Options and earnings announcements: an empirical study of volatility,trading volume,open interest and liquidity

{Journals}eufm/6_2/t189/makeup/t189.3d
European Financial Management, Vol. 6, No. 2, 2000, 149±171
Options and earnings announcements:
an empirical study of volatility, trading
volume, open interest and liquidity
Monique W. M. Donders
MeesPierson Derivatives, Rokin 55, 1000 AE Amsterdam, The Netherlands,
e-mail: monique.donders@meespierson.com
Roy Kouwenberg
Econometric Institute, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam,
The Netherlands,
e-mail: kouwenberg@few.eur.nl
and Ton C. F. Vorst
Department of Finance and Erasmus Center for Financial Research, Erasmus University
Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands,
e-mail: vorst@few.eur.nl
Abstract
In this paper we study the impact of earnings announcements on implied volatility,
trading volume, open interest and spreads in the stock options market. We find that
implied volatility increases before announcement days and drops afterwards. Also
option trading volume is higher around announcement days. During the days before
the announcement open interest tends to increase, while it returns to regular levels
afterwards. Changes in the quoted spread largely respond to higher trading volume
and changes in implied volatility. The effective spread increases on the event day and
on the first two days following the earnings announcement.
Keywords: earnings announcements; volatility; volume; spreads; open interest.
JEL classification: G13, G14.
1. Introduction
Many papers in both finance and accounting literature study stock price and trading
volume reactions to earnings announcements. Of particular interest are the earnings
# Blackwell Publishers Ltd 2000, 108 Cowley Road, Oxford OX4 1JF, UK and 350 Main Street, Malden, MA 02148, USA.

Corresponding author: Ton C.F. Vorst, Erasmus Center for Financial Research, Erasmus
University Rotterdam, PO Box 1738, 3000 DR Rotterdam, The Netherlands.

announcement studies by Ball and Brown (1968), Beaver (1968), Jones and
Litzenberger (1970), Foster (1977), Morse (1981), Verrecchia (1981), Cready (1988),
Ball and Kothari (1991) and Lee (1992), among others. Presuming there exists a
positive relationship between expected earnings and the market value of the share of a
firm, an unanticipated change in the reported earnings of a firm should be
accompanied by a change in the stock price. Apart from a price reaction, there may
be a volume effect. If investors have heterogeneous beliefs, receive slightly different
information, interpret identical information differently or if information arrives
sequentially to investors, higher levels of trading volume around information releases
such as earnings announcements are likely. Much less attention has been paid to
reactions to these information releases in the options market. Exceptions are Whaley
and Cheung (1982) in a paper on the efficiency of the CBOE around earnings
announcements, Schachter (1988) on the open interest in stock options, Philbrick and
Stephan (1993) and Amin and Lee (1994) on volume effects and Patell and Wolfson
(1981, 1984) and Donders and Vorst (1996) on implied volatilities.
Reactions in the option market may be more informative about information
processing for several reasons. Black (1975) was the first to suggest that the higher
leverage available in the options market might induce informed traders to transact
in options rather than in stocks. Since a 1% change in the stock price will induce at
least a 1% change in the option price, any price reaction to earnings announcements
in the stock will imply a more pronounced relative price change in the option.
For speculators, this leverage effect makes options more attractive than stocks.
Furthermore, the options market seems to be quicker in incorporating new
information into prices (e.g. Manaster and Rendleman (1982), Bhattacharya (1987),
Anthony (1988), Stephan and Whaley (1990), Chan, Chung and Johnson (1993),
Easley, O'Hara and Srinivas (1993) and DeJong and Donders (1997)). In addition to
the change in the stock price, speculators might be willing to place a bet on changes in
the (implied) volatility of the underlying asset, which is an important determinant of
the option price.
Furthermore, since open interest in options is endogeneous, as opposed to the
number of outstanding shares, changes in open interest may provide new insights
about information processing. Finally, trading options may overcome restrictions on
selling short stocks. This makes trading volume in both put and call options possibly
more informative than trading volume in stocks.
The study documented in this paper is the first in the literature that combines data
on implied volatility, trading volume, open interest and liquidity in options markets to
assess information processing around earnings announcements.
We expect to see the following effects. Because of the leverage effect in options and
short selling restrictions in the underlying stock, excess trading volume in options will
be higher than that in stocks, especially if the earnings information is interpreted
negatively. Open interest will increase before and decrease after the announcement
day, indicating that investors place delta- and vega bets before the information is
released and unwind their positions afterwards. We assume that investors buy, rather
than sell, options to anticipate on subsequent information releases. This excess
demand will cause option prices, and thus implied volatilities, to be higher than can be
explained by a simple theoretical model. After the information release, volatilities will
return to normal levels. Furthermore, both quoted and effective spreads will be higher
than normal during the announcement period to indicate the effect of asymmetric
information and higher transaction costs. Finally, since the number of private
150 Monique W. M. Donders, Roy Kouwenberg and Ton C. F. Vorst
# Blackwell Publishers Ltd, 2000

investors on the Amsterdam market is much higher than that on the Anglo-Saxon
markets, we expect slower information processing and more overreaction to news.
Most of these hypotheses are confirmed by the data in this study.
The outline of this paper is as follows. Section 2 contains a description of the data.
In section 3 through 6 we provide descriptions of the models and discuss the results
of the empirical analysis for implied volatility, trading volume, open interest and
liquidity respectively. Conclusions are in section 7.
2. Data
2.1. Announcements
This study covers the period 18 June 1991 through 30 December 1993, or 645 trading
days. Daily trading volume, open interest and quoted bid and ask prices for American
type call and put stock options traded on the AEX Options Exchange were generously
provided by the exchange. The sample consists of 40 firms with listed options, which
amounted a total of 903,190 records. We collected 190 quarterly, semi-annual and
annual earnings announcements from Beursplein 5 (a weekly information bulletin of the
Amsterdam Stock Exchange) and Financieele Dagblad (the major Dutch financial
newspaper). The earnings announcement dates are known well in advance, ranging
from at least 4 weeks to as long as 6 months. Dissemination of earnings news may occur
before, during or after trading hours. We categorize announcements made when the
exchange is closed as arriving immediately before opening the next trading day.
Information released during trading hours is assumed to be incorporated in trading
volume and closing prices of that day. Actual dividend amounts and ex-dividend dates,
needed to calculate implied volatilities, were obtained from the Officieele Prijscourant.
Since we expect any patterns in volume, open interest and spreads to be most
pronounced in options with a short time to maturity, we restrict our analysis to the
nearby option series with a time to maturity of at least 10 calendar days during the
complete event period. Options with a very short time to expiration are excluded from
the sample because the out-of-the-money series may have very low prices and
corresponding high spreads due to the existence of a minimum tick size of Dfl0.10.
2.2. Implied volatilities
For every day and each option class we follow Beckers (1981) by selecting only the call
and the put option with the shortest time to maturity (exceeding 10 calendar days) and
the strike closest to the forward price of the stock. We use the (present value of)
actual, not expected, dividends paid over the remaining lifetime of the option and
build a tree on the basis of the stockprice minus the present value of the dividends as
suggested in Hull (1997). Our binomial tree has at least a hundred time steps and a
bisection method to calculate implied volatilities for call and put options. For every
day and each option series, the interest rate used in the tree is the Amsterdam
Interbank Offered Rate (AIBOR) that matches the maturity of the option.
2.3. Event period
In this paper we study the 11-day period surrounding an earnings announcement.
The pre-event period is the 5-day period leading up to the information release
Options and Earnings Announcements 151

(days 5 1), the event day (day 0) is the day on which the new information is
available and the post-event period is the 5-day period following the dissemination of
earnings news (days ‡1 ‡5). We define dummy variables Di, i ˆ 5 ‡5 for all
days in the event period. The control period for each stock consists of all trading days
not included in the event period(s) for this stock.
2.4. Early exercise and expiration days
Call options on a dividend paying stock may be exercised early just before the ex-
dividend date. On the AEX Options Exchange, these early exercises decrease the open
interest in a series but are not recorded as a trade. Since these changes in open interest
are not related to the impact of earnings information releases, we delete the 5 trading
days surrounding each dividend payment in the analysis of changes in open interest.
For similar reasons, we exclude the 5 trading days surrounding each expiration in the
trading volume analysis. Option traders typically use these days to roll over expiring
contracts to the second nearby contract, resulting in large trading volume. These
reported high levels of trading are not related to expected volume on non-
announcement days, however.
2.5. Excess volatility, trading volume, open interest and spreads
The cross-sectional analysis of volatilities, volume, open interest and spreads around
earnings announcements requires a normalization of the variables over stocks with
very different characteristics and an adjustment for the effects of phenomena not
associated with the earnings information dissemination. Therefore, we construct series
of abnormal stock return volatilities (AVit) and call and put option implied volatilities
(AIVCit and AIVPit) as volatilities in excess of their control period average. The
construction of all variables is described in Appendix 1. We define abnormal trading
volumes for stocks (AVSit) and call and put options (AVCit and AVPit) and excess
quoted and effective spreads in a similar fashion.
The construction of a well-behaved time series for open interest is slightly more
complicated because of two empirical characteristics of the variable. First, open
interest tends to increase with the age of the contract, i.e. open interest is higher when
expiration is close. In order to avoid spurious correlations caused by this seasonality,
we differentiate the time series and exclude the trading day immediately following each
expiration. Second, the average daily change in option open interest is close to zero for
several firms in our sample. Dividing the observed daily change by the average daily
change in the control period, as we do for trading volume, results in non-stationary
time series for open interest. Therefore, we scale the daily change in open interest by
the daily trading volume for the options in the control period.
2.6. Good and bad news
We can classify news as good or bad only in relation to prior investor expectations. In
the absence of a reliable proxy for market consensus estimates for the earnings
of stocks traded on the Amsterdam Stock Exchange, we make the simplifying
assumption that the stock market is (weak-form) efficient. In this case, all relevant
information is incorporated into the stock price and we define news as good (neutral,
bad) if an announcement is being followed by a positive (zero, negative) cumulative

abnormal return on the stock. Since, we are interested in option price reactions as
determined by, e.g. changes in implied volatility, this classification does not create a
correlation with the endogenous variables. The cumulative abnormal return for firm i
is calculated over the 2 days including and following the announcement day and is
defined as:
CARi ˆ
X
1
t ˆ 0
(Rit RMt ) (1)
with Rit and RMt the returns on day t relative to the announcement for firm i and the
market index M respectively. As a market index, we use the AEX-index, which is a
market-cap weighted sum of prices of the 25 stocks with the highest trading volume on
the Amsterdam Stock Exchange in the previous 3 years.
We compute abnormal returns over the 2 consecutive days to take into account the
possibly slower information processing in the stock market. That this way to
characterize good and bad news is reasonable, is confirmed by the results in Amin and
Lee (1994) who show that, when announcements are partitioned on the basis of the
actual stock price change instead of Value-Line forecasts, there is a larger difference
between the good and the bad news samples. It is also consistent with Epps (1975) who
derives a model in which volume on transactions in which the price ticks up is greater
than volume on downticks.
We define bad news dummy variables BADi, i ˆ 5 1 for all days in the pre-
event period. Using the cumulative abnormal return measure, 86 (104) announce-
ments classify as bad (good) news.
3 Implied volatilities
3.1. Theory and tests
For many option pricing models, the implied volatility in option prices is
approximately equal to the average expected stock return volatility until the
expiration date. Merton (1973) shows this is true for the Black-Scholes (1973) option
pricing model with volatility that changes deterministically over time while Heynen,
Kemna and Vorst (1994) show the same result holds for the stochastic volatility model
of Hull and White (1987) and the (E)GARCH model of Duan (1995).1
We assume, for simplicity, that stock return volatility is constant (2
normal) over time
except for the scheduled news announcement days on which volatility is higher
(2
high).2
If T is the number of days in the remaining lifetime of the option, then the
implied volatility IVT before the information release is equal to:
IVT ˆ
(T 1)
T
2
normal ‡
1
T
2
high
0
@
1
A
u
u
u
t (2)
1
For a more elaborate discussion of these models we refer to Donders and Vorst (1996).
2
This assumption seems very restrictive. Donders and Vorst (1996), however, find
approximately the same results if a GARCH specification with a structural break in the
volatility on the announcement day is used.

If there are no other scheduled information releases before the maturity of the
option, the average volatility drops to 2
normal after the announcement. To test this
model, we estimate the following models for stock volatility AVit and call and put
option implied volatility, AIVCit and AIVPit respectively:
AVit ˆ 0 ‡
X
5
k ˆ 5

i Di ‡
X
0
k ˆ 5

k BADk ‡ it (3a)
AIVCit ˆ 0 ‡
X
5
k ˆ 5

i Di ‡
X
0
k ˆ 5

k BADk ‡ it (3b)
AIVPit ˆ 0 ‡
X
5
k ˆ 5

i Di ‡
X
0
k ˆ 5

k BADk ‡ it (3c)
If there is no trading on information prior to the event and if information is
processed quickly, coefficients for all dummy variables except D0 should not differ
from zero significantly for stock return volatility. Consistently, for implied volatility,
we expect the coefficients for the dummy variables D 5 through D 1 to be significantly
positive and upward sloping. In the model described above, coefficients for dummies
D0 through D‡5 should not differ significantly from zero.
If there are restrictions on the short selling of stocks, the (absolute) price reaction to
bad news may be less pronounced than that to good news. In this case, we expect the
coefficients for the bad news dummies to be negative.
3.2. Results
Results for stock return standard deviations and call and put option implied
volatilities are in Table 1.3
We find that stock returns are significantly more volatile
than normal from day 2 through day ‡2. On the event day itself, stock return
volatility is approximately 30% point higher than on normal days. Higher volatilities
in the pre-event period may indicate that some investors bring private information to
the market while the 14% higher volatility on day ‡1 indicates that information
processing is slow. The negative coefficients for the bad news dummies can be
interpreted as evidence for restrictions on selling short stock.
Significantly positive and increasing values for the pre-event dummy coefficients in
columns 2 and 4 agree with the simple model of implied volatility stated above. We
also find a significant but lower coefficient for the event-day dummy. This last result
differs from Donders and Vorst (1996), but is consistent with the higher stock return
volatility on day ‡1. This difference can be explained by the larger dataset that is used
in this study, which also includes smaller and less liquidly traded stocks. In the post-
event period implied volatility declines to normal levels. This return to normal levels is
3
The Durbin-Watson statistic of this model indicates that the error terms are positively serially
correlated. Therefore, standard errors of the estimated coefficients are too large and t-statistics
are conservative. Although an AR(2) model for the error terms removes this problem, it will
lead to biased estimates of the coefficients. We consider this a more serious problem than
conservative t-values and we therefore do not include the autoregressive terms in the model.

slower than in Patell and Wolfson (1981), probably due to the larger number of
private (as opposed to institutional) investors.
Figure 1 shows the average level (over 190 events) of annualized daily stock return
volatilities4
over the event period. In Figure 2 we compare implied (or expected)
volatilities to subsequently realized volatilities. The realized volatility is defined as the
standard deviation of the stock returns over the remaining lifetime of the option. The
solid line denotes realized volatility while the two dotted lines are for call and put
option implied volatilities. Figure 2 shows that both call and put implied volatility
overestimate the subsequently realized stock return volatility around earnings
Table 1
Stock return volatility and option implied volatility around earnings announcements
This table contains estimated coefficients and t-values for the models in (3a) through (3c) for the
period June 1991 through December 1993. The number of events is 190 for 40 firms. The event
period is defined as the 11 days surrounding the earnings announcement day. Coefficients are
estimated using a pooled regression technique where time series for the individual firms were
stacked. Bad news is defined as an announcement with negative cumulative abnormal stock
returns over days 0 and ‡1. 86 (104) events classify as bad (good) news.
Stocks t-value Calls t-value Puts t-value
Constant 0.000 0.00 0.0000 0.00 0.0000 0.00
D 5 0.0150 0.79 0.0220 3.09 0.0175 2.42
D 4 0.0103 0.55 0.0212 2.97 0.0249 3.45
D 3 0.0369 1.95 0.0416 5.84 0.0415 5.73
D 2 0.0494 2.61 0.0488 6.85 0.0444 6.14
D 1 0.0839 4.44 0.0544 7.64 0.0499 6.89
D0 0.2895 15.32 0.0244 3.42 0.0232 3.21
D‡1 0.1448 10.64 0.0013 0.25 0.0074 1.41
D‡2 0.0349 2.56 0.0055 1.07 0.0041 0.80
D‡3 0.0241 1.77 0.0068 1.33 0.0086 1.65
D‡4 0.0083 0.61 0.0009 0.19 0.0035 0.67
D‡5 0.0153 1.12 0.0009 0.18 0.0006 0.12
BAD 5 0.0195 0.72 0.0183 1.79 0.0046 0.44
BAD 4 0.0074 0.27 0.0066 0.67 0.0096 0.92
BAD 3 0.0346 1.28 0.0201 1.97 0.0166 1.60
BAD 2 0.0411 1.51 0.0261 2.56 0.0140 1.35
BAD 1 0.0655 2.42 0.0277 2.71 0.0172 1.66
BAD0 0.1121 4.14 0.0177 1.74 0.0071 0.68
4
Daily stock return volatility is defined as the annualized absolute stock return on that day:
Vit ˆ j Rit j

252
p
; Rit ˆ ln
Pit
Pit 1
0
@
1
A

announcements. This effect is most pronounced 1 day before and 3 days after
the earnings announcement. Statistical analysis5
shows that call and put implied
volatility are also significantly higher than realized volatility in the control period:
‡0.41% (calls) and ‡2.15% (puts) respectively. The event dummies for the day prior
to the announcement ( 1) and the day 3 days afterwards (‡3) are significantly
positive. The analysis shows that on average options tend to be too expensive,
specially put options, compared with their replication price in frictionless markets.
Moreover, around earnings announcements the overestimation of option prices
increases. The results indicate a tendency of slow information processing combined
10.00
20.00
30.00
40.00
50.00
-5 -4 -3 -2 -1 0 1 2 3 4 5
days relative to announcement
stock return daily volatility
Fig. 1. Stock return daily volatility around earnings announcements
20.00
22.00
24.00
26.00
28.00
30.00
-5 -4 -3 -2 -1 0 1 2 3 4 5
days relative to announcement
realized
and
implied
volatility
stock return realized volatility
call implied volatility
put implied volatility
Fig. 2. Realized and implied volatilities around earnings announcements
5
Regression results available from the authors on request.

with a preference for long positions in the option market (high buying pressure). These
effects could be explained by the dominant influence of private investors on the
Amsterdam market.
4. Trading volume
In addition to the numerous papers that link information processing to price changes,
there are several event studies that use trading volume to determine whether an event
has informational content, see e.g. Beaver (1968), Foster (1973), Morse (1981),
Bamber (1986), Karpoff (1986) Ajinkya and Jain (1989), Grundy and McNichols
(1989), Kim and Verrecchia (1991a, 1991b), Cready and Mynatt (1991) and Harris and
Raviv (1993). Although most papers focus on transaction volume in common stock,
the models apply equally well to other financial instruments whose prices are
determined by the value of the firm, such as stock options.
Beaver (1968) is the first to suggest using volume to test investors' reactions to the
release of information. He argues that volume, in conjunction with price changes,
reflects two things: a lack of consensus, or agreement, about how a newly disclosed
piece of (public) information should be interpreted and the extent to which that
information changes individual investor expectations. As such, trading volume reflects
the sum of differences in traders' reactions while the change in the price reflects only
the marginal reaction. In Karpoff's (1986) model there are two distinct ways in which
informational events affect trading volume. First, investor disagreement leads to
increased trading. Second, trading volume will also be higher if investors interpret
information identically but have divergent prior expectations. In a market with non-
zero transaction costs, volume increases caused by an informational event persist after
the event period. Harris and Raviv (1993) assume that traders share common prior
beliefs and receive common information but differ in the way they interpret this
information. There are two types of speculative risk-neutral traders in the model. The
responsive and unresponsive groups agree on whether a given piece of information is
favourable or unfavourable but they disagree on the extent to which the information is
important. In this model, trading volume occurs when, and only when, cumulative
information switches from favourable to unfavourable or vice versa. One of the results
of the model is that volume shows positive autocorrelation.
While in most models trading volume is assumed to increase with the degree of
asymmetric information, George, Kaul and Nimalendran (1994) show that trading
can be negatively related to the degree of information asymmetry. In their model,
disagreement arises from private information. In the absence of transaction costs,
anything that increases disagreement among traders in equilibrium creates volume.
However, in the model, private information also implies an adverse selection problem
that increases transaction costs and decreases the specialist's willingness to trade.
Whether volume increases or decreases with an increase in information asymmetry
depends on whether liquidity trading decreases in transaction costs at an increasing or
decreasing rate.
In our empirical analysis of trading volume in stocks (AVSit) and options (AVCit
and AVPit) around earnings announcements, we use a regression model with dummy
variables for the event period similar to that in section 3.1. In the model for options
we include two additional explanatory variables. First, contemporaneous trading

volume in stocks corrects option volume for increased hedging activity in the
announcement period. Second, as Patell and Wolfson (1979) and Donders and Vorst
(1996) provide evidence of higher absolute values of stock returns in announcement
days and corresponding patterns in implied volatilities from option prices in the pre-
and post announcement period, we use the abnormal level of implied volatility to
correct for any volatility bets in the options market. Specifically, we estimate:
AVSit ˆ 0 ‡
X
5
k ˆ 5

i Di ‡
X
0
k ˆ 5

k BADk ‡ it (4a)
AVCit ˆ 0 ‡ 1ASVit ‡ 2AIVCit ‡
X
0
k ˆ 5

i Di ‡
X
0
k ˆ 5

k BADk ‡ it (4b)
AVPit ˆ 0 ‡ 1ASVit ‡ 2AIVPit ‡
X
0
k ˆ 5

i Di ‡
X
0
k ˆ 5

k BADk ‡ it (4c)
with AVSit, AVCit and AVPit abnormal levels of trading volume in stocks and call and
put options, respectively. If there is no trading on private information in the pre-event
period, we expect the coefficients for D 5 through D 1 to be zero. The coefficient for
variable D0 should be large and positive while the Harris and Raviv (1993) model also
predicts positive coefficients for the post-event dummies. Short selling restrictions may
reduce trading volume in stocks, not necessarily in (put) options.
4.2. Results
Daily abnormal trading volumes in the market for the underlying stocks as well as
in call and put options are reported in Table 2. The results in the first column of the
table show that trading volume in stocks is higher than normal only on days 1 to
‡2 (and day ‡5) and peaks at the announcement day itself. This finding is consistent
with earlier studies, e.g. Bamber (1986), Lee (1992) and Amin and Lee (1994). A
significantly positive AR(1) coefficient (not reported here) is consistent with the
model in Harris and Raviv (1993). On the average, volume in the stock market is
155% higher than during the control period at this day. Negative, although not
significant, coefficients for the bad news dummies in the days leading up to and
including the event day may signal restrictions on the short selling of stocks for the
general public.
Columns three and five give the abnormal volume reaction to earnings news in
call and put options, respectively. The significance of the stock volume coefficient
indicates that trading across the option and equity markets is contemporaneously
correlated and that stock volume accounts for a significant portion of the daily
variations in option market volume. Controlling for the increase in stock volume and
changes in implied volatility we find that abnormal trading volume for call and put
options reaches a maximum level on the event day itself.6
The higher coefficients of
the day 0 dummy for options than for stocks support the hypothesis that options
6
Excluding the two extra explaining variables does not change our results significantly.

markets provide traders with an alternative means to respond to earnings
announcements.
The most striking difference between stock and option volume reactions occurs in
the days leading up to the information release. For options, we find increased levels of
trading at least 4 days before the announcement. This finding is consistent with
Anthony (1988) and Amin and Lee (1994) who find that option trading volume
increases 3±10 days before earnings announcements.
The different patterns in call and put trading volume may indicate that informed
traders prefer put options as a vehicle of response to earnings news. This intuition is
confirmed by the differences between the magnitude and significance of the implied
volatility coefficients for calls and puts. These results show that investors place
volatility bets by buying or selling put options rather than calls.
Table 2
Stock and option volume around earnings announcements.
This table contains estimated coefficients and t-values for the models in (4a) through (4c) for the
period June 1991 through December 1993. Expiration days are excluded, reducing the number
of announcements from 190 to 176. Since we found no evidence of significant autocorrelation in
excess trading volume at any reasonable lag for either stocks or options in time series
regressions for each individual firm, all time series are stacked. Excess trading volume in options
is corrected for contemporaneous excess stock volume and excess implied volatility.
Stocks t-value Calls t-value Puts t-value
Constant 0.0000 0.00 0.0007 0.05 0.0690 3.69
ASVit 0.3200 34.37 0.2201 17.48
AIVCit, AIVPit 0.0885 1.83 0.8496 13.80
D 5 0.0026 0.01 0.1184 0.62 0.0112 0.04
D 4 0.0324 0.21 0.5552 2.90 0.5686 2.20
D 3 0.0879 0.58 0.4154 2.17 0.8175 3.16
D 2 0.1070 0.72 0.5166 2.71 1.0294 4.00
D 1 0.3789 2.61 1.0579 5.68 2.0361 8.10
D0 1.5512 10.81 2.0320 11.03 2.1509 8.64
D‡1 0.9963 8.59 1.1376 8.60 1.3215 7.37
D‡2 0.3080 2.98 0.2226 1.68 0.4284 2.40
D‡3 0.1561 1.51 0.2175 1.64 0.2767 1.54
D‡4 0.1581 1.51 0.1911 1.43 0.2502 1.38
D‡5 0.2719 2.60 0.4040 3.02 0.2040 1.13
BAD 5 0.1249 0.60
BAD 4 0.2519 1.20
BAD 3 0.1710 0.81
BAD 2 0.0836 0.39
BAD 1 0.0365 0.17
BAD0 0.3186 1.53

5. Open interest
Although trading volume in the options market may be informative about the price
discovery process in financial markets, volume in itself gives no indication of the
direction of the transactions. As mentioned before, the number of option contracts,
as opposed to the number of stocks outstanding, is endogenous. Therefore,
changes in open interest may provide additional evidence with respect to the
informational role of options. Under the general assumption of higher uncertainty
about the future value of the stock, both hedgers and speculators may seek to
increase their exposure to the options market. Hedgers may have an increased
interest in limiting the downside risk of their stock portfolios while speculators will
enlarge option positions to place a bet on either a relatively large upward or
downward movement in the stock price or changes in the volatilities implied in
option prices. Therefore, we expect open interest to be higher in the pre-
announcement period than in the control period. Schachter (1988) finds just the
opposite, namely that open interest declines prior to earnings announcements,
especially for contracts with high vegas. He gives no theoretical explanation for
this intuitively unappealing result. However Wilson (1997) shows this decline in
open interest around earnings announcements is due to the coincidental timing of
contract expirations. Adjusting for these expirations, open interest in fact increases
around earnings releases.
To study patterns in changes in open interest around earnings announcements, we
apply a regression model similar to that in section 3.1 where dummy variables are
assumed to pick up any abnormal changes in open interest in the days surrounding the
earnings information release. We estimate:
AOICit ˆ 0 ‡
X
5
k ˆ 5

i Di ‡
X
0
k ˆ 5

k BADk ‡ it (5a)
AOIPit ˆ 0 ‡
X
5
k ˆ 5

i Di ‡
X
0
k ˆ 5

k BADk ‡ it (5b)
with AOICit and AOIPit relative changes in open interest in call and put options
respectively. If investors make either price or volatility bets before an announcement,
we expect coefficients for D 5 through D 1 to be positive. In the post-event period
investors will close their positions and open interest will decline to normal levels,
resulting in negative coefficients for dummies D0 through D‡5.
5.2. Results
Table 3 contains results for changes in open interest in the eleven days surrounding
the earnings information release. Changes in the number of option contracts in
existence provide additional evidence for the hypothesis that investors use the
options market to trade on information and place volatility bets. Increases in the
open interest of both call and put options are significantly larger than normal in
the pre-announcement period. The estimated coefficients for the dummy variables in
this period show that open interest is at its maximum level on the event day.

Negative changes in open interest in the post announcement period indicate that
investors close option positions. We find no evidence of differences in the behaviour
of open interest before good and bad news. The significantly lower trading volume in
put options on day 1 in case of bad news seems to be proportional to the change in
open interest.
Although our finding of increases (decreases) in the open interest before (after)
earnings announcements are intuitively appealing, they clearly contradict Schachter's
(1988) results. But as remarked before his results might be due to coincidental timing
of option expirations.
6. Liquidity and transaction costs
In the literature on market micro structure there are several theories that explain the
bid-ask spread. Stoll (1989) distinguishes three cost components: order processing
Table 3
Change in option open interest around earnings announcements.
This table contains estimated coefficients and t-values for the models in (5a) and (5b) for the
period June 1991 through December 1993. Expiration days and ex-dividend days are excluded,
reducing the number of announcements from 190 to 173. We found no evidence of significant
autocorrelation in the time series for the individual firms, nor did we find any significant cross
correlations between the 40 firms in the sample.
Calls t-value Puts t-value
Constant 0.1410 17.20 0.1026 8.76
D 5 0.0690 0.64 0.1720 1.10
D 4 0.1030 0.96 0.3620 2.40
D 3 0.2078 1.88 0.3360 2.28
D 2 0.3051 2.76 0.3561 2.40
D 1 0.2871 2.55 0.3516 2.25
D0 0.0661 0.58 0.3053 1.94
D‡1 0.0730 0.88 0.1747 1.56
D‡2 0.1685 1.97 0.4338 3.75
D‡3 0.4177 4.69 0.3192 2.55
D‡4 0.0539 0.62 0.0278 0.23
D‡5 0.0384 0.49 0.0265 0.24
BAD 5 0.0056 0.03 0.0060 0.02
BAD 4 0.0102 0.06 0.0802 0.37
BAD 3 0.0929 0.59 0.0792 0.37
BAD 2 0.1716 1.08 0.0385 0.18
BAD 1 0.0451 0.27 0.0198 0.08
BAD0 0.0211 0.12 0.1682 0.74

costs, inventory control costs and adverse selection costs. The order processing costs
represent a fee charged by market makers for standing ready to match buy and sell
orders. Inventory holding costs (Ho and Stoll, 1981, 1983) compensate dealers for
holding non-optimal portfolios. As a supplier of immediacy, the dealer is frequently
obliged to assume an inventory that differs from his or her expected-utility
maximizing optimal portfolio. Since the dealer is risk-averse, he will demand a
premium for any such trade. The third type of cost arises in the presence of
asymmetric information between the market maker and his potential counterparties in
trading, see for example Bagehot (1971), Glosten and Milgrom (1985) and Kyle
(1985). The market maker charges a fee on every transaction so that the expected loss
from trading with informed traders is compensated with expected profits from trading
with liquidity or `noise' traders.
There are two alternative theories for market liquidity around earnings announce-
ments depending on the way public disclosures are characterized (Kim and
Verrecchia, 1994). In the first theory it is assumed that some market participants
process earnings announcements into private information about the firm's perfor-
mance at some cost. These informed judgements create or increase information
asymmetry that causes the market makers to set wider quotes, making the market less
liquid.7
This theory predicts higher quoted spreads immediately after earnings
announcements.
The second theory hypothesizes an economy in which there exist informed traders
who are endowed with superior knowledge of the performance of the security. One
interpretation of this approach is that there exist shareholders affiliated with a firm
who have superior information based on their affiliation. Market makers know that
these traders exist and that they will come to the market immediately before the firm
releases the earnings information. In this period of greatest information asymmetry,
market makers will quote wider bid-ask spreads. The public disclosure of the
information ameliorates the adverse selection problem by partially, or fully, revealing
to market makers information known by traders. Consequently, market makers will
lower the spread after the announcement is made and the information asymmetry is
reduced.
Evidence in empirical studies of changes in the bid-ask spread around earnings
announcements is inconclusive. Venkatesh and Chiang (1986) find virtually no
increase in the quoted spread for earnings or dividend announcements that were not
preceded by another announcement in the prior 30 days. They concluded that, on
average, there is normal information asymmetry before announcements. In an
intraday analysis of the stock market, Lee, Mucklow and Ready (1993) show that, for
a sample of NYSE stocks, both quoted and effective spreads widen immediately
before earnings news. That the effect is most pronounced for announcements with
larger subsequent price changes is consistent with the asymmetric information model.
After controlling for volume, spreads are not abnormally high in the days following
earnings announcements.
7
Under some additional assumptions, trading volume in these illiquid markets can still be
higher since volume is driven up by informed traders at the time of the announcement. This
model is therefore not necessarily incompatible with the model for trading volume in section
4.

The effective or realized spread can be interpreted as the (ex post) difference
between the price at which an investor can buy and subsequently sell (or vice versa).
Both asymmetric information and inventory control theories predict that the realized
spread for a round trip transaction is smaller than the quoted spread because every
transaction has an impact on prices quoted by the dealer. Under the inventory cost
model this is because the dealer lowers (raises) both bid and ask prices after a dealer
purchase (sale) in order to induce transactions that will equilibrate inventory. Under
the adverse information cost model bid and ask prices are changed in a similar way to
reflect the information conveyed by transactions. Amin and Lee (1994) find a slight
increase in the effective spread in the post-announcement period for stock options
traded on the CBOE.
Previous studies show that the bid-ask spread is inversely related to trading volume
or the number of transactions. See e.g. Tinic and West (1972), Copeland and Galai
(1983), Venkatesh and Chiang (1986), Berkman (1992) and Lee et al. (1993).
Furthermore, since the risk-averse dealer requires a premium over undesired inventory
levels, the option spread is related to some measure of risk of the underlying stock.
Therefore, we adjust the spread measures for the expected patterns in trading volume
and implied volatility. Effectively, we estimate the following models:
AQSCit ˆ 0 ‡ 1AVCit ‡ 2AIVCit ‡
X
5
k ˆ 5

Options and earnings announcements: an empirical study of volatility,trading volume,open interest and liquidity

Recommended

Recommended

More Related Content

Similar to Options and earnings announcements: an empirical study of volatility,trading volume,open interest and liquidity

Similar to Options and earnings announcements: an empirical study of volatility,trading volume,open interest and liquidity (20)

More from Nicha Tatsaneeyapan

More from Nicha Tatsaneeyapan (20)

Recently uploaded

Recently uploaded (20)

Options and earnings announcements: an empirical study of volatility,trading volume,open interest and liquidity