As it is in (Tax) Haven: Market Attitudes towards Secrecy Shopping

Evelina Gavrilova∗ Aija Polakova†

Preliminary This Draft: August 3, 2017 First Draft: January 4, 2017

Abstract

We study asset price reactions to news on rms' decisions to acquire aliates located in known secrecy and tax havens. Our sample consists of data on the S&P 500 companies in the period 1997 - 2016. Results indicate a 2 to 4 percent increase in stock prices following acquisition deal news regarding aliates in tax and secrecy havens. We attempt to disentangle secrecy from tax planning, and nd that the positive eect is driven by low tax rates. In addition, for the top 50 largest multinational rms we nd that the share prices increase after signing of tax information exchange agreements between the United States and haven countries, consistent with a positive reaction to increased transparency.

JEL classication: G12, G32, H26 Keywords: event study, secrecy, tax haven

∗NHH Norwegian School of Economics: Department of Business and Management Science and Norwegian Center for Taxation (NoCeT). E-mail: [email protected] †NHH Norwegian School of Economics: Department of Business and Management Science and Norwegian Center for Taxation (NoCeT). E-mail: [email protected]

1 1 Introduction

The common tenet between high-prole fraud cases like Enron, Parmalat and Olympos is the existence of a constellation of shell companies through which losses of the parent were hidden. From the perspective of opportunistic managers as rational white collar criminals, an increase in the complexity of the corporate structure decreases the probability of detection by authorities or shareholders. Therefore, it is reasonable to assume that any investor could get jittery with the acquisition of yet another shell company. Yet, one limited liability aliate does not a fraud make, as simple tax avoidance could be the innocuous reason behind maintaining a complex corporate structure. In this paper we ask: how concerned are investors when faced with the probability of fraud? Do they distinguish between the tax and the fraud motives? To answer these questions, we investigate the eect on the stock price of opening an aliate in a secrecy or low-tax jurisdiction.1 The former gives opportunistic managers the perfect means to hide their crimes, while the latter is associated with the standard motive of tax avoidance. It is well known that the existence of aliates in secrecy jurisdictions impedes the audit- ing of companies by market analysts, shareholders and tax authorities. As Schjelderup (2016) notes, the benecial owners and annual reports of companies in secrecy jurisdictions can remain non-public. This leads to moral hazard problems for management and to greater obfuscation of companies' true nancial state and liability for investigating tax- or law-enforcement au- thorities. Unfortunately, the provision of low tax rates is often inextricably linked to secrecy, and, thus, what looks like tax avoidance can turn out to be the precursor of bad news and managerial diversion (in the spirit of Kim et al., 2011; Desai et al., 2007). Even managers themselves seem to be cognizant of the double interpretation by investors of having aliates in havens (Akamah et al., 2016). We hypothesize that the acquiring of an aliate in a secrecy jurisdiction will seem to outsiders as providing opportunities for managerial fraud and should lead to a reduction in the stock price. We remain agnostic on the market attitude towards tax avoidance. On one hand, cash reserves can be seen as valuable by investors, while, on the other hand, potential future tax nes can have a negative impact on the stock price. To make sure that the market is indeed concerned about secrecy foremost, we explore the reaction of stock prices to an additional type of event - the signing of bilaterial tax information exchange agreements (TIEAs). A TIEA between the domestic country and a secrecy jurisdiction increases the transparency of the corporate structure to domestic authorities and investors without impacting the tax bill, and this should lead to an increase in the stock prices of rms exposed to the secrecy jurisdiction. To test our hypotheses, we use proprietary data on historical ownership at the rm-level from the Orbis database from 1997 to 2016, merged with data on mergers and acquisitions from the Zephyr database for the S&P 500 rms. We link the acquisition events to an event window of stock market prices and look at what are the abnormal returns due to acquiring an aliate in a haven jurisdiction as opposed to an aliate in a non-haven jurisdiction. We use alternative denitions of secrecy and tax havens: Financial Secrecy Index by Tax Justice Network (2015), bank haven list by Johannesen & Zucman (2014) and corporate tax rates from Egger et al. (2006). We nd no eect on stock prices following acquisition deals in secrecy havens. When we look specically at banking secrecy havens, we nd an increase of 6 percent following rumours

1Following Schjelderup (2016), we distinguish between tax havens and secrecy jurisdictions. The former are countries with low taxes, while the latter are countries which allow for creation of private information. In the remaining text we will refer to the sum of them as havens.

2 of a deal. On the other hand, when we look at the tax rate we nd an increase of 2.8 percent following announcement of a deal, and 2.2 percent following deal completion. All in all, we nd that secrecy does not raise concerns among investors, while possible tax planning is considered as good news. In addition, when we look at the eect of signing of bilateral TIEAs between the United States and haven countries, we nd that the stock returns of companies increase by 5 to 20 percent. This is consistent with the market viewing transparency favorably. Likely, in the case of companies exposed to tax avoidance opportunities, the signing of a transparency agreement minimizes agency problems on the side of the manager by increasing the probability of detection, consistent with Desai et al. (2007). Our hypothesis on the eect of secrecy is guided by the implications of fraud participation. Yet, this is only one of the variety of reputational concerns that can cause a drop in the rm's stock price. The fact that these concerns exist has been shown recently in a survey by Graham et al. (2013). They nd that 69 percent of surveyed executives do not engage in tax planning due to reputational concerns, yet they do not detail what is the implied variety of such concerns. Akamah et al. (2016) hypothesize that such reputational concerns can cause managers to hide their haven aliates in the guise of the more general geographic area (i.e. a subsidiary in Luxemburg would be reported as being in Europe). Akamah et al. (2016) nd that there is indeed a reporting avoidance behavior when tax and secrecy havens are implicated. In contrast to these general reputational concerns, we nd no eect on stock prices of secrecy and a positive eect of tax planning. One reputational concern, that we try to pinpoint with our data, relates to consumer boycott in response to the tax-paying brand. One recent example is Starbucks in the United Kingdom in 2012, which likely suered a decrease in sales due to revelations of avoiding corporate tax payments in the United Kingdom. In this vein, Hanlon & Slemrod (2009) nd a greater decrease in the stock price for rms in the retail industry that use a tax shelter. In contrast, our estimates show that our results are not driven by retail aliates, which display similar returns to the main sample. Desai et al. (2007) note that the corporate tax in the United States was inaugurated in the beginning of the last century with the idea that auditing by tax authorities can serve as a certication service for minority shareholders. Yet, the existence of secrecy jurisdictions and the impediments they pose to shareholders, analysts and authorities, serve to obfuscate the true nancial state of a rm and, ultimately, to cast the shadow of fraud on it. Not only that, but it reputation-wise lumps together entrepreneurs with drug lords who launder money. However, we show that investors have no hard feeling on this matter, as secrecy does not seem to be of a concern. Related Literature Our contribution is in line with previous literature that has asserted that tax planning may occur in combination with managerial opportunism (see e.g. Kim et al., 2011; Hanlon & Slemrod, 2009; Desai et al., 2007). Kim et al. (2011) use rm-level data to show that rms with higher tax-sheltering capabilities are more likely to experience future stock price crashes. The complex structure arising from aliates in many jurisdictions gives opportunistic managers the opportunity to stockpile negative news until a tipping point. In our setting this translates to rational expectations of a decrease in the stock price following the opening of an aliate in a haven. We nd a more general eect because we remain agnostic towards the true nancial state of the perspective rm; thus, we identify the lack of precautionary perceptions rather than certainty about bad events to follow. Our empirical strategy is similar to the one by Hanlon & Slemrod (2009), who use news articles to create an event-based sample. By rst selecting the rms and then looking at

3 events, we have both a natural treatment and a control group of events - the former, when a rm acquires an aliate in a secrecy jurisdiction, and the latter for acquisitions in other jurisdictions. This allows us to construct the counterfactual trend to the events that we study. In addition, we have a higher number of acquisition observations, and we are better able to observe the existing structure of the rm, allowing us to determine and separate tax aggressiveness from private information concerns. In addition, we dierentiate between good and bad rumors, and implement an additional test for our hypothesis with the TIEAs.

2 Data

Companies. To test our hypothesis, we explore the stock price reaction to acquisition deal news of the S&P 500 companies. We obtained daily data on adjusted closing prices (adjusted for dividends and splits) on these rms from Yahoo! Finance from 1997 to 2016. We merged this data with data on mergers and acquisitions from the Zephyr database provided by Bureau van Dijk. We obtained data on rumour, announcement and completion dates of deals between acquirer rms and target rms. Rumour date is the date on which the deal was rst mentioned, as far as Zephyr researchers can ascertain. The unconrmed rumour report may be in the press, in a company press release or elsewhere. Announcement date is the date when details of the deal have been provided, when a formal oer has been made or when one of the companies involved in the deal has conrmed that the deal is to go ahead. Finally, completion date is the date when the deal has been announced as completed or in certain circumstances has received all approvals to go ahead. This information is obtained from advisor submissions, company annual reports and accounts, and company websites. We extracted data on all acquisition deals of the S&P 500 rms from 1997 to 2016. We dropped rms involved in nancial services, banking and insurance industries, as these rms are likely to acquire aliates in havens in the course of their normal business activity, rather than as a mean to purchase secrecy. Haven Countries. There does not exist any general or objective criteria that describe what a haven is; however, the term "haven" is a widely used expression. Hence, we use several denitions of havens in this paper. First, Tax Justice Network (2015) has developed the Financial Secrecy Index (FSI), which is a politically neutral ranking and ranks jurisdictions according to their secrecy and the scale of their oshore nancial activities. We extract the secrecy score for each country and use a rating of 60 and higher as a cut-o to dene a haven.2 These countries are Andorra, Anguilla, Antigua and Barbuda, Aruba, Bahamas, Bahrain, Barbados, Belize, Bermuda, Bolivia, Botswana, British Virgin Islands, Brunei Darussalam, Cayman Islands, Cook Islands, Curacao, Dominica, Dominican Republic, Gambia, Ghana, Gibraltar, Grenada, Guatemala, Guernsey, Hong Kong, Isle of Man, Jersey, Lebanon, Liberia, Liechtenstein, Macao, Macedonia, Malaysia (Labuan), Maldives, Marshall Islands, Mauritius, Monaco, Montenegro, Montserrat, Nauru, Panama, Paraguay, Philippines, Samoa, San Marino, Saudi Arabia, Seychelles, Singapore, St Kitts and Nevis, St Lucia, St Vincent and the Grenadines, Switzerland, Taiwan, Tanzania, Turkey, Turks and Caicos Islands, United Arab Emirates (Dubai), Uruguay, US Virgin Islands, Vanuatu, and Venezuela. Second, Johannesen & Zucman (2014) have compiled a list of the largest "bank havens", which includes havens with a large bank sector. These havens are Austria, Belgium, Chile, the

2We explore alternative threshold specications in the robustness tests.

4 Cayman Islands, Cyprus, Guernsey, the Isle of Man, Jersey, Luxembourg, Macao, Malaysia, Panama, and Switzerland. Third, we compile a list of countries that are likely to strike a sweetheart deal with multinational companies. These deals are Advance Pricing Agreements (APAs), which in some cases provide companies with tax rates lower than 1 percent. European Network on Debt and Development (2016) have compiled a list of the number of APAs in force in the European Union and Norway. We use 30 deals and more as a cut-o of the number of deals to dene a sweetheart country. These countries are Belgium, Czech Republic, France, Hungary, Italy, Luxembourg, Netherlands, Spain, and the United Kingdom. Finally, we dene havens in the traditional sense as tax havens by using data on worldwide corporate tax rates, kindly provided by Simon Loretz. In Table 1 we present summary statistics distinguishing between haven acquisition deals and non-haven acquisition deals. The rst two sets of columns show summary statistics for acquisition deals in non-havens vs acquisition deals in havens. Havens here were dened based on the FSI list. The last column shows the p-value for a t-test for dierence in means between the two sub-samples. For example, there is no signicant dierence between the deal value in havens vs non-havens. Also, both types of deals take similar time from rumour to completion, approximately 110 days. For both types of deals, in 30 percent of the cases the rumour, announcement and com- pletion dates coincide. These are usually deals for which there was no advance information, and therefore the three events were coded in the same day. This circumstance motivates us to distinguish between the three types of events in the empirical estimation. All the haven indicators such as secrecy score, bank haven list, and tax rate are higher for haven countries, while only the sweetheart indicator is higher for non-haven acquisions. We observe that 38 percent of the haven acquisitions are in countries on Zucman bank haven list. The average tax rate of acquired havens is 20 percent, while for non-havens it is 37 percent. Finally, we have over 10 thousand observations of acquisitions in non-haven countries, and only 266 for acquisitions in haven countries.

3 Identication Strategy

Our main hypothesis is that the market should react negatively to the secrecy that comes with acquiring an aliate in a haven country. To make sure that we identify a secrecy-specic component, rather than a low-tax one, we look at the reaction of the market to two types of events. First, we ask whether the market values dierently acquisition deals in havens, as compared to acquisition deals in non-havens, by varying the denition of havens. Second, we look at whether the eect that we nd is consistent with the reaction of the market to signing of TIEAs.

3.1 Acquisition Deals We consider 3 types of events in our empirical strategy: rumours about a given deal, ocial announcement and completion of the deal. We estimate the following type of specication:

R,A,C X Rft = (αiDft · NonH + βiDft · H) + δt + κf + Xftγ + ft; (1) i

5 where Rft is the one-day raw stock price for rm f at date t. We estimate separate coecients for each i-event: Rumour, Announcement and Completion for each Dft deal event. Dft is an indicator variable that takes on the value of 1 for a company's exposure for a three day period beginning with the acquisition deal date, and 0 otherwise. H is an indicator variable for haven status that takes on the value of 1 if the country where the target rm operates is considered as a haven, and 0 otherwise. NonH is an indicator variable for non-haven status that takes on the value of 1 if the country where the target rm operates is not considered as a haven, and 0 otherwise. Xft is the vector of rm-specic Fama-French factors. The specication also includes day xed eects and rm xed eects, δt and κf . The former will capture market-wide events, such as election eects. The latter will capture rm- specic heterogeneity, such as industry classication. By including these two variables, we pave the way to estimate a dierence-in-dierence (DiD) specication, where the treatment is the haven status of the acquired aliate. However, we want to inspect separately the coecients for haven and non-haven aliates; therefore, one can think of the dierence (βi−αi) as the DiD coecient. In the results section we present both the specication 1 and its DiD equivalent. We use three dierent measures of haven status. First, we dene a country as a secrecy haven if its secrecy score according to the Financial Secrecy Index is above 60. Second, we employ the bank haven list by Johannesen & Zucman (2014). With these two measures we hope to identify the fraction of the stock price increase that is due to the deal happening in a country with an active secrecy legislation. Given that these countries often have a low tax rate, we employ the corporate tax rate as the third measure of haven status. If secrecy does not matter to the market, then the βi coecients in all three cases should be equal. If secrecy is valued dierently than tax avoidance, then the coecients should be dierent.

3.2 Tax Information Exchange Agreements In order to verify our hypothesis on the importance of secrecy, we implement an additional test. We examine how the abnormal returns of a stock change following the signing of a bilateral tax information exchange agreement (TIEA) with a country, where the multinational rm has aliates. To that end we estimate a regression of the following type:

S,F X Rft = (αiTIEAft · H + βiTIEAft · H · HShareft + ζT IEAft · HShareft+ i

ηH · HShareft + δ2HShareft) + Xftγ + ft (2)

As compared to equation 1, the events i in equation 2 are the signing of the TIEA and TIEA coming into force, {S, F }. βi measures the treatment eect of signing a TIEA with a haven, controlling for the existing haven ownership. βi is identied by the time-series variation of the event window and by the cross-sectional variation in the exposure of the company to the given haven. Conceptually, we compare companies with a small exposure to havens to companies with a high exposure to havens. Note that in aid of precise identication this exposure is time-varying, as the companies make additional acquisition deals in havens while dierent TIEAs are signed or come in force.

6 4 Results

4.1 Acquisition Deals in Secrecy Havens In Table 2, we report the average eect on the share price of haven acquisition deals over three days. Each set of two columns shows the estimates of equation 1 and a dierence- in-dierence specication (DiD). We interpret the DiD estimates as the dierential eect of haven acquisitions with respect to all acquisitions and, eectively, as a test for the dierence in coecients between haven and non-haven acquisitions. In the rst two columns we show the main eects, where haven is dened as a country having an FSI secrecy score value above 60. In column 1 we observe that the coecients on haven interaction terms are not signicant, similar to the DiD coecients in column 2.3 Our main nding from the secrecy score-haven specications is a positive reaction of 2 percent for completing deals in non-haven countries. In columns 3 and 4 of Table 2 we repeat our main analysis by redening the haven variable - we restrict it to countries which have an active bank secrecy legislation, and which do not reveal information about the existing accounts of private individuals (Johannesen & Zucman (2014)). The existence of aliates in these specic havens would make it easier for the manager to hide his wealth in the case of corporate bankruptcy. If investors are concerned about the possible fraudulent behavior of managers, then the estimates should show a negative impact on the share price. Note that by selecting a list of rms which have survived until 2016 we are biasing this test against our hypothesis. These rms have likely suered less managerial diversion than rms that did not survive. With that in mind, we observe in column 4 that there is an overwhelming 6 percent average increase in the stock price following a rumour about acquiring an aliate in a haven, as a net eect of haven vs non-haven from column 3. This is equivalent to an 18 percent cumulative stock price increase over the three days of the event window. In columns 5 and 6 of Table 2 we present the results for the sweetheart countries. We generally nd positive coecients for all events, with DiD coecients revealing again a slightly higher enthusiasm for haven acquisitions with respect to rumour and completion date. The DiD coecients both show rst a 4 percent increase for rumours, and another 4 percent average increase for completion of deals. While the cumulative eect of deals in sweetheart countries is higher, we caution that these coecients could also reect the benets of trading with high- GDP countries in Western Europe. Therefore, we consider these results as less representative for the eects of secrecy shopping. Hence, in our main specication we nd no evidence that the market reacts negatively to acquisitions in secrecy havens. When we try to dierentiate with respect to bank secrecy, we nd that the market is rather likely to react positively to acquisitions in havens. This is surprising, given our hypothesis that there should be at least a cautionary negative eect, as the investment in a secrecy jurisdiction opens possibilities for a host of fraud opportunities. Robustness checks. In Table A.1 we show the results of a lag-lead analysis of the event date with the FSI-dened haven variable. The table presents the results for the whole specication, and the columns separate the coecients into the event type: rumour, announcement and completion. We nd no evidence of pre-trends in the haven acquisition events. There is small evidence of a pre-trend in the non-haven events, with a barely signicant negative eect in the

3In Table A.4 we separate between the three deal events in the last three columns, and we nd similar coecients for each of them on the non-haven interaction terms. Taking this together with column 1 in Table 2, it is obvious that these coecients are all driven by the completed deal event. Therefore, we present only pooled results in the main body of the article.

7 two days before the rumour, which could lead to an upward bias in the estimates on rumours about non-haven acquisitions. This biases the coecient against our hypothesis. In Table A.2 we show that the main results on FSI-dened haven variable are robust to weighting the regressions by market capitalisation of the rm, excluding same day deals (rumoured, annnounced and completed on the same day) and varying the cuto of the secrecy score. With respect to the bank haven list, we nd that the eect on rumours disappears when weighting, hinting that it is likely driven by the smaller rms in the S&P 500. In Figures A.1 and A.2 we graph the main results by calendar year.

4.2 Acquisition Deals in Tax Havens In Table 3 we present a specication with splines in the corporate tax rate to see what is the eect of the statutory corporate tax rate of the acquired rm on the stock price of the acquirer. In this way we want to distinguish whether the eect that we observe on secrecy can be generalized for all types of havens. We observe a positive eect in the main coecients for announcement and completion of deals. Both eects are approximately 2.5 percent and are representative of the excluded category of tax havens with tax rates up to 10 percent. There is no common pattern to the sign or size of the eect for the other bins for each event, making it apparent that the observed positive reaction is because of tax havens. This is furthermore conrmed in Table A.3, where we interact the deal event with the statutory tax rate and show that the average eect of an increase in the tax rate is not signicant. Therefore, we nd that investors view acquisitions of aliates in tax havens as positive news about the company. This is in contrast to our ndings about secrecy, which investors seem not to be concerned about.

4.3 Tax Information Exchange Agreements Figure 1 provides graphical evidence on the abnormal returns around signing of a TIEA and the TIEA coming into force for the top 50 largest rms in S&P 500. The eect that is plotted varies with companies' exposure to havens, and it is estimated from two separate regressions for both types of events: signing and enforcing a TIEA. In Figure 1 we observe a drop in the stock return immediately before the TIEA and an increase in the stock return after the signing of TIEA. We interpret this as evidence that the market values such tax information exchange agreements positively. The drop in the stock return and corresponding increases in the condence intervals seem to be driven by uncertainty about whether the treaty will be signed and whether it will come in force or it will be challenged. These results are broadly consistent with the previous literature.

4.4 Retail Sector In Table 4 we compare our estimates from the main specication on all industry sectors to estimates from only the retail sector. Hanlon & Slemrod (2009) nd that the reputational concerns of using tax shelters are strongest for companies in the retail sector. In our analysis we are looking at the importance of secrecy, rather than the tax bill, so our results are not directly comparable to those of Hanlon & Slemrod (2009). We dierentiate between two sector classications. First, we look at the eect on the stock price if the acquired aliates are in the retail sector. Given that S&P 500 companies are quite big, and some of them are active in several industry sectors, we want to distinguish between the retail arm vs the multi-sector arm of the parent. Second, we look at the eect on the stock

8 price if the acquiring parents are in the retail sector. In this specication we would expect that consumer backlash concern would be stronger, as these parents are primarily active in the retail sector. We nd that there is no dierence between the eect of opening an aliate in a haven or non-haven country, based on the FSI or bank haven list. The eect of rumours about bank havens remains almost constant in eect size across all specications. Therefore, we cannot claim that there is a dierential negative eect of opening a haven aliate in this industry with heightened reputational concerns.

5 Conclusion

In this paper we present evidence that stock market investors are not concerned when multi- national companies acquire aliates in secrecy jurisdictions. However, the increase in trans- parency is still considered as good news, albeit for a subsample of all rms. This hints that there might be a dierential impact with investors being less sensitive towards the probability of fraud, but more warm towards the decrease in secrecy. We nd that the possibilities for tax planning are considered as positive news. We interpret our ndings as evidence that investors distinguish between the tax planning and secrecy motives. Our positive result on tax havens is in contrast to the expected negative eect that underpins the reputational concerns outlined in the previous literature.

References

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9 Table 1: Descriptive Statistics of Acquisition Deals

Acquisitons of non-havens Acquisitions of havens Dierence Variable Mean SD Mean SD P-value

Deal value (th EUR) 981 426 4 766 927 907 354 4 407 290 0.76 Deal length (# days) 109 210 113 160 0.79 Deal in 1 day 0.31 0.46 0.33 0.47 0.28 Financial Secrey Index value 954 465 904 527 0.04 Secrecy score 14 21 70 4 0.00 Tax haven by Zucman list 0.01 0.10 0.38 0.49 0.00 Sweetheart 0.10 0.30 0.00 0.00 0.00 Tax rate 0.37 0.05 0.20 0.11 0.00 Number of deals 10 121 266 Notes: This table presents descriptive statistics for acquisition deals, distinguishing between non-haven acqui- sition deals and haven acquisition deals, according to the secrecy score of the Financial Secrecy Index. Deal value is the deal value in thousands of EUR. Deal length is the number of days from deal rumour date until deal completion date. Deal in 1 day is the proportion of deals that were rumoured, announced and completed on the same day. Financial Secrecy Index ranks jurisdictions according to their secrecy and the scale of their oshore nancial activities. Secrecy score is a qualitative measure of jurisdictions' secrecy, based on 15 secrecy indicators. Haven by Zucman list is a haven, based on list by Johannesen & Zucman (2014). Sweetheart is an indicator variable equal to 1 for a country whose government makes deals with multinational companies in favour of corporate tax payable, and 0 otherwise, based on European Network on Debt and Development (2016). Tax rate is statutory corporate tax rate. Number of deals gives the number of acquisition deals.

10 Table 2: Eect of Haven Acquisitions on Stock Returns

(1) (2) (3) (4) (5) (6) Type of Haven: FSI Secrecy Score Bank Haven Sweetheart

Rumour Deal*Haven 0.022 0.009 0.078*** 0.064*** 0.055** 0.043** (0.026) (0.026) (0.017) (0.020) (0.024) (0.018) Announced Deal*Haven -0.009 -0.031 0.016 -0.006 0.022 -0.001 (0.024) (0.024) (0.030) (0.030) (0.025) (0.021) Completed Deal*Haven -0.003 -0.027 0.030 0.005 0.066*** 0.044** (0.021) (0.020) (0.031) (0.031) (0.021) (0.019) Rumour Deal*Non-Haven 0.014 0.014 0.012 (0.014) (0.012) (0.012) Announced Deal*Non-Haven 0.016 0.023** 0.023** (0.013) (0.010) (0.009) Completed Deal*Non-Haven 0.028*** 0.025*** 0.022*** (0.009) (0.008) (0.008) Rumour Deal 0.014 0.014 0.012 (0.012) (0.012) (0.012) Announced Deal 0.023** 0.023** 0.023** (0.010) (0.010) (0.009) Completed Deal 0.025*** 0.025*** 0.022*** (0.008) (0.008) (0.008) Observations 1903762 1903762 1903762 1903762 1903762 1903762 R2 0.744 0.744 0.744 0.744 0.744 0.744 Specication Eq. (1) DiD Eq. (1) DiD Eq. (1) DiD Notes: The top of each set of two columns denotes the variable that underlies the denition of haven. In the rst two columns the reported coecients are on indicators for acquisition deal interacted with haven dummy according to Financial Secrecy Index for a 3 day event window. In columns 3 and 4 the acquisition deal is interacted with the bank secrecy haven from Johannesen & Zucman (2014). In columns 5 and 6 the acquisition deal is interacted with haven dummy according to number of Advance Pricing Agreements (sweetheart deals). All regressions control for parent and day xed eects. Robust standard errors clustered on parent company level are reported in parentheses. Statistical signicance at 10%, 5%, and 1% levels is denoted by *, **, and ***, respectively.

11 Table 3: Eect of Haven Acquisitions on Stock Returns. Tax Bins

(1) (2) Rumour Deal 0.015 0.014 (1.23) (1.21) Rumour Deal * Tax Bin 2 -0.009 -0.007 (-0.39) (-0.24) Rumour Deal * Tax Bin 3 -0.014 -0.014 (-0.81) (-0.77) Rumour Deal * Tax Bin 4 0.021 0.025* (1.60) (1.84) Rumour Deal * Tax Bin 5 -0.027 -0.031 (-1.16) (-1.32) Rumour Deal * Tax Bin 6 0.289* 0.281 (1.81) (1.63) Announced Deal 0.028*** 0.028*** (2.81) (2.79) Announced Deal * Tax Bin 2 -0.038 -0.032 (-1.15) (-0.67) Announced Deal * Tax Bin 3 -0.039** -0.038** (-2.38) (-2.11) Announced Deal * Tax Bin 4 -0.001 0.005 (-0.07) (0.43) Announced Deal * Tax Bin 5 -0.017 -0.022 (-0.77) (-0.99) Announced Deal * Tax Bin 6 -0.177 -0.201 (-1.01) (-1.24) Completed Deal 0.023** 0.022** (2.54) (2.48) Completed Deal * Tax Bin 2 -0.010 0.005 (-0.51) (0.09) Completed Deal * Tax Bin 3 0.006 0.007 (0.31) (0.39) Completed Deal * Tax Bin 4 0.021** 0.038** (2.35) (2.56) Completed Deal * Tax Bin 5 -0.011 -0.035 (-0.69) (-1.46) Completed Deal * Tax Bin 6 0.102 0.056 (1.17) (0.26) Observations 1903762 1899327 R2 0.744 0.744 Sample Full Exclude Same Day Deals Notes: The reported coecients are on indicators for acquisition deal interacted with tax bin dummy for an event window of 3 days. In column 2 we exclude the deals for which rumour, announcement and completion are all reported on the same date. Each tax bin variable is a dummy taking a value of 1 if the acquired rm is in a country with a tax rate, corresponding to the bin number. For example, tax bin 1 includes countries with a tax rate below 10 percent. Tax Bin 2 includes countries with tax rates between 10%-20%. Tax bin 1 includes aliates in Bahrain, Bermuda, Cayman Islands and Virgin Islands. Tax bin 2: Chile, Czechia, Hong Kong, Hungary, Iceland, Ireland, Latvia, Lithuania, Mauritius, Moldova, Poland, Romania, Serbia, Singapore, Slo- vakia, Switzerland, Taiwan. Tax bin 3: Austria, Canada, Chile, China, Croatia, Czechia, Denmark, Dominican Republic, Ecuador, El Salvador, Estonia, Finland, Georgia, Germany, Ghana, Greece, Iceland, Indonesia, Is- rael, Jordan, Kazakhstan, Korea, Luxembourg, Malaysia, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Russian Federation, Saudi Arabia, Singapore, Slovenia, South Africa, Spain, Sweden, Switzerland, Taiwan, Turkey, Ukraine, United Kingdom of Great Britain and Northern Ireland, Viet Nam. Tax bin 4: Angola, Argentina, Australia, Austria, Belgium, Brazil, Cameroon, Canada, China, Colombia, Costa Rica, Czechia, Denmark, Dominican Republic, France, Germany, India, Israel, Italy, Japan, Luxem- bourg, Mexico, Monaco, Morocco, Netherlands, New Zealand, Nigeria, Panama, Papua New Guinea, Peru, Philippines, Poland, Portugal, Puerto Rico, Qatar, Romania, Russian Federation, Spain, Thailand, Turkey, Uganda, Ukraine, United Kingdom of Great Britain and Northern Ireland, United States of America. Tax bin 5: Belgium, Egypt, France, Greece, Italy, Japan, United12 Arab Emirates, United States of America. Tax bin 6: Germany, Kuwait, United Arab Emirates. All regressions control for parent and day xed eects. Robust standard errors clustered on parent company level are reported in parentheses. Statistical signicance at 10%, 5%, and 1% levels is denoted by *, **, and ***, respectively. Figure 1: Cumulative Abnormal Returns after Tax Information Exchange Agreements

Notes: The gure represents the coecients on an indicator for signing and coming into force of tax information exchange agreement events interacted with haven dummy according to the Financial Secrecy Index, multiplied by the length of the window. The horizontal axis labels denote the number of days before or after the events which are included as part of the dummy variable for the agreement event, e.g., 4 refers to the return between the event date and four days after the event date while -4 refers to the return between four days prior to the event date and the event date. The thinner lines represent the 95% condence intervals using standard errors that are clustered by parent company.

13 Table 4: Eect of Haven Acquisitions on Stock Returns with Respect to the Retail Sector

(1) (2) (3) (4) (5) Main Retail sector Rest Retail sector Rest Aliates Parents Panel A. Financial Secrecy Index Secrecy Score Rumour Deal*Haven 0.009 0.037 0.003 0.344 0.005 (0.026) (0.030) (0.026) (0.506) (0.026) Announced Deal*Haven -0.031 -0.019 -0.026 -0.396 -0.026 (0.024) (0.029) (0.025) (0.433) (0.025) Completed Deal*Haven -0.027 0.043 -0.025 -0.025 -0.024 (0.020) (0.032) (0.020) (0.051) (0.020) Rumour Deal 0.014 0.016 0.014 0.022 0.011 (0.012) (0.011) (0.012) (0.014) (0.012) Announced Deal 0.023** 0.027*** 0.023** 0.020* 0.025*** (0.010) (0.009) (0.010) (0.010) (0.010) Completed Deal 0.025*** 0.024*** 0.024*** 0.024** 0.024*** (0.008) (0.008) (0.008) (0.010) (0.008) Panel B. Bank Haven List Rumour Deal*Haven 0.064*** 0.079*** 0.059*** 0.078 0.066*** (0.020) (0.027) (0.020) (0.134) (0.018) Announced Deal*Haven -0.006 -0.025 -0.000 -0.140 -0.002 (0.030) (0.042) (0.030) (0.111) (0.031) Completed Deal*Haven 0.005 -0.000 0.010 0.034 0.004 (0.031) (0.051) (0.031) (0.054) (0.032) Rumour Deal 0.014 0.015 0.013 0.022 0.011 (0.012) (0.011) (0.012) (0.015) (0.012) Announced Deal 0.023** 0.027*** 0.023** 0.020* 0.025*** (0.010) (0.009) (0.010) (0.011) (0.010) Completed Deal 0.025*** 0.025*** 0.024*** 0.024** 0.023*** (0.008) (0.008) (0.008) (0.010) (0.008) Observations 1903762 1897941 1903093 1890245 1902232 R2 0.744 0.744 0.744 0.744 0.744 Notes: Specication (1) is the baseline specication from Table 2. Further, the sample in specication (2) only includes acquisitions of aliates in retail sector, and specication (3) excludes these acquisitions. The sample in specication (4) only includes acquisitions by parent rms in retail sector, while specication (5) excludes such acquisitions. In Panel A the main reported coecients are on indicators for acquisition deal interacted with haven dummy according to Financial Secrecy Index for a 3 day event window. In panel B the acquisition deal is interacted with the bank secrecy list from Johannesen & Zucman (2014). All regressions control for parent and day xed eects. Robust standard errors clustered on parent company level are reported in parentheses. Statistical signicance at 10%, 5%, and 1% levels is denoted by *, **, and ***, respectively.

14 A Additional Tables

Table A.1: Eect of Haven Acquisitions on Stock Returns. Lag Lead Analysis

Rumour Announcement Completed F(2).Haven -0.011 0.027 -0.076 (0.044) (0.057) (0.056) F(1).Haven 0.008 0.030 -0.093 (0.047) (0.057) (0.059) Haven 0.021 0.033 -0.100 (0.058) (0.067) (0.064) L(1).Haven 0.036 0.037 -0.104 (0.088) (0.094) (0.073) L(2).Haven 0.054 0.078 -0.084 (0.136) (0.132) (0.096) L(3).Haven 0.021 0.113 0.001 (0.137) (0.114) (0.089) F(2).Non-Haven -0.022** 0.008 0.010 (0.011) (0.010) (0.007) F(1).Non-Haven -0.024* 0.007 0.009 (0.013) (0.013) (0.008) Non-Haven -0.023 0.009 0.010 (0.016) (0.016) (0.010) L(1).Non-Haven -0.018 0.016 0.014 (0.023) (0.021) (0.013) L(2).Non-Haven -0.009 0.045 0.035** (0.033) (0.029) (0.017) L(3).Non-Haven 0.015 0.067** 0.061*** (0.037) (0.028) (0.017) Observations 1895857 R2 0.744 Notes: The table shows the lag and lead analysis of the main specication, where the lag-lead variables are the haven or non-haven dummies (according to their secrecy score), interacted with dummies for rumour, announcement and completion of the deal. All the interaction terms are included and estimated in one speci- cation. All variables are in dierences except the third lag, which is in levels. E.g. L(2).Haven is the second lag of the rst dierence of the Haven dummy. F stands for forward or lead. All regressions control for parent and day xed eects. Robust standard errors clustered on company level are reported in parentheses. Statistical signicance at 10%, 5%, and 1% levels is denoted by *, **, and ***, respectively.

15 Table A.2: Eect of Haven Acquisitions on Stock Returns. Robustness Tests

(1) (2) (3) (4) (5) Main Weight by Excluded Secrecy score> 70 Secrecy score> 50 Market Cap Same Day Panel A. Financial Secrecy Index Secrecy Score Rumour Deal*Haven 0.009 0.047 0.009 0.027 0.005 (0.026) (0.043) (0.028) (0.027) (0.015) Announced Deal*Haven -0.031 -0.070* -0.027 -0.054 -0.030 (0.024) (0.041) (0.028) (0.035) (0.020) Completed Deal*Haven -0.027 -0.017 -0.018 0.029 0.002 (0.020) (0.031) (0.040) (0.033) (0.018) Rumour Deal 0.014 0.011 0.015 0.014 0.014 (0.012) (0.026) (0.012) (0.012) (0.012) Announced Deal 0.023** 0.010 0.023** 0.023** 0.024** (0.010) (0.016) (0.010) (0.010) (0.010) Completed Deal 0.025*** 0.023*** 0.028*** 0.024*** 0.025*** (0.008) (0.009) (0.010) (0.008) (0.008) Observations 1903762 1680345 1899327 1903762 1903762 R2 0.744 0.775 0.744 0.744 0.744 Panel B. Bank Haven List Rumour Deal*Haven 0.064*** 0.043 0.070*** (0.020) (0.033) (0.023) Announced Deal*Haven -0.006 -0.018 0.009 (0.030) (0.040) (0.044) Completed Deal*Haven 0.005 0.033 0.045 (0.031) (0.051) (0.064) Rumour Deal 0.014 0.012 0.014 (0.012) (0.026) (0.012) Announced Deal 0.023** 0.010 0.024** (0.010) (0.016) (0.010) Completed Deal 0.025*** 0.023*** 0.028*** (0.008) (0.009) (0.010) Observations 1903762 1680345 1899327 R2 0.744 0.775 0.744 Notes: Specication (1) is the main specication from Table 2. Specication (2) weights the regressions by market capitalization of the acquiring parent rm. Specication (3) excludes deals rumoured, announced and completed in the same day. Specication (4) increases the threshold for target companies to be classied as havens to secrecy score over 70. Specication (5) reduces the threshold for target companies to be classied as havens to secrecy score over 50. In Panel A the reported coecients are on indicators for acquisition deal interacted with haven dummy according to Financial Secrecy Index for a 3 day event window. In Panel B the acquisition deal is interacted with haven dummy according to bank secrecy list from Johannesen & Zucman (2014). All regressions control for parent and day xed eects. Robust standard errors clustered on parent company level are reported in parentheses. Statistical signicance at 10%, 5%, and 1% levels is denoted by *, **, and ***, respectively.

16 Table A.3: Eect of Haven Acquisitions on Stock Returns. Tax Rate

(1) (2) (3) (4) Rumour deal * Tax rate 0.00026 -0.00756 (0.029) (0.021)

Announced deal * Tax rate -0.02395 -0.01055 (0.036) (0.023)

Completed deal * Tax rate 0.01324 0.00572 (0.021) (0.022)

Rumour deal 0.01493 0.04551*** (0.012) (0.012)

Announced deal 0.02566** 0.04987*** (0.010) (0.013)

Completed deal 0.02298*** 0.04764*** (0.009) (0.014) Observations 1903762 1903762 1903762 1903762 R2 0.744 0.744 0.744 0.744 Notes: The main reported coecients are on indicators for acquisition deal interacted with target tax rate for an event window of 3 days. Column (1) is the specication that controls for all deals, while specications (2) to (4) only consider the eect of rumour, announcement and completion of deals alone, respectively. All regressions control for parent and day xed eects. Robust standard errors clustered on parent company level are reported in parentheses. Statistical signicance at 10%, 5%, and 1% levels is denoted by *, **, and ***, respectively.

17 Table A.4: Eect of Haven Acquisitions on Stock Returns

(1) (2) (3) (4) Panel A. Financial Secrecy Index Secrecy Score

Rumour deal * Haven dummy 0.02219 0.01402 (0.026) (0.029)

Announced deal * Haven dummy -0.00852 0.00928 (0.024) (0.031)

Completed deal * Haven dummy -0.00343 0.00629 (0.021) (0.031)

Rumour deal * Non-haven dummy 0.01374 0.04077*** (0.014) (0.012)

Announced deal * Non-haven dummy 0.01585 0.04399*** (0.013) (0.011)

Completed deal * Non-haven dummy 0.02769*** 0.04846*** (0.009) (0.012) Dierence-in-dierence (Haven - Non-haven) -0.02657 -0.02857 -0.03703 -0.04065 (0.02) (0.027) (0.029) (0.029) Panel B. Zucman List

Rumour deal * Zucman haven dummy 0.07756*** 0.09915*** (0.017) (0.031)

Announced deal * Zucman haven dummy 0.01625 0.09803** (0.030) (0.040)

Completed deal * Zucman haven dummy 0.02999 0.09065** (0.031) (0.041)

Rumour deal * Zucman non-haven dummy 0.01358 0.04362*** (0.012) (0.011)

Announced deal * Zucman non-haven dummy 0.02273** 0.04762*** (0.010) (0.011)

Completed deal * Zucman non-haven dummy 0.02462*** 0.04817*** (0.008) (0.012)

Dierence-in-dierence (Haven - Non-haven) 0.04176*** 0.06398*** 0.05041 0.04249 (0.031) (0.020) (0.038) (0.04) Panel C. Sweetheart List

Rumour deal * Sweetheart dummy 0.05505** 0.10517*** (0.024) (0.026)

Announced deal * Sweetheart dummy 0.02216 0.11127*** (0.025) (0.027)

Completed deal * Sweetheart dummy 0.06638*** 0.11929*** (0.021) (0.026)

Rumour deal * Non-sweetheart dummy 0.01222 0.04119*** (0.012) (0.011)

Announced deal * Non-sweetheart dummy 0.02273** 0.04510*** (0.009) (0.011)

Completed deal * Non-sweetheart dummy 0.02232*** 0.04491*** (0.008) (0.012) Dierence-in-dierence (Haven - Non-haven) 0.04405** 0.06398*** 0.06618*** 0.07437*** (0.019) (0.021) (0.022) (0.023) Observations 1903762 1903762 1903762 1903762 R2 0.744 0.744 0.744 0.744 Notes: Column (1) is the specication that controls for all deals, while specications (2) to (4) only consider the eect of rumour, announcement and completion18 of deals alone, respectively. In Panel A the reported coecients are on indicators for acquisition deal interacted with haven dummy according to FSI. In panel B the acquisition deal is interacted with the bank secrecy list. In Panel C the acquisition deal is interacted with haven dummy according to number of APAs (sweetheart deals). Figure A.1: Eect of Haven Acquisitions by Year: Secrecy Havens

Notes: The gure represents the coecients on an interaction term between haven dummy, according to FSI secrecy score, and acquisition deal dates. The horizontal axis labels denote years.

19 Figure A.2: Eect of Haven Acquisitions by Year: Bank Havens

Notes: The gure represents the coecients on an interaction term between haven dummy, according to bank secrecy haven list from Johannesen & Zucman (2014), and acquisition deal dates. The horizontal axis labels denote years.

20