firm opacity and informed trading around spinoffs
TRANSCRIPT
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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