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Firm Opacity and Informed Trading around Spinoffs

Yuan Wen

Associate Professor of Finance

State University of New York at New Paltz

ABSTRACT

This paper examines the prevalence of informed trading around corporate spinoffs and the

relation between firm opacity and informed trading using option market data. We find that

option volatility spread and volatility skewness for the five days prior to the spinoffs are able

to explain the abnormal stock returns in the spinoff announcement days, suggesting that there

is informed trading in the options market prior to spinoffs. We also find that informed trading

is more prevalent for firms that are more opaque prior to the spinoff. Furthermore, we find

that informed trading decreases after spinoffs.

Keywords: Spinoffs; Insider (Informed) Trading; Opacity

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1. Introduction

Corporate restructuring through spinoffs has been increasing at an accelerated pace in recent years

(Chemmanur and Liu, 2011, Feldman, Gilson, and Villalonga, 2014). A record $250.9 billion

worth of spinoffs were completed globally in 20151. These restructuring actions are intended to

refocus the firm on its core business and mitigate undervaluation caused by “diversification

discount” (Feldman, Gilson, and Villalonga, 2014, Slovin, Sushka, and Ferraro, 1995). Prior

studies find that abnormal returns associated with spinoffs are in the order of 2.4%-4.3%

(Krishnaswami and Subramaniam, 1999).

Gains from spinoffs could come from multiple channels. Firstly, spinoffs help to mitigate

undervaluation caused by diversification discount. Berger and Ofek (1995) suggest that diversified

firms tend to trade at a discount relative to single-segment firms. The discount could be caused by

lack of expertise, negative synergy of unrelated business segments or value destroying cross-

subsidy. Restructuring through spinoffs refocuses the firm on its core business where the managers’

area of expertise lies and mitigates the effects of negative synergy and costly cross-subsidy.

Secondly, the positive valuation effect could arise from increased information production by

institutional investors and their affiliated analysts (Chemmanur and Liu, 2011). The increase in

information production arises for the following reasons. 1) It is more difficult to analyze

diversified firms than to analyze pure-play firms because the former have larger sizes, more

complex organizational structures and greater opacity. Also, diversified firms suffer from more

agency problems (Kim and Pantzalis, 2003). Therefore, it is more costly to produce information

about diversified firms from the perspective of analysts, reducing their incentive to cover these

firms and the quality of their research (Bhushan, 1989). 2). Analysts specialize in industries.

1 http://blogs.wsj.com/cfo/2016/05/31/spinoffs-push-parents-on-new-strategies/

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However, diversified firms operate in multiple industries. It is difficult to map analyst expertise

to operations of diversified firms (Gilson et al., 2001). Refocus brought about by spinoffs reduces

the cost of information production for analysts and increases their incentive to cover these firms

and the quality of their research. 3). Different investors may have expertise in producing

information about some segments of the conglomerate firm but not about others. Spinoffs allow

them to focus their equity investment and information production in those segments, thereby

increasing their expected profits from information production (and therefore their incentive to

produce information) (Chemmanur and Liu, 2011).

Spinoffs are usually compared to other forms of corporate restructuring such as equity

carve-outs. In a spinoff, shares of a subsidiary are transferred to the current shareholders of the

parent firm. The parent firm does not retain any interest in the subsidiary. Spinoffs result in two

independent firms. Unlike spinoffs where no external financing is raised, equity carve-outs

involve unseasoned public offerings of subsidiary equity to outside investors. The parent firm

normally maintains a controlling interest in the subsidiary.

The change in the cost of information production could also be different following different

forms of restructuring. Chemmanur and Liu (2011) develop a model where firms choose between

three different restructuring mechanisms including spinoffs, equity carve-outs and tracking stock

issuance. They argue that in an equity carve-out, the unclean break-up of the parent and the

subsidiary makes it hard for institutional investors and their affiliated analysts to evaluate the two

firms resulting from the restructuring. To the contrary, the clean break-up of the parent and the

subsidiary in a spinoff makes it easier to evaluate the resulting new firms. Therefore, Chemmanur

and Liu (2011) suggest that the reduction in information production cost is the highest in spinoffs,

lower in carve-outs and the lowest in tracking stock issues. However, synergy loss is the highest

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in spinoffs, lower in carve-outs and the lowest in tracking stock issues. Given the difference in

information production changes, firms that are the most undervalued (i.e. firms where insiders

have the most favorable private information) choose to implement spinoffs (because good firms

will benefit more from better information production that helps to differentiate them from bad

firms). In addition, the fact that firms engaging in spinoffs will have the greatest loss of synergy

(caused by the clean breakup) suggests that for these firms, the magnitude of undervaluation

caused by diversification is likely to be greater than the value of synergy. To the contrary, firms

that are less undervalued will choose equity carve-outs or tracking stock issuance because the loss

of synergy is likely greater than the magnitude of undervaluation for these firms. Firms that are

the least undervalued choose to stay consolidated.

Spinoffs also have some tax advantages that are not available for other forms of

restructuring. According to Section 355 of the Internal Revenue Code, a parent firm can distribute

control of shares in a child firm to its shareholders without triggering gain at either the corporate

or the shareholder level. “Control” of shares is defined as shares representing at least 80% of the

total combined voting power and at least 80% of any non-voting shares. For this reason, spinoffs

are tax free for both the parent firm and its shareholders.

Given that spinoffs are associated with significant abnormal stock returns, we examine

informed trading around spinoffs. Spinoffs are unscheduled events. Unlike scheduled news release

such as earnings announcements, unscheduled news release is more likely to facilitate profitable

trading for informed traders. Implied volatility of options increases prior to scheduled information

events, making options “expensive” and then drops off sharply immediately after the information

release, leaving the change in option price low for each dollar of equity price change (Patell and

Wolfson, 1979). The profitability of informed trading around unscheduled events is likely greater

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because it is less affected by implied volatility increase prior to the events. We investigate the

prevalence of informed trading by examining the relationship between abnormal stock returns

associated with spinoffs and the volatility spread/volatility skewness of options prior to the

spinoffs. Furthermore, we examine how opacity and organizational complexity prior to

restructuring affect informed trading since the increase in information production is a major source

of shareholder gains. We suggest that opacity gives rise to informed trading because informed

traders have greater informational comparative advantage to uninformed traders in an opaque

environment. In addition, firms that are more opaque and/or more complex will gain more from

increased information production, leading to higher stock returns following the restructuring. The

potentially larger profit could give rise to more pronounced pre-announcement informed trading

for these firms.

Although a large body of literature conjectures that spinoffs reduce information

asymmetries between informed investors and uninformed investors, alternative views exist that

suggest the use of private information could be more pronounced after spinoffs (Huson and

MacKinnon, 2003). Gorton and Pennacchi (1993) and Subrahmanyam (1991) suggest that the

benefits of private information are likely to be amplified by spinoffs. This is because after a spinoff

that makes the firm less diversified, the benefits of private information are less likely to be

offset/diluted by changes in the value of the segments where the informed traders do not have

private information (Huson and MacKinnon, 2003). In addition, Kim and Verrecchia (1994, 1997)

and Lundholm (1991) suggest that additional public information can increase the informational

disparity between informed traders and uninformed traders and amplify the benefits of private

information. Furthermore, Huson and MacKinnon (2003) posit that the informational advantage

of the informed traders is the greatest immediately after the spinoff completion, with the private

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information gradually getting incorporated into the firm’s equity price. We examine the change

in informed trading after spinoffs using option market data. We do not make any assumptions

about the direction of change.

We find evidence supporting the view that there is informed trading prior to spinoffs. To

be more specific, options volatility spread and volatility skewness for the five days prior to the

spinoffs can predict the sign and magnitude of the abnormal stock returns associated with the

spinoffs. Further, we find that firms that are more opaque prior to the spinoffs are more prone to

informed trading. In addition, informed trading in the options market tends to decrease in the

aftermath of spinoffs. Our findings have important policy implications. Informed trading of stocks

are closely monitored by regulators. However, less attention is paid to the option market as a venue

for informed trading. Our findings suggest that it is important to increase monitoring of informed

option trading.

Our findings add to the literature on informed trading around major corporate events. There

is limited research on informed trading around corporate spinoffs. A recent exception is

Augustin et al. (2015). Augustin et al. (2015) examine abnormal options trading volume as a

measure of informed trading and find that 13% of all spinoff deals exhibit symptoms of informed

trading in the pre-announcement period. This result supports our findings about the prevalence

of informed trading around spinoffs. Our paper is different from Augustin et al. (2015) in that we

use volatility spread and volatility skewness that are directly inferred from option prices to detect

informed trading in the options market. Another major difference between our paper and

Augustin et al. (2015) is that we take one step further and examine how opacity affects informed

trading. Opacity gives rise to informed trading because informed traders have greater

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informational comparative advantage to uninformed traders in an opaque environment. Our

finding points to the importance of better disclosure in reducing insider trading.

2. Hypothesis

Given that spinoffs are associated with significant abnormal stock returns, we hypothesize that

informed trading is prevalent prior to spinoffs:

H1. There is informed option trading around spin-offs

We further hypothesize that Informed trading is positively associated with organizational

complexity and firm opacity because: 1) Opacity can motivate private information gathering and

create an informational comparative advantage for those incurring the informational gathering

costs (Diamond, 1985). 2). Spinoff will induce information production. The increase in

information production tends to be more pronounced for firms that are more opaque prior to the

spinoff, leading to higher stock returns (and therefore greater profit from informed trading) for

these firms following the spinoff. 3). Spinoffs will mitigate diversification discount to a larger

extent for firms that are more complex prior to restructuring, leading to higher stock returns and

greater informed trading profit following the spinoff.

H2: Firm opacity and organizational complexity are positively associated with the

prevalence of informed trading

We also look at how the prevalence of informed trading changes in the aftermath of

spinoffs.

H3. Informed trading decreases after spinoffs

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3. Data and Research Design

Our main tests are based on options data. We get option-related data from the OptionMetrics

Ivy DB US database, which is available from 1996 to 2015. Therefore, we construct our initial

sample by identifying spinoffs from 1996 to 2015. We obtain spinoff announcement dates and

company names from the SDC Platinum Mergers and Acquisitions database. We are able to

gauge the relatedness of the parent firm and spun-off child by examining the SIC codes of both

firms. Stock price-related data are from CRSP. We calculate abnormal stock returns around

spinoffs based on stock returns and market returns. Bid-ask spread, a proxy for opacity, is

calculated from the closing bid price and closing ask price from CRSP. We calculate

organizational complexity based on the Herfindahl of segment sales. Data for segment sales

are from the Compustat Historical Segment database. Firm-specific variables such as firm size,

equity market to book ratio, leverage, plant, property and equipment, and ROA are from

Compustat. After merging spinoffs data with the other databases, we have 491 firm-events

involving 342 firms in the sample with event dates ranging from July 1, 1998 to December 11,

2014. Definitions of variables are included in Table 1. Summary statistics for the variables are

reported in Table 2.

[Insert Table 1 here]

[Insert Table 2 here]

3.1 Variable Construction

Our measures of informed trading are based on four variables – volatility spread (𝑉𝑆),

volatility skew (𝑆𝐾𝐸𝑊) and two measures of suspicious trading created by Acharya and Johnson

(2010) –𝑀𝐴𝑋 and 𝑆𝑈𝑀. The implied volatility is derived from the Black-Scholes model, in

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which call option price is ωc = S N(d1) – Xe –δT N(d2), where d1 = [In (S/X) + (r- δ + σ2/2)T]/

σT1/2 ; d2= d1- σT1/2 and put option price is is ωp = Xe –δT N(-d2) – SN(-d1)

𝑉𝑆 is measured as the difference between the implied volatility of call options and that of put

options (Cremers and Weinbaum, 2010, Gharghori, Maberly and Nguyen, 2015).

𝑉𝑆𝑖𝑡 = 𝐼𝑉𝑖,𝑡𝑐𝑎𝑙𝑙𝑠 − 𝐼𝑉𝑖,𝑡

𝑝𝑢𝑡 = ∑ 𝑤𝑗,𝑡𝑖 (𝐼𝑉𝑗,𝑡

𝑖,𝑐𝑎𝑙𝑙 − 𝐼𝑉𝑗,𝑡𝑖,𝑝𝑢𝑡)

𝑁𝑖,𝑡

𝑗=1 , (1)

where 𝑗 represent each pair of call and put options matched by strike price and maturity

and 𝑁𝑖,𝑡 is the number of legitimate pairs of options on stock 𝑖. 𝑤𝑗,𝑡𝑖 is the weight for each pair of

call and put options based on the average open interest in the corresponding call and put options.

To address the problem of thinly traded options, we apply the following filters following Gharghori,

Maberly and Nguyen (2015): 1) We exclude options with an absolute value of delta greater than

0.98 or less than 0.02. 2) We only include options whose maturities range between 10 to 100 days.

3) We exclude options with a bid price of 0 or a bid-ask spread greater than the mid-point of bid

price and ask price. 4) We exclude options with zero open interest.

𝑉𝑆 is suggested to be a positive predictor of stock returns (Cremers and Weibaum, 2010).

If informed traders have positive information about the firm, they will either buy call options or

sell put options, which increase the price of call options, inducing a higher implied volatility

inverted from call options relative to put options. Since spinoffs are generally associated with

significantly positive abnormal stock returns (Krishnaswami and Subramaniam, 1999), informed

traders knowing about the upcoming spinoff announcement are likely to buy call options or sell

put options of the parent company, pushing up the volatility of call options relative to that of put

options and therefore increase the VS of the company.

𝑆𝐾𝐸𝑊 is measured as the difference between the implied volatility of out-of-the-money

put options and that of at-the-money call options (Gharghori, Maberly and Nguyen, 2015),

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𝑆𝐾𝐸𝑊 = 𝐼𝑉𝑖,𝑡𝑂𝑇𝑀𝑃 − 𝐼𝑉𝑖,𝑡

𝐴𝑇𝑀𝐶 (2)

where 𝐼𝑉𝑖,𝑡𝑂𝑇𝑀𝑃 is the implied volatility of out-of-the-money (OTM) put options for stock 𝑖

on day 𝑡 and 𝐼𝑉𝑖,𝑡𝐴𝑇𝑀𝐶 is the implied volatility of at-the-money (ATM) call options for stock 𝑖 on

day 𝑡. We use the same filters that we use in calculating the volatility spread to address the

problem of thinly traded options. Further, following Jin, Livnat and Zhang (2012) and Gharghori,

Maberly and Nguyen (2015), we define out-of-the-money put option to be the one whose delta is

the closest to -0.3 given that delta is higher than -0.45 and less than -0.15. At-the-money call

options are those whose delta is the closest to 0.5 among all eligible options that have a delta within

the range of 0.4 and 0.7. No weighting is required when calculating volatility skewness because

only one pair of call/put is chosen per day for each firm-event. Jin, Livnat and Zhang (2012)

suggest that the advantages of inferring moneyness from delta include: 1) Delta implies the

probability that the option will be in the money on the expiration date, 2) Deltas provide indications

about the liquidity of the options, 3) It allows for comparison of moneyness between options of

different maturities and strikes prices. The options chosen for the calculation of volatility skew

generally have active trading volume. The average turnover (trading volume/open interest) for

these options is 0.782.

The rational for using 𝑆𝐾𝐸𝑊 as a measure of informed trading is that informed traders

with negative private information tend to buy out-of-the-money put options, which drives up the

expensiveness of OTM put options relative to ATM call options. Xing, Zhang, and Zhao (2010)

find that high 𝑆𝐾𝐸𝑊 is associated with stock return underperformance over periods up to 6 months.

2 We test the robustness of our results by restricting our sample options to those with at least 20% turnover. Our

main results hold with this restriction.

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Further, they find that larger (more positive) earnings surprises are associated with less-

pronounced 𝑆𝐾𝐸𝑊. They suggest that the presence of the 𝑆𝐾𝐸𝑊 is driven by informed trading.

𝑀𝐴𝑋 and 𝑆𝑈𝑀 are based on residuals from the following two regression specifications,

following Ordu and Schweizer (2015). The first one is the unconditional variant and the second

one is the conditional variant.

𝑉𝑂𝐿𝑐𝑎𝑙𝑙 = 𝛼 + 𝜀𝑐𝑎𝑙𝑙, (3)

𝑉𝑂𝐿𝑐𝑎𝑙𝑙 = 𝛼 + 𝛽1 ∙ 𝑉𝑂𝐿𝑚𝑎𝑟𝑘𝑒𝑡 + 𝛽2 ∙ 𝑅𝐸𝑇𝑚𝑎𝑟𝑘𝑒𝑡 + 𝛽3 ∙ 𝐿𝑉𝑂𝐿𝑠𝑡𝑜𝑐𝑘 + 𝛽4 ∙ 𝐿𝑅𝐸𝑇𝑠𝑡𝑜𝑐𝑘 + 𝛽5 ∙

𝐿𝑉𝑂𝐿𝑐𝑎𝑙𝑙 + 𝜀𝑐𝑎𝑙𝑙, (4)

Where 𝑉𝑂𝐿𝑐𝑎𝑙𝑙 is the standardized call option volume, measured as (call options volume-mean

of call options volume)/standard deviation of call options volume. Mean and standard deviation of

call option volume are calculated from daily call option volume in the period 7 month prior to the

spinoff announcement date to 3 month prior to the spinoff announcement date. 𝑉𝑂𝐿𝑚𝑎𝑟𝑘𝑒𝑡 and

𝑅𝐸𝑇𝑚𝑎𝑟𝑘𝑒𝑡are the contemporaneous market volume and return. 𝐿𝑉𝑂𝐿𝑠𝑡𝑜𝑐𝑘 and 𝐿𝑅𝐸𝑇𝑠𝑡𝑜𝑐𝑘 are the

lagged volume and lagged return of the stock. 𝐿𝑉𝑂𝐿𝑐𝑎𝑙𝑙is the lagged dependent variable and 𝜀𝑐𝑎𝑙𝑙

is the residual.

We estimate the above 2 specifications for each spinoff firm using daily data for the period

beginning 90 days prior to the spinoff announcement date and ending 6 days prior to the spinoff

announcement date. For every specification, we compute the standardized regression residuals.

𝑀𝐴𝑋 and 𝑆𝑈𝑀 are based on the standardized regression residuals. The unconditional / conditional

𝑀𝐴𝑋 is the maximum of the daily standardized residual during the period [day-5, day-1] estimated

from equation (3) / (4). The unconditional/conditional 𝑆𝑈𝑀 is the sum of the daily standardized

residual during the period [day-5, day-1] estimated from equation (3) / (4). We also estimate the

𝑀𝐴𝑋 (𝑆𝑈𝑀) from a five-day window three months prior to the spin-offs and use it as a benchmark

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as in Ordu and Schweizer (2015). In our main regression specifications (Equations 9 and 10), our

𝑀𝐴𝑋 (𝑆𝑈𝑀) measures are the raw 𝑀𝐴𝑋 (𝑆𝑈𝑀) minus the benchmarks.

We measure organizational complexity as the segment-sale-based Herfindahl index

(Naveen, 2006), measured as = 1 − ∑ [ ( 𝑠𝑒𝑔𝑚𝑒𝑛𝑡 𝑠𝑎𝑙𝑒𝑠𝑖)2

( 𝑐𝑜𝑚𝑝𝑎𝑛𝑦 𝑠𝑎𝑙𝑒𝑠)2 ]𝑛𝑢𝑚𝑠𝑒𝑔𝑖=1 . We measure opacity as the bid-

ask spread. We calculate the bid-ask spread as the difference between ask price and bid price,

scaled by the midpoint of the two.

3.2.Testing Predictive Ability of Option Volatility Spread and Volatility Skew

If informed investors trade options on the private information they possess, option volatility

spread (VS) and option skewness (SKEW) in the period leading up to the announcement will be

able to predict abnormal stock returns in the announcement period. To examine whether VS and

SKEW can predict abnormal announcement-period stock returns, we estimate the following

regression specifications:

𝐶𝐴𝑅𝑖 = 𝛽0 + 𝛽1 ∙ 𝑉𝑆𝑖 + ∑ 𝛾𝑗𝑛𝑗=1 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖𝑗 + 𝜖𝑖 (5)

𝐶𝐴𝑅𝑖 = 𝛽0 + 𝛽1 ∙ 𝑆𝐾𝐸𝑊𝑖 + ∑ 𝛾𝑗𝑛𝑗=1 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖𝑗 + 𝜖𝑖 (6)

Where 𝐶𝐴𝑅 is the cumulative abnormal stock return for day 0 and day 1. Abnormal stock

return is estimated using the market model:

𝐸(𝑅𝑖𝑡) = 𝛼𝑖 + 𝛽𝑖𝑅𝑚𝑡

ARit = 𝑅𝑖 − ��𝑖 − ��𝑖𝑅𝑚𝑡

𝐶𝐴𝑅𝑖(0,1)= ∑ 𝐴𝑅𝑖𝑡10

Statistical test of abnormal returns is based on the cross-average of the CAR:

𝐶𝐴𝐴𝑅(0,1) =1

𝑁∑ 𝐶𝐴𝑅𝑖

𝑁

1

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The Standard deviation of CAAR(0,1) is estimated from the cross section of event-window

abnormal returns:

𝑆(𝐶𝐴𝐴𝑅) = √1

𝑁(𝑁 − 1)∑ [𝐶𝐴𝑅𝑖,(0,1) − 𝐶𝐴𝐴𝑅(0,1)]2

𝑁

𝑖=1

The standardized cross-sectional test statistic for the null hypothesis that the cumulative

average abnormal return is equal to zero is:

𝐶𝐴𝐴𝑅(0,1)

𝑆(𝐶𝐴𝐴𝑅)

The main explanatory variables in Equation 5 and Equation 6 are VS and SKEW

respectively. 𝑉𝑆 is volatility spread prior to announcement. 𝑆𝐾𝐸𝑊 is volatility skew prior to

announcement. We examine 𝑉𝑆 and 𝑆𝐾𝐸𝑊 for each of the 5 days leading up to the announcement

(day -5 to day -1).

A stylized example may help to illustrate the happenings around spinoffs. Dean Foods Co.

announced its spinoff of the WhiteWave Foods Co. on October 17 2012. For the ten trading days

prior to the announcement, the stock price was mostly under $16, with an average of $15.15. On

the announcement date, the price jumped to $16.96 at the market close. The next day, price

continued to rise, closing at $17.94. The cumulative abnormal stock return for the announcement

date and the following day was 0.1839. Option SKEW was higher in the days leading up to the

announcement than in normal days. Compared to an average SKEW of 0.0587 from day -10 to day

-6, the average SKEW was 0.0666 for the period starting at day -5 and ending at day -1. Not

surprisingly, SKEW declined after the announcement date, to an average of 0.0030 for the 5 trading

days after the announcement date.

3.3 Investigating How Opacity and Organizational Complexity Affect Informed Trading

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We examine how opacity and organizational complexity affect informed trading by

estimating the following two specifications:

𝑀𝐴𝑋𝑖 = 𝛽0 + 𝛽1𝐶𝑜𝑚𝑝𝑙𝑒𝑥𝑖𝑡𝑦𝑖 + 𝛽2𝑂𝑝𝑎𝑐𝑖𝑡𝑦𝑖 + ∑ 𝛾𝑗𝑛𝑗=1 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖𝑗 + 𝜖𝑖 (7)

𝑆𝑈𝑀𝑖 = 𝛽0 + 𝛽1𝐶𝑜𝑚𝑝𝑙𝑒𝑥𝑖𝑡𝑦𝑖 + 𝛽2𝑂𝑝𝑎𝑐𝑖𝑡𝑦𝑖 + ∑ 𝛾𝑗𝑛𝑗=1 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑖𝑗 + 𝜖𝑖 (8)

where MAX and SUM are measures of informed trading that are based on the Acharya and Johnson

(2010) model.

3.4 Investigating How Informed Trading Changes after Spinoffs

Huson and MacKinnon (2003) measure informed trading using the Ferreira et al (2011)

method. Ferreira et al. (2011) suggest that firm-specific return variation measures the rate at which

private information is incorporated into prices via trading. To be specific, the firm-specific stock

return variation is estimated by 1-R2 of the regression of the Fama-French 3-factor model:

𝑅𝑖,𝑡 − 𝑅𝑓,𝑡 = 𝛼𝑖 + 𝛽1 ∙ (𝑅𝑚,𝑡 − 𝑅𝑓,𝑡) + 𝛽2 ∙ 𝑆𝑀𝐵𝑡 + 𝛽3 ∙ 𝐻𝑀𝐿𝑡 + 𝜖𝑖,𝑡 (9)

Huson and MacKinnon (2003) conduct the tests using the logistic transformation of 1-R2,

namely: Log [(1-R2)/ R2] and call it PrivateInfo, where R2 is the R-squared from the above

regression. For all spinoffs, Huson and MacKinnon (2003) estimate the equation for the pre-

spinoff and post-spinoff period separately. Pre-spinoff period is from day -300 to day -50. Post-

spinoff period is from day 50 to day 300. We follow Huson and MacKinnon (2003) and estimate

the following model to examine the change in the prevalence of informed trading in the aftermath

of spinoffs.

PrivateInfoj = α+ β1jPost-spinoff + ∑ 𝛽𝑟𝑗𝑛𝑟=2 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑗𝑟 +𝜖𝑗 (10)

Huson and MacKinnon (2003)’s tests are based on stock price data. Easley et al. (1998)

conjecture that if the options market is more attractive to informed traders, option price may reflect

15

new information before stock prices does so. Easley et al. (1998)’s empirical analysis suggests

that option market is a venue for information-based trading. Given that informed traders tend to

choose the options market rather than the stock market for their information-based trading, we use

option data to measure informed trading before and after spinoffs. We use a unique measure of

informed trading based on the combination of the Acharya and Johnson (2010) model and the

Ferreira et al (2011) method.

The Acharya and Johnson (2010) model describes the “normal” trading volume of options, as

specified in equation (4):

𝑉𝑂𝐿𝑐𝑎𝑙𝑙 = 𝛼 + 𝛽1 ∙ 𝑉𝑂𝐿𝑚𝑎𝑟𝑘𝑒𝑡 + 𝛽2 ∙ 𝑅𝐸𝑇𝑚𝑎𝑟𝑘𝑒𝑡 + 𝛽3 ∙ 𝐿𝑉𝑂𝐿𝑠𝑡𝑜𝑐𝑘 + 𝛽4 ∙ 𝐿𝑅𝐸𝑇𝑠𝑡𝑜𝑐𝑘 + 𝛽5 ∙

𝐿𝑉𝑂𝐿𝑐𝑎𝑙𝑙 + 𝜀𝑐𝑎𝑙𝑙,

We estimate the regression model for the two windows around each firm-event: one is day

-300 to -50 (pre-spinoff) and the other is day +50 to day +300 (post-spinoff). We calculate the R2

for each of the two windows. Then we do a log transformation of the R2 : Log [(1-R2)/ R2] and

call it Opt_InformedTrading. Then, we estimate the following model to compare the prevalence

of informed trading before and after the spinoff.

Opt_InformedTradingj = α+ β1jPost-spinoff + ∑ 𝛽𝑟𝑗𝑛𝑟=2 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠𝑗𝑟 +𝜖𝑗 (11)

Where post-spinoff is an indicator variable that takes the value of 1 if the trading days fall into the

[day+50, day +300] range. It equals 0 if the trading days fall into the [day-300, day -50] range.

4 Empirical Results:

We find a 2.21% average cumulative abnormal stock returns (CAR) around spin-offs (for day 0

and day 1), which is statistically significant at the 1% level. Figure 1 provides a visual description

of the average abnormal stock returns from day -15 to day 1. The horizontal axis in Figure 1

16

represents days from the announcement date and the vertical axis represents the cross-sectional

average abnormal stock return for each day, which is estimated from the market model. The

abnormal stock returns seem to be small in magnitude on the days leading up to the announcement

date. The announcement day and the following day (day +1) see significantly positive abnormal

stock returns, with the magnitude of abnormal stock returns being 1.6% and 0.6% respectively.

[Insert Figure 1 here]

To investigate the prevalence of informed trading, we examine the relationship between

CAR and options volatility spread (VS)/ options skewness (SKEW). For the kth coefficient of all

our regression models, the standard errors are calculated as

𝑆��(𝛽��) = √1−𝑅𝑌𝐻

2

(1−𝑅𝑋𝑘𝐺𝑘2 )∗(𝑁−𝐾−1)

𝑆𝑌

𝑆𝑋𝑘

,

where H is the set of all the independent variables, Gk is the set of all the independent variables

except Xk, 𝑅𝑋𝑘𝐺𝑘

2 is the R-squared of Xk on all other independent variables, N is the number of

observations and K is the number of independent variables.

We find a significantly positive relationship between CAR and VS for day -1 and day -3

(see Columns 1 and 3, Panel A of Table 3). We also find that CAR is negatively related to SKEW

for all the 5 days prior to the announcement day (see Columns 1-5, Panel B of Table 3). This can

be caused by informed traders with positive information buying call options before the spinoff

announcement, which increases the implied volatility of call options relative to put options, or

informed traders with negative information buying put options, which increases the expensiveness

and implied volatility of put options relative to call options. The results are consistent with our

17

hypothesis that informed traders use their private information regarding the spinoffs in options

trading.

We include control variables such as firm size, opacity, relatedness (an indicator variable

that equals 1 if the spun-off child is in the same industry as the parent), complexity, ROA, leverage

and market to book ratio. We find some evidence that firms that are more opaque or more complex

prior to spinoffs exhibit higher abnormal stock returns (See Columns 1-5, Panel A of Table 3 and

Columns 1-5, Panel B of Table 3 respectively). Firms with higher leverage also seem to have

higher abnormal stock returns (Columns 1-5, Panel A and Column 3, Panel B of Table 3). This is

consistent with the wealth transfer hypothesis proposed by Maxwell and Rao (2003), which

suggests that spinoffs represent a wealth transfer from bondholders to stockholders.

[Insert Table 3 here]

Further, we examine the relations between opacity, organizational complexity and

suspicious informed trading activities (measured by MAX and SUM). We find MAX to be

positively related to bid-ask spread, our main measure of firm opacity (see Columns 1-2 , Table

4), suggesting that informed trading is more prevalent for firms that are more opaque prior to the

spinoff. This finding is consistent with our hypothesis 2. However, we do not find complexity to

be significantly related to either measure of informed trading.

[Insert Table 4 here]

We also examine the change in information environment from pre-spinoff period to post-

spinoff period. The main measure of information environment is based on the R2 from equation

(4). We do a log transformation of the R2 : Log [(1-R2)/ R2] and call it Opt_InformedInfo, where

Opt stands for options. Opt_InformedInfo is a positive measure of the prevalence of informed

trading. We also use the log transformation of the R2 from equation (9), which is based on equity

18

returns, as an alternative measure of information environment and call it PrivateInfo. The

results are reported in Table 5. Post-spinoff is an indicator variable that takes the value of 1 if the

trading day falls into the range [day+50, day+300] and 0 if it falls into the [day-300, day-50]

range. We find that post_spinoff is negatively associated with Opt_InformedInfo (Column 1,

Table 5), suggesting that informed trading of options decreases from pre-spinoff period to post-

spinoff period. The result implies that improved information environment after spinoffs reduces

the competitive advantage of informed option traders. However, we do not find the same

pattern in the equity market (Column 2, Table 5). This is likely driven by the fact that the

options market is the preferred venue for informed trading.

[Insert Table 5 here]

5. Conclusion

Prior studies find positive and significant abnormal stock returns associated with corporate

spinoffs. We suggest that the positive abnormal stock returns can provide profitable trading

opportunities for informed traders. We examine the prevalence of informed trading prior to

spinoffs using option market data and find evidence that informed trading does exist prior to

spinoffs. Further, we examine how firm opacity affects the prevalence of informed trading. We

find that firms that are more opaque prior to spinoffs exhibit more symptoms of informed

trading, consistent with the view that informed traders have a greater informational comparative

advantage at firms that have an opaque information environment. In addition, we find that the

prevalence of informed trading decreases after spinoffs, consistent with the view that spinoffs

encourage information production and reduce information asymmetry between informed traders

and uninformed traders.

19

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22

Table 1: Definitions of Variables Variables Definitions Options Skewness (SKEW) Difference between the implied volatility of out-of-money put options and

that of at-the-money call options Option volatility spread (VS) Difference between the implied volatility of call options and that of put

options

CAR Cumulative abnormal stock return

Size Log (total assets)

Opacity (ask price-bid price)/mid-point of bid price and ask price

Relatedness An indicator variable that equals 1 if the spun-off child is in the same industry

as the parent

Complexity (segment-sale-based

Herfindahl)

Organizational complexity based on Herfindahl of segment sales

ROA Return on assets

Leverage Long term debt/total assets

MTB Equity market to book ratio.

Opt_PrivateInfo Log [(1-R2)/ R2] , where R2 is estimated from equation (4)

PrivateInfo Log [(1-R2)/ R2] , where R2 is estimated from equation (9)

MAX Measure of informed trading, estimated from Equation (4)

SUM Measure of informed trading, estimated from Equation (4)

Table 2: Summary statistics

Table 2 reports the summary statistics for the main variables included in our analysis. OBS=491

Variables Mean Median Std Dev Max Min Option skewness .0334 .0241 .0596 .4797 -.1270 Option volatility

spread -.0060 -.0043 .0416 .2207 -.1993

CAR .0221 .0066 .1364 2.7361 -.7647 Size 8.9433 9.0093 1.8357 12.8659 3.0339 Opacity .0032 .0008 .0081 .0822 -.0020 Complexity .7663 .8557 .2466 1 .2807 ROA .0192 .0330 .1563 1.2472 -1.3692 Leverage .2328 .2028 .2243 2.6158 0 MTB 1.7849 1.3981 1.3693 13.9390 .6223 MAX .4246 .1453 2.4209 10.8513 -7.8259 SUM .4342 .0347 3.9525 14.9911 -16.4191 Opt_PrivateInfo 2.3587 2.2215 1.0711 5.2339 -1.3888 PrivateInfo .9591 .8292 .8565 5.5973 -.6610

23

Table 3: OLS Regression of Cumulative Abnormal Stock Return on VS and SKEW Panel A of Table 3 describes the relationship between cumulative abnormal stock return and options volatility

spread. Panel B of Table 3 describes the relationship between cumulative abnormal stock return and options

skewness. OBS=491

Panel A

Dependent variable: CAR

day -1

(1)

day -2

(2)

day -3

(3)

day -4

(4)

day -5

(5)

Option volatility spread .1775***

(.0583)

.0409

(.0517)

.1362**

(.0631)

.0315

(.0606)

.0465

(.0654)

Size -.0026*

(.0015)

-.0018

(.0015)

-.0029*

(.0015)

-.0022

(.0015)

-.0027*

(.0015)

Opacity .5122*

(.3060)

1.5089***

(.4419)

.6465*

(.3667)

.6025*

(.3099)

.6480*

(.3523)

Relatedness -.0022

(.0050)

.0003

(.0050)

-.0022

(.0050)

-.0012

(.0050)

-.0015

(.0051)

Complexity -.0026

(.0106)

-.0032

(.0106)

-.0020

(.0107)

-.0034

(.0107)

-.0035

(.0107)

ROA .0341**

(.0160)

.0343**

(.0159)

.0321**

(.0161)

.0329**

(.0162)

.0351**

(.0162)

Leverage .0308***

(.0108)

.0299***

(.0109)

.0319***

(.0109)

.0303***

(.0110)

.0303***

(.0109)

MTB .0009

(.0018)

.0016

(.0018)

.0008

(.0018)

.0013

(.0018)

.0012

(.0019)

Cons .0297

(.0190)

.0177

( .0192)

.0315

(.0192)

.0250

(.0193)

.0295

(.0192)

Adj_R2 .0484 0.0494 0.0390 0.0302 0.0322

Panel B

Dependent variable: CAR

day -1

(1)

day -2

(2)

day -3

(3)

day -4

(4)

day -5

(5)

Option skewness -.1510**

(.0629)

-.1283**

(.0602)

-.1266*

(.0660)

-.1722***

(.0607)

-.1570***

(.0526)

Size .0002

(.0023)

.0002

(.0022)

-.0017

(.0022)

-.0006

(.0023)

-.0011

(.0022)

Opacity -.8416

(.5619)

-1.6788

(1.1200)

-2.1209***

(.6244)

-.9436

(.6261)

-1.004

(.6219)

Relatedness -.0088

(.0076)

-.0081

(.0075)

-.0085

(.0075)

-.0061

( .0078)

-.0076

( .0075)

Complexity .0295*

(.0162)

.0273*

(.0157)

.0280*

(.0160)

.0310*

(.0164)

.0292*

(.0158)

ROA .0093

(.0295)

-.0084

(.0289)

.0021

(.0288)

.0073

(.0295)

.0026

(.0286)

Leverage .0267 .0216 .0301* .0231 .0210

24

(.0182) (.0185) (.0178) ( .0193) (.0185)

MTB .0022

(.0028)

.0025

(.0027)

.0021

(.0027)

.0025

(.0028)

.0007

(.0027)

Cons -.0140

(.0294)

-.0120

(.0289)

.0087

(.0292)

-.0064

(.0305)

.0039

(.0294)

Adj_R2 0.0164 0.0102 0.0344 0.0233 0.0263

Table 4: OLS Regression of MAX and SUM on Opacity and Organizational Complexity

Table 4 describes the relationship between informed trading and firm opacity. N=491

Dependent variable: MAX SUM

(1) (2)

Size -.1546**

(.0679)

-.2191**

( .1102)

Opacity 35.0337*

(18.4218)

59.7308**

(29.3485)

Complexity -.2931

(.4821)

-.1306

(.7800)

Market to book ratio .0988

(.0879)

.1276

(.1436)

Leverage -.1261

(.4765)

-.3336

( .7781)

PPE .7352

(.4478)

1.4389**

(.7317)

ROA 1.2301

(.7590)

1.7937

(1.2338)

Relatedness -.0479

(.2279)

.4478

(.3710)

Cons 1.5850*

(.8510)

1.4785

(1.3738)

Adj_R2 0.0130 0.0160

25

Table 5: Change in Informed Trading in the Aftermath of Spinoffs

Table 5 describes the change in informed trading from pre-spinoff period to post-spinoff period.

Dependent variable: Opt_PrivateInfo PrivateInfo

(1) (2)

Post-spinoff -.2683**

(.1117)

-.0456

(.0827)

Size -.1957***

(.0399)

-.0563*

(.0295)

Opacity -101.4716

(107.4479)

318.8275***

(79.5855)

Complexity -.4315*

(.2430)

.9155***

(.1800)

Market to book ratio -.1695***

(.0503)

-.0765**

(.0372)

Leverage .2726

(.3104)

.5998***

(.2299)

PPE -.1603

(.2226)

.1685

(.1649)

ROA .7443

(.6060)

-1.166***

(.4489)

Cons 4.9431***

(.5010)

.6203*

(.3711)

Adj_R2 0.0768 0.2169

26

Figure 1: Abnormal Stock Returns from Day -15 to Day 1

-0.004

-0.002

0

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

0.018

-16 -14 -12 -10 -8 -6 -4 -2 0 2

Ab

no

rmal

_ret

urn

Days From the Annoucement Date

Abnormal_return