Options Trading Activity
and Firm Valuation
Richard Roll, Eduardo Schwartz,
and Avanidhar Subrahmanyam
UCLA
The Issue


Ross (1976) -- options can improve market
efficiency by expanding contingencies
covered by traded securities (they help to
complete the market). Allocational efficiency.
Also, since informed traders may prefer to
trade options rather than stock (more
leverage), options may allow agents to trade
more effectively on their information, thus
improving informational efficiency.
The Issue, contd.


Cao and Wei (2007) find that informational
asymmetries play a more dominant role in
influencing options liquidity (relative to
stocks).
Easley, O’Hara and Srinivas (1998),
Chakravarty, Gulen, and Mayhew (2004) find
that options order flows contain information
about future direction of the underlying stock
price.
The Issue, contd.



If prices reveal more information, then
resources are allocated more efficiently,
which translates to higher firm valuations.
In addition, greater informational efficiency
could reduce investment risk because market
prices reflect information more precisely.
These arguments suggest that firms with
higher options trading volume should be
more informationally efficient and thus valued
more highly.
A point of clarification




The mere listing of an option does not necessarily
imply a valuation benefit.
If the options market has insufficient volume, the
valuation benefit from listing would be minor because
informed traders see no advantage to trading in
options (Admati and Pfleiderer, 1988).
Any valuation benefit of options listing should depend
on the amount of trading activity.
To the best of our knowledge, the relation between
options trading activity and firm valuation has not
been examined previously.
The Analysis


We analyze the effect of options trading
volume on firm value after controlling for
other variables that may also affect firm value
such as firm size, share turnover, return on
assets, capital expenditures, leverage and
dividend payments.
Following other studies we use a measure of
Tobin’s q as the valuation metric.
Findings


We find strong evidence that firms with more
options trading volume have higher value.
Firms with more options trading activity in a
given period tend to have improved financial
performance in the next period.

This is consistent with the premise that options
trading, by enhancing information flows, may lead
to better corporate resource allocation.
Findings, contd.

The results also show that the effect of
options trading on firm valuation is
greater in stocks with low analyst
following.

This indicates that the impact of options
trading on information production is larger
in stocks where investment analysis
produces
comparatively
less
public
information.
Data



Options trading data from Option Metrics – 1996 to
2005: 10 years of daily data (we aggregate to total
annual options volume for each stock).
Matched with data from Compustat on Tobin’s q and
a set of control variables.
Tobin’s q is computed as the sum of the market
capitalization of the firm’s common equity, the
liquidation value of its preferred stock, and the book
value of its debt divided by the book value of the
firm’s assets (total firm q). All results go through if
we use M/B of equity instead of q.
Control variables



A proxy for the firm’s leverage, long-term debt to
total assets, is intended to measure the likelihood of
distress, LTD. We expect higher LTD, lower q.
Profitability, ROA, intended to capture the notion that
more profitable firms may have more favorable
investment opportunities. On the other hand, high
ROA may also mean that the firm is in a mature
phase, and has limited growth opportunities. The
relation between ROA and q is an empirical issue.
Share turnover in the underlying stock: liquidity
effects arising from stock trading activity as opposed
to options activity.
Controls, contd.



A direct measure of investment opportunities
is capital expenditures divided by sales
(CapX) —high values should mean greater q.
A dummy variable for whether the firm pays
a dividend proxies for capital constraints
(firms that pay dividends may have more free
cash flow, which may potentially be used to
overinvest in marginal projects).
Firm size (market value of firm’s shares).
Number of firms with
nonmissing data
Year
All firms
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
6366
6430
6157
5874
5633
5150
4883
4653
4603
4064
Positive
options
volume
1342
1575
1717
1686
1638
1503
1597
1565
1705
1655
Natural
bifurcation
of sample
Summary Stats
All Firms
Variable
Tobin’s q
Options
volume
Size
Share
turnover
ROA
CapX
LTD
DivDum
Positive Options Volume
1.915
1877
1.151
0
Standard
Deviation
3.364
23434
2.197
1.533
1.885
0.949
-0.063
0.566
0.183
0.319
0.026
0.040
0.114
0
Mean
Median
Variable
Mean
Median
Standard
Deviation
Tobin’s q
Options
volume
2.250
6487
1.450
394
2.929
43223
12.61
2.636
Size
Share
Turnover
5.239
2.241
1.025
1.601
19.92
2.468
0.553
25.02
0.269
0.466
ROA
CapX
LTD
DivDum
-0.0072
0.2631
0.1852
0.3911
0.0406
0.0489
0.1348
0
0.2947
6.1213
0.2075
0.4880
Correlation matrices (all firms)
Tobin’s
q
Mature
Firms?
Options
Volume
Size
Share
turnover
ROA
CapX
Options volume
0.0915
Size
0.0628
0.4136
Share Turnover
0.1055
0.0902
-0.0099
ROA
-0.1175
0.0156
0.0401
-0.0786
CapX
0.0100
-0.0013
-0.0030
-0.0015
-0.0102
LTD
-0.0464
-0.0154
-0.0100
-0.0555
-0.0806
0.0152
DivDum
-0.0960
0.0146
0.1497
-0.1331
0.1475
-0.0125
LTD
0.0824
Correlation matrices,
Firms with Positive Options Volume
Tobin’s q
Options
Volume
Size
Share
turnover
ROA
CapX
Options volume
0.1798
Size
0.1057
0.4679
Share Turnover
0.1514
0.1375
-0.0787
ROA
-0.0500
0.0261
0.0734
-0.0613
CapX
0.0155
-0.0028
-0.0068
0.0045
-0.0200
LTD
-0.1350
-0.0393
-0.0340
-0.0976
-0.0744
0.0125
DivDum
-0.1639
0.0040
0.1823
-0.2945
0.1781
-0.0240
LTD
0.0661
Correlations



The correlation between q and options
volume is strongly positive (also with
share turnover and CapX)
q is negatively related to leverage as
well as the dividend dummy
q is negatively related to ROA: mature
phase with fewer opp. for growth?
Preamble to main analysis


For sample with positive options
volume: sorted into deciles volume each
year. For each decile we compute the
average value of q across all years.
Indicative that firms with higher options
volume have higher q.
Figure 1: Average Tobin’s q vs. options volume
Significant
Sort by size and then by volume



To allow for independent variation in
size and volume.
Average q for the resulting 25
portfolios.
q increases with volume for every size
group.
Options volume and q by size
Size quintile
1
Options
volume quintile
2
3
4
5
1
1.382
1.548 1.770 1.596 1.569
2
1.456
1.603 1.840 1.792 2.278
3
1.714
1.931 2.151 2.316 2.490
4
2.008
2.382 2.782 2.858 3.038
5
2.202
2.764 3.130 3.370 4.283
Strong
effect
Regressions

Year-by-year cross-sectional regressions
and then test the significance of the
time series coefficients. t-statistics are
corrected by procedure Newey/West:
residuals of the c-s regressions are
likely to be serially correlated due to the
autocorrelation in q.
Regression results – all firms
Variable
Optvol
Size
Stkturn
ROA
CapX
LTD
Divdum
Coefficient
t-statistic
0.1297
4.83
0.9875
2.18
0.1259
3.55
-0.8672
-3.01
0.0125
2.81
-1.0970
-2.73
-0.4326
-4.88
Average adjusted R2=8.55%
Average number of firms: 5381
Dependent Variable: Tobin’s q
Time series of annual cross-sections
t-statistic corrected by Newey/West
Regression results – firms with positive
options volume
Variable
Coefficient
Optvol
0.1188
3.38
Size
0.7017
2.09
Stkturn
0.1437
2.75
ROA
-0.5756
-1.25
CapX
0.0632
1.78
LTD
-1.631
-4.31
Divdum
-0.7293
-5.31
Average adjusted R2=12.05%
Avg no. of firms: 1557
t-statistic
Summary of results

Tobin’s q is positively and significantly related
to options trading; the effect is economically
significant, 16% to 23% increase in q for a
one sigma increase in options volume, ceteris
paribus


q is also negatively related to leverage and
the dividend dummy, consistent with
proposed hypotheses
Stock trading activity also bears a positive
relation with q
Robustness Checks

Various checks were performed and in
all cases the central results are
unchanged





Skewness
Panel regression
Endogeneity issues
Industry effects
Additional explanatory variables
Results with log(options volume) for
firms with positive options volume
Variable
Ln(Optvol)
Size
Stkturn
ROA
CapX
LTD
Divdum
Coefficient
t-statistic
0.1978
3.72
0.8288
2.70
0.0873
2.35
-0.6761
-1.43
0.0569
1.66
-1.638
-4.47
-0.7923
-5.57
Average adjusted R2=12.61%
Average number of firms: 1557
This checks whether the skewness in options
volume affects the results; it doesn’t.
From now on we use Ln(optvol).
Panel Regression: pools cross-section
and time-series data
Variable
Ln(Optvol)
Size
Stkturn
ROA
CapX
LTD
Divdum
Panel Estimates
Coefficient
t-statistic
0.1097
12.98
2.786
26.57
-0.0857
-7.48
-0.0573
-0.22
0.3299
1.03
-0.6712
-2.97
-0.9649
-16.07
Balanced Panel, to accommodate serial correlation and cross correlation in
the errors, using the Parks (1967) Procedure (see Appendix.) Firms
included must be present in all years (502 firms).
Endogeneity



One could argue, albeit implausibly, that high
q firms may attract more attention and this
may translate to greater options volume
(reverse causality).
One simple way to address this issue is to
consider the relation between q and oneyear lagged options trading volume.
Then we use an Instrumental Variable
approach.
Regression results using
lagged options volume
Variable
Coefficient
t-statistic
Lag(Ln(Optvol))
0.0879
5.56
Size
1.381
2.60
Stkturn
0.1779
2.06
ROA
-0.0912
-0.30
CapX
0.0717
1.74
LTD
-0.9991
-4.80
Divdum
-0.8275
-5.27
Average adjusted R2=10.19%
Average number of firms: 1359
An instrument



We need an instrument for options trading volume
that is inherently unrelated to q. Finding such an
instrument is a difficult endeavor and inevitably
involves an element of subjectivity.
We propose that options volume may be related to
the average absolute moneyness, the relative
difference between the stock’s market price and the
option’s strike price (correlation 0.19).
An alternative instrument for options volume is the
total open interest in options within a given year.
IV estimation (2SLS)
Variable
IV(optvol)
Size
Stkturn
ROA
CapX
LTD
Divdum
Moneyness as instrument
Open interest as instrument
Coefficient
t-statistic
Coefficient
t-statistic
0.3485
2.75
0.1385
2.84
0.1839
1.90
1.118
2.91
0.1348
3.07
0.1367
2.63
-0.7009
-1.53
-0.6277
-1.35
0.0539
1.71
0.0586
1.72
-1.551
-4.66
-1.608
-4.47
-0.6509
-7.01
-0.7415
-5.78
Average adjusted R2=13.23% Average adjusted R2=11.53%
Average number of firms: 1557
First equation: q as a function of same variables, using optvol from
Second equation: optvol as a function of instrument and size.
Main result is not due to reverse causality.
Year-by-year coefficients on ln options
volume (dependent variable – Tobin’s q)
Without industry controls
With Fama and French
(1997) industry controls
Year
Coefficient
t-statistic
Coefficient
t-statistic
1996
0.1452
4.06
0.1351
3.79
1997
0.1788
5.52
0.1659
5.18
1998
0.2236
6.28
0.2029
5.73
1999
0.5553
8.41
0.5025
7.49
2000
0.2820
9.01
0.2405
7.54
2001
0.1524
6.05
0.1410
5.68
2002
0.0898
4.92
0.0885
5.03
2003
0.0873
4.80
0.0835
4.80
2004
0.1123
5.87
0.1085
5.87
2005
0.1518
7.98
0.1433
7.68
Are unusual years driving the results; they’re not. Are industry
outliers (e.g., the tech bust) are responsible; they aren’t.
Other robustness checks


Results are robust to scaling options volume
by shares outstanding, and to using log
transformation of the positive controls.
To test that option trading activity does not
proxy for stock riskiness which could
potentially affect q: return volatility is not
significant in the overall regression for Tobin’s
q and the options volume variable remains
significant.
Options trading and future firm
performance: Further analysis



Identify the mechanism by which options
trading enhances firm value.
If options trading activity leads to better
corporate resource allocation, then there may
be a relation between future firm profitability
and options trading.
We regress ROA (our measure of financial
performance) on lagged values of options
volume and control variables.
Firm performance and options
trading (LHS variable=ROA)
Panel Estimates
One-year lagged
variables
Ln(Optvol)
Size
Stkturn
ROA
CapX
LTD
Divdum
Coefficient
0.0042
0.0041
-0.0019
0.4283
0.0411
-0.1135
0.0456
t-statistic
12.35
0.74
-4.63
9.32
1.65
-5.98
14.45
Parks procedure to account for autocorrelation
Persistence
Firm performance and options


There is a positive relation between
future ROA and current options activity
This supports the information channel:
that more options trading is associated
with greater informational efficiency,
which, in turn, leads to improved
resource allocation.
Options Trading and Investment
Sensitivity to Stock Price



The degree to which managers obtain
information from market prices to make
investment decisions can be captured by the
sensitivity of corporate investment to market
prices.
Several papers have theoretically and
empirically analyzed this sensitivity.
But, managers might learn more from market
prices when options volume is greater
(produces private information).
Corporate Investment



Sum of capital expenditures and R&D
expenses scaled by beginning of year
book assets.
We look at the interaction variable of q
with options volume.
We also include q to capture the
baseline effect of market valuation on
investment (both lagged) and other
controls.
Corporate Investment (LHS)
Panel Estimates
Explanatory
variables
Lagged 1 yr
Tobin’s q
Coefficient
t-statistic
0.4168
8.29
Ln(Optvol*q) 0.1793
InvAssets
0.4436
8.33
Return (t+1)
-0.0604
-2.20
Cash flow
2.805
8.35
6.32
Corporate Investment



Positive sensitivity of investment to
stock price (q).
Greater sensitivity to q when options
trading is high.
Supports notion that options trading
contributes to information production,
which managers use in making
corporate investment decisions.
Information Asymmetry



Effect of options trading on valuation may be
more pronounced in stocks with greater
levels of informed trading.
Difficult to find a measure for level of
informed trading in options markets.
We use the PIN (probability of informed
trading, computed with stock data) measure
of Easley, Hvidkjaer and O’Hara (2002) as a
proxy for information asymmetry.
Information Asymmetry


Using the structure of a sequential
trade market microstructure model,
they derive an explicit measure of the
probability of information based trading
(PIN) for an individual stock
For stocks with high PINs the effect of
option volume on valuation should be
greater.
Information Asymmetry
LOVOL
Year
Interaction
Variable
LOVOL*PINL
Coefficient t-statistic Coefficient t-statistic
1996
0.2297
2.82
0.0645
1.77
1997
0.4388
5.14
0.1334
3.81
1998
0.2948
4.95
0.0650
2.66
1999
0.5390
7.29
0.1563
5.31
2000
0.3795
4.03
0.1150
2.95
2001
0.3227
3.02
0.0907
2.31
The effect of options volume on q is stronger in stocks where more information
is produced by the trading process.
Information Asymmetry


Options volume variable remains
significant and the interaction of options
volume with PIN is positive and mostly
significant.
Suggestive evidence that the effect of
options volume on q is stronger in
stocks where more information is
produced by the trading process.
Security analysts and options
trading


Options volume could proxy for another
measure of information production, the
extent of analysts following. If no. of analyst
following a company is included in the
regressions: not significant whereas option
volume remains significant.
Results are also robust to the inclusion of the
dispersion of long-term growth forecasts by
analysts which is forward looking measure of
uncertainty.
A role for analyst following

The effect of options in information
production may be greater in stocks
with low analyst following, where little
public information is produced and
trading on private information may be
more important.

In these cases private information may
play a stronger part in information
production.
Testing the impact of analyst
following


We sort the sample each year into three
groups by analyst following, and label
them 0,1,2.
We interact options volume with this
indicator variable and include the
interaction variable in the regression.
Regression results with interaction
variable for analyst following
Variable
Ln(Optvol)
Ln(Optvol)*ANALYS
Size
Stkturn
ROA
CapX
LTD
Divdum
Coefficient
0.2573
-0.0293
0.9940
0.0183
-0.6138
0.0565
-1.5869
-0.7511
t-statistic
4.23
-3.78
2.96
2.38
-1.37
1.67
-4.55
-5.61
Interpretation of results with
inclusion of analyst following


The impact of options volume on Tobin’s q is
stronger in firms with less analyst following,
but it remains significant even for firms with
large analyst following.
Suggests private information production is
more important in stocks where investment
analysts produce less public information.
Bottom Line



The amount of options trading is associated
with higher firm valuations.
This result is consistent with the dual notions
that more options trading is associated with
greater informational efficiency of prices and
superior resource allocation.
The results survive when subjected to a
variety of robustness checks, including
different specifications of volume.
Bottom Line, contd.


There is a positive link between future firm
performance and current options volume, suggesting
that options trading enhances information production
and, in turn, resource allocation.
The role of options volume on valuation is stronger in
firms with less analyst following where it is likely that
less public information is produced
 In these stocks, private information production
through trading may be more important for
resource allocation
Issues of Interest




The key point of our paper is that the degree to
which an option is traded, not its mere listing, is
associated with higher valuations.
It would be interesting to consider whether this
notion extends to other scenarios.
For example, some countries have futures contracts
on individual stocks, and the effect of such contracts
on valuation could be ascertained.
Analyzing the impact of index options and futures on
market valuation would also be of interest.