1. Can Options Predict Stock Behavior?: A Literature Review on
the Information Content Contained in Options Markets
By: Steven Kislenko
Abstract
There has been a large amount of research done on options. In particular, there is a growing
amount of literature looking at the information content contained within options. Researchers in
this area of study have looked to see if information priced into options is useful in predicting
future stock behavior. This literature review will analyze the research done regarding the
information content of options. This includes looking at the pricing in of future events like
earnings announcements. In addition, this literature review will look at option characteristics that
may help predict the future performance or direction of stock movement.
Keywords: Information content, Options, Stock returns
Submitted under the supervision of Professor Aamir Khan, Carlson School of Management,
University of Minnesota, Spring 2015

2. Introduction
Traders consistently look for opportunities to generate returns above and beyond market
returns. Traders look at different asset classes (including stocks, bonds, and treasuries to name a
few) and strategies to trade the market. Options are one type of security that traders use to bet on
the market to generate positive returns for themselves and/or for investors. In essence, an option
is the right, but not the obligation, to buy or sell some underlying asset at a future date at a
specified price. The most common type of option is a stock option, which gives the owner of the
option the right to buy or sell a stock sometime in the future at a specified price (known as a
strike price). The former is called a call option. The latter is called a put option.
A growing body of literature has looked at the information content contained within
option prices. In essence, because options are valued based on stocks movements and because
option volume is so large, researchers have theorized that option behavior may help predict
future movements in stock prices. Therefore, if studied carefully, a trader may be able to profit
by looking at option patterns that can predict future increases or decreases in stock prices.
This literature review will look at this growing body of research regarding the
information content priced into options. This literature review will focus on two different
relationships. First, this literature review will discuss how inherent characteristics of options (like
open interest and implied volatility) help predict future stock behavior. Second, the literature
review will discuss the relationship between external events and option prices and how options
potentially price in the event prior to the stock doing so, which may mean that options lead future
stock behavior. Through this analysis, a better understanding of the relationship between stock
options and stocks will be developed.

3. Implied Volatility and Stock Predictions
The volatility of an option may play a key factor in predicting future stock behavior. In
essence, many researchers have theorized that the implied volatility contained within options
leads future stock volatility. Initial research done by Chiras and Manaster (1978) saw that the
implied volatility of stock options (measured as implied standard deviation) does a better job in
predicting future volatility of a stock as compared to a stock’s historical volatility. This
evaluation is disputed by Canina and Figlewski (1993), as they state there is no correlation
between implied volatility on S&P 100 index options and the future volatility of the S&P 100.
Therefore, implied volatility by itself may or may not be useful in predicting future volatility.
It is also important to compare implied volatility with other models that may forecast
future volatility. Day and Lewis (1992) compared implied volatility with GARCH (Generalized
Autoregressive Conditional Heteroscedasticity) and EGARCH (Exponential GARCH) models.
They determined that none of the models have a clear advantage in forecasting future volatility,
as implied volatility adds more information to GARCH and EGARCH conditional volatilities in
some circumstances and GARCH and EGARCH models add information to implied volatilities
in other circumstances. However, this may be the result of less mature option markets at the time
of research, according to Mayhew and Stivers (2002). Mayhew and Stivers (2002) conducted an
analysis of implied volatility and compared this to other models that help forecast future
volatility. They found that in active options markets, implied volatility contains more
information (and therefore outperforms) GARCH models and other time-series volatility models.
However, if there is a less actively trade option market (or there is a firm with low option
volume), implied volatility performance deteriorates relative to other time-series models.
Therefore, depending on the situation and circumstance, overall implied volatility may do a

4. better job in modelling the future volatility of stocks as opposed to other models, although this
idea is debated based on the research done in the preceding paragraph.
Implied volatility may also predict return characteristics. Diavatopoulos, Doran, and
Peterson (2008) looked to see whether firm-specific (i.e. idiosyncratic) implied volatility does a
better job in predicting future returns than other measurements of volatility (like historical
volatility). The researchers concluded, based on the information provided, that implied volatility
seemingly does a better job of predicting future returns as compared to other measures of
volatility. Because of this, historical volatility isn’t as important in modelling future returns.
Furthermore, the researchers created three sample portfolios: one that follows the market (i.e.,
buying and holding the S&P 500), one that involves going long high implied volatility stocks and
shorting low implied volatility stocks, and one that involves going long high realized (i.e.
historical) volatility stocks and shorting low realized volatility stocks. Starting with a $10,000
investment in 1996 and holding each portfolio through 2005, the results showed that the implied
volatility strategy nearly tripled in value while the historical volatility strategy did not gain at all.
The market strategy only gained 50% in that time period. Therefore, following an implied
volatility strategy may benefit an investor, as implied volatility may better predict future stock
returns. However, as stated before, the lack of consistency in predicting future volatility may
limit the overall effectiveness of implied volatility.
Option Volume and Open Interest Impact Stock Behavior
Option trading volume may also play a key factor in predicting future stock behavior.
Easley, O’Hara, and Srinivas (1998) showed that informed traders (i.e., traders with private
information) drive information content into options. Furthermore, the researchers saw a stronger
information content effect when there was bad news about a stock as opposed to good news.

5. Coupled with the fact that, at the time, there were limits on short selling, traders may be attracted
to options markets to act on private negative information. Therefore, informed traders, in driving
option volume, may give hints as to where a stock is heading. However, this relationship
between options and stocks is not one-dimensional, as stock price changes lead option volume,
but not vice versa. The researchers attribute this to hedging of different option strategies
Pan and Poteshman (2006) also see option volume leading stock behavior, although both
researchers looked at this idea through the framework of ratios. In this case, the researchers were
interested in seeing whether put-call ratios based on option volume lead stock behavior (i.e.,
dividing put option volume by call option volume for a stock). The researchers determined that
stocks that have low put-call ratios outperform stocks with high put-call ratios by 0.40% over
one day and about one percent over one week. This is primarily driven by informed traders with
nonpublic information trading in the stock market.
Johnson and So (2012) also looked at option volume through the framework of ratios, but
instead chose to focus on the option to stock volume ratio. They state that firms with low option
to stock volume ratios outperform against firms with high option to stock volume ratios by
0.34% per week. This follows a similar pattern seen in the preceding paragraph, although the
return performance is slightly weaker on a weekly basis. However, as was stated in the preceding
paragraph, private information drives the results seen in the research. Johnson and So also add
that this ratio leads outperformance in an environment where short sales are costly and where
leverage is low.
Both options ratios may help predict future stock behavior. However, Blau, Nguyen, and
Whitby (2014) saw that these ratios work best under different timeframes of analysis. In this
case, put-call volume ratios predict future stock returns better when looking at option volume at a

6. daily level. This means that option to volume stock ratios better predict stock returns when
looking at option volume at a weekly or monthly level. When one looks at negative information
in relation to option volume ratios, the relation between the put-call volume ratio and future
stock returns is not very strong. By contrast, the option to stock volume ratio better predicts
future stock returns at all time horizons (daily, weekly, and monthly) when there is negative
information. Therefore, although both predict future stock behavior, the put-call volume ratio
does not appear to be as effective when an individual has a longer time horizon for analyzing
options. This may mean that the option to stock volume ratio is more robust in many situations
for predicting future stock returns.
Like option volume, option open interest may also be useful in helping predict stock
behavior. Bhuyan and Chaudhary (2005) looked at CBOE open interest and compared stock
trading strategies based on open interest with other types of passive and active strategies. The
researchers determined that open interest trading strategies provide better accuracy and overall
better returns than other active and passive trading strategies in United States stock markets. In
addition, by trading open interest, an investor limits risky behavior. This amplifies the value of
trading on open interest. Therefore, beyond price, even open interest seem to be very useful in
providing information about future behavior of equity stocks.
Stock Order Imbalance Predicts Future Stock Returns
Hu (2014) looked at another feature of options: order imbalance. Before diving into this
topic, order imbalance must be defined. Order imbalance means that there is an excess of buy or
sell orders in the market that other buyers and sellers are not able to fill. In essence, it becomes
impossible to match all buy and sell orders between different market participants. This is
especially a problem for market makers that take on the opposite side of an order. If transactions

7. come in to sell options, market makers are usually on the other end to buy the options. However,
it is difficult for market makers to offload options given infrequent option transactions in
general. To reduce risk exposure, most market makers delta hedge, meaning they buy or sell
short shares to offset the risk of holding a naked option position. This translates the order
imbalance over to the stock market, which creates a stock order imbalance.
Hu (2014) was particularly interested in the information content that stock order
imbalances have as a result of market makers dealing with the risk exposure from holding
options in a naked position. When looking at stock order imbalances caused by delta hedging
options, he saw that this stock order imbalance positively predicts future stock returns. This trend
does not reverse in the long run, so in theory stock order imbalances induced by options
permanently predict future stock behavior. This behavior is amplified when there is an active
market for the stock and where there are informed investors trading the stock. Stock order
imbalances caused by things other than delta hedging options do not have this same information
content. Therefore, when looking to predict future stock returns, an investor should also look at
stock order imbalances caused by option trading in an actively traded stock, as this may help
predict future stock returns.
Firm-Specific Events/Announcements and Options
Different types of firm announcements may lead future stock prices. Take earnings
announcements. Billings and Jennings (2011) looked to see how sensitive option prices are to
upcoming earnings announcements. Given that options are useful for trading on private
information, the researchers found that option prices do lead future stock prices when there is a
lot of pre-announcement private information available (including whether or not management
will trade different types of stocks). In addition, option prices are also very sensitive to the

8. number of analysts that follow a stock. According to the researchers, the more analysts there are,
the more information is priced into the option. Finally, the built-in anticipation for the impact of
an earnings announcement on stock prices will be greater if more sophisticated investors trade
the stock. Therefore, firm-specific characteristics and firm-specific private information most
likely drive future stock behavior before an earnings announcement.
Hayunga and Lung (2014) looked deeper into how market analyst consensus factors into
option pricing and anticipation of future stock behavior. When looking at revisions in market
analysts’ consensus recommendations, the researchers saw that options correctly price the
direction of the stock movement three to four days in advance of the revision, regardless as to
whether or not there is a downgrade or upgrade. Research previously done by Doran, Fodor, and
Krieger (2010) confirms this general finding, although their research did not specify how early
the options priced in the market analyst consensus revision. This seems to suggest that options
may have already priced in the recommendation change in advance of the actual event and
therefore, based on the type of announcement, may lead future stock price increases (in the case
of an upgrade) or future stock price decreases (in the case of a downgrade).
Veenman, Hodgson, Van Praag, and Zhang (2011) looked more closely at how
management behavior in exercising options and trading stocks may give an indication of future
stock behavior. The researchers theorized that management may be opportunistic in taking
advantage of private information about the firm. They saw that a stock option exercise coupled
with the liquidation of the shares from the exercise leads weak performance of the stock. Sales of
previously held stocks by management do not predict anything. On the other end, straight stock
purchases by management lead future positive earnings, while the purchase of stocks through an
option exercise does not lead future positive earnings behavior. Therefore, depending on the

9. context and situation, management may give an indication as to where the firm is heading, which
may lead future stock price downtrend and uptrends.
Finally, stock splits should also be looked at to determine their impact on stock behavior.
Chern, Tandon, Yu, and Webb (2008) looked at stocks on the NYSE, AMEX, and NASDAQ to
see how stock split announcements impacted stocks that have options and stocks that do not have
options. In general, the researchers noted that stock split announcements tend to be associated
with positive abnormal returns. However, stocks with options exhibit significantly lower returns
relative to non-option stocks, although there is some dispute as to whether or not this is true for
stocks on the NASDAQ. Regardless, this seems to suggest that options may have already priced
in stock split information in advance of the announcement. Therefore the options may mute
future stock movements up or down, as all information has already been traded on the options
market.
Conclusion
In short, options most likely have information content useful in predicting future stock
behavior. Certain option characteristics do a good job in predicting future stock behavior. In this
case, research seems to show that open interest and stock order imbalance induced by options
trading do a good job in predicting future stock returns. Option volume also does a good job,
although the efficacy of different ratios is debated. Implied volatility seems to be the weaker
option characteristic to use, as other volatility models may do a better job than implied volatility
in predicting future stock behavior.
In addition, options seem to price in events earlier than stocks do, indicating that options
may be a vector for trading on private information. Overall, options seem to do a better job in

10. correctly anticipating future announcements. In addition, management trading strategies also lead
future earnings announcements. Therefore, an investor should also look to management to see if
their trading behavior predicts future movements in stocks that will be caused by future events.
An investor can trade options and stocks based on just option characteristics. He or she
can also trade on future announcements or on management cues. Trading on both, however, will
allow an investor to take advantage of the full breadth of information content that options afford.
Therefore, when trading stocks, an investor should look at options first. Most likely, the options
have already priced in something that will lead stock behavior in the future.

11. References
Bhuyan, R., & Chaudhary, M. (2005). Trading on the information content of open interest:
Evidence from the US equity options market. Derivatives Use, Trading & Regulation,
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198&vid=1&hid=115
Billings, M., & Jennings, R. (2012). The option market’s anticipation of information content
in earnings announcements. Review of Accounting Studies, 16(3), 587-619. Retrieved
from http://link.springer.com.ezp1.lib.umn.edu/article/10.1007/s11142-011-9156-5
Blau, B. M., Nguyen, N., & Whitby, R. J. (2014). The information content of option ratios.
Journal of Banking & Finance, 43, 179-187. doi: 10.1016/j.jbankfin.2014.03.023
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announcements: Do options matter?. Journal of Banking & Finance, 32(6), 930-946.
doi: 10.1016/j.jbankfin.2007.07.008
Chiras, D. P., & Manaster, S. (1978). The information content of option prices and a test of
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12. 10.1016/0304-405X(78)90030-2
Day, T. E., & Lewis, C. M. (1992). Stock market volatility and the information content of
stock index options. Journal of Econometrics, 52(1), 267-287. doi:
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Diavatopoulos, D., Doran, J. S., & Peterson, D. R. (2008). The information content in implied
idiosyncratic volatility and the cross-section of stock returns: Evidence from the option
markets. Journal of Futures Markets, 28(11), 1013-1039. doi: 10.1002/fut.20327
Doran, J. S., Fodor, A., & Krieger, K. (2010). Option market efficiency and analyst
recommendations. Journal of Business Finance & Accounting, 37(5-6), 560-590. doi:
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13. Johnson, T. L., & So, E. C. (2012). The option to stock volume ratio and future returns. Journal
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option exercises: Relative information and incentives to manage earnings. Journal of
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5957.2011.02239.x

14. Methodology of “Trading in the Options Market around
Financial Analysts’ Consensus Revision”
By: Steven Kislenko
Hayunga and Lung (2014) looked to see how options behave in anticipation of financial
analysts’ consensus recommendations and revisions. In order to do this, the researchers used an
event study design. In essence, the researchers were looking to collect data on options based
around an event, which in this case is the consensus recommendation or revision. From this, the
researchers hoped to understand how options act before and during the event to see whether or
not options contain information about these consensus revisions in advance of the actual event.
Hayunga and Lung (2014) used four dependent variables to understand this mechanism.
First is option-implied stock price and abnormal return. Starting with the put-call parity equation
to calculate stock price, the researchers added in a factor for early exercise of calls and puts to
the put-call parity equation to come up with an option-implied stock price based on American
options. They then compared the option-implied return with the benchmark return of the stock
through a linear regression. The benchmark is calculated based on the average return seen 11 to
150 days before the event. They also controlled for the moneyness of the option and the maturity
of the option, as they do include delta and maturity as factors in their regression as part of the
sensitivity analysis. Finally, the benchmark established in the linear regression above was
subtracted from the observed stock price to get the abnormal option return.
The second dependent variable is volatility spread (Hayunga & Lung, 2014). The
volatility spread is the average difference between the implied volatility of a pair of put and call

15. options at time t. In other words, the researchers first subtract the implied volatility of a put
option from the implied volatility of a call option in a put-call pair. The researchers then multiply
this difference by the open interest of the put-call pair. The researchers calculate this for several
put-call pairs. They then sum these calculations together to come up with the volatility spread.
The average volatility spread is then calculated based on the average spread seen 11 to 150 days
prior to the event. The abnormal volatility spread becomes the difference between the average
volatility spread and the volatility seen in the period under research.
The third dependent variable is volatility skewness (Hayunga & Lung, 2014). Volatility
skewness is defined as the difference between the implied volatility of an out-of-the-money put
option and the implied volatility of an at-the-money call option. Restated another way, it is the
implied volatility of an out-of-the-money put option minus the implied volatility of an at-the-
money call option. An out-of-the-money put option has a delta between -0.125 and -0.375, while
an at-the-money call option has a delta between 0.375 and 0.675. The abnormal volatility
skewness is then determined by comparing the average volatility skewness seen 11 to 150 days
prior to the recommendation/revision to the volatility skewness seen in the period under research.
The last dependent variable is option volume. In this case, Hayunga and Lung (2014)
looked to the option-to-stock volume ratio. According to the researchers, Roll, Schwartz, and
Subrahmanyam (2010) saw that post-announcement returns are positively correlated with the
pre-announcement option-to-stock volume ratio. Because of this, Hayunga and Lung believe that
the ratio would be useful in analyzing option behavior before consensus recommendations or
revisions. To understand this behavior, the average option-to-stock volume ratio is calculated
based on the option-to-stock volume ratios seen 11 to 150 days prior to the

16. recommendation/revision. These average ratios are then compared to the ratios seen in the period
under research.
To collect all the data necessary, Hayunga and Lung (2014) got upgrade and downgrade
information from the Institutional Brokers’ Estimate System (IBES). They got options data from
OptionMetrics. Finally, they got stock prices and accounting data from the Center for Research
in Security Prices (CRSP). Option prices are considered to be the midpoint of the bid and ask
prices.
The researchers then cleaned the data by removing several types of options, including
options with zero open interest and call or put options with no corresponding put or call option,
respectively, that has the same time to maturity and strike price. The researchers then found the
average implied stock price for a pair of options. Next, the researchers cleaned the data regarding
the analysts’ recommendation/revisions so there were no duplicate recommendations and so that
the recommendations in the data analysis didn’t display typical analyst behavior like
piggybacking or herding. Finally, if other events occur within three days of the consensus
recommendation revision, the revision events were removed from the analysis. This created a
sample of about 12,000 revisions to look at. They then also controlled for firm characteristics
and revision magnitude.