Option Prices Leading Equity Prices: Superior Information ... · information discovery and superior...

Joshua Livnat

Option Prices Leading Equity Prices:

Superior Information Discovery or

Superior Information Processing?

Yuan ZhangWen Jin

1

Joshua LivnatNew York University

(Emeritus Professor)

Quantitative Management Associates

Yuan ZhangColumbia University

Wen JinQuantitative Management

Associates

Note: The opinions and recommendations herein are not those of Quantitative Management Associates LLC. These materials are strictly used for Academic purposes only.

Plan

�Introduction

�Background on Option Measures

�Information Discovery / Processing�Information Discovery / Processing

�Research Design

�Results

�Conclusion

2

Xing et al. (2010)

0.10%

0.20%

0.30%

0.40%

3

Volatility Skew Quintiles: Hedge Portfolio Alpha 21 BP per Week

-0.20%

-0.10%

0.00%

0.10%

1 2 3 4 5

ExRET Alpha

Xing et al (2010) – Longer Returns

2.00%

3.00%

4.00%

5.00%

4

-4.00%

-3.00%

-2.00%

-1.00%

0.00%

1.00%

2.00%

4 8 12 16 20 24 28

low

2

3

4

high

Weeks

Cremers and Weinbaum (2010)

60

80

100

120

5

-20

0

20

40

1 2 3 4 5

Return - 4 weeks Alpha

Volatility Spread Quintiles: Hedge Portfolio Alpha 51 BP over 4 Weeks

Research Question

�Why do option prices lead equity prices? – Non-mutually exclusive hypotheses:

�Do option trades reflect the traders’ �Do option trades reflect the traders’ superior ability to discover information?

�Do option trades reflect the traders’ superior ability to process information?

6

Preview of Results

� We focus on option volatility skews and volatility spreads between calls and puts, and examine scheduled and unscheduled events separately.

� We find evidence consistent with superior � We find evidence consistent with superior information discovery for both scheduled and unscheduled events.

� We find evidence consistent with superior information processing, but for unscheduled events only.

7

Implied Volatility Surface

8

Volatility Skew

4.5

5

5.5Im

plied V

ola

tility

OTM Put

3.5

4

0 5 10 15 20Strike Price

Implied V

ola

tility

Option Smile? Option Smirk?

ATM Call

SKEW= IVOTM put – IVATM call

The Art of Smile

10

Volatility Spread

� Weighted average of the differences in implied volatilities between call and put options matched on strike price and maturity, where the weights are the relative open interest.

SPREAD= Σ w (IVcall – IVput)

11

Why these “violations”?� Demand-based option models (Bollen and Whaley

2004, and Garleanu et al. 2009): changes in implied volatilities are driven by net buying/selling pressure.

� Positive news --> an excess of buyer-initiated call trades and/or seller-initiated put tradesand/or seller-initiated put trades

--> Lower volatility skews and/or higher volatility spread

� Negative news --> an excess of buyer-initiated put trades and/or seller-initiated call trades

--> Higher volatility skews and/or lower volatility spread

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Information Discovery Hypothesis

�Option trades reflect the traders’ superior ability to “discover” information:

� They have access to private information that equity traders do not.equity traders do not.


� The SEC charged three people who worked as accountants at Dell Inc. in 2005 with insider trading, saying that the men had made profits by trading put options on inside information that the company wouldn’t hit quarterly earnings target. WSJ. Sep 29, 2007. wouldn’t hit quarterly earnings target. WSJ. Sep 29, 2007.

� There was a massive surge in call option purchases the day before the announcement of the Perot Systems/Dell transaction: 2,539 call options traded the day before this buyout was announced, 242 times the four-week average. Bloomberg.com. Sep 21, 2009.

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�Option trades reflect the traders’ superior ability to “discover” information:

� They have access to private information that equity traders do not.equity traders do not.

� They discover public information that is not incorporated in both option and equity prices.

Information Processing Hypothesis

� Option trades reflect the traders’ superior ability to “process” information:

� They are better at detecting deceptive statements in conference calls. statements in conference calls.

� They are better at interpreting the implications of a product announcement for firm valuation.

� They are better able to employ merger prediction models based on either business knowledge, economic fundamentals, or market trading activities (Cao et al. 2005).

Research Design: Sample

� Scheduled information events: earnings announcements (1996-2010) and 10-K/10-Q filings (1996-2009)

� Unscheduled information events: Key Developments by Capital IQ (2002-2009)Capital IQ (2002-2009)

� SEC inquiries, expansions, reorganizations, client announcements, M&A transactions, executive/board changes, litigations, labor relations, product announcements, and strategic alliances.

� We make sure 10-K/10-Q filings and Key Dev events are at least three days away from earnings announcements.

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Research Design: Timeline

-1 10

XRETReturns

XRET=f(Base, Pre)

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-2-10-50

PreBaseOption

Measures

Information

Discovery

XRET=f(Base, Pre)

Presumably the informed

traders’ informational advantage

is largest immediately before

significant information releases.

(Skinner 1997)

Research Design: Timeline

-1 1 6 90

XRETPOST

0

XRETReturns

19

1 5Post

-2-10-50PreBaseOption

Measures

Information

Processing

XRETPOST=f(Base, Pre, Post)

After the news release, the only advantage to informed

traders is an information-processing advantage. (Kim and

Verrecchia 1994, Skinner 1997)

Data

� OptionMetrics: Closing prices, maturity, implied volatilities, Greeks, and open interest, etc.

� S&P Compustat: Earnings announcement dates.

� S&P Filing Dates: SEC filing dates.

� IBES Detail: Earnings surprises. � IBES Detail: Earnings surprises.

� CRSP: Abnormal returns.

� Key Developments: Dates of other announcements.

� DataExplorers: Short selling data.

� Options with positive open interest and expiring in (10, 60).

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Implied Volatilities—ATM Call

0.3

0.4

0.5

0.6

21

0

0.1

0.2

0.3

Base Pre Post

Mean Median

Earnings Announcement Sample

Option Measures: Mean

0.02

0.03

0.04

0.05

22


-0.02

-0.01

0

0.01

Base Pre Post

VolSkew VolSpread

Information Discovery:

Predicting Event Return


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Information Processing:

Predicting Post-Event Return


24


0.02

0.03

0.04

0.05

25

10-K/10-Q Sample

-0.02

-0.01

0

0.01

Base Pre Post

VolSkew VolSpread



10-K/10-Q Sample

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10-K/10-Q Sample

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Unscheduled Events

� Information discovery: access to private or inside information or discovery of public information: The former more relevant for non-scheduled events.

� Scheduled events (earnings announcements and 10-K/Q) are highly anticipated and carefully scrutinized.K/Q) are highly anticipated and carefully scrutinized.

� Unscheduled events are often less quantitative, less structured, and more difficult to interpret.

� Majority of equity price jumps occur with unscheduled firm-specific news events (Lee and Mykland 2008): Key Developments events.


0.01

0.02

0.03

0.04

29

-0.02

-0.01

0

0.01

Base Pre Post

VolSkew VolSpread

Key Developments Sample




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Sub-Sample Period Analyses

� Oct. 2000: Regulation Fair Disclosure (selective disclosure)

� Jul 2002: Sarbanes Oxley Act (effectiveness of � Jul 2002: Sarbanes Oxley Act (effectiveness of internal control over financial reporting)

� Dec 2002: Global Settlement (conflict of interest)

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33

10-K/10-Q Sample

Robustness Checks: Both Signals

Earnings Announcement Sample: Information Discovery

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Robustness Checks: Both SignalsEarnings Announcement Sample: Information Processing

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Other Robustness Checks

�Control for equity excess returns in the Base and Pre window

�Control for accruals information for the earnings announcement sample if disclosedearnings announcement sample if disclosed

�Analyze the implications of short-sale constraint

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Analysis of Short-Sale Constraints

Easy to short

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Data Source: DataExplorers

Hard to short

� For the earnings announcement analyses, we obtain qualitatively similar results to those reported with the following subsamples:

� Subsample with DataExplorers data available

Analysis of Short-Sale Constraints

� Subsample with DataExplorers data available

� Subsample with less than 40% utilization rate

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Trading Strategy

� PEAD strategy: Long in high decile of SURP and short in low decile of SUPR.

� SKEW (SPREAD) strategy: Long in low (high) quartile of SKEW (SPREAD) and short in high (low) quartile of of SKEW (SPREAD) and short in high (low) quartile of SKEW (SPREAD).

� COMBINED strategy: PEAD strategy but no long (short) position if a firm is in the short (long) quartile based on SKEW/SPREAD.

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Trading Strategy Results--Univariate

40

Trading Strategy--Regression

41

Annual Returns: Single Strategy

0.3

0.4

0.5

0.6

42

-0.2

-0.1

0

0.1

0.2

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

PEAD SKEW SPREAD

Single Strategies

400

500

600

700

800

PEAD

SKEW

SPREAD

43

0

100

200

300

400

Dec-9

5

Dec-9

6

Dec-9

7

Dec-9

8

Dec-9

9

Dec-0

0

Dec-0

1

Dec-0

2

Dec-0

3

Dec-0

4

Dec-0

5

Dec-0

6

Dec-0

7

Dec-0

8

Dec-0

9

Combined Strategies - PEAD

150

200

250

44

0

50

100

150

Dec-9

5

Dec-9

6

Dec-9

7

Dec-9

8

Dec-9

9

Dec-0

0

Dec-0

1

Dec-0

2

Dec-0

3

Dec-0

4

Dec-0

5

Dec-0

6

Dec-0

7

Dec-0

8

Dec-0

9

PEADSKEWMINUSPEAD PEADSPREADMINUSPEAD

Cumulative contribution to PEAD strategy from using option signals.

Annual Incremental Returns

0.08

0.1

0.12

0.14

0.16

0.18

45

0

0.02

0.04

0.06

0.08

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

PEAD/SKEW-PEAD PEAD/SPREAD-PEAD

Summary of Results

� We find evidence consistent with both superior information discovery and superior information processing:

� Volatility skews and volatility spreads immediately before (scheduled and unscheduled) events are before (scheduled and unscheduled) events are negatively associated with announcement returns. –Information Discovery

� Volatility skews and volatility spreads immediately after events are negatively associated with subsequent post-event returns for unscheduled events only. –Information Processing

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Contributions

� Better understanding of the interactions between the option and stock markets.

� Provide initial evidence on the implications of Regulation FD on the option market.Regulation FD on the option market.

� Useful for stock investors for ways to earn abnormal returns.

� Potentially useful for regulators.

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Thank You!

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With smile; not a smirk.

Who Leads?

� Stephen and Whalley (1990): stock market leads by 15 minutes

� Kumar et al. (1992): option market leads by 30 minutes

� Chan et al. (1993): option market does not lead

� Option trading imbalances: Easley et al. 1998 (20 � Option trading imbalances: Easley et al. 1998 (20 minutes), Cao et al. 2005, Pan and Putshman (2006);

� Option volatility skews: Bates 1991, Doran et al. 2007, Van Buskirk 2009, Xing et al. 2010 (6 months);

� Volatility spread between call and put options: Ofek et al. 2004, Cremers and Weinbaum 2010 (4 weeks).

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Private and Public Information

� Holthausen and Verrecchia (1990), Kim and Verrecchia (1997), Barron et al. (2005):

� Pre-announcement private information (“information discovery”)

� Public announcement (“information event”)� Public announcement (“information event”)

� Event-period private information (“information processing” or “differential interpretations”)� information an investor gleans by studying the error in

a financial reports, where the error arises from the application of random, liberal, or conservative accrual-based accounting practices and estimates

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Delta and Moneyness

�delta(call)=N(d1), delta(put)=N(d1)-1, where d1= [ln(S/K)+(r+δ2)/2)T]/δ√T.

� Bounded between 0 and 1 and measures the probability that the option would be in the money probability that the option would be in the money when it expires.

� It allows comparing moneyness levels between options with different underlying maturities and strike prices.

� Options with deltas close to -0.3 would maximize option traders’ returns if the stock prices go down.

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Why Negative Skews?

� “Crashophobia” (Rubinstein 1994)

�A negative volatility risk premium. (Bakshi and Kapadia 2003) Kapadia 2003)

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Call-Put Parity

�European options on non-dividend paying stocks:

�C-P=S-PV(K)

� Call and cash vs put and share, both worth � Call and cash vs put and share, both worth max (ST, K) on T.

�American options on non-dividend paying stocks:

�S>=PV(K)+C-P

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Implied Volatility Functions

� Constant implied volatilities (Black and Scholes 1973)

� Deterministic implied volatilities (Emanuel and MacBeth 1982): highly unstable over time

� Stochastic implied volatilities (Black 1976): unrealistic parameter values

� Supply and demand model (Bollen and Whaley 2004): excess buying pressure relates to IVF

55

Supply and Demand of Options

� Under dynamic replication, the supply curve for option series is a horizontal line (i.e., unlimited supply).

� In reality, however,

� The ability of professional arbitrageurs to exploit � The ability of professional arbitrageurs to exploit mispriced securities is limited (Shleifer and Vishny1997; Liu and Longstaff 2000).

� A market maker will not sell an unlimited number of contrasts. As his position grows large and imbalanced, his hedging costs and/or volatility risk exposure also increase, and he is forced to charge a higher price (Bollen and Whaley 2004).

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Characteristics of Option Traders

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Source: Pan and Poteshman (2006)

What do Option Traders Trade

ITM OTM

58

Source: Bollen and Whaley (2004), 20 most active option classes

ITM

OTM ITM

OTM

End large users have large net positions in OTM put options

(Garleanu et al. 2009).

Why is the Predictive Ability not Arbitraged Away

• Behavioral bias (Hong and Stein 1998; Hong et al. 1999).

• Short-sale constraints and other transaction costs • Short-sale constraints and other transaction costs (Ofek et al. 2004).

• Investors’ tendency to avoid idiosyncratic volatility when eliminating arbitrage opportunities (Ali et al. 2003, Mendenhall 2004).

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How is IV calculated

�For options on individual stocks, which are American, implied volatilities are calculated using a binomial tree, taking into account dividend payments and the possibility of dividend payments and the possibility of early exercise.

--OptionMetrics

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Volatility Skew and Spread

� Identify all call and put options with a maturity in the window [+10, +60] and open interest>0.

� SKEW: Difference in implied volatility between OTM put and ATM call options (Xing et al. 2010).put and ATM call options (Xing et al. 2010).

� SPREAD: Weighted average of volatility spread between call and put options matched on strike price and maturity, where the weights are scaled open interest (Cremers and Weinbaum 2010).

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Robustness Checks

� Earnings Announcement Sample: Information Discovery

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Where do Informed Traders Trade� Option traders and equity traders are not necessarily

different/segmented traders. � Why informed traders may trade in the option market?

� Options are not redundant securities due to imperfect market (information asymmetry, trading frictions, etc.) (Back 1993; Easley et al. 1998)Easley et al. 1998)

� Options market may provide a more effective venue for informed trading (lower cost, higher leverage, and truncated payoff) (Black 1975, Amin and Lee 1997, Cao et al. 2005)

� Short-sale constraint in the equity market.

� We seek to examine, if informed traders trade in the option market, how and when they become informed.

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Background� Implied volatility is the average expected variance rate

per unit time from any valuation date to expiration.

� Conventional option pricing models have two implications regarding implied volatilities:

� The implied volatility is independent of strike prices;� The implied volatility is independent of strike prices;

� The implied volatility is the same for all put options and call options with the same strike price and expiration date (“call-put parity”). (For European options with no dividend payments)

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Option Prices Leading Equity Prices: Superior Information ... · information discovery and superior...

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