Were We Better Off Without the Invention of Tracking

TRACKING STOCKS: A TWO-STAGE EQUITY SEPARATION PROCESS

November 9, 2013

ABSTRACT

Tracking stocks (a subsidiary share) split a company into two (or more) equity claims, but still allow the subsidiary to remain wholly owned by the parent while trading on the fundamentals of the business unit. They gained popularity in the 1990s but were mostly gone by 2007. Issuing firms significantly underperformed the market in the three years prior to announcing the intention to issue a tracking stock. That announcement created positive significant abnormal market returns, but the post-issuance performance had significant negative returns for the parent and tracking stock. Many firms opted to retire the structures, and this study focuses analyzes the lifespan of the structure from pre-issuance to post-retirement. On average, the announcement to retire the structure resulted in a positive, significant abnormal return to the parent and the tracking stocks followed by positive, significant abnormal returns for the parent and tracking stock on the retirement date, signaling that the market expected a subsequent event. Further, the parent had a positive, significant abnormal post-retirement return and often followed with a sale or spin-off of the subsidiary. When examining the entire picture from pre-issuance to post- retirement, it seems that the market believes – over the long-run – that tracking stocks are part of a two-stage equity separation process.

KEYWORDS: divestitures; tracking stocks; targeted stocks

I. INTRODUCTION

On September 15, 2008, Bank of America (BoA) agreed to acquire Merrill Lynch (ML) for about $50 billion as the credit crisis claimed one of America's oldest financial companies.

Roughly two years later, as BoA struggled with record operating losses and a declining stock price, ML‘s investment banking and brokerage business were generating huge profits, and the marriage was in trouble. The Wall Street Journal reported that BoA would consider issuing a ML tracking stock to raise capital in response to a request from the Federal Reserve which asked for contingency plans if conditions deteriorated for the bank. Three years later, as Yahoo reviewed pitches for a potential sale/breakup of the company, it explored the establishment of a tracking stock(s) that would attempt to mirror the value of the underlying Asian assets. Then on October

10, 2013, Liberty Interactive Corporation announced plans to create a QVC Group tracking stock as part of a recapitalization of the Liberty Interactive Group tracking stock. These events renewed interest in the structure and why they hardly exist anymore. Several financiers suggested that one successful issuance could lead to a re-emergence of the structure.

A tracking stock is a type of common stock that "tracks" or depends on the financial performance of a specific business unit or operating division of a company but does not have a legal claim on the unit‘s assets. It is a claim on the assets of the parent. By the end of 1999, the total market value of outstanding tracking stocks exceeded $400 billion.1 However, the introduction of accounting changes in 2003 forced companies to recognize stock compensation expenses, and that may have diminished the appeal of the structure. In the years since the dot.com boom, like many of the companies of that era, most of the tracking stocks have long since vanished.

1 According to information published by Spin-Off Advisors, LLC (www.spinoffadvisors.com) 2

By 2009, only one firm in the U.S., Liberty Media, still had tracking stocks. In 2010, the firm announced plans to simplify its extremely complex structure by converting its largest tracking stock into an asset-backed stock and spinning off the two other subsidiaries with tracking stocks. The result would be for the combined company to have two classes of common stock trading on Nasdasq. Despite this, on February 23, 2012, Liberty unveiled a plan to recapitalize into two tracking stocks, Liberty Interactive and Liberty Ventures. The company said the Interactive tracking stock would include QVC, a 34% stake in HSN, and $500 million in cash, while Ventures would include interests in Expedia, TripAdvisor, Time Warner, Time

Warner Cable, and AOL, along with $1.3 billion in cash. Now it is poised to recapitalize again with a tracking stock at the center of its plans. Does this mean that we can expect see more tracking stock offerings in the near future? What happened to all the previous tracking stocks?

The answer may lie in why tracking stocks arose in the first place.

In order to fully examine the tracking stock structure, we assemble what we believe is the entire population of tracking stocks and analyze the structure using both operating performance and market performance. Our paper contributes in several ways. We are the first to undertake a comprehensive study of the entire lifespan of the tracking stock structure by analyzing the population from pre-issuance to post-retirement. We are also the first to fully study the wealth effects of the elimination (rather than creation) of the structure and to significantly incorporate operating performance. Our finding that the tracking stock structure is viewed as a temporary form of restructuring, is an incremental contribution to the divestiture literature. Further, our finding that the long-term drift in post-retirement returns in the same direction as the initial reaction at the time of the retirement announcement contributes to the under-reaction literature.

We examine the direction and magnitude of earnings, price, volume, and volatility reactions to various events during the tracking stock lifespan. We find that in the three years before the issuance announcement, the parent had significantly negative buy and hold excess returns (BHERs) (-377.14%).2 This is somewhat consistent with Clayton and Qian (2004), which shows insignificant positive raw BHERs in the three years prior to the announcement (76.5%).3

We find positive significant issuance announcement CARs (2.80%), confirming what was documented by previous studies4. On the issuance date, the parent has a positive, but insignificant, abnormal return (.18%). This differs from Clayton and Qian (2004), which find that firms issuing tracking stocks experience positive significant abnormal returns (3.12%) on the issuance date.5 For the tracking stocks, we find insignificant CARs at issuance (.27%).

The findings of Ritter (1991) and Loughran and Ritter (1995) suggest that because tracking stocks create positive announcement effects, firms might have taken advantage of irrational market prices to issue the new securities. If this is true, we would expect to see long- term under-performance following the issuance of tracking stocks that could eliminate any wealth gains from the announcement effect. We find that both the parent and tracking stocks have declining return on assets (ROAs) and significantly negative CARs (-99.52% & -106.53%, respectively) in the three years after issuance. This is counter to Lauterbach and Vu (2007), which find that the parent stock response is insignificant in the short and long run.

2 We present results using the market model with the CRSP equally weighted benchmark for the (-1,1) window. The results are quantitatively similar when we use the market model with the CRSP value weighted benchmark, market- adjusted returns (equal or value weighted), or comparison-period mean adjusted returns. 3 Based on the market model with the CRSP equally weighted benchmark. Our raw BHER is 101.23%. Their BHERs are presented raw and against benchmarks based on industry and size and size and book-to-market. 4 Our study only includes firms that issued the tracking stock, where a number of prior studies documented the wealth effects for any firm announcing a tracking stock issuance. 5 Our sample period is longer and includes more issuances than Clayton and Qian (2004). 4

According to Billett and Vijh (2004), ―if the poor long-term returns reflect problems that were not foreseen, then we would expect many reversals of the tracking stock structure in favor of the old one-stock structure. We would also expect complete divestitures of tracked businesses by spin-off or sell-off. In addition, we would expect positive market reaction following the announcement of such reversals and divestitures.‖ This study confirms that after the long-term period of under-performance, most firms decided to eliminate the tracking stock structure.

Both parent and subsidiary have positive significant CARs when announcing a tracking stock retirement (4.02% and 10.4%, respectively). The positive market reaction to the announcements of issuance and elimination, flanking a period of long-term under-performance, is consistent with Billett and Vijh (2004), but inconsistent with Clayton and Qian (2004), which documented that tracking stocks and parent stocks do not significantly under- or outperform benchmarks over the three years following issuance. In comparison, our results are consistent with Klein, Rosenfeld, and Beranek (1991) and Vijh (2002), which document positive returns

(3.67% on average) following the announcement of offers by parent firms to re-acquire or sell- off carve-out stocks and suggest carve-outs are a temporary form of asset restructuring. Our results are also similar to the positive significant reactions to announcements of share repurchases (3.54% on average). It has been found that long term performance improves following share repurchases (around 12%), as well as following full reacquisition of carve-outs

(15.59%). It appears that the retirement of a tracking stock structure confers some of the same benefits to shareholders as carve-out reacquisition and share repurchases.

We extend the work of Billett and Vijh (2004) and Clayton and Qian (2004), which documented negative wealth effects of tracking stocks and their parent firms over extended periods after issuance and before retirement announcement, by focusing on what happens when a

firm announces that it is retiring its tracking stock, when it completes the retirement, and in the period after the retirement. Solely focusing on returns at the time of retirement announcement, though, would have been inadequate, and it seemed necessary to examine the performance at retirement and over an extended period following the retirement to determine the full impact of not only that event but of the entire tracking stock lifespan. ―Were we better off without the invention of tracking stocks?‖ - can only be answered by including the long-term post-retirement performance of the parent because the announcement period returns do not tell the full story.

Interestingly, both parent and tracking stocks had positive significant CARs on the retirement date (1.7% & 2.2%, respectively). This was not expected, absent any new information, but was the indicator that the market expected a subsequent event. In fact, the great majority of former tracking stock subsidiaries were sold off or spun off on the three years after the retirement. During that same period, the parent had mixed operating results and positive significant CARs (21.62%). Like Lakonishok and Vermaelen (1990) for fixed price tender offers and Ikenberry, Lakonishok, and Vermaelen (1995) for open market share re-purchases, we find a positive significant abnormal return in the period following retirement. The long-term drift in our post-retirement returns is in the same direction as the initial reaction of the price at the time of the retirement announcement, which suggests the market, on average, under-reacts at the time of the announcement.6

Guided by Bamber, Barron, and Stevens (2011), we also test cumulative abnormal relative volume (CARV) from daily volume data centered on the event date for each firm where the return is replaced with log-transformed relative volume, as well as the cumulative absolute abnormal return (CAAR) and absolute cumulative abnormal relative volume (ACARV) as

6 According to Kadiyala and Rau (2002), long-run abnormal returns reflect investors‘ tendency to under-react, first to short-term information available prior to the event and subsequently to information conveyed by the event itself. 6

proxies for volatility. The parent has significant positive CARV around the issuance date of the tracking stocks (95.38%), announcement date of the retirement (123.48%), and retirement date

(27.78%). For the volatility, there is significant positive absolute CAAR around the issuance date

(3.89%), announcement date of the retirement (4.96%), and the retirement date (4.31%), as well.

There is also significant positive absolute ACARV around the issuance date (168.77%), announcement date of the retirement (196.55%), and the retirement date (150.98%). When assessing the CARVs for the tracking stock, we find positive significant results around the announcement date of the retirement (250.43%) but negative insignificant results around the retirement date (-2.11%). The announcement date of the retirement (13.78%) and the retirement date (4.64%) volatility are also positive and significant. There is also significant positive absolute ACARV around the announcement date of the retirement (276.53%) and around the retirement date (235.82%).

The rest of this paper is organized as follows. In Sections 2 and 3, we review the basics of divestitures and tracking stock structures, along with the extant literature, respectively. Data is discussed in Section 4. We outline our research methodology in Section 5. The results and their implications are discussed in Section 6. Finally, the conclusion, future research ideas, and closing remarks are provided in Section 7.

II. DIVESTITURE/TRACKING STOCKS BASICS

Modern economic history has been divided into merger waves. During the third and fourth merger waves (1965-1989), acquirers frequently bought firms in unrelated industries.

Sometimes this was done to maximize shareholder value, smooth out cyclical bumps, hedge an investment portfolio, or diversify the firm‘s holdings. During the 1980s in particular, diversification was the driving force behind many large corporate marriages. 7

Starting with the fifth merger wave (1992) and continuing today, companies became more likely to acquire firms in the same or a related industry, choosing targets that complemented and strengthened their capacity to serve customers - making horizontal and congeneric mergers (increasingly across geographic borders) the focus of their merger activity.

The idea of the focused firm led to the 1990s being known as the decade of divestitures. Even though business combinations were abundant during this period, more and more businesses began disposing of units that were acquired in previous waves. 7

A decision to focus on "core business" for the benefit of both the company and its shareholders is one of the most often cited reasons for corporate divestitures structured as (1) a sell-off by exchanging a subsidiary's assets or stock for cash, other assets, or in settlement of debt; (2) a spin-off by distributing the subsidiary's stock pro rata to the parent's shareholders as a dividend; (3) a split-off by distributing the subsidiary's stock to the parent's shareholders in exchange for shares of the parent's stock; (4) a split-up by distributing the stock of two or more subsidiary companies to the parent's shareholders in exchange for all the parent's stock followed by liquidation of the parent; or (5) an equity carve-out by creating a new legal entity and publicly offering its shares, while retaining control.

Each structure has its pros and cons, and each creates a separate claim on the unit being divested. There is another divestiture structure, the tracking stock8, which emerged on the landscape in the 1980s and rose to popularity in the 1990s during the dot.com boom when many companies had Internet divisions that they wanted to monetize, but not sell. The tracking stock

7 Lipton, M., 2006. Merger waves in the 19th, 20th and 21st centuries.

8 Tracking stock is sometimes referred to as alphabet stock, letter stock, or targeted stock. The names alphabet stock and letter stock arose out of General Motors‘ acquisitions of Electronic Data Systems and Hughes Aircraft in the 1980s, which created GM-E and GM-H, respectively. The term targeted stock was coined by Lehman Brothers when they assisted USX Corporation with their tracking stock equity restructuring in the early 1990s. 8

structure was heavily promoted to solve this problem by helping large corporations unlock the value of high-performing divisions by creating a new equity issue that mirrors the performance of a particular subsidiary, while the parent would still control the unit. Additionally, tracking stocks were a way for firms to retain their best employees since they could offer stock option plans in the unit's shares; to assist with the completion of mergers; and to preserve the internal capital markets, operating synergies, and co-insurance effect of diversified firms. They can be issued in a distribution to current shareholders as dividends (like a spin-off); in a sale to new investors to raise cash as an IPO (like a carve-out); or in an acquisition as currency.

Numerous studies have documented that increasing firm focus by divestiture is associated with an increase in firm value, regardless of whether the divestiture is accomplished by an asset sale, a spin-off, or a carve-out.9 This benefit, however, is unachievable with tracking stocks.

Unlike those structures which also divide the firm into two or more firms with distinct boundaries, a tracking stock does not legally separate the balance sheet of the tracked business from the company as a whole.

Tracking stocks split the company‘s operations into two (or more) publicly traded equity claims, but allow the unit to remain wholly-owned by the parent, and can be issued Like conventional common stocks, the tracking stock legally represents an equity stake in the diversified parent. Unlike their conventional counterparts, however, they are designed to trade on the fundamentals of a particular business unit, rather than on the whole corporation and are

9 Lang and Stulz (1994), Berger and Ofek (1995, 1999), and Comment and Jarrell (1995) document that focus and firm value are positively related and that firm value increases when focus is improved. John and Ofek (1995), Desai and Jain (1999), and Vijh (1999) find that focus-increasing divestitures are associated with greater wealth gains for samples of asset sales, spin-offs, and carve-outs, respectively. Hite and Owers (1983), Miles and Rosenfeld (1983), and Schipper and Smith (1983) study the announcement returns of spin-offs, and Schipper and Smith (1986) study the announcement returns of carve-outs. 9

created with features that often limit the rights of the shareholders. Thus, tracking stocks preserve the internal capital market while other forms of restructuring destroy it.

Spin-offs, carve-outs, and tracking stocks can reduce information asymmetry around the subsidiary; help align managerial incentives with those of the shareholders by providing firms the option of tying management compensation to areas within their direct control; and increase value for the parent. Carve-outs and spin-offs provide the parent the opportunity to divest non- core or inefficient assets, which can lead to a reduction in firm inefficiencies. They, however, create new and distinct equity claims on some portion of a firm's assets, whereas tracking shares are not separate company shares. They do not represent legal ownership interest in the assets of the subsidiary and have virtually no effect on the assets controlled by the parent. A tracking stock effectively creates a quasi-pure play without a legal separation of corporate assets and liabilities. The complete legal separation of businesses removes potential conflicts arising from the division of cash flows. This distinction is important, as many benefits of divestitures are believed to result from the complete separation of non-synergistic businesses.

III. LITERATURE REVIEW

While there is a large literature stream on divestitures, there are only a handful of studies on tracking stocks. Prior literature has found that the market reacts positively to the announcement of a divestiture but the level of reaction differs across methods, with spin-off announcements realizing the largest gains.10 Logue, Seward, and Walsh (1996) were the first to compare tracking stock to other forms of corporate restructuring. They found positive average abnormal announcement period returns. Billett and Mauer (2000), D‘Souza and Jacobs (2000),

10 Schipper and Smith (1986), Vijh (1999), D‘Souza and Jacob (2000), and Clubb and Stouraitis (2002) 10

Elder and Westra (2000), and Harper and Madura (2002) document mean announcement returns of around 3% for tracking stocks, which are of the same magnitude as the announcement returns for spin-offs and carve-outs and consistent with Logue, Seward, and Walsh (1996). The evidence appeared to suggest that the tracking stocks create net benefits to shareholder wealth.

Lee and Madhavan (2010) conducted a meta-analysis of the divestiture-performance relationship and found that across studies, divestiture has a positive, statistically significant effect on post-divestiture performance, whether the measures are accounting or market based. The source of this value creation, however, is difficult to identify and validate. For tracking stocks, the investigation of post-issue long-run performance provides some puzzling results.

Zuta (2000) established that Berger and Ofek (1995) excess value measures improve in the year following the issuance of tracking stock. Chemmanur and Paeglis (2001) and Boone,

Haushalter and Mikkleson (2003) jointly examine tracking stocks and carve-outs, and both find that for both tracking stocks and carve-outs, short-term stock price reactions are positive but long-term stock and operating performances are negative. Harper and Madura (2002) and Billett and Vijh (2004) show that the post-issue performance of tracking stocks typically underperforms the market, whereas Clayton and Qian (2004) documents that tracking stocks and parent stocks do not significantly under- or outperform benchmarks over the three years following issuance.

Ritter (1991), Loughran and Ritter (1995), Harper and Madura (2002) and Lauterbach and Vu (2007) suggest that firms might take advantage of irrational prices/investors to issue new securities. Montier (2002) argues that one explanation is that rational managers attempt to exploit the transitory irrational demand for certain units of the business. Firms may knowingly take advantage of this transitory window of opportunity by issuing over-valued stocks when one

division of a firm is highly regarded in the markets or exhibit temporarily high multiples. While tracking stocks may increase firm value when the price-multiple of unit-comparable firms are near their peak, valuation is expected to subsequently decline after the issues. In the other words, insiders may knowingly issue over-valued tracking stocks. This argument is consistent with the positive announcement returns and disappointing post-issue stock performance.

Billett and Vijh (2004) documented some key results related to post-issue performance.

They found that tracking stocks earned significantly negative buy-and-hold excess returns during a three-year period following the issue date. They also found significantly negative returns surrounding the earnings announcements during their sample period. This contrasts with the post-issue returns of spinoffs, which are known to be positive, and carve-outs, which are known to be insignificant. Clayton and Qian (2004) believe this post-issue underperformance led firms to rethink the structure, contributing to its extinction. Miles (2005) documented that announcements of tracking stock elimination was a positive wealth event for the majority of the parent shareholders and a positive significant event for the tracking shareholders.

Among studies on divestitures, a number provide evidence that an equity carve-out is usually the first stage of a two-stage process. In Schipper and Smith (1986), two-thirds of carved-out subsidiaries were later reacquired by the parent, divested, spun-off, or liquidated.

Klein, Rosenfeld, and Beranek (1991) found that 85% were followed by a second event. Perotti and Rossetto (2007) models carve-outs as a way for the parent to obtain information from the market on the value of the subsidiary as an independent entity. Though costly, the listing generates information about the optimal allocation of ownership of the subsidiary and improves the decision to exercise the option to sell or reacquire control.

Gleason, Madura, and Pennathur (2006) document favorable market reaction for both parents and carved-out units when the units are re-acquired, whereas, Klein, Rosenfeld, and

Beranek (1991) show that parents experience significantly positive announcement returns when the follow-on event is a sell-off, but an insignificantly positive return when the follow-on event is a re-acquisition. Both sell-off and re-acquisition announcements have a strong positive impact on the carved-out unit‘s return in Klein, Rosenfeld, and Beranek (1991). Gleason, Madura, and

Pennathur (2006) suggest it is possible that the market equates carve-out re-acquisition to a parent share repurchase as their results are similar to studies on stock repurchases11. The study reports a significant CAR of 1.66% for the re-acquisition announcement to parents and 10.93% to carved-out units as well as insignificant BHERs of 3.31% in the year after re-acquisition.

For repurchase announcements, Ikenberry, Lakonishok, and Vermaelen (1995) finds an average market reaction of 3.54% and documents that firms have significant positive abnormal returns of 12% in the four-year period following repurchases. The picture that emerges is that firms that announce an open market repurchase tend to perform abnormally well in the long run.

IV. DATA

In 1984, GM consummated the acquisition of EDS with shares of GM-E, the first tracking stock. For the period beginning 1980, we searched Factiva for stories containing key words ‗tracking stock‘, ‗alphabet stock‘, ‗letter stock‘, ‗lettered stock‘, ‗target stock‘, or ‗targeted stock.‘ Details such as issuer, subsidiary, and announcement dates were obtained from those articles. If two sources disagreed on the announcement date, we carefully read the articles and in all cases choose the earlier date. In addition, we received the data set from Danielova (2008).

11 See Vermaelen (1981); Ikenberry, Lakonishok, and Vermaelen (1995); Grullon and Michaely (2004). 13

We obtained issuance and retirement dates from the first and last trading dates on CRSP; announcement dates of subsequent restructuring events from Factiva; number of shares outstanding and book value of equity from Compustat (or SEC filings if not available in

Compustat); and stock prices and returns, along with market returns from CRSP. From this information, we calculate the market value of equity (number of shares outstanding * closing price) and book-to-market ratio at issuance and retirement.

Almost seventy firms announced an intention to issue a tracking stock structure, however for various reasons only thirty-two actually did (issuing 52 tracking stocks). After eliminating one tracking stock that was traded in Japan (even though the parent was traded in the US) and one that was a preferred stock (its information was not available on CRSP), we were left with 50 tracking stocks to analyze. Nearly all of the companies that issued the structures have undergone corporate reorganizations and have eliminated their tracking stock structures. The eliminations were attributable to the parent firm being acquired in its entirety; the sale of the tracked division or liquidation of the divisional assets; the spin-off of the tracked division; or the conversion of the tracking stock back into common stock of the parent corporation.

The data set is small, but it includes the entire population of completed tracking common stock issuances in the US. As a population study, our statistical tests will not suffer from small sample bias; nevertheless, we conduct and document them in the following section.

Descriptive Statistics

The parents are typical publicly traded firms in the US with a market capitalization at tracking stock issuance of $22 billion (median $3.9 billion). In 1999 (during the height of the tracking stock phenomena), firms in the S&P 500 had an average market value of $21.4 billion

(median $8.7 billion)12. At issuance, the tracking stocks have average market capitalization of

$3.1 billion (median $819 million). When measured by total assets, the average parent firm size is $24.125 billion (median $6.27 billion), while the tracking firm size is $4.338 billion (median

$806.94 million). The book-to-market ratio averages 0.77 for the tracking stocks and 0.58 for the parent stocks. The average lifespan of the tracking stock was 4.34 years (median 3.94 years).

Krishnaswami and Subramaniam (1999) report that the ratio of subsidiary to parent market value averages 0.213 for spin-offs, and Vijh (1999) reports that the ratio of offering value to the parent market value averages 0.159 for carve-outs.13 Tracking stocks are similar to those other divestitures with a subsidiary to parent market value of .142. By retirement, while the median market capitalization for the parent stock had grown to over $8 billion, the median market capitalization for the tracking stock had fallen to $212 million. The total assets of the tracking stocks had also declined (to $1 billion), while the parent firm saw an increase (to $53 billion). The average book-to-market ratio has increased significantly for both the parent and the subsidiary (to 2.67 and 2.21, respectively), indicating deteriorating market conditions.

V. METHODOLOGY

We compute CARs, CARVs, CAARs, and ACARVs around the following dates:

 Announcement Date of Issuance of Tracking Stock

 Issuance Date of Tracking Stock

 Announcement Date of Retirement of Tracking Stock

 Retirement Date of Tracking Stock

12 Source: Standard & Poor‘s 13 Calculated from information contained in Table 2 in Krishnaswami and Subramaniam (1999). 15

Then, we calculate a series of operating performance measures, CARs, and BHERs for the following long-term periods:

 The three year period before issuance

 The period between issuance and retirement (or 3 years, whichever comes first)

 The three year period after retirement (or delisting, whichever comes first)

The measures were calculated for the parent issuing the tracking stock (PS), the tracking stock

(TS), and the combined entity (PS + TS) 14 for all the periods except the pre-issuance, issuance announcement date, and post-retirement periods (only PS was calculated).

Short-Term Abnormal Returns

We examine the period around the issuance announcement, issuance, retirement announcement, and retirement dates. If the market is efficient, at least in its semi-strong form as defined in Fama (1970), stock prices should incorporate the new information, which is relevant for valuation, quickly and correctly.

Fama (1991) noted that event studies with narrow windows around announcement dates have many methodological benefits. In particular, the standard event study procedure attenuates the joint-hypothesis problem, as the results from narrow windows are much less sensitive to a particular asset-pricing model. Therefore, we use four event windows to estimate the market reaction: (-10, +10), (-5, +5), (-3, +3), and (-1, +1). Our estimation period is a 250-day period ending 90 days prior to the announcement to minimize any potential run-up related to the announcement.15

14 The combined abnormal returns can only be obtained by first computing the PS and TS CARs and then combining these returns in proportion to their market values. 15 For TS Issuance Date, our estimation period is a 90-day period starting 15 days after the event. 16

It should be noted, however, that the decision to retire a tracking stock is frequently the result of a long-term study of possible methods of corporate reorganization. The chosen window may capture some changes attributable to reorganization; a window further removed from the announcement may not accurately reflect the realities of the changing firm. Potential bias introduced through the choice of comparison period should be against finding abnormality. To ensure the robustness of our results, we use four models: 1) Market; 2) Market-adjusted return;

3) Mean-adjusted return; and 4) Raw return, along with various benchmarks: 1) CRSP equal- weighted index,16 2) CRSP value-weighted index, 3) S&P 500, 4) size-decile portfolio returns; and 5) industry portfolio returns.

In order to determine whether our short-term results are statistically significant, we compute a variety of parametric test statistics and non-parametric test statistics. Because of the skewness of stock returns, in general, and in our population, in particular, we use Johnson‘s skewness-adjusted t-statistic along with a computationally intensive bootstrap re-sampling technique because the skewness is severe and the population size is small to determine our statistical significance.

Tracking stocks are new issuances, and event studies around new issuances are faced with the problem that data is not available to obtain estimates of the benchmark parameters in a pre-event period (Ibbotson 1975). Therefore, calculating the short-term abnormal return at issuance for TS is tricky. We calculate the CAR using a 90-day estimation period starting 15 days after issuance for each tracking stock. In addition, following Clayton and Qian (2004), we

16 CRSP value and equal weight indexes include all stocks listed on the NYSE, Amex, and Nasdaq. We present equally weighted returns rather than value-weighted returns since equally weighted portfolio returns better capture the extent of underperformance than value-weighted portfolio returns. 17

examine the issuance date return to a current shareholder (an investor who holds the company stock prior to the issuance) based on the type of issuance transaction (dividend, IPO, or merger).

For tracking stocks distributed as dividends, we calculate the issuance date return as the total closing market value of PS + TS on issuance day, divided by the market value of PS the day before, minus one. For tracking stocks issued as IPOs, we use an estimation window of a 90-day period from 15 trading days after the issuance date to 105 days after the issuance date and calculate the CARs. Because firms that issue tracking stock in a merger experience a large change in value with the addition of the acquired firm and because acquired firms are mostly privately held - so we have no information on their market value prior to the trading of the tracking stock - we don‘t calculate their return on the issuance date.

Volume and Volatility

A growing body of research in accounting and finance examines the reaction of trading volume to new information. The typical volume event study employs a single-index market model borrowed mutatis mutandis from abnormal returns event studies.17 Market volume (or log turnover) is used to explain individual security volume (or log turnover), and predicted residuals are used to test for zero mean abnormal volume within a specified event window. We use daily volume data centered on a single date for each firm replacing the return with log-transformed relative volume.

The market response to a retirement announcement is also likely to include an increase in volatility so we investigate this possibility. Bailey, Karolyi and Salva (2006), Fernandes and

Ferreira (2008), Gomes, Gorton and Madureira (2006) and Rogers and Van Buskirk (2008),

17 Ajinkya and Jain (1989) state that ―Although a well-developed economic theory such as CAPM for returns does not seem to exist for trading volume, a number of theoretical papers linking trading volume to information releases can be used to motivate a trading volume market model.‖ Tkac (1999) develops a theoretical model justifying the use of the trading volume market model. 18

among others, use the absolute of abnormal return or abnormal volume as a volatility-based measure of the information content of an announcement. The increase in the cumulative average abnormal return and cumulative average abnormal volumes prior to the announcement date are common instruments to measure the extent of information leakage prior to the announcement.

We use the same indices, benchmarks, estimation options, event windows, and test statistics that are used to determine the CARs, in order to calculate the average CARVs, CAARs, and ACARVs in the volume and volatility studies.

Long-Term Abnormal Returns

There is a substantial body of literature that goes beyond documenting announcement effects induced by corporate events to analyzing the long-term abnormal returns following those events. Abnormal long-term performance signals that measuring abnormal returns only around an announcement does not accurately capture the total value created (destroyed) by an event.

This can be partially explained by the ―under-/over-reaction‖ hypothesis that investors tend to over-react to some events such as IPOs, but under-react to others such as dividend initiations.18

We measure long-term abnormal returns for PS and TS for the periods before issuance and between issuance and retirement (or three years, whichever is first), as well as the period after retirement for PS because a number of studies show that the valuation effects of restructuring are not necessarily completed at the time of the announcement.19

Examining long-term abnormal returns is more complicated and controversial than measuring announcement effects. According to Barber and Lyon (1997); Lyon, Barber and Tsai

(1999); and Drobetz, Kammermann and Wälchli (2003), there are five main hurdles in

18 According to studies by DeBondt and Thaler (1985); Barberis, Shleifer, Vishny (1998); and Kent, Hirshleifer, and Subrahmanyam (1998) 19 Agrawal, Jaffe, and Mandelker (1992); Rau and Vermaelen (1998); Cusatis, Miles, and Woolridge (1993); Ikenberry, Lakonishok, and Vermaelen (1995); and Loughran and Vijh (1997) 19

measuring long-term abnormal returns and assessing their significance: benchmark bias, rebalancing bias, skewness bias, cross-sectional dependence, and new listing bias. Cowan and

Sergeant (2001) added a sixth – overlapping horizon bias. Numerous studies such as those, along with Kothari and Warner (1997) and Fama (1998), have discussed the pros and cons of BHERs,

CARs, annual excess returns in calendar time, and the Fama-French three and four factor models and have debated the appropriate tests of long-term excess returns because of these hurdles.

Many of the biases are mitigated with large sample sizes and careful construction of benchmark portfolios; however, we have a small population.

BHERs capture the investor experience more accurately, while the remaining methods give more reliable test statistics. The literature on long-term stock performance heavily emphasizes results from the buy-and-hold approach despite these well-known problems because

BHERs capture the investor experience more accurately and avoid biases arising from microstructure issues when portfolio performance is measured with frequent rebalancing20. Other biases are also reduced if the value weight portfolio performance is examined, but BHERs tend to magnify under-performance. CARs are also problematic21, but they give more reliable test statistics than BHERs. To overcome potential criticism of any methodology, we examine all measures of abnormal returns over a three-year holding period.

First, we calculate CARs and BHERs are using one year windows of (+1, +250), (+251,

+500), (+501, +750), as well as a three year window of (+1, +750) for PS and TS. Again, we use four models: 1) Market; 2) Market-adjusted return; 3) Mean-adjusted return; and 4) Raw return,

20 See Blume & Stambaugh (1983), Roll (1983), and Ball, Kothari, and Shanken (1995). 21 For example, they have an upward bias caused by bid-ask spread bounce and the simple summing of daily returns can produce cases of non-realistic returns of less than 100 percent for a given event interval. 20

along with various benchmarks: 1) CRSP equal-weighted index, 2) CRSP value-weighted index,

3) S&P 500, 4) size-decile portfolio returns; and 5) industry portfolio returns.

Addressing the weaknesses

To address the various weaknesses of the buy-and-hold approach, we benchmark with four different characteristic-matched control firms matched by industry, size, and a book-to- market (as a proxy for risk); earnings-to-price (as proxy for long-term growth); return on assets

(as a proxy for operating performance); and prior stock return (as a proxy for momentum). We use the t-test and Wilcoxon test to determine the statistical significance for the control firm

BHERs. The selection of multiple benchmarks is necessary because, at present, there is no consensus about the relevant factors in stock returns.

To identify the control firms, we use the following procedures: Each sample firm is paired with the firm in its two-digit SIC code that has the closest market value of equity (MV) in year t-1. The sample firm and its matched firm must be listed on the same exchange, and the matched firm cannot have a tracking stock. Then, we select matches for book-to-market, earnings-to-price, return on assets, and prior stock return using the matching value within the subset of firms whose market value lies between 70% and 130% of the sample firm value.

Book-to-market is an industry normalization of the book-value of equity to market-value of equity (BTM) ratio; earnings-to-price is the earnings per share divided by the market price per share (EP); return on assets (ROA) is operating income before depreciation divided by book value of assets; and the prior stock return (MOM) is the past 12-month return through the end of

May of the ranking year.

When a sample firm has no match for a particular characteristic after matching for industry and size in year t-1, we try to match within the 70%-l30% filter using all firms without

regard to SIC code.22 If a matched firm is delisted, we replace it with the next-closest firm.

According to Vijh (1999), the size and book-to-market matching firms are an especially important benchmark for subsidiaries, while the size and earnings-to-price matching firms are an especially important benchmark for parents. After matching the sample firms with their benchmark firms, we compute the BHER for each stock j over a period of t trading months:

∏ ∏ (1) where is the return on stock j on date t, and is the return on the matched firm on date t.

As with the short-term returns, we compute a variety of parametric and non-parametric test statistics and use the bootstrapped Johnson‘s skewness-adjusted t-statistic to determine statistical significance. Though it is not included among the biases previously mentioned, we also observe a leptokurtic distribution of the stock returns in our data. The skewness-adjusted test is not analytically adjusted for kurtosis, however, the two forms of non-normality tend to go together. Simulation evidence suggests that the skewness-adjusted t-statistic, when bootstrapped, is robust to both problems. We use the wild bootstrap for the greatest robustness based on general results in the econometrics and statistics literature.23

Lyon, Barber and Tsai (1999) recommend Johnson‘s skewness-adjusted t-statistic for heteroskedasticity, as well, but caution that the ―misspecification in nonrandom samples is pervasive.‘‘ They assume that the observations are cross-sectionally uncorrelated. The assumption holds in random samples of event firms, but is violated in nonrandom samples like ours. Jegadeesh and Karceski (2009) introduced the HSC t test, a test of long-run performance

22 Size matching proves to be important only when firms are drawn from the smallest third of firm size and the top third of performance (measured by return on assets). Performance matching is important when sample firms have historically performed either well or poorly. 23 We appreciate Professor Arnie Cowan for the suggestion. 22

that is robust in nonrandom samples that allows for heteroskedasticity and autocorrelation. We added the HSC t test with a wild bootstrap to determine our statistical significance.

Operating Performance

We analyze operating performance with four measures - return on book value of assets

(ROA-BV)24, return on sales (ROS), return on market value of assets (ROA-MV)25, and cash- flow return on assets (ROA-CF)26 27 - using operating income before depreciation.28 We calculate abnormal operating performance (AOP) for each firm as its operating performance minus a matched firm‘s operating performance from the year before through three years after issuance (retirement). We follow the same matching process from the long-term abnormal returns, except size is measured as book value of equity (BV).

Like Eberhart, Maxwell, and Siddique (2004), we follow the calculation of AOP for each of firm by computing the time-series mean and median29 of the measure and use the time-series volatility to estimate the standard error. This is the equal-weighted abnormal operating performance measure. Then, we calculate the value-weighted average abnormal operating performance using the book value of assets to measure the value weights; so the weight applied to a firm is the ratio of its book value of assets to the total book value of assets for the sample firms. Again, we compute the time-series mean and median and use the time-series volatility of

24 Used the average of beginning- and ending-period book value of total assets (6). 25 Book value of total assets (item 6) less book value of common equity (item 60) plus market value of common equity (item 2.5 times item 199). 26 Operating income before depreciation (item 13) plus decrease in receivables (2), decrease in inventory (3), increase in accounts payable (70), increase in other current liabilities (72), and decrease in other current assets (68). 27 Should be less susceptible to earnings management than ROA but has much wider dispersion among firms. 28 It is perceived as a cleaner measure of the productivity of operating assets than earnings. 29 Due to the skewness of accounting ratios, it is typical to report median values in studies examining operating performance. Kaplan (1989), Healy and Palepu (1990), Degeorge and Zeckhauser (1993), Jain and Kini (1994), Mikkelson, Partch, and Shah (1997), Loughlin and Ritter (1997), and McLaughlin, Safieddine, and Vasudevan (1996), among others, all report median values. This however, produces conceptual problems if there is a positive covariance between operating performance ratios and changes in scale. A highly profitable firm that grows (in size) can more than offset multiple unprofitable firms that declines (in size). Therefore, the use of medians does not capture the extent of this covariation. 23

these measures to estimate the standard error. To measure statistical significance, we compute Z- statistics to conduct Wilcoxon matched-pairs signed-rank tests of the hypothesis that the distribution of sample firm and matched firm ratios are identical.

Multivariate Analysis

We extend our study to a multivariate analysis of the CARs, BHERs, and ROAs and regress the abnormal performance on several explanatory and control variables.

To explain the abnormal returns upon the issuance announcement, we use CAR (-1, 1) as the dependent variable in the following regression:

+ ε (2) where TSRun (PSRun) is the percent change in stock price in the three months before the issuance announcement as a proxy for momentum; TSROA (PSROA) is the ROA in the quarter before issuance announcement to proxy for performance; TSDebt is the debt ratio of TS measured as the debt of TS divided by total assets for the quarter prior to issuance announcement as a measure of leverage; and TSCashHol is the cash to total asset ratio of TS for the quarter prior to issuance announcement as a measure of liquidity.

In addition, there are four dummy variables - Relatedness that equals 1 if TS has the same three-digit SIC code as PS; IssType that equals 1 if the tracking stock is issued as a dividend, 0 otherwise; Bubble that equals 1 if TS was issued during the overvalued Internet Bubble Period30; and NewEcon equals 1 if TS is a wireless, internet, or high technology firm.

Next, we use several variables to explain the BHERs after issuance for PS and TS with

BHER (1, 750) as the dependent variable in the following regression:

30 Following Ljungqvist and Wilhelm (2003), we define the period as January 1999 through December 2000. 24

+ ε (3) where ROA is the ROA in the quarter before issuance; InstOwn is the percentage of institutional ownership; DivYield is the ratio of dividends to price at issuance; EarnYield is the ratio of earnings to price ratio at issuance; DivPayout is the ratio of dividends to earnings at issuance; and BTM is the book-to-market ratio at issuance.

To explain the abnormal returns upon the retirement announcement for PS and TS, we use CAR (-1, 1) as the dependent variable in the following regression:

+ ε (4) where PerOut is the time from tracking stock issuance to retirement announcement; TS(PS)ROA is the ROA in the quarter before retirement announcement; TS(PS)Run is the percent change in stock price in the three months before the retirement announcement; TSDebt is the debt ratio of

TS measured as the debt of TS divided by total assets for the quarter prior to retirement announcement; and TSCashHol is the cash to total asset ratio of TS for the quarter prior to retirement announcement. In addition, there are three dummy variables – RelSize, which is the ratio of TS market value to PS market value at retirement announcement; Relatedness, which is a dummy variable that equals 1 if TS has the same three-digit SIC code as PS; and RetType is a dummy variable that equals 1 if the unit is re-integrated into the parent, 0 otherwise.

Then, we examine the 750-trading day BHER for PS following retirement, where BHER

(1, 750) is the dependent variable in the following regression:

ε (5) 25

where ROA is the ROA in the quarter before retirement; InstOwn is the percentage of institutional ownership; DivYield is the ratio of dividends to price at retirement; EarnYield is the ratio of earnings to price ratio at retirement; DivPayout is the ratio of dividends to earnings at retirement; and BTM is the book-to-market ratio at retirement.

Last, we investigate the change in operating performance following issuance and retirement, where ΔROAt,t-1 (change in ROA from year t to t+1) is the dependent variable in the following regression:

(6) where ROAt is the level of ROA in year t, ROAt-1, t is the change in ROA from year t-1 to t,

PS(TS) Assetst is the log of total assets in year t (a proxy for size), PS(TS) Debtt is the debt scaled by total assets ratio in year t (a proxy for leverage), and PS(TS) Capt is the book value of assets scaled by sales. We chose ROA over ROE or other metrics because it is not affected by financial leverage or the way a firm computes separately stated items like extraordinary items.

VI. RESULTS

The firms that issue tracking stocks significantly underperform (-377.14%) in the three years prior to announcing a tracking stock issuance. At announcement, it has a positive significant CAR of 2.8%. Around the issuance date, neither the parent nor the tracking stock has statistically significant abnormal returns (.18% and -.27%, respectively). After issuance, however, the long term returns of both the parent and the tracking stock underperform the market

(-99.52% and -106.53%, respectively). These results are consistent with Billett and Vijh (2004), but inconsistent with the results of Clayton and Qian (2004), which documented that tracking

stocks and parent stocks do not significantly under- or outperform benchmarks over the three years following issuance.

We find that the parent company has significant positive CARs around the announcement date of the retirement of tracking stocks (4.02%), as well as around the retirement date of the tracking stocks (1.7%). The same trend applies to the tracking stocks, which have significant positive CARs around the announcement date of the retirement (10.4%) and retirement date

(2.2%). These results are consistent with Billett and Vijh (2004) and Clayton and Qian (2004).

Since many tracking stocks contain provisions for payment of a premium upon conversion, it is expected that they would have a positive market reaction to the announcement of the elimination of the tracking stock structure, but it is unclear why there are still positive, significant returns on the actual retirement date, absent any new information.

All of our models and test statistics, including the un-tabulated calendar time, RATS, and

Fama-French models, generate similar results; therefore, the results appear to be robust with respect to the adopted methodologies.

Interestingly, we find that, using the event-time approach, there is significant underperformance for the parent in the first three years after the retirement when using the buy and hold approach. The long term CARs are positive and slightly significant. Barber and Lyon

(1997) show that CARs and BHERs can produce different inferences on the same set of data.

CARs require monthly rebalancing, which may lead to an inflated long-horizon return on the reference portfolio. This effect is likely to be attributed to bid-ask bounce and nonsynchronous trading. In the calendar-time, RATS, and Fama-French methods, underperformance is robust for the BHERS, including when adjusted for size.

When using volume data, we find that the parent has significant positive CARV around the announcement date of the retirement of the tracking stocks (123.48%) and retirement date of tracking stocks (27.78%), as well as around the issuance date of tracking stocks (95.38%). For the tracking stock, we find significant positive CARV around the announcement date of the retirement (250.43%) but significant negative CARV around the retirement date (-2.11%). The volatility data provides similar evidence of information content for the short term windows. The parent company has positive significant ACAR around the announcement date of the retirement of the tracking stocks (4.96%) and retirement date of tracking stocks (4.31%), as well as around the issuance date of tracking stocks (3.89%). For the tracking stock, there is significant positive

ACAR around the retirement announcement date (13.78%) and retirement date (4.64%).

The abnormal stock returns seem to be driven by a few of the variables. The debt ratio of the subsidiary has a significant effect because the unit‘s financial leverage is still backed by the parent‘s assets, while being monitored by its own shareholders. Subsidiaries with lower leverage may require more monitoring, but they will be monitored and/or controlled by the parent. In fact, parents may be more likely to improve units with low debt because they may not have been sufficiently monitored, and are expected to benefit more from re-acquisitions of low-debt subsidiaries. Those units with high debt have less financial flexibility, and may therefore experience more favorable valuation effects.

The cash holdings of the subsidiary are also a significant driver of the abnormal returns.

Cash-constrained units can experience liquidity problems. Those with low cash holdings ratios may benefit more from re-acquisition because they gain access to the parents‘ liquidity. We find the expected negative relation between the ratio and abnormal returns. Finally, the relatedness of

the subsidiary to the parent is a driver. The more similar the subsidiary and parent, the more likely it is that both will benefit from potential synergies. A parent is more likely to be able to resolve information asymmetry when reintegrating a closely related subsidiary. The parent may also be better able to improve it if it exercises some control.

The operating results mirror the stock returns. Holthausen and Larcker (1996) documented similar results with reverse leverage buyouts. In addition, Jain and Kini (1995) report that operating profitability tends to decline after an IPO. They suggest that this decline may be explained in part by the worsened incentives of managers. Because many of the tracking stocks were issued to use as a compensation tool, this may partially explain our results, as well.

As a point of comparison, previous studies have shown that the average spin-off firm outperforms the market after the event, while the average carve-out firm considerably underperforms the market. Spin-off parents (subsidiaries) show significant positive BHERs of

15.9% (55.8%) for the three-year post-event period. Carve-out parents (subsidiaries), in contrast, exhibit significant negative BHERs of -34.3% (-17.2%) for the three-year post-event period. Our results most closely resemble what has been documented with carve-outs, which also have continued parental ownership and tend to be eliminated.

The superior performance of spin-off firms seems to be driven by superior post-issue operating performance. The profitability of spin-off parents and subsidiaries improves in the long run, while the profitability of carve-out and tracking stock subsidiary firms‘ declines. This may be attributable to the benefits of the independence of the subsidiary. Following a spin-off, the parent company completely loses its influence on the subsidiary, whereas the parent company retains a controlling interest in the subsidiaries following carve-out and tracking stock

transactions. This distinction is important, as many benefits of divestitures are believed to result from the complete separation of non-synergistic businesses.

Our findings are consistent with Klein, Rosenfeld, and Beranek (1991) and Vijh (2002) which document positive returns following the announcement by parent firms to re-acquire or sell-off carve-out stocks, as well as with Ikenberry and Vermaelen (1996), which documents positive returns following the announcement of offers by firms to re-acquire their shares. This validates our initial assertion that if retirement of a tracking stock confers some of the same benefits to shareholders as carve-out retirement and share repurchases, then we expect a favorable market reaction to the announcement of tracking stock retirements.

There are two key findings. First, on average, the elimination of tracking stocks (as evidenced on the announcement and actual retirement dates) results in positive wealth effects for both PS and TS after a long-term period of under-performance. This follows the findings of

Ritter (1991) and Loughran and Ritter (1995) which suggested that because tracking stocks create positive announcement effects, firms might take advantage of irrational market prices to issue new securities, leading to long-term under-performance. It also follows the findings of

Klein, Rosenfeld, and Beranek (1991) and suggests the market may perceive the tracking stock as a temporary form of asset restructuring and the first part of a two-stage equity separation.

Second, for a given firm, there can be substantial differences in the reaction of the tracking and parent stocks. This differential price reaction suggests that the tracking stock successfully separates a firm into distinct economic entities and may indicate that the tracking stock structure leads to conflicts of interest between the tracking and parent shareholders. Many firms, therefore, take the logical next step and spin off the tracking subsidiary into a distinct legal

entity. In fact, a quarter of the eliminations are accomplished with a spin-off or other equity separation. This is also consistent with the findings of Klein, Rosenfeld, and Beranek (1991).

Robustness Checks

We checked our results for the effects of outliers and contemporaneous news. Even after eliminating those observations our results were robust with respect to our methodologies. As stated previously, we used a number of approaches for the long-term abnormal performance, including CARs, BHERs, the calendar-time portfolio approach, and the Fama-French three and four factors. The inferences are similar with all methods. Ibbotson (1975) recognized that the parameter estimates for new issues could not be estimated in a pre-event estimation period since the stock was not previously quoted. Ibbotson pioneered the Return Across Securities (RATS) method which allows the estimate of beta to vary during the returns window. As a robustness check, we used the RATS method, along with the modified form used in Agrawal, Jaffe, and

Mandelker (1992), which adjusts for size effects; the RATS method with the Fama-French three and four factor models, and the RATS method with the Fama-French three and four factor models estimated with the calendar-time portfolio approach for robustness. These inferences are similar to what had previously been documented.

VII. CONCLUSIONS

Our study examines of the long-term performance of tracking stocks and the firms that issue them and is the first to focus on the entire lifespan of the tracking stock structure. It complements the existing empirical literature on tracking stocks by providing evidence on their long-term under-performance and positive abnormal returns around retirement announcements and retirements. We thoroughly examine the universe of tracking stock restructurings using many different measures and benchmarks and have robust results for all out findings. 31

Our main conclusions are that elimination of the tracking stock structure seems to create wealth gains for shareholders and is the first step in a two-step equity separation process. This elimination follows a long-term period where the tracking stocks under-perform appropriate benchmarks by an average of more than 10% per year in the three years following issuance. The announcement to eliminate the tracking stock structure results in a significant and positive excess return of around 10% to the tracking stock shareholders and 4% to the parent shareholders.

To draw further implications, we contrast our evidence on tracking stocks with existing evidence for spin-offs and carve-outs. Previous literature shows that the pro forma combined firms after spin-offs earn significant and positive long-term excess returns in addition to the positive announcement excess returns. Spin-offs create greater wealth gains for the combined shareholders than do tracking stocks. Our population behaves more like carve-outs with a prolonged period of under-performance, followed by significant positive abnormal return upon the announcement of the elimination of the structure. Tracking stocks, like their carve-out counterparts, suffer from lack of independence from the parent. Many of the tracking stocks (as well as carve-outs) are eliminated with the use of the spin-off divestiture structure.

The market reaction at the announcements of issuance and elimination suggest that, like with carve-outs in Klein, Rosenfeld, and Beranek (1991), tracking stock structures are perceived as a temporary form of asset restructuring. When compared to the spin-off literature, it seems that firms would have been better off choosing a spin-off over a tracking stock in the beginning.

We find that unification of the firm‘s capital is a better choice than one with a tracking stock and that the tracking stock structure is a temporary form of restructuring. Further, we find that there is a long-term drift in the post-retirement returns in the same direction as the initial reaction at the time of the retirement announcement.

TABLE 1: Divestiture Comparison

SPIN-OFF CARVE-OUT TRACKING STOCK DESCRIPTION Parent distributes shares of IPO of subsidiary Parent distributes shares of subsidiary subsidiary to existing to existing shareholders; IPO of shareholders subsidiary; or Merger currency DEGREE OF SEPARATION Complete Partial Notional REVERSIBLE No Dependent Yes CASH PROCEEDS Yes (upstream dividend) Yes Yes, if IPO option TAXABILITY Generally tax-free Potential capital gains tax, but Tax-free avoidable depending on percentage carved out and tax basis CONSOLIDATION No Yes if IPO for less than 20% Yes ASSET TRANSFER TO NEW COMPANY Yes Yes No BOARD OF DIRECTORS Separate Separate Same PARENT CONTROL OF NEW CO. CASH FLOWS No Generally no Mostly yes

TABLE 2: Descriptive Statistics

Panel A: Parent (PS)

BV of Assets BV of Equity Market Price(I) (per Market Cap (I) P/E Ratio (I) (I) (I) share) BTM (I) Mean 22,006,144.60 29.08 24,124.89 15.11 42.24 0.58 Median 3,900,276.00 17.30 6,267.25 12.17 28.82 0.39 Std Dev 64,650,278.10 73.93 52,774.66 14.79 29.93 0.75 Skewness 5.31 1.42 5.15 2.35 1.05 3.06 Kurtosis 31.34 5.73 31.46 7.32 0.34 9.65 Std Error Mean 8,965,380.49 10.25 7,318.53 2.05 4.15 0.10

BV of Assets BV of Equity Market Price (R) (per Market Cap (R) P/E Ratio (R) (R) (R) share) BTM (R) Outstanding Period Mean 27,764,259.00 2.42 53,348.82 12.63 37.63 2.67 4.34 Median 8,311,548.00 15.84 10,937.50 12.04 32.56 0.44 3.94 Std Dev 72,775,914.10 142.16 100,193.93 7.91 24.11 13.10 3.18 Skewness 5.12 -4.92 2.91 0.61 0.75 5.82 1.97 Kurtosis 28.33 27.01 8.49 1.06 0.26 33.94 5.88 Std Error Mean 12,480,966.30 24.38 17,183.12 1.36 4.13 2.25 0.44

Panel B: Tracking (TS)

BV of Assets BV of Equity Market Price(I) (per Market Cap (I) P/E Ratio (I) (I) (I) share) BTM (I) Mean 3,115,773.00 61.84 4,338.36 14.40 25.90 0.77 Median 819,335.00 3.94 806.94 8.96 21.13 0.49 Std Dev 5,559,863.00 629.54 8,997.27 31.25 18.57 1.16 Skewness 2.90 -5.13 3.02 5.09 1.56 3.04 Kurtosis 9.85 31.62 9.40 29.10 2.53 10.44 Std Error Mean 794,266.18 100.81 1,372.07 5.14 2.65 0.19

BV of Assets BV of Equity Market Price (R) (per Market Cap (R) P/E Ratio (R) (R) (R) share) BTM (R) Outstanding Period Mean 3,942,002.00 22.58 4,071.72 11.38 17.22 2.61 4.34 Median 211,653.00 -0.24 1,001.81 8.47 10.39 0.62 3.94 Std Dev 8,097,689.00 84.64 8,222.30 10.35 19.35 7.18 3.18 Skewness 2.65 3.52 3.27 1.34 1.94 5.20 1.97 Kurtosis 7.11 13.38 11.54 1.55 3.97 29.18 5.88 Std Error Mean 1,193,939.14 13.22 1,268.73 1.64 2.85 1.15 0.44 IQR 2,619,634.00 21.61 2,122.00 11.56 20.69 1.38 3.66 35

TABLE 3: PARENT PRE-ISSUANCE RETURNS

Panel A: Parent Pre-Issuance (BHER) Market Model, Equally Weighted Index

Precision Portfolio Weighted Postitive: Time- Generalized Skewness Mean Median EGLS CSectErr Rank Corrected Days CAR CAR CAAR Negative Patell Z StdCsect Z Z CDCSI Z Series t t Sign Z Test Z t

( -750,-501) -24.29% -3.07% 7.59% 18:22 0.34 0.178 0.135 0.48 -3.196*** -1.653* -0.108 1.920* -1.717$ (-500,-251) -37.32% -27.86% -18.10% 13:27< -2.259* -1.162 -0.798 -0.578 -4.912*** -2.056* -1.695* -2.002* -1.846$ (-250,-1) -15.47% -21.38% 6.00% 18:23 0.97 0.495 0.403 0.355 -2.036* -1.039 -0.251 0.789 -1.02 - - (-750,-1) 377.14% -68.55% -4.52% 15:26 -0.184 -0.062 -0.014 -0.067 28.657*** -2.436** -1.191 0.408 -3.558***

Panel B: Parent Pre-Issuance (CAR) Market Model, Equally Weighted Index

Precision Portfolio Weighted Postitive: Time- Generalized Skewness Mean Median EGLS CSectErr Rank Corrected Days CAR CAR CAAR Negative Patell Z StdCsect Z Z CDCSI Z Series t t Sign Z Test Z t

( -750,-501) -1.84% 2.64% 7.59% 22:18 0.34 0.178 0.172 0.585 -0.242 -0.162 1.161 1.920* -0.162 (-500,-251) -14.58% -17.66% -18.10% 15:25 -2.259* -1.162 -1.096 -0.733 -1.919* -1.179 -1.06 -2.002* -1.152 (-250,-1) 10.88% -13.80% 6.09% 18:23 0.97 0.495 0.429 0.372 1.432$ 0.74 -0.251 0.789 0.783 (-750,-1) -5.13% -4.21% -4.43% 20:21 -0.183 -0.062 -0.057 0.22 -0.39 -0.155 0.376 0.408 -0.155

The symbols $,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a generic one-tail nonparametric bootstrap of the indicated test.

TABLE 4: Issuance Announcement and Issuance Returns

Panel A: Parent Issuance Announcement Market Model, Equally Weighted Index

Precision Portfolio Weighted Postitive: Time- Generalized Skewness StdCsect CSectErr Rank Test Corrected Days Mean CAR Median CAR CAAR Negative Patell Z Z EGLS Z CDCSI Z Series t t Sign Z Z t

( -10,-2) 0.84% 1.29% 3.12% 28:23 1.916* 1.507$ 1.339$ 1.565$ 0.62 0.559 1.115 0.07 0.552 (-1,0) 2.33% 1.35% 3.46% 33:18>> 4.469*** 2.612** 2.308* 2.565** 3.634*** 2.471** 2.517** 2.474** 2.179* (0,+1) 2.09% 1.18% 2.71% 33:18>> 3.318*** 1.375$ 1.169 1.367$ 3.268*** 1.446$ 2.517** 1.674* 1.405$ (-2,+10) 0.27% -0.59% 0.23% 24:27 0.2 0.172 0.165 0.1 0.197 0.23 -0.007 0.141 0.232 (-10,+10) 3.91% 3.89% 7.19% 34:17>> 2.903** 2.182* 2.037* 2.146* 1.884* 1.848* 2.798** 1.053 1.872* (-1,+1) 2.80% 1.70% 3.85% 38:18>>> 4.011*** 2.125* 1.789* 2.070* 3.568*** 1.975* 3.920*** 2.442** 1.959$

Panel B: Parent Issuance Market Model, Equally Weighted Index

Precision Portfolio Weighted Postitive: Time- Generalized Skewness StdCsect CSectErr Rank Test Corrected Days Mean CAR Median CAR CAAR Negative Patell Z Z EGLS Z CDCSI Z Series t t Sign Z Z t

( -10,-2) 0.70% -0.44% 0.87% 19:22 0.625 0.512 0.518 0.282 0.546 0.58 -0.078 1.047 0.594 (-1,0) -0.06% 0.29% 0.42% 22:19 0.273 0.206 0.193 0.563 -0.099 -0.082 0.861 0.145 -0.082 (0,+1) -0.75% -0.58% -1.04% 18:23 -1.318$ -0.898 -0.8 -0.752 -1,251 -0.853 -0.391 -1.287$ -0.799 - - (+2,+10) -4.04% -2.09% -3.69% 17:24 -2.493** -2.217* 1.887* -1.329$ 3.1688*** -2.632** -0.704 -2.360** -3.082** (-10,+10) -3.16% -2.88% -2.54% 15:26( -1.172 -0.842 -0.82 -0.499 -1.622$ -1.394$ -1.330$ -0.883 -1.378 (-1,+1) 0.18% -0.01% 0.28% 20:21 0.134 0.103 0.093 0.324 0.251 0.197 0.235 -0.063 0.199

Table 4 continued

Panel C: Tracking Issuance

Precision Portfolio Weighted Postitive: Time- Generalized Skewness StdCsect CSectErr Rank Test Corrected Days Mean CAR Median CAR CAAR Negative Patell Z Z EGLS Z CDCSI Z Series t t Sign Z Z t

( -10,-2) 2.01% -2.30% 5.88% 1:2 0.691 0.366 0.37 0.452 1.003 0.274 -0.453 1.964* 0.315 (-1,0) 0.37% -1.12% 1.88% 1:2 0.424 0.21 0.209 0.307 0.402 0.097 -0.453 -0.288 0.104 (0,+1) 0.22% -0.48% 0.10% 23:25 1.938* 0.655 0.511 0.331 0.241 0.102 0.215 -1.638$ 0.101 - - (+2,+10) -1.30% -3.40% -8.32% 14:34<< 3.663*** -2.503** 1.731* -3.146** -0.666 -0.283 -2.389** -2.440** -0.271 (-10,+10) -0.90% -4.26% 0.03% 17:31( -2.747** -1.570$ -1.267 -2.311* -0.302 -0.22 -1.521$ -0.462 -0.218 (-1,+1) 0.27% -0.48% 2.46% 23:25 2.119* 0.717 0.56 0.387 0.243 0.125 0.215 -0.398 0.125

The symbols $,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a generic one-tail nonparametric bootstrap of the indicated test.

TABLE 5: Post-Issuance Returns

Panel A: Parent Post-Issuance (BHER) Market Model, Equally Weighted Index

Precision Portfolio Weighted Postitive: Time- Generalized Skewness Mean EGLS CSectErr Rank Corrected Days CAR Median CAR CAAR Negative Patell Z StdCsect Z Z CDCSI Z Series t t Sign Z Test Z t

- (+1,+250) -15.22% 1.42% -1.58% 21:20 -0.418 -0.271 0.233 0.129 -2.263* -1.4564$ 0.548 -0.905 -1.471$ (+251,+500) -18.73% -29.01% -20.54% 15:25 -1.385$ -0.77 -0.76 -0.677 -2.786** -1.980* -1.197 -0.848 -2.026* (+501,+750) -12.47% -3.82% 14.47% 17:19 0.78 0.431 0.376 1.029 -1.854* -1.11 0.033 1.442$ -1.138 - - (+1,+750) -99.52% -59.64% -7.66% 14:27( -0.775 -0.386 0.231 0.136 -8.546*** 3.401*** -1.643$ -0.179 -4.074***

Panel B: Parent Post Issuance (CAR) Market Model, Equally Weighted Index

Precision Portfolio Weighted Postitive: Time- Generalized Skewness EGLS CSectErr Rank Corrected Days Mean CAR Median CAR CAAR Negative Patell Z StdCsect Z Z CDCSI Z Series t t Sign Z Test Z t

- (+1,+250) -8.19% 6.24% -1.58% 21:20 -0.418 -0.271 0.243 0.137 -1.218 -0.808 0.548 -0.905 -0.825 - (+251,+500) -9.17% -1.42% -21.07% 20:20 -1.385$ -0.77 0.756 -0.666 -1.364$ -0.963 0.387 -0.848 -0.996 (+501,+750) -3.21% 1.41% 14.48% 18:18 0.78 0.431 0.443 1.189 -0.477 -0.344 0.367 1.442$ -0.347 - (+1,+750) -19.95% -14.35% -8.17% 20:21 -0.775 -0.386 0.377 0.214 -1.713* -1.06 0.235 -0.179 -1.109

Table 5 continued

Panel C: Tracking Post-Issuance (BHER) Market Model, Equally Weighted Index

Precision Portfolio Weighted Postitive: Time- Generalized Skewness EGLS CSectErr Rank Corrected Days Mean CAR Median CAR CAAR Negative Patell Z StdCsect Z Z CDCSI Z Series t t Sign Z Test Z t

(+1,+250) -71.38% -28.20% -10.82% 11:37<<< -0.789 -0.744 -0.2 -0.436 -6.958*** -2.389** -3.258*** 0.332 -3.983*** (+251,+500) -90.22% -9.89% -35.26% 13:35<< -1.913* -0.965 -0.32 -0.736 -8.794*** -2.057* -2.679** -0.056 -3.100*** - - (+501,+750) -106.53% -37.38% -28.89% 11:25< -2.176* -1.015 0.298 -0.338 10.384*** -1.802* -1.902* -0.554 -2.532** - - - (+1,+750) <-999.9% -108.64% -74.97% 7:41<<< 2.593** -1.118 0.003 -0.006 887.85*** -1.285$ -4.416*** -0.16 -1.806*

Panel D: Tracking Post Issuance (CAR) Market Model, Equally Weighted Index

Precision Portfolio Weighted Postitive: Time- Generalized Skewness EGLS CSectErr Rank Corrected Days Mean CAR Median CAR CAAR Negative Patell Z StdCsect Z Z CDCSI Z Series t t Sign Z Test Z t

- (+1,+250) 3.29% -4.45% -10.82% 22:26 -0.789 -0.744 0.613 -1.211 0.321 0.327 -0.074 0.332 0.25 - (+251,+500) -3.71% 0.20% -35.92% 0:24 -1.913* -0.965 0.812 -1.613$ -0.362 -0.227 0.505 -0.056 -0.228 - (+501,+750) -21.43% -15.57% -28.94% 16:20 -2.176* -1.015 0.869 -0.806 -2.089* -1.134 -0.231 -0.554 -1.208 - - (+1,+750) -16.50% 5.42% -75.68% 1:23 2.593** -1.118 0.925 -1.596$ -0.928 -0.471 0.794 -0.16 -0.481

The symbols $,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a generic one-tail nonparametric bootstrap of the indicated test.

TABLE 6: Retirement Announcement Returns

Panel A: Parent Retirement Announcement Market Model, Equally Weighted Index

Precision Portfolio Weighted Postitive: Time- Generalized Skewness Mean StdCsect CSectErr Rank Corrected Days CAR Median CAR CAAR Negative Patell Z Z EGLS Z CDCSI Z Series t t Sign Z Test Z t

( -10,-2) -0.24% 0.37% -1.34% 21:18 -0.625 -0.591 -0.559 -0.782 -0.158 -0.18 0.751 -0.356 -0.181 (-1,0) 3.17% 2.11% 5.94% 27:12>> 6.437*** 3.995*** 3.992*** 3.965*** 4.527*** 3.869*** 2.675** 2.075* 4.737*** (0,+1) 3.14% 2.21% 6.30% 29:10>>> 6.692*** 3.488*** 3.399*** 3.666*** 4.479*** 3.057** 3.316*** 2.666** 4.108*** (+2,+10) -0.91% -1.88% -0.72% 18:21 -0.603 -0.682 -0.658 -0.092 -0.615 -0.853 -0.21 -0.316 -0.856 (-10,+10) 2.87% 5.55% 5.35% 27:12>> 1.697* 1.723* 1.634$ 1.885* 1.265 1.503$ 2.675** 0.436 1.501$ (-1,+1) 4.02% 2.42% 7.40% 29:10>>> 6.615*** 3.982*** 3.869*** 3.841*** 4.685*** 3.688*** 3.316*** 2.318* 5.031***

Panel B: Tracking Retirement Announcement Market Model, Equally Weighted Index

Precision Portfolio Weighted Postitive: Time- Generalized Skewness Mean StdCsect CSectErr Rank Corrected Days CAR Median CAR CAAR Negative Patell Z Z EGLS Z CDCSI Z Series t t Sign Z Test Z t

( -10,-2) 5.22% 1.61% 3.53% 26:21 1.959* 1.625$ 1.444$ 0.554 2.380*** 2.372** 1.212 1.345$ 2.788** (-1,0) 8.90% 4.19% 14.18% 37:10>>> 11.906*** 5.448*** 4.782*** 5.147*** 8.617*** 4.576*** 4.429*** 4.793*** 6.745*** (0,+1) 8.23% 6.19% 14.43% 35:12>>> 11.873*** 4.661*** 4.300*** 4.715*** 7.970*** 3.842*** 3.844*** 3.558*** 4.793*** (+2,+10) -1.14% -0.33% -2.99% 22:25 -0.975 -1.393$ -1.252 -1.962* -0.522 -0.861 0.042 0.114 -0.896 (-10,+10) 14.47% 8.00% 17.31% 35:12>>> 5.005*** 4.703*** 3.762*** 3.199** 4.324*** 4.147*** 3.844*** 2.496** 5.801*** (-1,+1) 10.40% 6.73% 16.77% 36:11>>> 11.537*** 5.207*** 4.510*** 4.877*** 8.220*** 4.159*** 4.137*** 4.076*** 5.599***

The symbols $,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a generic one-tail nonparametric bootstrap of the indicated test.

TABLE 7: Retirement Returns Table A: Parent Retirement Market Model, Equally Weighted Index

Precision Portfolio Weighted Postitive: Time- Generalized Skewness Mean Median CSectErr Rank Corrected Days CAR CAR CAAR Negative Patell Z StdCsect Z EGLS Z CDCSI Z Series t t Sign Z Test Z t

( -10,-2) 2.06% 0.50% 3.96% 22:18 1.828* 1.452$ 1.310$ 1.755* 1.296$ 1.248 0.824 1.157 1.548* (-1,0) 1.57% 0.79% 2.52% 22:18 2.856** 1.892* 1.607$ 1.760* 2.098* 1.550$ 0.824 1.385$ 2.120* (0,+1) 1.17% 0.30% 1.62% 21:19 2.231* 1.184 1.071 0.844 1.564$ 1.135 0.507 0.127 1.364 - (+2,+10) 0.89% -0.68% 0.59% 13:21 0.016 0.017 0.016 0.617 -0.56 -0.735 -1.197 0.126 -0.741 (-10,+10) 3.00% -1.23% 7.34% 17:23 2.607** 1.633$ 1.511$ 2.090* 1.237 1.072 -0.758 1.007 1.300$ (-1,+1) 1.70% -0.09% 2.80% 19:21 2.821** 1.483$ 1.326$ 1.349$ 1.852* 1.262 -0.126 0.442 1.554$

Table B: Tracking Retirement Market Model, Equally Weighted Index

Precision Portfolio Weighted Postitive: Time- Generalized Skewness Mean Median CSectErr Rank Corrected Days CAR CAR CAAR Negative Patell Z StdCsect Z EGLS Z CDCSI Z Series t t Sign Z Test Z t

( -10,-2) 1.30% 1.14% 2.77% 23:19 0.947 0.892 0.847 1.075 0.58 0.741 1.031 1.532$ 0.892 (-1,0) 2.35% 1.16% 3.75% 23:19 3.233*** 1.588$ 1.449$ 1.397$ 2.226* 1.428$ 1.031 1.910* 1.588* (0,+1) 1.07% -0.42% 2.85% 20:22 1.859* 1.052 1 0.728 1.015 1.063 0.104 -1.601$ 1.052 (+2,+10) -0.29% 0.18% -3.53% 4:4 -0.325 -0.299 -0.363 -0.761 -0.128 -0.134 0.18 -0.634 -0.299 (-10,+10) 3.44% 1.80% 4.63% 24:18) 2.239* 1.392$ 1.293 1.570$ 1.007 1.091 1.341$ 0.445 1.392* (-1,+1) 2.20% 0.98% 5.39% 22:20 3.230*** 1.550$ 1.461$ 1.607$ 1.702* 1.320$ 0.722 -0.375 1.550*

The symbols $,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a generic one-tail nonparametric bootstrap of the indicated test.

TABLE 8: Post-Retirement Returns

Panel A: Parent Post-Retirement (BHER) Market Model, Equally Weighted Index

Precision Portfolio Weighted Postitive: Time- Generalized Skewness Mean Median CSectErr Rank Corrected Days CAR CAR CAAR Negative Patell Z StdCsect Z EGLS Z CDCSI Z Series t t Sign Z Test Z t

(+1,+250) -15.24% -17.66% 4.03% 13:21 0.545 0.35 0.305 0.523 -1.822* -1.384$ -1.197 -0.546 -1.389 (+251,+500) -0.88% 4.32% 20.13% 17:15 1.833* 1.444$ 1.597$ 1.303 -0.106 -0.14 0.524 0.806 -0.14 (+501,+750) -2.63% -1.03% 12.68% 15:17 1.507$ 1.207 1.237 1.364$ -0.314 -0.344 -0.183 1.026 -0.341 - (+1,+750) -64.81% -68.70% 36.84% 12:22( 2.245* 1.121 0.83 0.942 4.475*** -2.588** -1.540$ 0.743 -2.655*

Panel B: Parent Post-Retirement (CAR) Market Model, Equally Weighted Index

Precision Portfolio Weighted Postitive: Time- Generalized Skewness Mean Median CSectErr Rank Corrected Days CAR CAR CAAR Negative Patell Z StdCsect Z EGLS Z CDCSI Z Series t t Sign Z Test Z t

(+1,+250) -0.14% -2.96% 4.03% 16:18 0.545 0.35 0.343 0.573 -0.016 -0.014 -0.167 -0.541 -0.014 (+251,+500) 13.36% 22.86% 20.13% 21:11> 1.833* 1.444$ 1.620$ 1.352$ 1.598$ 1.967* 1.939* 0.806 1.913* (+501,+750) 9.75% 6.11% 12.72% 21:11> 1.507$ 1.207 1.15 1.212 1.166 1.216 1.939* 1.026 1.301$ (+1,+750) 21.62% 8.73% 36.88% 18:16 2.245* 1.121 1.057 1.128 1.493$ 1.045 0.519 0.743 1.109

The symbols $,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a generic one-tail nonparametric bootstrap of the indicated test.

TABLE 9: Volume

Panel A: Parent Issuance Announcement Market Model, Equal Weighted Index of Log-Transformed Volume Precision Portfolio Days Mean Median Weighted Postitive: Time- Generalized Rank Skewness CARV CARV CAARV Negative Patell Z StdCsect Z EGLS Z CDCSI Z Series t CSectErr t Sign Z Test Z Corrected t

( -10,-2) 71.88% 20.48% 101.57% 26:25 2.105* 0.936 0.877 0.956 1.968* 1.065 0.611 1.893* 1.096 (-1,0) 65.59% 50.48% 87.66% 35:16>>> 4.156*** 2.453** 2.305* 2.053* 3.808*** 2.744** 3.137*** 2.694** 2.789** (0,+1) 153.59% 142.43% 238.04% 41:10>>> 10.913*** 5.836*** 5.239*** 5.068*** 8.918*** 5.455*** 4.821*** 6.677*** 6.119*** (+2,+10) 70.16% 70.78% 34.93% 27:24 1.001 0.37 0.348 0.176 1.920* 0.852 0.891 1.093 0.872 (-10,+10) 295.61% 301.37% 355.04% 32:19 5.191*** 1.493$ 1.365$ 1.284 5.297*** 1.737* 2.295* 3.880*** 1.855* (-1,+1) 153.57% 191.93% 218.53% 37:14>>> 8.354*** 4.101*** 3.689*** 3.464*** 7.281*** 4.033*** 3.698*** 5.093*** 4.469***

Panel B: Parent Issuance Market Model, Equal Weighted Index of Log -Transformed Volume Precision Portfolio Days Mean Median Weighted Postitive: Time- Generalized Rank Skewness CARV CARV CAARV Negative Patell Z StdCsect Z EGLS Z CDCSI Z Series t CSectErr t Sign Z Test Z Corrected t

( -10,-2) 205.10% 111.26% 353.61% 25:16> 7.522*** 2.640** 2.520** 2.441** 7.535*** 2.571** 1.794* 4.909*** 2.871** (-1,0) 66.50% 43.85% 106.41% 28:13>> 4.913*** 2.785** 2.557** 2.445** 5.183*** 2.675** 2.732** 3.079** 3.068** (0,+1) 59.04% 51.72% 98.39% 28:13>> 4.480*** 2.733** 2.512** 2.489** 4.601*** 2.545** 2.732** 3.220*** 2.871** (+2,+10) 187.78% 155.47% 365.23% 26:15> 7.474*** 2.522** 2.512** 2.554** 6.899*** 2.448** 2.107* 4.589*** 2.486* (-10,+10) 488.26% 377.47% 872.21% 29:12>> 12.000*** 3.216*** 3.068** 3.053** 11.744*** 3.053** 3.045** 7.741*** 3.476** (-1,+1) 95.38% 65.15% 153.38% 28:13>> 5.775*** 3.021** 2.748** 2.651** 6.069*** 2.839** 2.732** 4.031*** 3.387**

Panel C: Tracking Issuance Market Model, Equal Weighted Index of Log -Transformed Volume Precision Portfolio Days Mean Median Weighted Postitive: Time- Generalized Rank Skewness CARV CARV CAARV Negative Patell Z StdCsect Z EGLS Z CDCSI Z Series t CSectErr t Sign Z Test Z Corrected t

( -10,-2) 491.30% 711.35% >999.9% 2:1 5.257*** 1.380$ 1.468 1.894$ 7.776*** 1.125 0.63 4.554*** 0.784 (-1,0) -788.32% -644.22% <-999.9% 13:35<< -83.620*** -3.952*** -3.902*** -4.383*** -26.469*** -3.321*** -2.968** -0.311 -3.081** (0,+1) -709.47% -572.04% <-999.9% 13:35<< -53.265*** -3.605*** -3.486*** -3.875*** -23.821*** -2.994** -2.968** -0.532 -2.795** (+2,+10) 547.75% 551.19% >999.9% 37:11>>> 18.233*** 4.382*** 4.549*** 4.515*** 8.670*** 4.591*** 3.963*** 4.667*** 2.736** (-10,+10) -125.90% -7.57% >999.9% 24:24 -5.523*** -0.745 -0.729 -0.925 -1.305$ -0.464 0.209 6.287*** -0.465 (-1,+1) -704.36% -572.04% <-999.9% 13:35<< -53.029*** -3.584*** -3.493*** -3.928*** -19.310*** -2.967** -2.968** 0.661 -2.775**

Table 9 Continued

Panel D: Parent Retirement Announcement Market Model, Equal Weighted Index of Log-Transformed Volume Precision Portfolio Days Mean Median Weighted Postitive: Time- Generalized Rank Skewness CARV CARV CAARV Negative Patell Z StdCsect Z EGLS Z CDCSI Z Series t CSectErr t Sign Z Test Z Corrected t

( -10,-2) 60.04% 32.23% 153.35% 21:18 3.022** 0.888 0.908 1.069 2.014* 0.742 0.861 1.162 0.758 (-1,0) 77.52% 61.92% 151.76% 25:14> 6.868*** 3.190*** 3.235** 3.330*** 5.516*** 3.217*** 2.145* 3.154*** 3.395** (0,+1) 108.07% 68.37% 199.93% 27:12>> 9.218*** 3.489*** 3.497*** 3.488*** 7.690*** 3.620*** 2.786** 4.387*** 3.923*** (+2,+10) 157.70% 47.96% 345.48% 25:14> 7.088*** 2.177* 2.219* 2.475** 5.290*** 2.005* 2.145* 4.375*** 2.179* (-10,+10) 341.22% 282.31% 737.34% 25:14> 9.967*** 2.029* 2.064* 2.266* 7.493*** 1.884* 2.145* 5.233*** 1.986* (-1,+1) 123.48% 75.32% 238.51 27:12>> 8.859*** 3.327*** 3.366*** 3.442*** 7.174*** 3.370*** 2.786** 4.255*** 3.548***

Panel E: Tracking Retirement Announcement Market Model, Equal Weighted Index of Log -Transformed Volume

Precision Portfolio Days Mean Median Weighted Postitive: Time- Generalized Rank Skewness CARV CARV CAARV Negative Patell Z StdCsect Z EGLS Z CDCSI Z Series t CSectErr t Sign Z Test Z Corrected t

( -10,-2) -116.09% -137.84% -70.15% 19:28 -1.931* - 0.705 - 0.663 - 0.177 - 2.138* -1.271 -1.062 -1.835* -1.246 (-1,0) 139.04% 130.62% 268.19% 39:8>>> 10.033*** 6.215*** 6.298*** 6.634*** 5.431*** 6.099*** 4.776*** 3.522*** 6.898*** (0,+1) 231.64% 193.47% 456.14% 43:4>>> 16.897*** 7.384*** 8.087*** 8.302*** 9.048*** 7.934*** 5.944*** 5.856*** 9.733*** (+2,+10) 304.61% 132.38% 614.88% 32:15>> 10.553*** 3.415*** 3.430*** 3.826*** 5.609*** 3.232*** 2.733** 4.352*** 3.623*** (-10,+10) 438.95% 317.77% >999.9% 30:17> 11.249*** 2.911** 2.887** 3.495*** 5.291*** 2.391** 2.149* 3.601*** 2.563** (-1,+1) 250.43% 222.54% 487.49% 42:5>>> 14.828*** 7.353*** 7.698*** 8.071*** 7.987*** 7.448*** 5.652*** 5.166*** 8.664***

Panel F: Parent Retirement Market Model, Equal Weighted Index of Log -Transformed Volume

Precision Portfolio Days Mean Median Weighted Postitive: Time- Generalized Rank Skewness CARV CARV CAARV Negative Patell Z StdCsect Z EGLS Z CDCSI Z Series t CSectErr t Sign Z Test Z Corrected t

( -10,-2) 101.93% 75.02% 290.23% 23:17) 6.000*** 1.466$ 1.444$ 1.681$ 3.942*** 0.965 1.342$ 2.939** 0.945 (-1,0) 16.07% 6.75% 66.65% 20:20 2.799** 1.169 1.16 1.495$ 1.318$ 0.559 0.392 0.448 0.547 (0,+1) 24.24% 18.71% 75.14% 22:18 3.581*** 1.607$ 1.663$ 2.147* 1.989* 1.036 1.025 1.341$ 1.043 (+2,+10) 4.06% 40.34% 39.71% 19:15 0.693 0.242 0.234 0.31 0.157 0.05 1.048 0.554 0.05 (-10,+10) 133.16% 167.20% 433.85% 24:16> 6.892*** 1.379$ 1.358$ 1.842* 3.371*** 0.69 1.659* 2.770** 0.666 (-1,+1) 27.78% 20.27% 103.91 21:19 3.710*** 1.447$ 1.458$ 1.886* 1.861* 0.768 0.709 1.278 0.749

Table 9 Continued

Panel G: Tracking Retirement Market Model, Equal Weighted Index of Log-Transformed Volume

Precision Portfolio Days Mean Median Weighted Postitive: Time- Generalized Rank Skewness CARV CARV CAARV Negative Patell Z StdCsect Z EGLS Z CDCSI Z Series t CSectErr t Sign Z Test Z Corrected t

( -10,-2) -135.21% -154.64% -120.57% 18:24 -2.860** - 0.675 - 0.651 - 0.276 -3.321*** -0.966 -0.681 -1.750* -0.984 (-1,0) -4.15% 17.46% 53.00% 24:18 1.187 0.411 0.399 0.952 -0.216 -0.092 1.172 0.303 -0.092 (0,+1) 11.46% 17.58% 158.32% 26:16> 2.883** 1.051 1.055 1.695* 0.597 0.379 1.789* -0.071 0.379 (+2,+10) -194.98% -344.43% -126.96% 3:5 -1.790* -0.329 -0.342 -0.046 -4.789*** -0.642 -0.601 -5.257*** -0.61 (-10,+10) -174.46% -165.71% -93.02% 18:24 -1.929* -0.352 -0.338 0.113 -2.805** -0.814 -0.681 -4.706*** -0.822 (-1,+1) -2.11% 17.46% 154.51% 24:18 1.763* 0.558 0.55 1.151 -0.09 -0.042 1.172 -0.313 -0.042

The symbols $,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a generic one-tail nonparametric bootstrap of the indicated test.

TABLE 10: Volatility

Panel A: Parent Issuance Announcement Market Model, Equally Weighted Index

Mean Median Precision-Weighted Portfolio Skewness Days Absolute CAR Absolute CAR Absolute CAAR StdCsect Z EGLS Z CDCSI Z Time-Series t CSectErr t Jackknife Z Rank Test Z Corrected t

( -10,-2) 7.61% 4.25% 29.80% 1.484$ 1.407$ 1.598$ 8.025*** 7.164*** 8.114*** 2.179* 10.127*** (-1,0) 4.76% 2.90% 9.36% 2.592** 2.594** 2.848** 10.640*** 6.460*** 8.601*** 3.130*** 11.716*** (0,+1) 6.72% 3.64% 11.59% 1.483$ 1.367$ 1.680* 15.032*** 5.946*** 8.451*** 5.389*** 9.580*** (+2,+10) 6.36% 5.15% 23.78% 0.083 0.079 -0.03 6.707*** 8.595*** 8.556*** -1.288$ 15.291*** (-10,+10) 11.33% 6.60% 67.99% 2.180* 2.310* 2.413** 7.816*** 7.608*** 9.530*** 2.249* 13.086*** (-1,+1) 6.31% 3.66% 14.41% 2.150* 2.003* 2.343* 11.517*** 5.388*** 7.168*** 4.406*** 8.968***

Panel B: Parent Issuance Market Model, Equally Weighted Index

Mean Median Precision-Weighted Portfolio Skewness Days Absolute CAR Absolute CAR Absolute CAAR StdCsect Z EGLS Z CDCSI Z Time-Series t CSectErr t Jackknife Z Rank Test Z Corrected t

( -10,-2) 5.12% 2.81% 27.77% 0.63 0.664 0.524 5.701*** 5.723*** 6.966*** 2.025* 8.672*** (-1,0) 3.04% 1.63% 8.31% 0.243 0.233 0.627 7.188*** 5.646*** 7.244*** 3.369*** 10.326*** (0,+1) 3.72% 2.86% 7.82% -815 -0.666 -0.587 8.792*** 5.575*** 9.409*** 2.955** 12.233*** (+2,+10) 7.52% 5.36% 24.78% -2.116* -1.435$ -1.015 8.366*** 6.464*** 7.795*** 0.832 11.836*** (-10,+10) 10.83% 7.93% 63.93% -0.719 -0.638 -0.213 7.887*** 6.902*** 9.244*** 3.134** 9.972*** (-3,+3) 5.23% 3.16% 21.90% -0.478 -0.403 -0.046 6.597*** 6.318*** 7.480*** 1.940* 9.133*** (-1,+1) 3.89% 2.56% 11.38% 0.15 0.136 0.426 7.491*** 5.491*** 7.238*** 3.342*** 9.843***

Panel C: Tracking Issuance Market Model, Equally Weighted Index

Mean Median Precision-Weighted Portfolio Skewness Days Absolute CAR Absolute CAR Absolute CAAR StdCsect Z EGLS Z CDCSI Z Time-Series t CSectErr t Jackknife Z Rank Test Z Corrected t

( -10,-2) 7.92% 3.63% 39.39% 0.785 1.048 1.289 3.312*** 1.331$ 1.318$ 1.429$ 2.043*** (-1,0) 4.34% 4.46% 11.84% 0.511 0.704 1.013 3.848*** 1.887* 1.863* 2.795** 1.777*** (0,+1) 4.48% 3.82% 9.88% -0.478 -0.342 -0.219 3.973*** 6.153*** 2.673** 1.944* 27.613*** (+2,+10) 4.24% 4.12% 33.28% 2.776** 3.895* 4.050* 1.771* 3.814*** 5.426*** 1.813* 4.689*** (-10,+10) 13.53% 5.18% 88.14% 0.782 1.05 1.293 3.703*** 1.350$ 1.383$ 3.137*** 2.141*** (-1,+1) 5.70% 5.66% 15.48% 0.051 0.056 0.313 4.127*** 5.264*** 7.972*** 2.683** 5.749***

Table 10 Continued

Panel D: Parent Retirement Announcement Market Model, Equally Weighted Index

Mean Median Precision-Weighted Portfolio Skewness Days Absolute CAR Absolute CAR Absolute CAAR StdCsect Z EGLS Z CDCSI Z Time-Series t CSectErr t Jackknife Z Rank Test Z Corrected t

( -10,-2) 5.52% 4.11% 29.92% -0.574 -0.517 -0.707 5.711*** 5.804*** 5.682*** 0.402 9.880*** (-1,0) 4.24% 2.52% 10.22% 4.133** 4.608*** 4.627*** 9.313*** 6.219*** 7.192*** 2.046* 10.001*** (0,+1) 4.33% 2.55% 9.82% 3.643*** 3.697*** 4.018*** 9.505*** 4.774*** 8.150*** 1.338$ 8.319*** (+2,+10) 5.44% 4.25% 27.65% -0.604 -0.535 0.082 5.630*** 8.667*** 6.966*** 0.4 16.571*** (-10,+10) 9.49% 8.23% 71.40% 1.717* 1.972* 2.368* 6.433*** 7.743*** 7.333*** 1.214 15.104*** (-1,+1) 4.96% 2.59% 13.83% 4.097*** 4.214*** 4.238*** 8.890*** 5.036*** 6.916*** 1.824* 8.310***

Panel E: Tracking Retirement Announcement Market Model, Equally Weighted Index

Mean Median Precision-Weighted Portfolio Skewness Days Absolute CAR Absolute CAR Absolute CAAR StdCsect Z EGLS Z CDCSI Z Time-Series t CSectErr t Jackknife Z Rank Test Z Corrected t

( -10,-2) 11.15% 8.33% 40.41% 1.618$ 1.624$ 0.586 7.440*** 6.759*** 3.215*** 0.154 13.709*** (-1,0) 10.23% 5.10% 18.87% 5.414*** 5.107*** 5.489*** 14.480*** 5.691*** 0.725 4.141*** 9.895*** (0,+1) 11.66% 7.68% 23.25% 4.746*** 5.090*** 5.631*** 16.507*** 6.616*** 1.587$ 5.263*** 12.384*** (+2,+10) 6.18% 3.95% 36.31% -1.334$ -1.045 -1.513$ 4.126*** 6.313*** 2.773** -0.917 11.444*** (-10,+10) 18.33% 9.76% 105.06% 4.733*** 4.255*** 3.715*** 8.006*** 5.969*** 1.057 1.357$ 11.112*** (-1,+1) 13.78% 9.09% 28.35% 5.215*** 5.160*** 5.569*** 15.930*** 6.517*** 1.149 4.910*** 12.601***

Panel F: Parent Retirement Market Model, Equally Weighted Index

Mean Median Precision-Weighted Portfolio Skewness Days Absolute CAR Absolute CAR Absolute CAAR StdCsect Z EGLS Z CDCSI Z Time-Series t CSectErr t Jackknife Z Rank Test Z Corrected t

( -10,-2) 5.90% 3.70% 31.57% 1.539$ 1.500$ 2.117* 5.684*** 4.251*** 7.161*** 0.867 10.185*** (-1,0) 3.42% 2.16% 6.06% 1.852* 1.677$ 1.842* 6.986*** 3.855*** 10.097*** -0.349 9.094*** (0,+1) 3.38% 2.00% 6.91% 1.172 1.1 0.857 6.903*** 3.768*** 6.870*** 1.054 7.089*** (+2,+10) 5.08% 3.66% 26.42% 0.03 0.029 0.592 4.894*** 6.068*** 6.006*** 0.813 8.634*** (-10,+10) 9.99% 6.37% 67.75% 1.674* 1.664$ 2.333* 6.296*** 4.262*** 8.314*** 1.187 9.817*** (-1,+1) 4.31% 2.23% 9.77% 1.484$ 1.382$ 1.423$ 7.192*** 3.643*** 8.232*** 0.23 6.809***

Table 10 continued

Panel G: Tracking Retirement Market Model, Equally Weighted Index

Mean Median Precision-Weighted Portfolio Skewness Days Absolute CAR Absolute CAR Absolute CAAR StdCsect Z EGLS Z CDCSI Z Time-Series t CSectErr t Jackknife Z Rank Test Z Corrected t

( -10,-2) 6.62% 4.69% 37.58% 0.964 0.961 1.356$ 4.229*** 4.638*** 7.635*** -1.538$ 12.042*** (-1,0) 4.29% 1.95% 9.01% 1.654* 1.544$ 1.514$ 5.817*** 2.780*** 7.466*** -0.656 5.830*** (0,+1) 3.14% 1.91% 14.72% 1.082 1.079 0.817 4.250*** 3.505*** 7.289*** 4.097*** 8.397*** (+2,+10) 4.39% 3.67% 41.77% -0.393 -0.423 -0.765 2.800** 3.224*** 2.321* 2.803** 3.934*** (-10,+10) 8.54% 5.69% 98.74% 1.472$ 1.393$ 1.751* 3.571*** 2.939** 6.954*** 1.917* 6.518*** (-1,+1) 4.64% 2.26% 19.39% 1.640$ 1.608$ 1.895* 5.132*** 3.019** 8.026*** 2.881** 6.664***

The symbols $,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a generic one-tail nonparametric bootstrap of the indicated test.

TABLE 11: Absolute Volume

Panel A: Parent Issuance Announcement Market Model, Equally Weighted Index

Mean Median Precision- Absolute Absolute Weighted Portfolio Skewness Days CARV CARV Average ACARV StdCsect Z EGLS Z CDCSI Z Time-Series t CSectErr t Jackknife Z Rank Test Z Corrected t

( -10,-2) 372.14% 332.12% 852.47% 1.198 1.228 1.478$ 14.093*** 8.560*** 9.877*** 1.936* 12.568*** (-1,0) 135.69% 111.41% 259.50% 2.544** 2.795** 2.270* 10.901*** 7.978*** 11.176*** 3.143*** 13.178*** (0,+1) 193.90% 161.09% 324.89% 5.900*** 6.488*** 6.192*** 15.577*** 8.565*** 14.052*** 5.775*** 13.801*** (+2,+10) 419.22% 263.04% 896.60% 0.625 0.602 0.481 15.876*** 7.226*** 9.388*** 3.279*** 12.232*** (-10,+10) 884.24% 656.19% >999.9% 1.789* 1.797* 1.801* 21.922*** 7.202*** 10.147*** 5.383*** 13.418*** (-1,+1) 228.21% 192.67% 432.35% 4.219*** 4.551*** 4.156*** 14.969*** 7.692*** 11.801*** 5.209*** 13.143***

Panel B: Parent Issuance Market Model, Equally Weighted Index

Mean Median Precision- Absolute Absolute Weighted Portfolio Skewness Days CARV CARV Average ACARV StdCsect Z EGLS Z CDCSI Z Time-Series t CSectErr t Jackknife Z Rank Test Z Corrected t

( -10,-2) 408.81% 312.56% 930.20% 2.633** 3.077** 2.769** 24.157*** 7.185*** 9.014*** 4.576*** 12.270*** (-1,0) 126.19% 80.29% 228.85% 2.826** 3.274** 3.139** 15.818*** 6.942*** 8.491*** 2.985** 13.189*** (0,+1) 116.97% 90.52% 219.96% 2.820** 3.337*** 3.470*** 14.662*** 6.947*** 8.437*** 2.786** 13.681*** (+2,+10) 398.32% 307.70% 954.54% 2.541** 3.264** 3.291** 23.537*** 7.529*** 8.193*** 3.462*** 10.898*** (-10,+10) 849.58% 708.05% >999.9% 3.175*** 3.944*** 3.851*** 32.866*** 7.312*** 8.465*** 6.211*** 14.316*** (-1,+1) 168.77% 123.95% 315.82% 3.036** 3.542*** 3.516*** 17.273*** 6.649*** 8.114*** 2.511** 13.677***

Panel C: Tracking Issuance Market Model, Equally Weighted Index

Mean Median Precision- Absolute Absolute Weighted Portfolio Skewness Days CARV CARV Average ACARV StdCsect Z EGLS Z CDCSI Z Time-Series t CSectErr t Jackknife Z Rank Test Z Corrected t

( -10,-2) 724.93% 711.35% >999.9% 1.297$ 2.194$ 2.675$ 29.132*** 3.292*** 3.798*** 5.468*** 3.666*** (-1,0) >999.9% >999.9% >999.9% -4.137*** -2.438** -2.582** 113.474*** 7.455*** -4.132*** 3.242*** 10.116*** (0,+1) >999.9% 964.78% >999.9% -3.764*** -2.299* -2.432** 110.556*** 7.359*** -4.018*** 3.559*** 9.927*** (+2,+10) 791.99% 686.81% >999.9% 4.491*** 5.909*** 5.913*** 31.826*** 9.285*** 1.618$ 4.049*** 18.426*** (-10,+10) >999.9% 910.71% >999.9% -0.689 -0.617 -0.659 37.143*** 7.934*** -1.11 7.524*** 11.612*** (-1,+1) >999.9% 964.78% >999.9% -3.739*** -2.298* -2.444** 90.624*** 7.414*** -3.995*** 3.421*** 10.042***

Table 11 Continued

Panel D: Parent Retirement Announcement Market Model, Equally Weighted Index

Mean Median Precision- Absolute Absolute Weighted Portfolio Skewness Days CARV CARV Average ACARV StdCsect Z EGLS Z CDCSI Z Time-Series t CSectErr t Jackknife Z Rank Test Z Corrected t

( -10,-2) 352.95% 242.37% 838.31% 0.862 0.923 1.105 18.831*** 6.082*** 6.808*** 3.014** 11.208*** (-1,0) 132.26% 93.73% 265.17% 3.455*** 4.381*** 4.760*** 14.969*** 7.925*** 11.431*** 2.992** 11.233*** (0,+1) 159.17% 88.34% 310.30% 3.669*** 4.467*** 4.593*** 18.014*** 6.902*** 10.288*** 4.056*** 9.030*** (+2,+10) 359.73% 257.68% 856.92% 2.339** 2.708** 3.213** 19.193*** 6.135*** 7.155*** 3.646*** 10.164*** (-10,+10) 856.45% 644.21% >999.9% 2.180* 2.543** 2.971** 29.913*** 6.655*** 7.501*** 5.971*** 12.420*** (-1,+1) 196.55% 124.54% 409.75% 3.575*** 4.423*** 4.726*** 18.163*** 7.291*** 9.920*** 4.263*** 9.436***

Panel E: Tracking Retirement Announcement Market Model, Equally Weighted Index

Mean Median Precision- Absolute Absolute Weighted Portfolio Skewness Days CARV CARV Average ACARV StdCsect Z EGLS Z CDCSI Z Time-Series t CSectErr t Jackknife Z Rank Test Z Corrected t

( -10,-2) 471.86% 431.16% >999.9% -0.73 -0.621 -0.192 18.842*** 7.664*** 8.448*** 3.192*** 14.381*** (-1,0) 167.10% 131.95% 378.24% 6.282*** 7.505*** 7.404*** 14.155*** 9.159*** 10.426*** 4.431*** 14.928*** (0,+1) 245.75% 193.47% 505.67% 7.411*** 8.793*** 8.583*** 20.817*** 9.250*** 12.253*** 6.562*** 13.631*** (+2,+10) 514.50% 363.45% >999.9% 3.552*** 4.436*** 5.039*** 20.545*** 7.172*** 9.057*** 4.830*** 11.247*** (-10,+10) >999.9% 949.56% >999.9% 3.107*** 3.855*** 4.778*** 27.355*** 8.815*** 9.757*** 7.635*** 17.402*** (-1,+1) 276.53% 222.54% 630.68% 7.486*** 8.808*** 8.750*** 19.126*** 9.591*** 11.886*** 6.307*** 15.030***

Panel F: Parent Retirement Market Model, Equally Weighted Index

Mean Median Precision- Absolute Absolute Weighted Portfolio Skewness Days CARV CARV Average ACARV StdCsect Z EGLS Z CDCSI Z Time-Series t CSectErr t Jackknife Z Rank Test Z Corrected t

( -10,-2) 458.14% 289.58% >999.9% 1.414$ 1.588$ 2.143* 25.207*** 5.889*** 6.223*** 6.528*** 11.012*** (-1,0) 123.35% 58.02% 252.41% 1.069 1.235 1.774* 14.397*** 5.859*** 7.228*** 2.857** 10.486*** (0,+1) 105.50% 78.34% 239.61% 1.510$ 1.904* 2.547** 12.314*** 6.331*** 8.062*** 2.505** 11.991*** (+2,+10) 346.20% 259.35% 787.33% 0.485 0.513 1.275 19.048*** 6.348*** 8.502*** 2.987** 11.823*** (-10,+10) 822.50% 660.32% >999.9% 1.391$ 1.584$ 2.550** 29.626*** 5.764*** 6.859*** 7.457*** 11.546*** (-1,+1) 150.98% 91.11% 356.07% 1.344$ 1.605$ 2.246* 14.388*** 5.532*** 7.243*** 3.248*** 10.452***

Table 11 Continued

Panel G: Tracking Retirement Market Model, Equally Weighted Index

Mean Median Precision- Absolute Absolute Weighted Portfolio Skewness Days CARV CARV Average ACARV StdCsect Z EGLS Z CDCSI Z Time-Series t CSectErr t Jackknife Z Rank Test Z Corrected t

( -10,-2) 678.76% 572.09% >999.9% -0.563 -0.511 -0.09 28.833*** 7.259*** 6.884*** 6.467*** 12.208*** (-1,0) 217.38% 200.82% 402.59% 0.454 0.48 1.165 19.559*** 7.313*** 10.279*** 4.027*** 14.025*** (0,+1) 137.79% 114.68% 461.47% 1.047 1.182 1.991* 12.398*** 6.459*** 9.114*** 3.250*** 10.885*** (+2,+10) 708.98% 588.08% >999.9% -0.416 -0.38 -0.186 30.072*** 4.411*** 6.429*** 8.524*** 5.735** (-10,+10) 993.62% 662.61% >999.9% -0.271 -0.253 0.32 27.591*** 6.618*** 7.527*** 11.492*** 10.827*** (-1,+1) 235.82% 217.64% 646.89% 0.589 0.634 1.403$ 17.325*** 7.071*** 10.329*** 4.440*** 12.509***

The symbols $,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a generic one-tail nonparametric bootstrap of the indicated test.

TABLE 12: Regression Results

Panel A: BHER_PS (equation 3) Parent Post-Issuance BHER (1, 750) is the dependent variable with the following independent variables: IssType is a dummy variable that equals 1 if the tracking stock is issued as a dividend, 0 otherwise; TSROA (PSROA) is the ROA in the quarter before issuance; TSDebt is the debt ratio of TS measured as the debt divided by total assets for the quarter prior to issuance; TSCashHol is the cash to total asset ratio of TS for the quarter prior to issuance; TSPE (PSPE) is the price/earnings ratio at issuance; TSBTM (PSBTM) is the book-to-market ratio at issuance; and RelSize is the ratio of TS to PS market value at issuance.

Source | SS df MS Number of obs = 34 ------+------F( 10, 23) = 1.18 Model | 43.489102 10 4.3489102 Prob > F = 0.3536 Residual | 84.8747103 23 3.69020479 R-squared = 0.3388 ------+------Adj R-squared = 0.0513 Total | 128.363812 33 3.88981249 Root MSE = 1.921

------ppisbher | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------+------IssType | -.4722569 .7992348 -0.59 0.560 -2.1256 1.181086 TSROA | 2.784776 5.067444 0.55 0.588 -7.698032 13.26758 PSROA | 7.288399 25.45822 0.29 0.777 -45.37595 59.95274 TSDebt | 2.020482 1.340917 1.93* 0.006 -.7534159 4.79438 TSCashHol | .0000323 .0000841 0.38 0.704 -.0001417 .0002064 TSPE | .0003072 .0037432 0.08 0.935 -.0074363 .0080506 TSBTM | .0337799 .0170742 1.98 0.060 -.0015408 .0691006 PSPE | -.0018812 .0022031 -0.85 0.402 -.0064387 .0026762 PSBTM | 1.969062 1.570351 1.25 0.222 -1.279456 5.21758 RelSize | -.0217232 .9265398 -2.03** 0.024 -1.68095 2.152437 _cons | -2.657144 1.459285 -1.82 0.082 -5.675906 .3616177 ------

Panel B: BHER_TS (equation 3) Tracking Post-Issuance BHER (1, 750) is the dependent variable with the following independent variables: IssType is a dummy variable that equals 1 if the tracking stock is issued as a dividend, 0 otherwise; TSROA (PSROA) is the ROA in the quarter before issuance; TSDebt is the debt ratio of TS measured as the debt divided by total assets for the quarter prior to issuance; TSCashHol is the cash to total asset ratio of TS for the quarter prior to issuance; TSPE (PSPE) is the price/earnings ratio at issuance; TSBTM (PSBTM) is the book-to-market ratio at issuance; and RelSize is the ratio of TS to PS market value at issuance.

Source | SS df MS Number of obs = 44 ------+------F( 7, 36) = 1.51 Model | 109.33087 7 15.6186957 Prob > F = 0.1939 Residual | 371.399375 36 10.3166493 R-squared = 0.2274 ------+------Adj R-squared = 0.0772 Total | 480.730245 43 11.1797731 Root MSE = 3.212

------trbher | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------+------ISSTYPE | -.568178 1.121085 -0.51 0.615 -2.841844 1.705488 TSROA | 10.86455 5.808801 1.87 0.070 -.916243 22.64535 TSDEBT | .000052 .0001934 2.41** 0.008 -.0003403 .0004442 TSCASHHOL | -.0003188 .000136 -2.34** 0.025 -.0005945 -.000043 TSPE | -.0024339 .0049869 -0.49 0.628 -.0125479 .0076801 TSBTM | .0160193 .0265547 0.60 0.550 -.0378361 .0698747 RELSIZE | -.0741958 .2952847 -0.25 0.803 -.673061 .5246694 _cons | -1.939025 .9280954 -2.09 0.044 -3.82129 -.0567602 ------

Panel C: CAR_PS (equation 4) Parent Retirement Announcement CAR (-1, 1) is the dependent variable with the following independent variables: RetType is a dummy variable that equals 1 if the unit is re-integrated into the parent, 0 otherwise; TSROA (PSROA) is the ROA in the quarter before retirement announcement; RelSize is the ratio of TS market value to PS market value at retirement announcement; TSDebt is the debt ratio of TS measured as the debt of TS divided by total assets for the quarter prior to retirement announcement; TSCashHol is the cash to total asset ratio of TS for the quarter prior to retirement announcement; Relatedness is a dummy variable that equals 1 if TS has the same three-digit SIC code as PS; TSRun (PSRun) is the percent change in stock price in the three months before the retirement announcement; Bubble is a dummy variable that equals 1 if TS was originally issued during the overvalued Internet Bubble Period defined the period as January 1999 through December 2000; NewEcon is a dummy variable that equals 1 if TS is a wireless, internet, or high technology firm; and PerOut is the time from tracking stock issuance to retirement announcement.

Source | SS df MS Number of obs = 27 ------+------F( 10, 16) = 1.54 Model | .037643508 10 .003764351 Prob > F = 0.2140 Residual | .039197303 16 .002449831 R-squared = 0.4899 ------+------Adj R-squared = 0.1711 Total | .076840811 26 .002955416 Root MSE = .0495

------CAR_PS | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------+------RETYPE | -.0162759 .0267869 -0.61 0.552 -.0730617 .0405098 TSRDA | .0724031 .081833 0.88 0.389 -.1010752 .2458813 PSROA | -.0091543 .060812 -0.15 0.882 -.13807 .1197613 TSDEBT | 3.59e-06 2.26e-06 1.59 0.131 -1.19e-06 8.37e-06 RELSIZE | -.0575941 .0338456 -1.70 0.108 -.1293435 .0141553 TSCASHHOL | -.0000947 .0000483 -1.96 0.068 -.0001971 7.76e-06 RELATEDNESS | -.0100001 .0282895 -0.35 0.728 -.0699711 .0499709 TSRUN | -.0134945 .0097504 -1.38 0.185 -.0341644 .0071755 BUBBLE | -.0531515 .033334 -1.59 0.130 -.1238164 .0175134 NEWECON | .0346387 .0382762 0.90 0.379 -.0465033 .1157807 _cons | .0656562 .0315383 2.08 0.054 -.001202 .1325144 ------

Panel D: CAR_TS (equation 4) Tracking Retirement Announcement CAR (-1, 1) is the dependent variable with the following independent variables: RetType is a dummy variable that equals 1 if the unit is re-integrated into the parent, 0 otherwise; TSROA (PSROA) is the ROA in the quarter before retirement announcement; RelSize is the ratio of TS market value to PS market value at retirement announcement; TSDebt is the debt ratio of TS measured as the debt of TS divided by total assets for the quarter prior to retirement announcement; TSCashHol is the cash to total asset ratio of TS for the quarter prior to retirement announcement; Relatedness is a dummy variable that equals 1 if TS has the same three-digit SIC code as PS; TSRun (PSRun) is the percent change in stock price in the three months before the retirement announcement; Bubble is a dummy variable that equals 1 if TS was originally issued during the overvalued Internet Bubble Period defined the period as January 1999 through December 2000; NewEcon is a dummy variable that equals 1 if TS is a wireless, internet, or high technology firm; and PerOut is the time from tracking stock issuance to retirement announcement.

Source | SS df MS Number of obs = 27 ------+------F( 10, 16) = 0.57 Model | .275646708 10 .027564671 Prob > F = 0.8120 Residual | .768152666 16 .048009542 R-squared = 0.2641 ------+------Adj R-squared = -0.1959 Total | 1.04379937 26 .04014613 Root MSE = .21911

------trreacar | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------+------RETTYPE| -.1269952 .1059029 -1.20 0.248 -.3514993 .097509 TSROA | .00841 .3596064 0.02 0.982 -.7539216 .7707416 TSDEBT | -3.78e-07 1.00e-05 -0.04 0.970 -.0000216 .0000208 RELSIZE | -.0406168 .1600157 -0.25 0.803 -.3798348 .2986013 TSCASHHOL | -.0003478 .000325 -1.07 0.300 -.0010367 .0003411 RELATEDNESS | -.1378194 .1029015 -1.34 0.199 -.3559607 .080322 TSRUN | .0000815 .041086 0.00 0.998 -.087017 .08718 BUBBLE | .0056168 .1497335 0.04 0.971 -.3118042 .3230377 NEWECON | .1705472 .1697387 1.00 0.330 -.1892828 .5303772 PROUT | .0075191 .0204545 0.37 0.718 -.0358424 .0508807 _cons | .1634396 .1423702 1.15 0.268 -.1383718 .465251 ------56

PANEL E: BHER_PS (equation 5) Parent Post-Retirement BHER (1, 750) is the dependent variable with the following independent variables: RetType is a dummy variable that equals 1 if the unit is re-integrated into the parent, 0 otherwise; TSROA (PSROA) is the ROA in the quarter before retirement; TSDebt is the debt ratio of TS measured as the debt of TS divided by total assets for the quarter prior to retirement; TSCashHol is the cash to total asset ratio of TS for the quarter prior to retirement; PSPE is the price/earnings ratio at retirement; PSBTM is the book-to-market ratio at retirement; and RelSize is the ratio of TS market value to PS market value at retirement.

Source | SS df MS Number of obs = 26 ------+------F( 8, 17) = 2.74 Model | 32.4172924 8 4.05216155 Prob > F = 0.0384 Residual | 25.1404041 17 1.4788473 R-squared = 0.5632 ------+------Adj R-squared = 0.3577 Total | 57.5576965 25 2.30230786 Root MSE = 1.2161

------ppostretbher | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------+------rettype | .8276398 .5175008 1.60 0.128 -.2641915 1.919471 tsroa | 1.778141 1.253955 1.42 0.174 -.8674719 4.423755 psroa | 1.628512 14.86942 0.11 0.914 -29.74323 33.00026 tsdbtr | .8173977 .8691097 0.94 0.360 -1.016264 2.651059 tscshhol | .6681434 1.532933 0.44 0.668 -2.566061 3.902348 psperatio | -.0043128 .0088956 -0.48 0.634 -.023081 .0144553 psbtmratio | -1.918993 .8598092 -2.23 0.039 -3.733031 -.1049538 relsize | 1.967716 .8322842 2.36 0.030 .2117495 3.723682 _cons | -1.486245 .8420786 -1.76 0.096 -3.262876 .2903853 ------

PANEL F: Change in PS ROA - Post-Issuance (Equation 6)

Parent Post-Issuance ΔROAt,t-1 (the change in ROA from year t to t+1) is the dependent variable with the following independent variables: ROAt is the level of ROA in year t, ROAt-1, t is the change in ROA from year t-1 to t, PSAssetst/TSAssetst is the log of total assets in year t (a proxy for size), PSDebtt/TS Debtt is the debt scaled by total assets ratio in year t, and PSCapt/TS Capt is the book value of assets scaled by sales.

Source | SS df MS Number of obs = 42 ------+------F( 10, 31) = 4.41 Model | .100952924 10 .010095292 Prob > F = 0.0007 Residual | .070920895 31 .002287771 R-squared = 0.5874 ------+------Adj R-squared = 0.4543 Total | .171873819 41 .004192044 Root MSE = .04783

------delPSROA | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------+------PSROAt | -.1491575 .1813363 -0.82 0.417 -.5189954 .2206803 DelPSROAtm1t | -.2356744 .1257955 -1.87 0.070 -.492236 .0208871 DelTSROAttpt | -.0097373 .0336184 -0.29 0.774 -.0783025 .0588278 TSROAt | -.0054509 .0125148 -0.44 0.666 -.030975 .0200733 DelTSROAtm1t | -.0479528 .0158557 -3.02 0.005 -.0802906 -.015615 LogPSAssets | -.0065207 .0128469 -0.51 0.615 -.0327222 .0196807 LogTSAssets | .0090514 .0088072 1.03 0.312 -.0089109 .0270137 PSDEBTR | -.0025777 .0461065 -0.06 0.956 -.0966125 .091457 TSDEBTR | .051783 .0345659 1.50 0.144 -.0187146 .1222806 PSCapt | .0009149 .0011257 0.81 0.423 -.001381 .0032107 _cons | -.033342 .0869662 -0.38 0.704 -.2107107 .1440267 ------

PANEL G: Change in TS ROA - Post-Issuance (Equation 6)

Tracking Post-Issuance ΔROAt,t-1 (the change in ROA from year t to t+1) is the dependent variable with the following independent variables: ROAt is the level of ROA in year t, ROAt-1, t is the change in ROA from year t-1 to t, PSAssetst/TSAssetst is the log of total assets in year t (a proxy for size), PSDebtt/TS Debtt is the debt scaled by total assets ratio in year t, and PSCapt/TS Capt is the book value of assets scaled by sales.

Source | SS df MS Number of obs = 40 ------+------F( 6, 33) = 166.49 Model | 109.656173 6 18.2760288 Prob > F = 0.0000 Residual | 3.62253573 33 .10977381 R-squared = 0.9680 ------+------Adj R-squared = 0.9622 Total | 113.278708 39 2.90458227 Root MSE = .33132

------DelTSROA | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------+------elTSROAttp1 | -.1974996 .0883416 -2.24 0.032 -.3772319 -.0177674 TSROAt | .3542158 .0200459 17.67 0.000 .3134321 .3949994 LOGTSASSETS | .1272171 .0673095 1.89 0.068 -.0097251 .2641592 TSDEBTR | .0905753 .2301797 0.39 0.696 -.3777287 .5588794 TSCAPINT | .0002939 .0003216 0.91 0.367 -.0003604 .0009482 _cons | -.5448702 .2608675 -2.09 0.045 -1.075609 -.0141312 ------

PANEL H: Change in PS ROA - Post-Retirement (Equation 6)

Parent Post-Retirement ΔROAt,t-1 (the change in ROA from year t to t+1) is the dependent variable with the following independent variables: ROAt is the level of ROA in year t, ROAt-1, t is the change in ROA from year t-1 to t, PSAssetst/TSAssetst is the log of total assets in year t (a proxy for size), PSDebtt/TS Debtt is the debt scaled by total assets ratio in year t, and PSCapt/TS Capt is the book value of assets scaled by sales.

Source | SS df MS Number of obs = 16 ------+------F( 8, 7) = 2.39 Model | .020176467 8 .002522058 Prob > F = 0.1344 Residual | .007398532 7 .001056933 R-squared = 0.7317 ------+------Adj R-squared = 0.4251 Total | .027574999 15 .001838333 Root MSE = .03251

------DelPSROA | Coef. Std. Err. t P>|t| [95% Conf. Interval] ------+------PSROAt | -.5899407 .2274537 -2.59 0.036 -1.127783 -.0520983 DelPSROAtm1t | .3276086 .6081617 0.54 0.607 -1.110465 1.765682 LogPSAssets | .0359958 .0301551 1.19 0.271 -.0353096 .1073013 LogTSAssets | -.0212562 .0158565 -1.34 0.222 -.0587507 .0162384 PSDEBTR | .0601019 .0241917 2.48 0.042 .0028976 .1173062 TSDEBTR | -.0180881 .0321405 -0.56 0.591 -.0940884 .0579121 pscapin | -.0052765 .0056647 -0.93 0.383 -.0186714 .0081183 _cons | -.0503043 .0996799 -0.50 0.629 -.2860099 .1854013 ------

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