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The effectiveness of tightening illegal insidertrading regulation: the case of corporate takeoversAnthony Boardman, Z. Stuart Liu, Marshall Sarnat & Ilan Vertinsky

Version of record first published: 07 Oct 2010.

To cite this article: Anthony Boardman, Z. Stuart Liu, Marshall Sarnat & Ilan Vertinsky (1998): The effectiveness oftightening illegal insider trading regulation: the case of corporate takeovers, Applied Financial Economics, 8:5, 519-531

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0960 Ð 3107 Ó 1998 Routledge 519

Applied Financial Economics, 1998, 8, 519 Ð 531

The e¤ ectiveness of tightening illegal insidertrading regulation: the case of corporatetakeovers

ANTHONY BOARDMAN* , Z . STUART LIU § ,MARSHALL SARNAT § and ILAN VERTINSKY*

*University of British Columbia, § Dalhousie University and §ICAS a¦ liated to theUniversity of Manchester

The impact of tightening the regulation of illegal insider trading in the United States isanalysed. It is argued that more e� ective regulation will reduce the price run-up intarget companies prior to takeover announcements. By comparing stock price re-sponses to takeover announcements during two distinct regulatory regimes Ð a regimeof lax regulation, prior to 1985, and a regime of stricter regulation, 1989 Ð 1991Ð inferences are made about the e� ectiveness of changes in illegal insider tradingregulation. Using this approach, strong evidence is found that stricter regulation wase� ective in reducing illegal insider trading. Tightening the regulation had a greaterimpact on negotiated takeovers than on those initiated by bidding. Evidence alsoindicates that, for negotiated takeovers, but not for takeovers initiated by bidding,insiders associated with acquiring ® rms sought fewer but more pro® table takeoversafter the e� ective tightening of regulation, possibly to compensate them for thereduction in the pro® t opportunities from illegal insider trading.

I . INTRODUCTION

During the 1980s several steps were taken to enhance compli-ance with insider trading regulations in the USA. In August1984, Congress passed the Insider Trading Sanction Act of1984 (ITSA), which signi® cantly increased both the civil andcriminal penalties for illegal insider trading. In November1988, Congress passed the Insider Trading and SecuritiesFraud Enforcement Act (ITSFEA), which increased the max-imum jail sentence to ten years and the maximum criminalpenalties from $100 000 to $1 million. It also held seniormanagement accountable for the failure of any employee tocomply with insider trading regulations. The enactment ofITSA and ITSFEA, accompanied by intensi® cation in theSecurities and Exchange Commission’s (SEC) enforcementprocedures, constitute landmark shifts in the e� ective regula-tion of insider trading in the USA. The major purpose of thispaper is to examine the impact and e� ectiveness of theseregulatory changes on illegal insider trading.

Although there is a very large literature on insider trad-ing, few studies have examined the impact of changing

insider regulations. Ja� e (1974) studied the e� ects ofstrengthening insider trading regulations between 1961 and1966 by comparing insider trading pro® ts in ® ve sub-peri-ods during the years 1961 to 1967. He estimated the averageabnormal returns of securities involved in insider trading asde® ned by the O� cial Summary of Insider Trading (theSummary), a report published by SEC on transactions ex-ecuted by corporate o� cers, directors and owners of 10% ormore of the common stock of publicly traded ® rms. Hefound no statistically signi® cant di� erences among the ab-normal returns earned in various sub-periods, and con-cluded that the regulatory changes had little or no e� ect onthe share prices in question. Seyhun (1992) extended Ja� e’sstudy by examining both insider trading pro® tability andvolume between 1975 and 1989, again using the Summary asthe database. Although a series of e� orts to strengthen insidertrading regulations were made during this period, Seyhun didnot ® nd a signi® cant impact of these changes on insidertrading pro® tability or on the volume of transactions.

An alternative way to analyse the impact of regulatorychanges on illegal insider trading, which we adopt in this

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1 Note that any regulation that restricts or prohibits accumulation of large stakes in target companies by insiders will dampen price run-upsprior to the announcement. For analysis of the impact of the Williams Act see Jarrell and Bradley (1980), Schipper and Thompson (1983)and Malatesta and Thompson (1993). See also OCE (1987).2 For mergers, Dodd (1980) estimates the average price run-up in the target’s shares during the ® ve days prior to a takeover announcementis 11.5%, with most of the increase in the last two days prior to the announcement. Keown and Pinkerton (1981), Asquith (1983) and Eckbo(1983) report similar patterns. Dodd (1980) reports a similar pattern for tender o� ers, although the results are not so pronounced. Incontrast, the OCE (1987) ® nds the stock price of target ® rms in 172 successful tender o� ers increases on average by 39% by the close of theday before the announcement. See also Jarrell and Bradley (1980), Jensen and Ruback (1983), Bradley et al. (1988), Jarrell et al. (1988), andJarrell and Poulsen (1989).

study, is to examine stock price responses to takeover an-nouncements, before and after changes in insider tradingregulations. It makes sense to focus on takeovers becausemost insider trading violations appear to be related totakeovers, probably due to the large pro® t opportunities.For example, Meulbroek (1992) found that of the 183 insidertrading cases initiated by the SEC in civil or administrativecases between 1980 and 1989, 145 (79%) were takeover-related.

Insider trading was ® rst regulated in the United States bythe Securities and Exchange Act of 1934. This Act requiresinsiders, which includes anyone who holds more than 10%of the target’s shares, to disclose material inside informationor refrain from trading. The Williams Act of 1968 and itsAmendments of 1970 broaden the disclosure requirementsand require a purchaser to ® le a Schedule 13D beforelaunching a tender o� er for more than 10% of the target’sstock.1

Despite the regulations, evidence on corporate mergersand takeovers reveals that there are substantial price run-ups in the stock price of targets prior to the takeoverannouncements. 2 Keown and Pinkerton (1981) argue thatthese price run-ups are, at least in part, due to insidertrading associated with the impending takeovers. Jarrelland Poulsen (1989) and Meulbroek (1992) estimated theproportion of the pre-announcement price run ups that isattributable to insider trading, but reached di� erent con-clusions. Jarrell and Poulsen (1989) found that a large frac-tion of the pre-announcement price run-up is due to pressspeculation and inaccurate identi® cation of the actual take-over announcement date. In contrast, Meulbroek (1992)concluded that 43% of the pre-announcement price run-upis due to illegal insider trading. If illegal insider trading hasan e� ect on stock market prices, then changes in illegalinsider trading activity due to regulatory changes will leadto movements of stock prices with di� erent magnitudes orwith other di� erent market response characteristics thanbefore. Comparison of the stock market behaviour oftargets near takeover announcements, before and after regu-latory changes, allows us to make inferences about thee� ectiveness of the regulatory changes in reducing illegalinsider trading. An advantage of this indirect approach isthat it does not require the explicit identi® cation of illegalinsider trades, which may be almost impossible to determineaccurately.

To test the e� ectiveness of the regulatory changes,we distinguish between two distinct regulatory regimesÐ a regime of lax regulation, prior to 1985, and a regime ofstricter regulation, 1989 Ð 1991. We then compare the stockprice behaviour near the takeover announcement ofa sample of takeover targets from the ® rst period toa sample of takeover targets from the second period. Inorder to make the samples comparable, the tagets had tomeet a number of selection criteria. Targets were only UScompanies. Also, they were only from the six industries thatwere most active in corporate takeovers. Furthermore, thevalue of the takeover had to exceed $100 million. In addi-tion, we separated the sample into targets in takeoversinitiated by bidding and targets in negotiated takeovers, andanalysed both groups separately. Finally, we excluded tar-gets that, in the two months prior to the successful takeoverannouncement, were reported as being involved in take-overs or were openly seeking an acquirer, as these com-panies could be thought to be in play’ and would be thesubject of media speculation. In the end, we constructeda sample with 37 targets in negotiated takeovers and 17targets in takeovers initiated by bidding during the earlierperiod, and 31 targets in negotiated takeovers and ninetargets in takeovers initiated by bidding during the laterperiod.

Even after applying these criteria, other factors unrelatedto the takeover might a� ect the targets’ stock prices. Thesefactors could be random or systematic. Three potentialfactors, which we did not explicitly control for, are thebidder’s foothold acquisition in the target, the method ofpayment, and whether the bid was friendly or hostile. Ifthese factors did not change systematically from the ® rstperiod to the second, they are not likely to bias our results.By selecting a reasonably large sample we expect that theseand other potentially biasing factors will average out’. Fur-thermore, these characteristics primarily a� ect the takeoverpremia which is related to post-announcement price move-ments and do not appear to be related to pre-announcementprice movements.

Our results strongly indicate that the tightening of regula-tion in the late 1980s was e� ective and signi® cantly reducedillegal insider trading. We also ® nd that tightening theinsider trading regulation had a greater impact on illegalinsider trading during negotiated takeovers than duringtakeovers initiated by bidding. Finally, we ® nd that after the

520 A. Boardman et al.

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3 See also the Companies Act of 1985 and the Companies Securities (Insider Dealing) Act of 1985.4 Council Directive 89/592 (November 13, 1989) Ð Under the directive all EEC member States were required to introduce insider tradingprohibitions by 1 June 1992. In practice, a great majority of the States had already introduced criminal sanctions, while others (e.g.Germany and Luxembourg) relied mainly on selfÐ regulatory instruments.5 The analysis is assumed to be carried out in a continuous time stochastic economy. Let (E , F , P) be a complete probability space. E is anevent set which consists all possible states of the world in a closed time interval [t0 - T 1 , t0 + T 2 ]; F is a s -® eld of subsets of E ; P isa probability measure on F . Uncertainty is resolved over time according to some ® ltration F = {Ft , t Î [t0 - T 1 , t0 + T2 ]}, where Ft is anincreasing family of sub- s -® elds of F such that Fs Ì Ft for s < t, and Ft0 + T 2

= F . A stochastic process Xt is observable at t meaning thatXt is Ft measurable or F-adapted. A stochastic process Xt is not observable at t meaning that Xt is not Ft measurable but Ft+ measurableinstead, where Ft+ =

Ts> t

Fs .6 The exogenous supply of the target ® rms’ shares can be interpreted as exogenous supply provided by noise traders.

introduction of tighter regulation, insiders associated withacquiring ® rms sought fewer but more pro® table takeoversif the takeover was negotiated, but not if it was initiated bybidding.

II . INSIDER TRADING REGULATIONSOUTSIDE THE USA

Although insider trading law evolved at a much faster pacein the United States, by now almost every country witha modern securities market has adopted some form of con-trol prohibiting or restricting insider trading. Practice, how-ever, varies widely from country to country. German law,for example, contains no speci® c prohibition of insider trad-ing (Rosen, various dates).

In the UK, the treatment of insider trading was origi-nally left to common law remedies which proved inadequatefor the task. The debate leading up to the restructuringof the British ® nancial system, or Big Bang’ as it iscommonly known, provided the impetus for a sweepingchange in the regulatory climate, culminating in the Finan-cial Services Act 1986.3 In essence, this replaced the self-regulation which up to that time had characterized British® nancial markets with what has been called a system ofself-regulation within a statutory framework’, or practi-tioner-based statutory backed regulation’, i.e. a complexcombination of statutory provisions backed up by the gen-eral law with much of the detail left to practitioners (McVea,1993).

Another, and perhaps the most signi® cant change in theregulation of insider trading outside of the United States,was the directive adopted by the European Community in1989 harmonizing the conditions for prohibiting insidertrading in publicly traded securities.4 The EEC directiveimposes minimum criteria de® ning the prohibition ofinsider trading; member States are of course entitled tointroduce more stringent rules. Three types of activity areprohibited: (a) trading as an insider; (b) disclosing insideinformation; (c) tipping or procuring third parties. But evenafter these changes, US law on insider trading is clearly themore pervasive.

III . MODELLING THE EFFECTS OFREGULATORY CHANGES ONPRICE MOVEMENTS

Meaningful inferences about illegal insider trading dependon the extent to which the market reveals inside informationthrough stock price adjustments. Inside information may befully revealed, partially revealed or not revealed at all. Inthis section we develop a model that permits us to makeinferences about the impact of regulatory changes on illegalinsider trading under di� erent amounts of information rev-elation. The model suggests that the impact of regulatorychanges on illegal insider trading can be inferred by examin-ing stock price movements if inside information is partiallyrevealed to the market. However, if information is notrevealed at all or is fully revealed, inferences with respect tothe e� ects of the regulatory changes cannot be made.

Regulatory changes, insider trading and price movements

Consider a successful corporate takeover that occurs duringa time period [t0 - T 1 , t0 + T 2 ], T 1 > 0 and T 2 > 0.5 Attime t0 , the takeover attempt is announced and the target isidenti® ed. There are two types of traders, referred to as(illegal) insiders and outsiders, engaged in the exchange ofthe target ® rm’s shares during the period [t0 - T1 ,t0 + T 2 ]. Suppose the total supply of the shares is anexogenously determined stochastic process St , which is notinstantaneously observable to either type of trader.6 Sup-pose further that the demand of each type of trader for theshares is a time-varying function which is exogeneouslygiven and known only to that type of trader (to be speci® edbelow).

Let the superscript i denote insiders and let o denoteoutsiders. Each type of trader possesses information at timet denoted by Y k

t (k = i, o). In addition, both types of tradersobserve the market signal V t , which represents the gain inmarket value of the target due to the takeover. The twotypes of traders, however, have di� erent beliefs about thegain in the intrinsic value of the target from the takeover.Let V i

t (V t , Y it) denote insiders’ expectations at time t of the

gain in the intrinsic value of the target from the takeover,

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7 Implicitly, we assume that v is the intrinsic value of the gain from the takeover which is a random variable measurable with respect to F .Let F k

t = s {V t , Y kt} (k = i, o) be the information sets of k type of traders, which is the s -® elds generated by the market signal V t , and the

private information Y kt . V t and Y k

t are stochastic processes measurable with respect to Ft . Then V kt (V t , Y k

t ) = E{v | F kt }.

8 It is possible that higher quality information might lead insiders to have a lower intrinsic value of the takeover than outsiders. But, ifinsiders had a lower value than outsiders they would not o� er a high enough price to successfully e� ect a takeover. They would be morelikely to sell the stock short than to buy it. If the prospects for the takeover were bad, it would not take place. By limiting our sample toonly successful takeovers, this situation is not likely to apply to our data.9 For the proof of this proposition, see Appendix A.

based on information available to them at time t. Similarly,let V o

t (V t , Y ot ) denotes outsiders’ expectations at t of the gain

in the intrinsic value of the target from the takeover, basedon information available to them at t. The functions V k

t ( ´ , ´ )(k = i, o) vary over time re¯ ecting other factors, in additionto V t and Y k

t (k = i, o), which change over time and alsoin¯ uence traders’ expectations.7

Prior to the takeover announcement, we assume an asym-metric information structure. During this period, insidersare better informed than outsiders in the sense that theyhave a higher expected value of the takeover than out-siders:8 V i

t (V t , Y it) > V o

t (V t , Y ot ), t < t0 . Assume further that

the market signal conveys no additional information toinsiders, i.e., ¶ V i

t/ ¶ V t = 0 for t < t0 . However, it may conveyinformation to outsiders, depending on whether inside in-formation is fully revealed, partially revealed or not revealedto the market. After the takeover announcement, insidersand outsiders are fully informed. Speci® cally, we assume:

Assumption (AS1)

Before the takeover announcement (i.e. t < t0 ), ¶ V it/¶ V t = 0,

and

(i) under full information revelation, ¶ V ot /¶ V t > 0 and

V it = V o

t ;(ii) under partial information revelation, ¶ V o

t /¶ V t > 0 andV i

t > V ot ; and

(iii) under no information revelation, ¶ V ot / ¶ V t = 0 and

V it > V o

t .

After the takeover announcement (i.e. t > to), ¶ V kt / ¶ V t = 0

(k = i, o) and V it = V o

t .

Let Dkt (V k

t , l) denote the demand for shares in the target® rm by each type of trader (k = i, o) and assume thatdemand is a function of the expected gain in the value of thetarget, V k

t , and the tightness’ of the regulatory environment,l. We assume that the demand for shares by insiders andoutsiders increases as the expected gain in the value of thetarget increases: ¶ Dk

t / ¶ V kt > 0, for k = i, o.

A higher value of l indicates tighter insider trading regula-tion. If the regulation became tighter, insiders would tradeless and their demand for shares would be less than other-wise, that is, ¶ Di

t/ ¶ l < 0. If the regulatory changes wereine� ective, then there would be no e� ect on the demand byinsiders, that is ¶ Di

t/ ¶ l = 0. As the regulations do not apply

to outsiders, tightening them or their enforcement wouldhave no direct impact on outsiders’ demand; consequently,¶ Do

t/ ¶ l = 0.The market value of the stock depends on the level of

information revealed to the market. Prior to the takeoverannouncement, under full revelation, insider information isfully revealed to the market and so V t = V i

t = V ot ; under no

information revelation, the market does not re¯ ect anyinside information and so V t = V o

t ; under partial informa-tion revelation we assume that the gain in the value of thetarget from the takeover, V t , is a weighted average of thetwo types of traders’ expectations, with the weights propor-tional to each type of traders’ demand. Formally.

Assumption (AS2)

Before the takeover announcement (i.e. t < t0 ):(i) under full information revelation, V t = V i

t ;(ii) under no information revelation, V t = V o

t ;(iii) under partial revelation,

V t =Di

t

Dit + Do

tV i

t + Dot

Dit + Do

tV o

t (1)

After the takeover announcement (i.e. t > t0 ), V t = V it = V o

t .

Notice that under partial revelation the total demand at t,which equals the total supply St , is not instantaneouslyobservable to any type of trader; outsiders are unable tointerpret precisely the information that insiders possess.

Using assumptions (AS1) and (AS2), it can readily beshown that if a change in V t arises when the regulation istightened, information is partially revealed to the marketand illegal insider trading regulation is e� ective in reducingillegal insider trading. Thus, the change in V t can be used asan indirect measure of illegal insider trading.

Proposition 19

If the regulatory change is e� ective, then before the an-nouncement (i.e. t < t0 ):

(i) under full revelation or no revelation, dV t/dl = 0;(ii) under partial revelation, if dV o

t /dV t < 1, thendV t/dl < 0.After the announcement (i.e. t > t0 ), then dV t/dl = 0.If the regulatory change is ine� ective, then dV t/dl = 0.

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1 0 Boardman et al. (1997) propose a variety of functional forms for modelling adjustments in stock price returns, allowing for both S-shapedmarket response functions and more ¯ exible functional forms. Nonetheless, their model requires the market adjustment process to operateover a few days (observations) and assumes the response function is continuous. The model used here re¯ ects our desire to capture sharpprice responses to the takeover announcement. This model speci® cation is convenient to estimate and ¯ exible enough to cover a large classof price reactions.1 1 For proof of this proposition, see Appendix A.

The condition ¶ V ot / ¶ V t < 1 under partial information rev-

elation can be interpreted as a stability condition. It con-strains outsiders from over-adjusting their expectationsupon observing the market signal V t .

By de® nition, V t is the di� erence between the value of thetarget at time t due to the takeover and the target’s value attime t without a takeover. Clearly, V t can be expressed interms of the incremental change in the stock price of thetarget ® rm due to the takeover at time t. Transforming theseprices into returns since the beginning of the takeover pro-cess yields the cumulative abnormal returns (CARs) to thetarget ® rm. This correspondence between V t and the cumu-lative abnormal return to the target at time t allows us toanalyse the V ts by examining the cumulative abnormalreturns.

Price movements during corporate takeovers

Building on Boardman et al. (1997), we model the cumu-lative abnormal returns as a di� usion process wherethe expected gain in the value of the target from thetakeover during an interval corresponds to the drift termof the di� usion process. The rate at which the gains arecapitalized into the cumulative abnormal returns dependson the amount and nature of takeover Ð related information¯ owing into the market, which may be quite di� erentbefore and after the takeover announcement. The cumulat-ive abnormal returns may exhibit a signi® cant change inthe drift after the announcement. To capture a poten-tially dramatic change in the rate of capitalization aroundthe announcement, we introduce a segmented di� usion pro-cess with a switch in its mean drift at the announcementtime.

As our focus is on price movements around the an-nouncement, we require a stochastic model in which theCARs are most accurate at the announcement time. Thisconcern leads us to model the cumulative abnormal returnsbuilt on time-re¯ ecting Brownian motion.

Time-reß ecting Brownian motion

Let W 1t be a standard Brownian motion starting at t0 - T 1 ,

and W 2t be a standard Brownian motion starting at t0 .

W 1t and W 2

t are independent. A time-re¯ ecting Brownianmotion is a stochastic process W t such that:

W t = 5 W 1t0

- W 1t

W 2t

t0 - T 1 < t < t0

t0 < t < t0 + T 2(2)

the motion on one side of the re¯ ection point t0 is simply themirror image of the motion on the other side. The instan-taneous variance is 0 when t = t0 and increases as t divergesfrom t0 .

The capitalization process of the takeover event

We model the capitalization process as a segmented di� u-sion process characterized by two Brownian motions withdi� erent linear drifts, switching at the announcement time.Let CARt be the cumulative abnormal returns to the targetat t, t Î [t0 - T 1 , t0 + T 2 ]. Let CA= R* denote the expectedtotal cumulative abnormal return over the entire eventperiod [t0 - T 1 , t0 + T 2 ]. The di� usion process of the CARsis then represented by the following di� erential equation:1 0

dCARt = 5 a CARtdt + s dW t

b (CA= R* - CARt)dt + s dW t

t < t0

t > t0(3)

where a , b > 0. Before the takeover announcement, theCARs are on average increasing or decreasing at an increas-ing rate, a CARt , depending on whether CARt > 0 or < 0.After the announcement, and CARs are on average increas-ing or decreasing at a decreasing rate, b (CA= R* - CARt),depending on whether CA= R* > 0 or < 0. For simplicity, weassume that the di� usion term, s , remains constant beforeand after the announcement.

Proposition 21 1

Let CA= Rt be the expected cumulative abnormal return attime t. The solution to the stochastic di� erential equation(Equation 3) is:

CARt = 5 m ± CA= R*e a (t ± t0 ) + s e t

t0

e a ( t ± s) dW s , t < t0

CA= R* - (1 - m )CA= R*e ± b (t ± t0 )

+ s e t

t0

e ± b (t ± s) dW s t > t0

(4)

where

m ± = limt ­ t0

CA= Rt

CA= R*

is the price run-up from the beginning of the event period tothe moment immediately prior to the announcement, and

m =CA= Rt0

CA= R*

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Table 1. The e¤ ects of regulatory changes on price run-ups

Information revelation

Full revelation Partial revelation No revelation

E� ective regulatory changes dm ±

dl= 0

d m ±

dl< 0

d m ±

dl= 0

dm

dl= 0

d m

dl= 0

d m

dl= 0

Ine� ective regulatory changes dm ±

dl= 0

d m ±

dl= 0

d m ±

dl= 0

dm

dl= 0

d m

dl= 0

d m

dl= 0

is the price run-up from the beginning of the event period tothe announcement at t0 , both expressed as proportions ofthe total price run-up.

When m ± ¹ m , CARt is a right continuous di� usion pro-cess with a jump at t = t0 . The jump re¯ ects the newinformation (the takeover announcement) which arrives att = t0 . With uncertainty, and when the market is e� cient,the parameter m will always be less than 1. If the market isine� cient, mis-pricing may occur and the target may betemporarily over-valued in which case m > 1.

The parameter a measures the rate of price run-up beforethe announcement, re¯ ecting how quickly inside informa-tion is incorporated into the market price and market an-ticipation of the potential takeover event. The parameterb measures the rate of price run-up after the announcement,re¯ ecting how quickly the market reacts to unanticipatednews and how it discounts the price to capture the uncer-tainty associated with the success of the takeover. In gen-eral, the more e� cient the market, the larger will be b . Onthe other hand, greater uncertainty about the outcome ofthe event leads to a smaller b .

IV . CONSEQUENCES OF REGULATORYCHANGES

This section examines the e� ects of changes in insider trad-ing regulation in terms of the parameters of the segmenteddi� usion process. Three hypotheses are developed.

Information revelation and the e¤ ectiveness of regulation

Since m ± is de® ned in terms of the expected cumulativeabnormal returns immediately prior to the announcementrelative to the expected total cumulative abnormal returnover the entire event period, d m ± /dl is an indirect measure ofdV t/dl when t is the moment immediately before the an-

nouncement. Similarly, d m /dl is an indirect measure ofdV t/dl when t = t0 . It follows from Proposition 1 thatd m ± /dl is an indirect measure of the e� ectiveness of changesin the regulatory environment, and d m /dl is always 0 irre-spective of the change in the regulatory environment.

Table 1 summarizes the e� ects of regulatory changes onthe price run-ups ( m ± and m ) under di� erent amounts ofinformation revelation and according to whether the regula-tory changes are e� ective or not. These follow directly fromProposition 1 and from the de® nition of m ± and m . If theregulatory changes were ine� ective, there would be nochange in the price run-up prior to the announcement or inthe price run-up immediately after the announcement. If theregulatory changes were e� ective, there would be no changein the price run-up prior to the announcement if theinformation was fully revealed or not revealed to the mar-ket. However, if the regulatory changes were e� ective andinformation was partially revealed then d m ± /dl < 0. Inall situations, d m /dl = 0. Clearly, we can only determinewhether the regulatory changes were e� ective and infor-mation was partially revealed or not. This leads to Hypo-thesis I.

Hypothesis I: The tightening of regulation in the late 1980swas e� ective and inside information was partially revealedto the market, that is, d m ± /dl < 0 and d m /dl = 0.

Negotiated takeovers versus takeovers initiated by bidding

A negotiated takeover is one in which the managements ofthe target and the acquiring ® rm enter into negotiationsbefore the announcement. This includes takeovers negoti-ated following a secret bid. If, on the other hand, an o� er ismade prior to the announcement and the two parties do notmeet o� cially before the announcement, the takeover isconsidered to have been initiated by bidding. Since negoti-ated takeovers are more susceptible to information leakageand the course of negotiation is usually longer and relatively

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1 2 See especially Jensen and Ruback (1983); see also Kyle and Vila (1991). However, for a competing hypothesis see Jarrell and Bradley(1980), and Malatesta and Thompson (1993).1 3 For proof of this result, see Liu et al. (1995). The variance Ð covariance matrix is a nonÐ diagonal matrix where the o� -diagonal elementsre¯ ect the correlations among the errors.

less secretive than the preparation of a bid, the takeoverprocess in negotiated takeovers is more vulnerable to illegalinsider trading and more responsive to tightening insidertrading regulation. Furthermore, in the case of takeoversinitiated by bidding, insiders have greater opportunities tosubstitute legal for illegal insider trading. Hence we expecta tightening of regulation would have a greater impact onnegotiated takeovers (denoted by superscript N) than onthose initiated by bidding (denoted by superscript B):

Hypothesis II: The impact of tighter regulation is greater onprice run-ups prior to the announcement of negotiatedtakeovers than on price run-ups prior to the announcementof those initiated by bidding, that is,

) dm ± N

dl ) > ) d m ± B

dl )Regulation and the truncation of the distribution of takeovers

Stricter insider trading regulation increases the costs oftakeovers to bidders. Therefore, as regulation tightens, in-siders may seek more pro® table takeovers with higher totalcumulative abnormal returns, thereby truncating the distri-bution of CA= R* for successful takeovers.1 2 Given our rea-soning leading to Hypothesis II, we further posit that thise� ect will be greater on average for negotiated takeoverthan for takeovers initiated by bidding.

Hypothesis III:

dCA= R*N

dl>

dCA= R*B

dl> 0

V. TESTING THE HYPOTHESES

To test our hypotheses we compare the parameters in themodels describing the dynamics of the system before andafter the tightening of regulation. In principle, we can eitherestimate the di� erential equations representing the dynam-ics of the CARs, Equation 3, or its solution, Equation 4. But,as Equation 3 does not contain the crucial parameters m ±

and m , Equation 4 is preferred for our purpose. Unfortunate-ly, when the price reaction is strong around the announce-ment time, the CARs may experience a large jump andestimating Equation 4 alone could be troublesome due tothe non-linear structure of the equation. Daily observationsof stock returns may not be able to identify the parametersof this model (although hourly or more frequent data might

be able to). To overcome this potential problem, we estimatea system of simultaneous equations consisting of a discreteapproximation of the segmented di� usion model (Equation3) and its solution (Equation 4):

D CARt = 5 a CARt + e t

b (CA= R* - CARt ± 1 ) + e t

t = - T , ¼ , - 1t = 1, ¼ , T

CARt = 5 m ± CA= R*e a t + j t

CA= R* - (1 - m )CA= R*e ± b t + j t

t = - T , ¼ , - 1t = 0, ¼ , T

(5)

where D CARt = CARt+ 1 - CARt for t = - T , ¼ , - 1and D CARt = CARt - CARt ± 1 for t = 1, ¼ , T ; day - 1 isthe last trading day before the announcement; day 0 is theday of the announcement or the ® rst trading day after theannouncement if it was made after the close of trading onthe previous day (i.e. - 1 < t0 < 0); m ± is the price run-upto the day immediately prior to the announcement (day- 1); m is the price run-up to the day of the announcement

(day 0); CARt is the cumulative abnormal return at day t;CA= R* is the expected total cumulative abnormal returnfrom - T to T ; a is the rate of price run-up prior to theannouncement; b is the rate of price run-up after the an-nouncement; and e t and j t are given by:

e t = 5 s (W t+ 1 - W t)s (W t - W t ± 1 )

t = - T , ¼ , - 1t = 1, ¼ , T

(6)

j t = 5 s ò t0 e a (t ± s) dW s

s ò t0 e b (t ± s) dW s

t = - T , ¼ , - 1t = 0, ¼ , T

By construction, the error terms, h (where h 9 = (e 9 , j 9 )),have a multi-variate normal distribution with mean zeroand variance Ð covariance matrix V .1 3 Consequently, estima-tion of the system of simultaneous equations (Equation 5)can be carried out by non-linear full information maximumlikelihood estimation that maximizes the concentratedlog likelihood function:

L (a , b , CA= R*, m 0 , m 1 ) = - 12 log | V | - 2T log(h 9 V ± 1h ) (7)

subject to the following constraint, which results from theconstruction of the time-re¯ ecting Brownian motion:

j 0 = 0 (8)

To allow for parameter shifts between the two regulatoryregimes, we specify a model where the parameters of Equa-tion 5 di� er between the two regimes. Let subscript 1 denotethe ® rst regime (lax regulation) and subscript 2 denote the

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1 4 We do not distinguish between domestic takeovers and international takeovers. Although these takeovers may di� er in various ways,they di� er more in terms of stock price premia than in information leakage, see Harris and Ravenscraft (1991) and Kang (1993).1 5 Several studies have shown that cumulative average abnormal returns to the target of unsuccessful takeovers exhibit patterns that di� erfrom those of successful takeovers, c.f. Bradley (1980), Dodd (1980), Asquith (1983), Bradley et al. (1983), Samuelson and Rosenthal (1986).

second regime (tighter regulation). This system, which werefer to as the two-regime segmented di� usion model, con-sists of the following four simultaneous equations:

D CAR1 t = 5 a 1 CAR1 t + e 1 t

b 1 (CA= R*1 - CAR1 t ± 1 ) + e 1 t

t = - T , ¼ , - 1t = 1, ¼ , T

CAR1 t = 5 m ±1 CA= R*e a 1 t + j 1 t t = - T , ¼ , - 1

CA= R*1 - (1 - m 1 )CA= R*1 e ± b 1 t + j 1 t t = 0, ¼ , T

(9)

D CAR2 t

= 5 a 2 CAR2 t + e 2 t

b 2 (CA= R*2 - CAR2 t ± 1 ) + e 2 t

t = - T , ¼ , - 1t = 1, ¼ , T

CAR2 t

= 5 m ±2 CA= R*2 e a 2 t + j 2 t

CA= R*2 - (1 - m 2 )CA= R*2 e ± b 2 t + j 2 t

t = - T , ¼ , - 1t = 0, ¼ , T

where e 1 t , j 1 t , and e 2 t , j 2 t , are de® ned analogously to e t andj t as in Equation 6.

Changes in the parameters between the two regimes arede® ned:

D a = a 2 - a 1

D b = b 2 - b 1

D m ± = m ±2 - m ±

1 (10)

D m = m 2 - m 1

D CA= R* = CA= R*2 - CA= R*1

Assuming that the error terms in the second regime areserially independent of those in the ® rst regime, then theconcentrated log likelihood function of the two regimemodel, L *, is simply the sum of the concentrated log likeli-hood functions of the segmented di� usion models for thetwo regimes:

L * (a 1 , b 1 , m ±1 , m 1 , CA= R*1 , D a , D b , D m ± , D m , D CA= R*)

= L (a 1 , b 1 , m±1 , m 1 , CA= R*1 ) + L (a 1 + D a , b 1

+ D b , m ±1 + D m ± , m 1 + D m , CA= R*1 + D CA= R*) (11)

To examine di� erences between the two regimes, we esti-mate Equation 11 by using a constrained optimization thatmaximizes L * subject to the following two constraintswhich result from the structure of the time-re¯ ectingBrownian motion:

j 1 0 = 0j 2 0 = 0

(12)

Estimates of D a , D b , D m ± , D m and D CA= R* provide uswith estimates of the changes in the parameters betweenthe regulatory regimes. They allow us to examine the e� ectsof regulatory changes on stock price movements and tomake inferences about the e� ectiveness of the regulationand changes in insider trading. But, before we can test thehypotheses developed in the previous section we must spec-ify them in terms of the parameters of the discrete timeversion of the two regime segmented di� usion model (Equa-tion 9).

First, consider the parameters corresponding to d m ± /dland d m /dl. Recall that in the continuous time segmenteddi� usion process (Equation 4) and in the discrete time ver-sion of the segmented di� usion model (Equation 5), m ± isthe price run-up immediately prior to the announcementtime and m is the price run-up from the beginning of theevent period to the announcement. Consequently, D m ± andD m correspond to d m ± /dl and d m /dl, respectively. Similarly,D m ± N and D m ± B correspond to d m ± N/dl and d m ± B/dl, re-spectively; and D CA= R*N and D CA= R*B correspond todCA= R*N/dl and dCA= R*B/dl, respectively.

VI . DATA SOURCES, SAMPLING CRITERIAAND ESTIM ATION OF THE CARs

The target ® rms in our sample were restricted to US ® rmsbecause they were subject to the same disclosure require-ments, while ® rms in other countries are subject to di� erentrequirements. No such restriction was placed on acquiring® rms.1 4 To control for sectoral di� erences, estimation wasconcentrated on a small number of industries and matched® rms over the two regulatory regimes. Target ® rms wereselected from only the six industries that were most active incorporate takeovers during the two periods: Banking andFinance (SIC code 6000s and 6100s), Food and Beverage(SIC code 2000s), Investment (SIC code 6711 and 6712),Insurance (SIC code 6300s and 6411), Oil and Gas (SICcode 1311, 1380s, 2900s and 4920s), and Retail (SIC code5200s Ð 5700s and 5900s).

We only considered successful takeovers with a value ofover $100 million.1 5 A successful takeover was de® ned as anacquisition of an independently listed company completedby 31 December 1991. This requirement excluded acquisi-tions of non-listed subsidiaries or strategic business units forwhich stock price data were unavailable. An acquisition ofa partial stake in a target was not considered as a takeover.We also excluded the acquisition of a stake in a target of lessthan 30% even if the acquirer already owned the remaining

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1 6 There are many di� erent ways to estimate the normal’ return or counter-factual Ð what would have happened in the absence of the event;see, for example, Brown and Warner (1980) and Armitage (1995). The market model, which is a variant of the capital asset pricing model(CAPM), is used most frequently to obtain expected market returns. However, recent empirical studies on market anomalies havechallenged the CAPM as an equilibrium speci® cation of the return generation process; see, for example, Ball (1992), and Fama and French(1992, 1993). Furthermore, the market index, such as the S&P 500, is at best an imperfect proxy of the theoretical market portfolio; see Roll(1977). Experience suggests that in practice it does not make much di� erence for our purpose whether the CARs are computed using theCAPM or any other reasonable method. Our method is much easier to conduct, and it is intuitively easier to understand.1 7 In the construction of the industry average, missing data were treated as no observation. In other words, if a ® rm’s return was missing ona particular date, the industry average for that date is the average return of the ® rms for which data were available on that date.1 8 We analysed the residuals from each of these models and could not reject the hypothesis that they are normally distributed at the 5%level of signi® cance. For each set of residuals we performed the chi-square test developed by D’Agostino et al. (1990), which is based onskewness and kurtosis, and the Shapiro Ð Wilk (1965) test, which is based on the variance. The lowest of the eight P-values was 0.11.

stock. This requirement ruled out acquisitions too small toproduce a signi® cant impact on the target’s stock price.

Other factors unrelated to insider trading might pro-duce signi® cant pre-announcement price movements. Forexample, a takeover war among suitors often inducesa strong price reaction. To avoid this problem, we excludedtargets that were reported as being involved in takeoversduring the two months prior to the successful one, based onthe Wall Street Journal Index and the New Y ork TimesIndex. Targets that openly sought a buyer within twomonths of a successful takeover or whose stock was sus-pended from regular trading within 10 trading days beforeor during the event period were not considered.

Takeover targets were identi® ed from the 100 MillionDollar Club in the annual report of Mergerstat Review(MR) published by Merrill Lynch Business Brokerage andValuation. To be included, the target and to be listed of theNew York Stock Exchange (NYSE), the American StockExchange (AMEX), or on the National Association of Se-curities Dealers Automatic Quotation system (NASDAQ)for a period of at least 15 trading days before and after thetakeover announcement. The entire 31 trading day eventperiod had to lie either in the period from 4 January 1982 to31 December 1984 (the lax regime), or in the period from3 January 1989 to 30 December 1991 (the stricter regime).Stock price information was obtained from the Daily Re-turn File of the Center for Research in Security Prices(CRSP).

Takeover targets that satis® ed our sample selectioncriteria were classi® ed as negotiated takeovers or take-overs initiated by bidding based on reports in The WallStreet Journal and the New Y ork Times news reports. In theperiod of lax regulation (1982Ð 1984), 50 takeover targetssatis® ed our sample selection criteria: 37 were identi® ed asnegotiated and 17 as takeovers initiated by bidding. In theperiod of tight regulation (1989 Ð 1991), 40 takeover targetssatis® ed our sample selection criteria, 31 of which wereidenti® ed as negotiated and nine as takeovers initiated bybidding.

We measured the daily abnormal returns to a target bythe mean adjusted return, that is, the excess returns toa target over the average returns to the industry, where theindustry average returns were based on an equally weighted

portfolio of ® rms in the industry excluding the targets in oursample.1 6 Firms included in an industry portfolio had anSIC code within the range speci® ed for the industry, andwere continuously listed for trading from 4 January 1982 to31 December 1984, or from 3 January 1989 to 30 December1991.1 7 The daily abnormal returns were averaged across all® rms in each sub-sample and then the CARs were obtainedby summation in the usual way.

VII . EMPIRICAL RESULTS

Table 2 sets out the results from estimating the segmenteddi� usion model (Equation 5) separately for the two regula-tory regimes and for the two types of takeovers: negotiatedtakeovers ( negotiated’) and takeovers initiated by bidding( bidding’).1 8 Table 3 gives the results of estimating the tworegime segmented di� usion model (Equation 9) for negoti-ated takeovers and takeovers initiated by bidding. It con-tains estimates of the changes in the parameters between theperiod of lax regulation and the period of stricter regulationas de® ned by Equation 10.

Information revelation and the impact of tighter regulation

Table 2 shows that for the earlier period (1982 Ð 1984), theaverage price run-up just prior to the announcement, m ± ,was 44.2% for negotiated takeovers and 42.4% for thoseinitiated by bidding. However, for the later period(1989 Ð 1991), the average price run-up just prior to theannouncement was only 13.3% for negotiated takeoversand 21.1% for takeovers initiated by bidding. Clearly,the average price run-up just prior to the announcementdropped dramatically from the ® rst regime to the second inboth types of takeovers. It dropped by 70% for negotiatedtakeovers, and by 50% for those initiated by bidding.

The average price run-up from the beginning of theperiod until just after the announcement, m , changed onlyslightly from one period to the other. Table 2 shows that inthe period 1982 Ð 1984, m was 99.3% for negotiated takeoversand 102.7% for those initiated by bidding; the relevant® gures for the period 1989 Ð 1991 were 96.0% and 95.0%,respectively.

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Table 2. Estimated segmented di¤ usion models during lax and tight regulatory regimes

1982 Ð 1984 1989 Ð 1991

Parameter Negotiated Bidding Negotiated Bidding

a 0.27224 0.28422 0.17092 0.12300(10.981) (6.9077) (3.9368) (1.2601)

b 0.26112 0.44567 0.97484 0.69358(2.1239) (2.3473) (5.9254) (4.6409)

CA= R* 0.24397 0.24657 0.34148 0.24148(71.246) (83.374) (432.48) (74.366)

m ± 0.44211 0.42361 0.13299 0.21062(23.130) (15.242) (13.419) (5.3172)

m 0.99330 1.0273 0.96014 0.95000(55.970) (47.126) (102.11) (26.008)

L 219.72 195.07 229.03 163.71No. of ® rms 33 17 31 9No. of observations 31 31 31 31

Separate estimation of the segmented di� usion models for negotiated takeovers and for takeoversnegotiated by bidding, for the lax regime and for the tight regulatory regime. Speci® cation of eachmodel is given by the system of two simultaneous equations presented below, with t = 0 being theannouncement date. The window for the time series starts 15 days before the announcement and ends15 days after the announcement. The number of ® rms (No. ® rms) reports the number of target ® rmsfor which the average cumulative abnormal returns are constructed. L is the value of the log likelihoodfunction. Asymptotic t-values are in parentheses.

D CARt = 5 a CARt + e t

b (CA= R* - CARt) + e t

t = - T , ¼ , - 1t = 1, ¼ , T

CARt = 5 m 0 CA= R* exp(a t) + e t

CA= R* - (1 - m 1 )CA= R* exp( - b t) + e t

t = - T , ¼ , - 1t = 0, ¼ , T

1 9 Another way to check the reliability of our results is to compare them to other studies. Jarrell and Poulsen (1989) and Meulbroek (1992),who used a similar method to identify the correct announcement date, report price run-ups prior to the takeover announcement similar tothose we obtained for events that occurred in the earlier period.

Table 3 shows that the change in the average price run-upjust prior to the announcement from the ® rst regime to thesecond, D m ± , is statistically signi® cant for both types oftakeovers. The t-value for D m ± is - 15.3 for negotiatedtakeovers and - 4.6 for takeovers initiated by bidding. Thechanges in the average price run-ups including the an-nouncement day, D m , are not statistically signi® cant at the0.05 level. As D m ± is statistically signi® cantly di� erent fromzero for both types of takeovers but the D m s are not, there isfairly strong evidence in support of Hypothesis I that in-formation was partially revealed to the market and theregulatory changes were e� ective.

One concern regarding this dramatic reduction in theaverage pre-announcement price run-up, m ± , is whether itmight be due to incorrect choices of the announcementdates. To avoid any potential bias, announcement dateswere carefully checked and it was appropriate to adjustthem backwards by one or two days from the date they were® rst reported in the Wall Street Journal. If the announce-ment dates were over-adjusted in the second period then, inaddition to reducing D m ± , this would also reduce D m . But, as

mentioned above, m does not change signi® cantly betweenperiods. Furthermore, the mean adjustments for the twoperiods are very similar (1.5 days for the ® rst period versus1.65 days for the second period). Therefore, there does notappear to be any bias due to the choices of the announce-ment dates.1 9

The parameters a and b provide additional informationabout the e� ectiveness of tighter regulation. The parametera measures the rate of price run-up before the announce-ment. Under partial information revelation, it re¯ ects theamount of inside information ¯ owing into the market priorto the announcement and the e� ciency of the market toincorporate this inside information into stock prices. If themarket’s ability to digest information remained unchangedover time, then a change in a across regulatory regimeswould re¯ ect a change in the amount of inside information¯ owing into the market prior to the announcement. Moree� ective regulation would reduce the amount of informationreaching the market prior to the announcement, whichshould lead to a smaller value of a . Furthermore, since all ofthe information regarding the impending takeover is known

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Table 3. Estimated two-regime segmented di¤ usion model for negotiated takeovers and those initiated by bidding

Parameter Negotiated Bidding Parameter Negotiated Bidding

a 1 0.27408 0.26440 D a - 0.10316 - 0.14482(11.145) (6.4547) ( - 2.0677) ( - 1.3707)

b 1 0.26112 0.43856 D b 0.71372 0.26443(2.1419) (2.3252) (3.4892) (1.0979)

CA= R*1 0.24397 0.24650 D CA= R* 0.09751 - 0.00506(71.497) (82.423) (27.841) ( - 1.1538)

m ±1 0.45994 0.43264 D m ± - 0.32695 - 0.22153

(24.288) (15.937) ( - 15.297) ( - 4.6252)m 1 0.99329 1.0272 D m - 0.03315 - 0.07657

(56.329) (47.058) ( - 1.6589) ( - 1.8018)L * 449.26 358.96No. of ® rms 64 26No. of observations 62 62

Estimated changes in the average cumulative abnormal returns for each type of takeover. The estimation is conducted by combining thetwo segmented di� usion models speci® ed below, with t = 0 being the announcement date. Window starts at 15 days before theannouncement and ends 15 days after the announcement date. Subscript 1 indicates the period of lax regulation between 1982 Ð 1984, andsubscript 2 the period of tight regulation between 1989 and 1991. The number of ® rms (`No. Firms’) reports the total number of target ® rmsin the pooled sample. L * is the value of the log likelihood function. Asymptotic t-values are in parentheses.

D CAR1 t = 5 a 1 CAR1 t + e 1 t

b 1 (CA= R*1 - CAR1 t ± 1 ) + e 1 t

t = - T , ¼ , - 1t = 1, ¼ , T

CAR1 t = 5 m ±1 CA= R*1 exp(a 1 t) + j 1 t

CA= R*1 - (1 - m 1 )CA= R*1 exp(b 1 t) + j 1 t

t = - T , ¼ , 1t = 0, ¼ , T

D CAR2 t = 5 (a 1 + D a )CAR2 t + e 2 t

(b 1 + D b ) ((CA= R*1 + D CA= R*) - CAR2 t ± 1 ) + e 2 t

t = - T , ¼ , - 1t = 1, ¼ , T

CAR2 t = 5 (m ±1 + D m ± )(CA= R*1 + D CA= R*) = exp((a 1 + D a )t) + j 2 t

(CA= R*1 + D CA= R*) - (1 - (m 1 + D m ))(CA= R*1 + D CA= R*) exp((b 1 + D b ) t) + j 2 t

t = - T , ¼ , - 1t = 0, ¼ , T

2 0 Table 2 shows that a decreased from the earlier regime to the later regime for both types of takeovers. For negotiated takeovers, itdeclined by 36% from 0.27 to 0.17; for takeovers initiated by bidding, it declined by 57% from 0.28 to 0.12. Table 3 reports that thedi� erence ( D a ) is statistically signi® cantly di� erent from zero for negotiated takeovers at the 0.01 level, but not for takeovers initiated bybidding. Table 2 also shows that b increased from 0.26, in the ® rst period to 0.97 in the second period for negotiated takeovers, and from0.45 in the ® rst period to 0.69 in the second period for takeovers initiated by bidding. Table 3 shows that the di� erence (D b ) is statisticallysigni® cant for negotiated takeovers but not for those initiated by bidding.

after the announcement, a drop in a implies an increase in b ,the rate of the estimated average price run-up after theannouncement. Therefore, if the regulation was more e� ec-tive in the second period, a should decrease and b shouldincrease from the ® rst regime to the second.

The results in Tables 2 and 3 show that a decreased andb increased for both types of takeovers. The di� erences (D aand D b ) were statistically signi® cantly di� erent from zerofor negotiated takeovers, but not for takeovers initiated bybidding.2 0 In aggregate these results provide additionalsupport for Hypothesis I.

Negotiated takeovers versus takeovers initiated by bidding

Table 3 shows that the change in the pre-announcementprice run-up for negotiated takeovers, D m ± N, is - 32.7%

and the change in the pre-announcement price run-upfor takeovers initiated by bidding, D m ± B, is - 22.2%.Both changes are statistically signi® cant. These resultsare consistent with and support Hypothesis II, which main-tains that the tightening regulation had a greater e� ect onthe pre-announcement price run-up for negotiated take-overs than for takeovers initiated by bidding: | D m ± N | >| D m ± B | .

Furthermore, the changes in a and b were much larger fornegotiated takeovers than for takeovers initiated by bid-ding. As we discussed above, these changes were statisticallysigni® cant for negotiated takeovers, but not for takeoversinitiated by bidding. This evidence suggests that stricterregulation had a greater e� ect on the amount of information¯ owing to the market in negotiated takeovers than in take-overs initiated by bidding.

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2 1 One possible explanation for the di� erence is results can be traced to the samples employed. Earlier studies based their analyses onreported insider transactions, most of which are legal and, therefore, relatively insensitive to regulatory change. Our research designfocuses on illegal insider trading which of course is the target of the regulation.

Regulation and the truncation of the distribution of takeovers

Table 3 shows that for negotiated takeovers, CA= R*N in-creased signi® cantly over the two periods by 42%, from 0.24to 0.34. CA= R*B decreased a little over the two periods, butthis change was not signi® cant. This evidence providespartial support for Hypothesis III:

dCA= R*N

dl>

dCA= R*B

dl> 0

Tightening the regulation led, on average, to more pro® t-able negotiated takeovers than before, thus truncating thedistribution of negotiated takeovers. However, it had nosigni® cant e� ect on the distribution of takeovers initiated bybidding.

Comparison of this result and the results discussed earlierraises some questions. Our earlier results indicate that theregulatory changes had signi® cant e� ects on the pre-announcement price run-ups for both types of takeovers,but our results regarding Hypothesis III suggest that theregulatory changes did not have a signi® cant e� ect on themagnitude of the total abnormal returns to targets in take-overs initiated by bidding. This apparent inconsistency canbe explained, at least in part, by the greater opportunity tosubstitute legal for illegal trades in the case of takeoversinitiated by bidding. If the takeover is started with an openbid for the target, the bidder can coordinate the timingbetween pre-takeover trades and the takeover announce-ments. In some circumstances, this coordination enables thebidder to accumulate su� cient target shares legally beforedeclaring the takeover. If this is indeed the case, then ane� ective change in regulation would reduce illegal insidertrading without leading the bidder to seek more pro® tabletakeovers.

VI II . SUMMARY AND CONCLUSIONS

Our main results can be summarized as follows:

As has been reported in other studies, we also ® nd thatinsider trading reveals inside information to the market.Speci® cally, we found that inside information is partiallyrevealed to the market.However, in contrast to previous studies, which found noimpact of regulatory changes on insider trading, we ® ndthat tightening insider trading regulation during the late1980s was e� ective in reducing illegal insider trading.2 1

Substantial evidence exists that the tighter regulation ofinsider trading had more impact on illegal insider tradingduring negotiated takeovers than during those initiatedby bidding.

In negotiated takeovers, tighter regulation led insiders torequire more pro® table takeovers, possibly to compen-sate them for the reduction in the pro® t opportunitiesfrom illegal insider trading. As insider trading regulationbecomes stricter, less pro® table takeovers may be de-terred. In takeovers initiated by bidding, insiders may beable to substitute legal trading before the takeover forillegal trading during the takeover.

Our methodology permits testing of the impact of anyevent that is manifested in stock market prices. It can alsoserve as a tool for studying changes in market capitalizationprocesses in general. The speci® c application in this paper,i.e. the study of the e� ectiveness of tightening insider tradingregulation in the USA, can easily be applied to other coun-tries or other regulatory changes. The data requirements forour method of studying insider trading are less onerous thanstudies that use direct evidence of insider trading.

ACKNOWLEDGEMENTS

The authors acknowledge ® nancial support from theKrueger Center for Financial Research, and the SocialScience and Humanities Research Council of Canada, andthe helpful comments of K. Head, Y. Landskroner, A.Parikh, I. Venezia and an anonymous referee.

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APPENDIX (A

Proof of Proposition 1

Consider ® rst when t < t0 . By assumption (AS2), V t = V it

under full information revelation and V t = V ot under no

revelation. Since under either full revelation or no revel-ation, the regulatory variable l does not enter the equilib-rium condition, it follows that dV t/dl = 0. Under partialinformation revelation, from assumption (AS2) we have

dV t

dl= 1

(Dit + Do

t )2 3 1 ¶ Dit

¶ lDo

t - Dit¶ Do

t

¶ V t

dV t

dl 2 V it

+ 1 ¶ Dot

¶ V t

dV t

dlDi

t - Dot

¶ Dit

¶ l 2 V ot 4

+ 1 Dot

Dit + Do

t 2¶ V o

t

¶ V t

dV t

dl. (A1)

Solving Equation (A1) for dV t/dl yields,

dV t

dl=

Dot (V i

t - V ot )

¶ Dit

¶ l

Dit(V i

t - V ot )

¶ Dot

¶ V t+ (Di

t + Dot )2 - Do

t (D it + Do

t )¶ V o

t

¶ V t

(A2)

Since ¶ Dit/ ¶ l < 0 and, by assumption (AS1), V i

t - V ot > 0,

then Dot (V i

t - V ot ) ¶ Di

t/ ¶ L < 0. Since ¶ Dot / ¶ V t > 0, then

Dit (V i

t - V ot ) ¶ Do

t / ¶ V t > 0. Finally, ¶ V ot / ¶ V t < 1 implies that

(Dit + Do

t )2 - Dot (Di

t + Dot ) ¶ V o

t / ¶ V t > 0. Therefore, dV t/dl < 0.When t > to , the information asymmetry disappears.

Therefore, under rational expectations it must be thatV t = V i

t = V ot . Since l does not enter the equilibrium condi-

tion, dV t/dl = 0. This completes the proof.

Proof of Proposition 2The solution to the stochastic di� erential equation(Equation 3) is:

CARt = 5 CA= Rt0 ± d ea (t - t0 - d ) + s ò t

t0 - d ea (t - s - d )dW s t < t0

CA= R* - (CA= R* - CA= Rt0) e - b (t - t0 )

+ s ò tt0

e ± b (t - s)dW s t > t0

(A3)

with d > 0. Now using the de® nition of m ± and m and letd ® 0, Equation A3 converges to Equation 4. This com-pletes the proof.

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