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Discussion Notes for Presentation at Yale University October 4, 2001

Discussion Notes for Presentation at Yale University October 4, 2001 The Effect of Takeovers on Shareholder Value. Sanjai Bhagat University of Colorado, David Hirshleifer Ohio State University, Robert Noah Analysis Group (Cambridge, MA) http://bus.colorado.edu/faculty/bhagat.

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Discussion Notes for Presentation at Yale University October 4, 2001

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  1. Discussion Notes for Presentation at Yale University October 4, 2001 The Effect of Takeovers on Shareholder Value Sanjai Bhagat University of Colorado, David Hirshleifer Ohio State University, Robert Noah Analysis Group (Cambridge, MA) http://bus.colorado.edu/faculty/bhagat

  2. The Question Do takeovers improve target and bidder firm value? Other stakeholders not considered: • Employees • Customers/Suppliers • Bondholders

  3. Two important challenges to estimating value effects of takeovers • I: Truncation Dilemma (of Window Length) • Short return window: Since not all bids succeed, return is only a fraction of the value effects of successful takeovers. • Long return window: Can capture full value effects. But, return includes greater noise, and raises questions of benchmark specification.

  4. Two important challenges to estimating value effects of takeovers • II: Revelation Bias • Bidder’s return at the time of bid gives a wrong estimate of the market’s valuation of the bidder’s gain from takeover, because • Some bidders deliberately time bid announcement with unrelated negative announcements. Wall Street’s version of Wag the Dog (WSJ 12/18/98). • The form of the offer and the very fact of an offer may convey information about the bidder’s stand-alone value.

  5. “It's Wall Street's version of `Wag the Dog.’ ” • “Over the past week, both Mattel and Coca-Cola have announced acquisitions on the same day they also issued warnings about disappointing earnings. ... No one is suggesting that either company unveiled its acquisition solely to divert attention from its problems... But it is also clear that the acquisitions, like the [Iraq] bombings, helped shift attention away from other less favorable developments.'' • WSJ, `Heard on the Street', 12/18/98, p. C1

  6. Revelation Examples • Returns to bidding firm shareholders. • Fact of an Offer • Good news • Bidder expects high cash flow. • Bad news • Poor internal investment opportunities. • Bidder management with empire-building propensities. • Cash vs. Exchange Offer • Stock Offer: Bad news, lemons problem with equity issuance • Cash offer: Good news that not issuing equity.

  7. Goals of the Paper • Document stock returns associated with tender offers in a comprehensive dataset (1962-97; soon to be through 2000). • Develop and apply a method to address the truncation dilemma. • Develop and apply a method to disentangle shareholder value improvements from revelation about stand‑alone value. • Examine several economic hypotheses about takeover contests.

  8. Solution to Dilemma of Window Length problem • Probability Scaling Method • Like traditional methods, uses short return window. • Method adjusts return from short window upward to reflect the probability of success of bid.

  9. Solution to Dilemma of Window Length and Revelation Bias problems • Intervention Method • Focuses on the returns to the bidder when something happens (while the bid is outstanding) that changes the probability of success of the bidder.

  10. What might change the probability of success of a bidder? • Litigation by target firm. • Arrival of other bidders. • Objection by a government regulatory agency (FTC, Dept. of Justice). • Defensive measures by target (poison pill, lock-up provision).

  11. Arrival of a second bidder: • Decreases probability of success of the first bidder. • If takeover is in the interest of the first bidder, the first bidder’s stock price declines at the arrival of the second bidder. • If takeover is not in the interest of the first bidder, the first bidder’s stock price rises at the arrival of the second bidder. • Note: Decline/rise in the first bidder’s stock price is not related to the stand-alone value of the first bidder, but only reflects value from the takeover.

  12. Findings • Value improvements from tender offers are positive in 95% of the sample using the Intervention Method (89% using the Probability Scaling Method), and are on average 50% of target value using either method. • Sample average revelation effect is zero. But time variation in revelation effects. • Average perceived value improvement larger than estimates based on short-window returns. • Bradley-Desai-Kim (1988): 30% of target value. • Intervention and Probability Scaling Methods: 40% of target value.

  13. Findings • Bidders do not profit from buying targets: Average premia about the same as the value improvement. • Similar value improvements pre- and post-Williams Act. • Takeover by a large bidder of a small target creates a greater value improvement. (=> ‘Cultural’ clashes more severe in marriage of equals.)

  14. Findings • Value improvements as a fraction of target value not especially high during merger boom of 1992-1997. However, these takeovers have created large dollar increases in value because of their large scale! • Larger value improvements for friendly (business complementarities) takeovers compared to hostile (discipline of bad managers) takeovers.

  15. Illustration of the Truncation Dilemma Target Stand‑Alone Value: $ 100 Target Post‑Takeover Value: $ 140 Bidder Stand‑Alone Value: $ 200 Prior probability of successful bid: 0 Post-offer probability of success: 0.6 Combined pre-bid expected value: 100 + 200 = 300. Just after the initial bid: 100 + 200 + .6(40) = 324. Combined equity return: (324/300) - 1 = 8.0%. True percentage value improvement: (40/300) = 13.33%

  16. Illustration of the Truncation Dilemma (2) Stock market’s prior perception of bidder + target discounted value: 100 + 200 = 300. Stock market’s post-bid combined valuation: 100 + 250 = 350. Combined bidder/target equity return: (350 - 300)/300 = approx. 16.7%

  17. Illustration of the Intervention Method Case 1: Large Improvement from Takeover Target Stand‑Alone Value: $ 100 Target Post‑Takeover Value: $ 140 FB Stand‑Alone Value: $ 250 E[Price FB Pays|FB Succeeds]: $ 120 E[Price FB Pays|Competing Bid, FB Succeeds]: $ 130 Pr(FB Succeeds) .6 Pr(FB Succeeds|Competing Bid) .4

  18. Stock Price Reaction of FB to Competing Bid Stock price when FB announces offer: 250 + .6(140 ‑ 120) = 262. When competitor appears: 250 + .4(140 ‑ 130) = 254. FB's stock return: (254 ‑ 262)/262 = ‑ 3%.

  19. Sources of FB’s Return FB’s price reaction to the competing bid reflects: (1) FB pays more if he succeeds. (2) FB has a lower probability of succeeding. (2) ==> FB return decreases with improvement.

  20. Case 2: Zero Improvement from Takeover Post‑takeover value of target = $100. Stock price when FB announces offer: 250 + .6(100 ‑ 120) = 238. When competitor appears: 250 + .4(100 ‑ 130) = 238. Stock return = 0%.

  21. One-to-One Mapping of Value Improvement to Stock Returns Improvement Stock Return 40%-3% 0%0% Given the other parameters, 1:1 mapping. Can infer improvement without revelation bias.

  22. Specifics of the Intervention Method 4 dates t = 0: Time prior to first bid. t = 1: Arrival of first bid. t = 2: Time prior to arrival of competing bid. t = 3: Arrival of competing bid.

  23. y : Market value of bidder not related with takeover. t : Bidder's profit from takeover conditional on t. Pt : Bidder's price at t. Hence, P1 = y + 1 , P3 = y + 3 . (11)

  24. V0 : Non-takeover target value. V1 , V3 : Post-takeover target value reflecting takeover gains. B1 , B3 : Price ultimately paid by a successful first bidder.  : Fraction of target held by first bidder prior to first bid. Hence, 1 = Pr(S|q1) {a[V1 - V0] + (1-a)[V1 - B1]}, 3 = Pr(S|q3) {a[V3 - V0] + (1-a)[V3 - B3]}. (12)

  25. Assume, V3 = V1 = V . [See footnotes 16, 17] Also, R3 = P3 / P1 - 1. (V/V0) - 1 = {[R3(P1/V0)] / [Pr(S|q3) - Pr(S|q1)]} + a + {(1 - a) [l(B1/V0) + (1 - l)(B3/V0)]} - 1 , (14) where, l = Pr(S|q1) / [Pr(S|q1) - Pr(S|q3)]. Strong Agency / Hubris Hypothesis: (V / V0 ) - 1 = 0. (V / V0 ) - 1 > 0. Implies joint value improvement.

  26. The Probability Scaling Method of Estimating Value Changes Value Improvement = [Combined Initial Bidder and Target Return] / [(Probability a First Bidder arrives and wins) + (Probability a First Bidder arrives but a Later Bidder wins)] (9)

  27. DATA • MERC and SDC datasets. • Table 1: 794 tender offers during 1962-1997. • Figure 2 : Percentage of • Successful takeovers, • Multiple (two) bidder takeovers, • Hostile takeovers, • All cash offers. • Figure 7 : Median dollar shareholder returns to • Bidders, • Targets, • Combined entity, • over various sub-periods during 1962-1997.

  28. Table 4, Model B: Entry of second bidder significantly lowers probability of success of first bidder.

  29. ESTIMATES OF VALUE IMPROVEMENTS (V/V0) - 1 = {[R3(P1/V0)] / [Pr(S|q3) - Pr(S|q1)]} + a + {(1 - a) [l(B1/V0) + (1 - l)(B3/V0)]} - 1 , (14) where, l = Pr(S|q1) / [Pr(S|q1) - Pr(S|q3)]. (V/V0) - 1: IRATIO : Joint value improvement brought by takeover. R3 : Bidder abnormal return at entry of second bidder = -.3% (median = -.3%). P1/V0 : Relative size of bidder versus target = 4.68 (1.80).

  30. Pr(S|1) : Unconditional probability of success of first bidder = 530/794 =.6675. Pr(S|3) : Probability of success of first bidder given arrival of competing bid = 35/137 =.2555.  : Fraction of target's equity owned by first bidder = .025 (median = .000). B1/V0 : Average price at which first bidder wins in full sample = 1.435 (1.384). B3/V0 : Average price at which first bidder wins given arrival of competing bid = 1.440 (1.421).

  31. Table 3 IRATIO = 49.6% (median = 43.4%). 95% of IRATIO estimates are positive. Histogram of IRATIO: Figure 8.

  32. SENSITIVITY ANALYSIS 1. Use transaction-specific probabilities of success using the logit models of Table 4. Last four rows in Table 3. 2. Sensitivity of mean of estimated IRATIO to simultaneous variation in each of the estimated parameters in the direction of lower IRATIO: Mean IRATIO remains positive with simultaneous 12% shift in all four estimate parameters. 3. Table 3: Parameter estimates from Bhagat-Shleifer-Vishny (1990). IRATIO= 36.7% (21.2%)

  33. SENSITIVITY ANALYSIS • 4. Table 3: Parameter estimates from Betton-Eckbo (1998). IRATIO= 52.2% (47.0%) • 5. Parametric derivation of distribution of mean IRATIO. • 6. Model Specification: Table 10 • If the arrival of a competing bid causes an upward revision in the expected post-takeover value of the target to the first bidder => K > 1. • If the first bidder fails to acquire the target, the first bidder will successfully acquire another similar target at a similar premium=> g > 0. • An unsuccessful first bidder can sometimes profit by selling its holdings to a successful competing bidder=> Pr(S2|q3) > 0.

  34. Comparison of the Intervention Method, and • Probability Scaling Method to • Traditional Methods • Table 5 • Traditional Method combined return (CIBR) = 28.3% • Intervention Method combined return (IRATIO) = 43.4% • Probability Scaling Method combined return = 41.1% • Work to be done • Estimate revelation effects in different classes of transactions. • Is revelation more adverse when pay with equity than when pay cash? • Different revelation in hostile versus friendly transactions?

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