index log(RV)” on binary variable containing whether or not an acquisition was announced on that day

  12. Data • Of 247 total observations, the binary variable was “True” only 12 times • Acquisitions range in size from small software makers to multi-billion dollar deals • All acquisitions, no matter the scope, were treated the same

  13. STATA Regression regress diff binary Source | SS df MS Number of obs = 247 -------------+------------------------------ F( 1, 245) = 0.02 Model | .009389691 1 .009389691 Prob > F = 0.8827 Residual | 105.38513 245 .430143388 R-squared = 0.0001 -------------+------------------------------ Adj R-squared = -0.0040 Total | 105.39452 246 .428433007 Root MSE = .65585 ------------------------------------------------------------------------------ diff | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- binary | .028678 .1941022 0.15 0.883 -.353644 .411 _cons | 3.169239 .0427831 74.08 0.000 3.084969 3.253508

  14. Conclusions • The binary variable is clearly not significant • Many sources of error here • In future, I will attempt to use either only large or only small acquisitions or add a variable for acquisition size • I also want to extend study to other stocks or time periods

  15. Extensions • Need to research more common methodologies for this type of study • Expand to other equities, and perhaps bring in returns

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