equity leaps calls vs. stocks: an empirical study for long ... · pdf fileequity leaps calls...
TRANSCRIPT
1
Equity LEAPS Calls vs. Stocks: An Empirical Study
for Long-Term Speculation
S. LEILA BEHESHTI SHIRAZI, Graduate School of business, University of Malaya, 50603
Kuala Lumpur, MALAYSIA
Mobile no: 00989121999142
Email: [email protected]
AND
IZLIN ISMAIL, Senior Lecturer of finance, Graduate School of business, University of Malaya,
50603 Kuala Lumpur, MALAYSIA
Mobile No: 0060122687157
Email: [email protected]
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
2
ABSTRACT
Long-Term Equity Anticipation Security or LEAPS is a call option introduced as a more
conservative security that can replicate a common stock position. This study’s objective is to
examine the effect of applying the strategy of “Buying In-The-Money LEAPS Calls vs.
Purchasing Stocks” proposed by CBOE on the performance of traders in terms of risk and
return trade-off and the risk-adjusted performance in practice, using a sample of 54 common
stocks listed on NYSE and NASDAQ and 54 LEAPS calls on the same underlying stocks listed
on CBOE during 2008-2010. The results indicate that LEAPS calls are not a preferred
financial instrument to replace common stocks for risk-averse traders. When the stock market
experiences a progressive downturn trend, the portfolios of LEAPS calls provide much higher
negative returns, significant loss and poor performance as well as higher levels of volatility
relative to the portfolios of common stocks. The results of this study also suggest that risk-
seeking traders, who can tolerate the higher level of risk in compensation for higher returns,
choose the portfolio of LEAPS calls with high Book-To-Market (BTM) ratio assets. This
portfolio is less volatile relative to the portfolio of LEAPS with low BTM ratio and provides
higher rates of return in comparison to the portfolios of common stocks in favorable market
conditions.
JEL Classification: G11, F39
Keywords: Equity LEAPS call, common stock, long-term speculation, return, volatility, mean,
variance, risk-adjusted performance, Sharpe ratio, Treynor ratio, Jenson Alpha.
I. Introduction
Over the last four decades, many studies have conducted on derivatives (e.g. , Sears and
Trennepol, 1982; Lapan et al. ,1991; Jarrow and Turnbull , 1995; Wilmot et al.,1997; Mc
Millan, 2002, Farhi and Borgi, 2009) and a large body of the literature has been concentrated
on studying the hedging function of derivatives (see Warner, 1977; Mayers and Smith, 1982;
Smith and Stulz, 1985; Zimmerman, 1988; Blanchet-Scalliet and Jeanblanc, 2001; Lotz,
1999). However, there is limited study on the speculative use of these securities (e.g. Bauer,
Cosemans & Eichholtz, 2008), whereas many traders in the marketplace speculate on price
movements of the underlying assets (Kumar, 2007). Thus, this paper look at speculative use
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
3
of derivatives in one of the largest and most volatile option exchange, Chicago Board Option
Exchange (CBOE).
Speculation on derivatives is usually carried out in a short-term and entails running the risk of
loss in the expectation of high reward (Farhi & Borghi, 2009). Bauer, Cosemans & Eichholtz
(2008) found evidence that most individual investors who trade short-term options to
speculate on stock price movements have incurred substantial losses on their investments.
They concluded that the poor market timing was the main determinant of their worse
performance. However, it is believed that a longer maturity derivative like Long-Term Equity
Anticipation Security (LEAPS)1 can reduce the risk of speculating on derivatives when there
is no need to time the market precisely (Apostolou et al., 2005; Thomsett, 2009). At the same
time, LEAPS provide the opportunity of gaining leverage in the stock market and speculate
on favorable price moves of the underlying assets in a long-term2. The leverage inherent in
this type of option can magnify the returns on investment, whereas investors only pay a
fraction of total capital required for the securities (Apostolou et al., 2005; Lasher, 2007).
Since, due to the effect of leverage, small changes in the value of the underlying assets
generate great changes in the value of the options (Wilmott, Howison & Dewynne, 1997).
1 LEAPS is a long-term option with the expiration date of up to three years (Lasher, 2007). LEAPS were introduced by
Chicago Board Option Exchange (CBOE) in 1990 as a new investment tool (Roth, 1994; Allaire & Kearney, 2002). It
provide a longer time frame for option traders to benefit from favorable price moves in the market (Apostolou et al., 2005).
2 Usually it is believed that the term speculation refers to a short-term financial action and the term investment to a long-term
one (Brandes, 2003; Hiriyappa, 2008). However, Nagarajan & Jayabal (2011) avoid distinguishing them based on their
holding period and explain that the distinctions between speculation and investment are the degree of risk involved and the
motives of traders. The element of risk involved in speculation is significantly higher than that of investment. A speculator
tends to take a higher level of risk when anticipates a higher level of return in future. Also, an investor’s motive is to increase
his/her income from the securities whereas a speculator’s motive is the capital appreciation. Thus, even those who buy and
hold securities for decades, may be classified as speculators, except only the rare few who are primarily motivated by
income or safety of principal rather than selling at profit. Thus, in this paper the term investors and long-term speculators are
used interchangeably when the motives of both investors and long-term speculators are earning profit from price fluctuation
in the future.
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
4
According to Lasher (2007), gains from LEAPS calls can be sometimes 4-5 times greater
than those of the underlying stocks).
Recently, many financial advisors and option specialists (Finnegan, 1977; McMillan, 2002;
Taylor, 2008; Rahemtulla, 2009, Zigler, 2010) suggest traders and long-term speculators to
purchase LEAPS calls instead of stocks or replace the existing stocks in their portfolio of
assets with LEAPS calls. Moreover, CBOE has introduced a new investment strategy which
is called “Buying In-The-Money LEAPS Calls vs. Purchasing Stocks” to inspire investors to
buy LEAPS calls rather than underlying stocks. Therefore, the new generations of market
participants who are more risk takers have been encouraged to purchase LEAPS calls instead
of common stocks in order to obtain greater returns from the favorable price moves of the
underlying over a longer period of time (Allaire & Kearney, 2002; Kolb & Overdahl , 2007).
According to Thomsett, (2009) buying LEAPS calls can be a more conservative approach
relative to purchasing common stocks outrights in a volatile stock market because investors
will not put their whole capital at risk and just limit the risk to the premium amount paid.
However, it is significant to note that the leverage effect can also magnify the risk of
investment on LEAPS (Lasher, 2007). We were unable to find a study in literature
representing the performance of the long-term speculators by applying this strategy in
practice. So, the risk and return tradeoff as well as the risk-adjusted performance of LEAPS
calls in comparison with underlying stocks are still under question. Investors are unaware of
the actual level of returns and risks that they will experience through adopting this strategy in
order to ensure the preference of LEAPS calls over Equity stocks.
According to McMillan (2002) LEAPS calls can be used to construct a long-term portfolio
of stocks but with less capital outlay. Consistently, Taylor (2008) suggests investors can
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
5
create a portfolio of stocks with LEAPS because LEAPS can provide them greater
diversification, manageable risk, and higher return. Thus, this study attempts to measure the
risks, returns and risk-adjusted performances of equity LEAPS calls and those of the
underlying stocks in the context of portfolios of assets in order to compare them together and
explore any preference of each. The risk-adjusted performance of these portfolios would be
measured through Sharpe, Treynor and Jenson. Also this study tries to increase the
understanding of investors or long-term speculators about their success or failure in the
market by buying LEAPS calls instead of common stocks.
The results of this study can help those investors or long-term speculators who do not have
sufficient capital to purchase various expensive stocks in the stock market and instead are
willing to buy LEAPS calls in the derivative market to enjoy from favorable price moves.
This study also would make a contribution to the literature in the area of speculating in
options, specifically LEAPS calls. It is also anticipated that this study will motivate others to
conduct further research on speculating in LEAPS calls within different periods of time and
investigate the performance of investors or long-term speculators adopted this investment
strategy in the financial markets.
This paper proceeds as follows. Section II discusses the previous studies on the return and
risk of options as well as the performance of the traders holding options for speculation.
Section III, describes data collected for this study. This is followed by the methodology
employed in the study to measure the monthly returns, risk and risk-adjusted performance of
the portfolios of LEAPS calls and the portfolios of the underlying stocks in the section IV.
Section V, presents our empirical results and discusses our findings. Section VI discusses the
conclusion, limitation of the study and a few suggestions for further research.
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
6
II. Literature Review
A. Speculation on Options and LEAPS
Many investors, nowadays, prefer to trade options rather than stocks to save transaction
costs, to avoid tax exposure and to bypass stock market restrictive rules (Kolb & Overdahl,
2007). On the other hand, the values of options depend on the price of the underlying stock
and buying options is regarded as a substitute for direct purchase or sale of stocks (Bodi,
Kane & Marcus, 2009). Thus, some investors trade options to speculate on the price
movement of the underlying stock and obtain larger gains (Kolb & Overdahl, 2007).
According to Roth (1994) educated traders with speculative motives have moved toward
trading LEAPS rather than short-term options. Since Longer expiration period of LEAPS
overcomes the ongoing struggle of option traders with time. Moreover, LEAPS provides less
leverage for investors because the buyers of LEAPS have less time premium erosion to fight
against than the buyers of short-term options (CBOE, 2001). The fact can make the LEAPS
less volatile and risky comparing to short-term options (Holland & Wingender, 1997; Weiyu
Guo, 2003).
There are limited numbers of studies on LEAPS in literature. Among the few existing
studies on LEAPS, there are some empirical explorations concerning the pricing of SPX
LEAPS (Bakshi et al., 2000) and Equity LEAPS (weiyu guo, 2003) through Black-Scholes
model, the volatility dynamics of LEAPS on S&P 500 stock market index (Bollerslev &
Mikkelsen, 1999), trading volume of LEAPS (weiyu guo, 2003), and the relationship between
the introductions of LEAPS and changes in the value of underlying stocks (Lundstrum &
Walker, 2005). However there are a few studies on the levels of returns and risk of these
securities.
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
7
B. Returns and Risk of the Options and the LEAPS Calls
Options, due to their inherited leverage, generate magnified returns on the investment
(Apostolou et al., 2005; Kolb & Overdahl, 2007; Gurusaour, 2009). Evidence on S&P index
shows that the returns of call options are significantly larger than those of the underlying
stocks; by average two per cent per week (Coval & Shumway, 2001). Since options have a
convex payoff (Begley & Feltham, 1999; Guy, 1999; Bryan, Hwang & Lilien, 2000) while
the payoff of stocks is linear.
According to Allaire and Kearney (2002), the implied volatility of LEAPS calls is very
high that is the great concern of traders intending to invest on these securities. The volatility
sometimes prevents the traders to invest on LEAPS calls. It implies that LEAPS calls are
more volatile and risky in comparison with underlying stocks. On the other hand, the
volatility factors can create a significant variation in a portfolio returns (Coval and Shumway,
2001). Banerjee et al. (2007) finds evidence that VIX3 variables (volatility) significantly
affect excess returns for most portfolios and this relationship is stronger for portfolios that
have higher beta values (like a portfolio of options).
Crouhy, Galai & Mark (2002) and MacMilan (2002) postulate that an options portfolio
has higher beta and risk profile comparing to a stocks portfolio. The high beta of an option
portfolio makes its returns significantly volatile. Sears and Trennepohl (1982) find evidence
that systematic risk or market variance for an options portfolio is significantly greater than
that of stocks portfolio. They conclude that the systematic risk can be reduced with the
greater chance of diversification and elimination of unsystematic risk in a portfolio.
3 VIX or volatility index is introduced by Chicago Board Option Exchange in 1993. This index is calculated
from the S&P 100 (OEX) stock index options and originally computed on a minute-by-minute basis from the
implied volatility of eight OEX option series.
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
8
Moreover, Lowel (2009) explains that Deep-In-The Money (DITM) call strategy is the
best way to artificially own a stock with half money outlay and less risk. DITM LEAPS calls
have more potential to earn significantly huge returns in comparison with ATM LEAPS calls
because they have higher delta.
C. Speculative Performance on Derivatives and LEAPS calls
Studies over different time periods suggest that the most speculators have made net trading
losses in future market (e.g. Steward, 1949; Ross, 1975; Hieronymus,1977) as well as in the
stock market (see Barber et al. ,2004). Teweles and Jones (1987) argue that traders
mistakenly believe they can forecast prices and obtain profits, but they forget the possibility
of losses as well. Dusak (1973) finds that the average holding period returns over the period
1952 to 1967 on the future contracts over the same period were closed to zero for speculators.
Moreover, Chapman (2010) investigates the risk and return of a strategy in which speculators
use credit to maximize the probability of achieving gains. He finds that adopting such a
strategy would eventually lead to large losses and negative expected profits for the
speculator. Consistently, Clark et al. (2008) represents that a portfolio of derivative
speculators underperform a portfolio of hedgers, specifically when separated by credit risk
exposure.
In contrast, Hodrick and Srivastava (1984) examines the risk-return trade-off of speculating
in forward currency markets and finds that the strategy was profitable. They indicate that to
earn profit speculator’s willingness to absorb a substantial variance of profits is required.
Also, Changyun (2003) examines the behavior and performance of speculators and hedgers in
15 U.S. futures markets and observes that speculation (hedging) is positively (negatively)
correlated with subsequent abnormal returns after controlling for market risk. He concludes
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
9
that speculators can outperform hedgers and speculation is significantly profitable in future
markets comparing to hedging.
Bharadwaj and Wiggins (2001) in their study investigate if the market for LEAPS written
on the S&P 500 index is efficient enough to preclude violations of put-call parity and the box
spread pricing relationship. They find that LEAPS puts are overpriced relative to calls about
80% of the time, but the discrepancy is seldom enough to produce a reliable arbitrage profit
after transaction costs.
III. Data
The sample underlying stocks are selected among the stocks listed on New York Stock
Exchange (NYSE) and NASDAQ stock market. Also the sample equity LEAPS calls have
been chosen from the listed LEAPS calls on CBOE. As S&P 500 EWI closely mirrors the
sample underlying stocks and the sample LEAPS calls, it is considered as our benchmark in
this study.
The samples are not selected randomly this study and several criteria are considered in the
sampling. First, we have picked the sample securities from different industries to meet the
diversification principle of MPT model and remove the unsystematic risk of the portfolios.
Second, we have chosen the samples based on their book-to-market (MTB) ratios because
several studies have represented a strong relationship between the assets returns and their
Book-to-Market ratios (see Rosnberg, Reid and Lanstein, 1985; Davis, 1994; Chan, Hamao,
and Lakonishok,1991; Capaul, Rowley and Sharpe, 1993). Also, Fama and French (1995)
observed that two classes of stocks tend to do better than the market as a whole: small caps
stocks and stocks with a high book-to-market ratio. Following this study, Barber, Lehavy,
and Trueman (2007) designed two portfolios of high BTM and low BTM and measured their
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
10
returns. This issue, hence, has also taken into consideration in this study and we have
constructed two portfolios of stocks and two portfolios of equity LEAPS calls, one with high
BTM ratio and another with low BTM ratio (totally 4 portfolios of assets). Third, among
equity LEAPS calls written on the underlying stocks, only DITM equity LEAPS calls are
selected to examine the strategy proposed by CBOE.
With regard to the number of assets in a portfolio, Statman (1987) shows that a portfolio
including 30-40 stocks can effectively achieve efficient diversification. Chung (2000)
indicates a well-diversified portfolio include at least 27 securities. Consistently, Wang and
Yang (2007) based on the ordinary least square method (OLS) and GARCH Model find that
the optimal portfolio size in terms of the number of stock holdings is between 21 and 28 with
portfolio returns maximized and volatility minimized. Therefore, we have decided about the
sample size of 27 securities in each of the four equally weighted portfolios in this study.
We roll over LEAPS calls in the portfolios over through a process of selling the old option
and then purchasing a new one with the same strike price but a later expiry date (Allaire &
Kearney, 2002). This enables us to examine if a long-term speculation on LEAPS calls allows
the underlying assets to appreciate over the time and create profit for the speculators. Thus,
LEAPS calls with 2 year expiration date traded in January 2008 are rolled over in Aug 2009
at the same strike price but later expiry date (i.e. January 2011). Moreover, these portfolios
are rebalanced in August 2009 to be equally-weighted.
As equity LEAPS calls are good investment strategy when the market is bullish and The Wall
Journal reported in April 2008 that the economy of USA was expected to flourish in the
middle of the year 2008, we have decided on the three year period for the study from January
2008 to December 2011.
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
11
IV. Method
As the strategy of Buying In-The-Money LEAPS Calls vs. Purchasing Stocks is going to be
examined in the context of portfolios of assets, we have employed the Modern Portfolio
Theory (MPT) of Markowitz (1952) in this study. Moreover, we use the strategy of buy and
hold to measure the returns, risks and risk-adjusted performance of these portfolios. Since
this strategy is a very popular investment strategy in the financial markets because most of
investors believe good assets usually grow over a long period of time, even if they seem to
decline at some points. Moreover, small investors prefer this type of investment strategy
because they are willing to find a way to minimize their transaction costs.
A. Measuring Investor Return and Risk
We define investor return as the monthly changes in the price of all stocks and LEAPS calls
in this study. In a fashion similar to the studies of stock returns by Fisher and Lorie (1968),
and stock and bond returns by Ibbotson and Sinquefield (1976), we measure monthly returns
of investment over the period of this study. Like the prior studies, we have not taken into
consideration the costs associated with commission, tax, and transactions for the sake of
simplicity. Thus, we calculate the monthly returns of common stocks as
R i,t = [(P i,t + D i,t) / P i,t-1] -1, (1)
Where Ri,t is the return of the common stock i at the end of the time t; Pi,t is the closing price
of the stock i at the end of time t; and Di,t is the dividend received from the stock i during the
time t and reinvested at the end of time t.
We obtain the returns of LEAPS calls in accordance with the study of Xiaoyan Ni (2007) on
the returns of short-term call options. So, the returns of an individual LEAPS call from one
expiration date to next is calculated as
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
12
R i,t = [Max(S t - K, 0) / P] -1, (2)
Where Ri,t is the return of the equity LEAPS call of the stock i at the end of time t; S t is the
closing price of the stock i at the end of time t; K is the strike price of the LEAPS calls at its
expiration date; and P is the premium amount paid to buy the equity LEAPS Call of the stock
i.
Since this study is conducted in the context of portfolio, we consequently have to measure the
returns of the portfolios of assets, rather than those of the individual assets. Based on MPT,
the returns on each portfolio is calculated as the weighted sum of the returns of the securities
within the portfolios:
R P,t = ∑27
i=1 wi,t . Ri,t (3)
Where R P,t is the return of each portfolio of asset at the end of time t; w i,t is the weight of
asset i in the portfolio at the end of time t; R i,t is the return of the asset i at the end of time t.
Investor risk is defined as the standard deviation of the monthly returns on each portfolio.
That is, we measure the volatility of each portfolio by calculating the standard deviation of
the portfolio’s returns. To measure the risk of each portfolio, we also estimate the beta or
systematic risk of these portfolios. We calculate the Beta (β) of each portfolios through
regression the portfolio’s returns against the market returns.
B. Measuring Investor risk adjusted Performance
Pilotte and Sterbenz (2006) investigate the risk-returns characteristics of two equally
weighted portfolios of bills and bonds by applying both Ex-ante and Ex-post Sharpe and
Treynor Ratios. We follow the same methodology in this study to measure the risk-adjusted
performance of these portfolios. However, we only apply Ex-post Sharpe and Ex-post
Treynor Ratios. Since, we intend to evaluate the past investment performance of these
portfolios unconditionally.
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
13
Generally, Sharpe ratio is defined as the ratio of the excess return to standard deviation of
return. We define the excess return (XRt) on the portfolios as the difference between 1-month
holding period return on each portfolio and the return on a U.S. government bond expiring in
3 years. Thus, the Ex-post Sharpe Ratio is
EX Post Sharpe Ratio= 1/T Ʃt t=1 XRp,t ,
SD (XRp,t)
Where XRt is the excess return on the portfolio p and SD (XRp,t) is the standard deviation of
XRt. Hereby, I rewrite the equation as
EX Post Sharpe Ratio= 1/TƩtt=1(Rp,t – Rf,t) , (4)
Also we use the Treynor ratio to measure the excess return per unit of market risk in the
portfolio of assets. Since this ratio is more appropriate measure when an investor holds a
well-diversified portfolio and the unsystematic risk of investment is diversified away. The
Traynor ratio is defined as:
EX Post Treynor Ratio= 1/TƩt t=1(Rp,t – Rf,t) , (5)
βp,t
Where RP,t is the return of the portfolio p at the end of time t, Rf,t is the return on T-bond at
the time t, and βP is the beta or systematic risk of a portfolio of assets.
Jenson’s alpha as another risk-adjusted measure is used in this study to determine the
abnormal returns of these portfolios over the study period. Jenson’s Approach is based on the
Capital Asset Pricing Model (CAPM) which its coefficient is the measure of performance. By
applying a regression model, we intend to find the intercept term (α) and measure the
abnormal return of each portfolio as
Rp,t - Rf = αp + βp (Rm,t – Rf)+ ept , (6)
P,t ,t
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
14
Where RP,t is the return on the portfolio p at the end of time t, Rf, is the return on T-bond
during the time t, and βP is the beta or systematic risk of the portfolio, Rm is the return on the
equally weighted market index (S&P 500 EWI) at the time t, ept is error term.
As CAPM uses only one variable beta to describe the returns on a portfolio of assets with
return of the market as a whole, we also use the three-factor model developed by Fama and
French (1993) to measure the performance of these portfolios. Like Barber et al (2007), we
estimate the abnormal returns of the portfolios by following monthly time-series regression
for each portfolio p
Rp,t - Rf,t = αp + βp (Rm,t – Rf,t)+ bs SMBt+ bv HMLt + ept , (7)
Where SMBt is the return on an equally-weighted portfolio of small-cap stocks at the time t
minus the return on an equally-weighted portfolio of big-cap stocks at the time t, and HMLt is
the return on an equally weighted portfolio of high BTM stocks at the time t minus the return
on an equally weighted portfolio of low BTM stocks at the time t. The regression yields
parameter estimates of αp, βp, bs, and bv, where the intercept αp represent the abnormal return
on the p.
V. Research Results
A. Risk and return tradeoff
Table I provides descriptive statistics for the risk and return tradeoff on all the four
hypothetical portfolios as well as that of the S&P500 EWI. During the January 2008-
December 2011 period, the means on the returns of all the four portfolios are negative and
also that of the market index is negative. That is, the long-term speculators holding each of
these portfolios incur loss within the period of the study. However, the means on the returns
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
15
of the LEAPS call portfolios are negatively greater negative than those of the stock
portfolios. The long-term speculators holding the portfolios of LEAPS calls experience
significantly greater negative rates of return and consequently much more losses relative to
the long-term speculators holding the portfolios of stocks. The greater negative rates of
return on the portfolios of LEAPS calls are consistent with the theoretical prediction that
leverage magnified the rates of return.
Table I. Monthly Returns on the Portfolios, Jan 2008 –Dec 2010
Stock portfolio
with HBTM
Stock portfolio
with LBTM
LEAPS Call
portfolio with
HBTM
LEAPS call
portfolio with
LBTM
S&P 500
EWI
Mean -0.201802494 -0.215923601 -1.235422759 -2.811359586 -0.183125236
Standard Error 0.026766961 0.030016351 0.129285051 0.406720552 0.02420025
Median -0.174138737 -0.184084924 -1.090235309 -1.590298701 -0.150206902
Sample Variance 0.025792927 0.032435328 0.60172648 5.95517786 0.021083476
Standard Deviation 0.160601764 0.180098106 0.775710307 2.440323311 0.145201502
Beta 1.083251327 1.167457889 0.871171190 -1.015977203 1.07387653
Skewness -0.376937597 -0.266347776 -0.617176252 -0.849119265 -0.36246228
Kurtosis 0.4024833263 0.360557358 -0.398825966 -0.437582559 0.489478238
Range 0.557287644 0.804700174 2.921221297 8.357676167 0.54188886
Minimum
(lowest return) -0.495430456 -0.58378258 -3.083731169 -8.043450536 -0.511184533
Maximum
(highest return) 0.061857188 0.220917593 -0.162509871 0.314225631 0.030704327
Sum -7.264889791 -7.773249639 -44.47521934 -101.2089451 -6.592508489
Count 36 36 36 36 36
Confidence Level
(95.0%) 0.054339819 0.060936432 0.262462606 0.825686611 0.04912912
Note. The table represents the mean on the monthly rates of return and the volatilities of the four portfolios
of stocks and LEAPS calls as well as those of S&P EWI within a three year period.
Moreover, the betas of the stock portfolios indicate that the portfolios move in the same
direction as the market. The stock portfolio with low BTM ratio, due to its greater beta, is
more volatile in comparison with the stock portfolio with high BTM ratio. The low beta of
Fig
ure
4.1
Mo
nth
ly R
etu
rns
of
the
Hy
poth
etic
al
Port
foli
os
an
d S
&P
500 E
WI,
Jan
uary
2008
-Dec
emb
er 2
010
Jan
-08
M
ar-0
8
M
ay
-08
Ju
l-o8
S
ep-0
8
N
ov
-08
Jan
-09
M
ar-0
9
Ma
y-0
9
J
ul-
o9
Sep
-09
N
ov
-09
J
an
-10
M
ar-1
0
Ma
y-1
0
J
ul-
10
Sep
-10
N
ov
-10
Sto
ck p
ort
foli
o
wit
h H
igh B
TM
LE
AP
S c
all
port
foli
o w
ith
Hig
h B
TM
Sto
ck p
ort
foli
o
wit
h L
ow
BT
M
LE
AP
S c
all
port
foli
o w
ith
Low
BT
M
Sto
ck p
ort
foli
o
of
S&
P 5
00 E
W
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
16
the LEAPS call portfolio with high BTM ratio implies that it is less volatile than the stock
portfolios and the market index. However, the negative beta of the LEAPS call portfolio with
low BTM ratio implies that the portfolio moves in the opposite direction to the market, but it
is roughly as risky as the S&P 500 EWI.
The negative values of Skewness, in table I, show that the distribution of data is not
normal and they are skewed toward left. The distribution of returns on the portfolio of stocks
is moderately skewed whereas the distribution of returns on the portfolio of LEAPS call is
highly skewed. Also the negative values of kurtosis demonstrate flatness of the peak of the
LEAPS calls portfolios and the positive values of kurtosis indicate the sharpness of the peak
of the stock portfolios and market index.
It is significant to note that the mean on the returns of the LEAPS calls portfolio with
low BTM ratio is significantly less negative than that of the LEAPS calls portfolio with high
BTM ratio. In the other words, the portfolio of LEAPS calls with low BTM ratio generates
the greatest negative rates of return and loss in comparison to the other portfolios.
The table reveals that the LEAPS call portfolios, due to their significantly higher standard
deviation, are more volatile and risky than the portfolios of stock and the market index. Our
expectations in this study on the fact that the higher level of risk yields the higher level of
returns is not realized with the portfolios of LEAPS calls; i.e. the long-term speculators
holding the portfolios of LEAPS calls experience the higher levels of risk for the lower levels
of returns. The volatility of LEAPS calls portfolio with low BTM ratio is even significantly
than the LEAPS calls portfolio with high BTM ratio.
The Figure1 illustrates that the average returns of the portfolios of stocks and LEAPS
calls as well as S&P EWI are not normally distributed and they are negatively skewed toward
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
17
left. It represents that the variation in the returns for the portfolios of stocks and that of the
S&P500 EWI are approximately in the same range, but the variation in the returns of the
portfolios of LEAPS call are significantly great. In general, the portfolios of LEAPS calls
have showed worse performance within the period of the study with the low level of returns
and the high level of volatility. In fact, the long-term speculators holding the portfolio of
LEAPS calls would acquire a larger amount of risk and lower level of return.
Figure1. Returns Distribution of the Portfolios of Assets
Note. No transaction cost, tax cost and reinvestmentrisk are considered into the calculations.
The Pearson unpaired two-sample T-test is also used to determine whether there is a
significant difference between the mean values of the monthly returns on each of the two
portfolios of assets. Table II indicates that the null hypotheses are rejected and there are
statistically significant differences between the means of the stock portfolios and those of the
0
0.25
0.5
0.75
1
1.25
1.5
1.75
2
2.25
2.5
2.75
3
-8 -7 -6 -5 -4 -3 -2 -1 0 1 2
Pro
ba
bil
ity
Return (%)
Stock portfolio
with High BTM
LEAPS
portfolio with
High BTM
Stock portfolio
with low BTM
LEAPS
portfolio with
low BTM
Stock portfolio
of S&P 500
EWI
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
18
LEAPS call portfolios. Also the difference between the means of the LEAPS call portfolios
with high BTM ratio and low BTM ratio are statistically significant. However the results
reveal that the difference between the means of two stocks with high BTM ratio and low
BTM ratio is not statistically significant, due to the p-value of greater than 0.05. difference
between the means of two portfolios of stocks are not significantly different when the P-value
is greater than 0.05.
Table II – The Result of Pearson unpaired Two-sample T-test
Variables Difference in
means
Difference in
Standard error T-value P-value
hbtmsp
hbtmleapsp 1.033621 0.1320268 7.8289 0.0000
lbtmsp
lbtmleapsp 2.595437 0.4078267 6.3641 0.0000
hbtmsp
lbtmsp 0. 0141206 0.402174 0.351l 0.7266
hbtmleapsp
lbtmleapsp 1.575937 0.4267742 3.6927 0.0004
Hbtmsp
lbtmleapsp 2.609557 0.4076004 6.4022 0.0000
lbtmsp
Hbtmleapsp 1.0195 0.1327237 7.6814
0.0000
Note. The confidence level chosen for t-test is 95%. This level of confidence corresponds to α = .05. Since
we have tested the null hypothesis that the two means are equal, a two-tailed test is used. Also, the degree of
freedom is selected 70.
B. Risk-adjusted performance of the portfolios
The means of the sharpe ratios for all the four portfolios and that of the S&P 500 EWI are
negative because the portfolios generate negative excess returns within the period of the
study. However, the negative values of sharpe ratios are difficult to interpret. Israelsen (2004)
proposed a modification to the sharpe ratio when the excess returns are negative. He
introduced an exponent to the denominator. This exponent is made up of the excess return
divided by its absolute value. The equation of the modified Sharpe Ratio is as
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
19
Smod = RP - Rf (9)
σP^((R
p - R
f)/abs(R
p - R
f))
Table III represents that portfolios of LEAPS calls with the lower (or more negative)
modified Sharpe ratios are worse investment alternatives in comparison with the portfolios of
stocks with higher (or less negative) modified Sharpe ratios for the long-term speculators in
the terms of reward per unit of total risk. Moreover, the portfolio of LEAPS call with low
BTM ratio performs significantly poorer than the portfolio of LEAPS call with high BTM.
On the other hand, the results of the modified Sharpe ratios reveal that when the market is
down a stock portfolio with low BTM ratio can even perform better than the market portfolio
for long-term speculation.
Table III. Monthly Ex-Post Sharpe Ratio of the portfolios, Jan 2008 – Dec 2010
Stock portfolio
with High BTM
Stock portfolio
with Low BTM
LEAPS portfolio
with High BTM
LEAPS portfolio
with Low BTM S&P 500 EWI
Normal Modified Normal Modified Normal Modified Normal Modified Normal Modified
Mean -0.35809 -0.03025 -0.35529 -0.00851 -1.62499 -0.97780 -1.16233 -6.93819 -1.45438 -0.02875
Standard
Error 0.02677 0.00847 0.03002 0.03246 0.16667 0.10029 0.16667 0.98908 0.16903 0.00387
Median -0.33043 -0.03200 -0.32345 -0.03767 -1.43782 -0.86518 -0.66196 -3.94209 -1.22446 -0.0251
Standard
Deviation 0.16060 0.05085 0.18010 0.19475 1 0.60173 1 5.93446 1.01418 0.02324
Sample
Variance 0.02579 0.00259 0.03243 0.03793 1 0.36207 1 35.2178 1.02857 0.00054
Kurtosis -1.02483 19.7892 0.16056 30.7869 -0.39882 -0.39883 -0.43758 -0.43381 -0.48948 1.01058
Skewness -0.37694 3.84131 -0.26635 5.40859 -0.61718 -0.61718 -0.84912 -0.86303 -0.73625 0.04845
Minimum -0.65172 -0.08360 -0.72315 -0.10966 -4.00772 -2.4115 -3.30634 -19.6899 -3.74577 -0.07678
Maximum -0.09443 0.22887 0.08155 1.08728 -0.24185 -0.14553 0.11848 0.11848 0.03914 0.03914
Sum -12.8912 -1.08890 -12.7905 -0.30644 -58.4997 -35.20082 -41.8438 -249.775 -52.3579 -1.03488
Largest(1) -0.09443 0.22887 0.08155 1.08728 -0.24185 -0.14553 0.11848 0.11848 0.03914 0.03914
Smallest(1) -0.65172 -0.08360 -0.72315 -0.10966 -4.00772 -2.41155 -3.30634 -19.6899 -3.74577 -0.07678
Confidence
Level(95.0%) 0.05434 0.01720 0.06094 0.06589 0.33835 0.20359 0.33835 2.00793 0.34315 0.00786
Note. The ex-post Sharpe ratio for the portfolios is calculated by dividing the ex post premiums on those
portfolios by the corresponding standard deviation of their returns . The ex- post premium is the difference
between the mean of the monthly rates of return of the portfolios taken from table 1 and the rate of return on 3-
year US government bond.
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
20
As illustrated in Table IV, the means of the monthly Treynor ratios for the three
portfolios of stocks with high BTM, stocks with low BTM, and LEAPS call with high BTM
ratios have taken negative values because of their negative excess returns. However, the
mean of the monthly Treynor ratio for the portfolio of LEAPS call with low BTM ratio has
wrongly taken a positive value. Since the negative excess return and the negative beta of the
portfolio has resulted in the positive value of Traynor ratio. The fact makes it impossible to
interpret the values of Treynor ratios and rank the performance of the portfolios accordingly.
Table IV. Monthly Ex-Post Treynor Ratio of the portfolios, Jan 2008 – Dec 2010
Stock
portfolio with
High BTM
Stock
portfolio with
Low BTM
LEAPS
portfolio with
High BTM
LEAPS
portfolio with
Low BTM
S&P 500 EWI
Mean -0.209464312 -0.237423306 -1.446928885 2.7918536 -0.194058934
Standard Error 0.024709834 0.030016351 0.148403726 0.4003245 0.022553821
Median -0.183926603 -0.205584629 -1.280271112 1.589995 -0.163380151
Standard
Deviation 0.148259005 0.180098106 0.890422359 2.4019469 0.135322928
Sample Variance 0.021980733 0.032435328 0.792851977 5.769349 0.018312295
Kurtosis -1.024833263 0.160557358 -0.398825966 -0.4375826 -0.489478238
Skewness -0.376937597 -0.266347776 -0.617176252 0.8491193 -0.736246228
Minimum -0.480526027 -0.605282285 -3.568565171 -0.2845789 -0.499799192
Maximum 0.033932281 0.199417889 -0.215353622 7.9416649 0.005223045
Sum -7.540715244 -8.547239013 -52.08943986 100.50673 -6.986121611
Largest(1) 0.033932281 0.199417889 -0.215353622 7.9416649 0.005223045
Smallest(1) -0.480526027 -0.605282285 -3.568565171 -0.2845789 -0.499799192
Confidence
Level(95.0%) 0.05016363 0.060936432 0.30127558 0.8127019 0.045786691
Note. The Ex-post Treynor ratio for the portfolios is also calculated by dividing the ex post premiums on
those portfolios by the corresponding Beta or systematic risk.
To address this issue, Kothari and Warner (2001) use Jenson Alpha in their study to
examine the performance of the mutual funds when the excess return is negative.
Consistently, we measure the performance of these portfolios and their abnormal return
through the coefficient α which is obtained from our regression analysis. It is significant to
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
21
mention that the time-series regression is conducted for each portfolio of asset separately to
find the coefficient α.
Table V. indicates that the abnormal return or the performances of the portfolios stocks
are roughly the same within the period of the study. The values of α for both of these
portfolios are equally 0.00029. It means that they perform similarly the same and no one is
better off the other. In consistent with the results of the Sharpe ratio, the values of α for the
portfolios of LEAPS calls are very smaller (or greater negative) comparing to those of the
portfolios of stocks. It implies that their performances and the abnormal returns they can earn
are significantly poorer than the portfolios of stocks.
Table V. The Results of Time-series Regression on the Portfolios of Stocks, Jan 2008 – Dec 2010
Regression result for the portfolio of stocks with HBTM
Regression result for the portfolio of stocks with LBTM
_cons -.0002917 .0113744 -0.03 0.980 -.0234606 .0228771 hml .2414776 .0679007 3.56 0.001 .1031684 .3797868 smb -.002656 .0893912 -0.03 0.976 -.18474 .179428 mmrf 1.103969 .0362171 30.48 0.000 1.030197 1.177741 hbtmspmrf Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total .902751885 35 .025792911 Root MSE = .02872 Adj R-squared = 0.9680 Residual .026392104 32 .000824753 R-squared = 0.9708 Model .876359781 3 .292119927 Prob > F = 0.0000 F( 3, 32) = 354.19 Source SS df MS Number of obs = 36
. regress hbtmspmrf mmrf smb hml if tin(3421m1,3509m10)
_cons -.0002916 .0113744 -0.03 0.980 -.0234604 .0228772 hml -.7585224 .0679007 -11.17 0.000 -.8968315 -.6202132 smb -.0026551 .0893912 -0.03 0.976 -.184739 .1794287 mmrf 1.10397 .0362171 30.48 0.000 1.030198 1.177742 lbtmspmrf Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 1.13523638 35 .032435325 Root MSE = .02872 Adj R-squared = 0.9746 Residual .026392069 32 .000824752 R-squared = 0.9768 Model 1.10884431 3 .36961477 Prob > F = 0.0000 F( 3, 32) = 448.15 Source SS df MS Number of obs = 36
. regress lbtmspmrf mmrf smb hml if tin(3421m1,3509m10)
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
22
Table VI. The Results of Time-series Regression on the Portfolios of LEAPS Calls, Jan 2008 –
Dec 2010
Regression result for the portfolio of LEAPS calls with HBTM
Regression result for the portfolio of LEAPS calls with LBTM
The value of Alpha for the LEAPS call portfolio with HBTM is -0.5859, whereas this
value is about 3 times smaller (i.e. -1.6681) for the LEAPS call portfolio with LBTM ratio.
It implies that the the LEAPS call portfolio with HBTM ratio is better off the LEAPS call
portfolio with LBTM and it is a better investment alternative for long-term speculators
intending to replace their stocks with LEAPS calls.
In the other hand, the values of R square for the portfolios of LEAPS calls are
subsequently 0.27 and 0.18 which is very low. It indicates that the three-factor model of
Fama and French (1993) can not perfectly predict the return on a portfolio of option,
particularly LEAPS calls. However the result of our study with the R squares of 0.97 and
_cons -.5859263 .2751935 -2.13 0.041 -1.146477 -.0253755 hml 2.929713 1.642802 1.78 0.084 -.4165652 6.275992 smb -5.666539 2.162748 -2.62 0.013 -10.07191 -1.261165 mmrf 1.938632 .8762434 2.21 0.034 .1537827 3.723482 hbtmleapsp~f Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 21.0604231 35 .601726375 Root MSE = .69482 Adj R-squared = 0.1977 Residual 15.4488456 32 .482776424 R-squared = 0.2665 Model 5.61157754 3 1.87052585 Prob > F = 0.0180 F( 3, 32) = 3.87 Source SS df MS Number of obs = 36
. regress hbtmleapspmrf mmrf smb hml if tin(3421m1,3509m10)
_cons -1.668079 .9162128 -1.82 0.078 -3.534343 .1981856 hml 7.078108 5.469448 1.29 0.205 -4.062794 18.21901 smb -15.8994 7.200526 -2.21 0.035 -30.5664 -1.232413 mmrf 1.882966 2.917313 0.65 0.523 -4.059406 7.825338 lbtmleapsp~f Coef. Std. Err. t P>|t| [95% Conf. Interval]
Total 208.431234 35 5.95517812 Root MSE = 2.3133 Adj R-squared = 0.1014 Residual 171.242878 32 5.35133994 R-squared = 0.1784 Model 37.188356 3 12.3961187 Prob > F = 0.0943 F( 3, 32) = 2.32 Source SS df MS Number of obs = 36
regress lbtmleapspmrf mmrf smb hml if tin(3421m1,3509m10)
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
23
0.98 subsequently for the stock portfolio with HBTM and the stock portfolio with LBTM are
consistant with the findings of Fama and French (1993)
C. Findings of the Research
The results of the study show that the returns on the portfolios of LEAPS call are
significantly lower than those of the portfolios of underlying stock, while these portfolios
have possess higher level of volatility. It is significant to note that during the given period of
the study, the higher levels of risk or volatility of the LEAPS call portfolios are not
compensated with the higher levels of returns. Adversely, these portfolios have provided
greater loss for the long-term speculators. The results of the ex-post Sharpe ratio and Jenson
Alpha also indicate significant poor performance for the portfolios of LEAPS calls, especially
for the LEAPS call portfolio with LBTM ratio. That is the portfolios of LEAPS calls have
less abnormal returns (or higher negative excess return) comparing to the portfolios of stocks.
Unlike the prior study conducted by Bauer et al. (2008) on the main reason of substantial
loss on short-term, this study reveals that the poor market timing is not involved and in the
absence of requiring to timing the market, the only reason of substantial loss is leverage. On
the other hand, the better performance of the LEAPS call portfolio with HBTM relative to the
LEAPS call portfolio with LBTM ratio is consistant with the study of Fama and French
(1995) about the outperformance of stocks with high book-to-market ratio.
VI. Conclusion and Recommendation
In the last 2007, many financial analysts and stock market experts had expected the US stock
market to move up and boom in the following year. Based on the technical analysis, they
were looking for a bullish trend in the US stock market in 2008. They were anticipating the
future prices in the stock market would increase and market participants would obtain capital
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
24
gains. According to Business News Journal in the 20th December 2007, seven market
analysts suggested investors to put their money in US stock markets in 2008 to enjoy high
profitability. Based on these recommendations we constructed the portfolios of assets for the
beginning of 2008 in order to examine the strategy of "Buying In-The-Money LEAPS Calls
vs. Purchasing Stocks" in practice rather than in theory. Since the best time to purchase
LEAPS calls is when the underlying market is expected to move upward, it was anticipated
high performance for those who following this strategy within this period of time. However,
unlike what had been expected by the stock market analysts, the year 2008 was a terrible year
for most equity investors. In fact, the financial/credit crisis caused a downturn in consumer
and business spending in the year which subsequently reflected in equity markets as well.
The sudden fall in stock prices in 2008 resulted in significant losses for the markets
participants. This downward trend of US stock market even continued in the years 2009 and
2010.
The results of this study represent that all the four portfolios and S&P500EWI have
experienced significantly negative rates of return and poor performance within the given
period of study for their holders. Apart from the last observation (in December 2010) when
the stock portfolios and S&P 500 EWI turned to yield positive rates of return, the other prior
observations of the study showed significant negative rates of return and performance for the
portfolios. The fact is the result of the worse general economy of US during the period and
abrupt downturn of stock markets.
Within the period, the portfolios of LEAPS calls have had significantly lower rates of
return and higher risk and worse performances relative to the portfolios of common stock. It
implies that investing on LEAPS calls instead of the underlying stocks is not a good strategy,
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
25
even when it is predicted the market moves favorable to practice the strategy. There are
situations in the market when everyone predicts the general economy is providing positive
signal about the future prospects of the market and the market will improve, but everything
turns out adversely and the market moves downward. In such situations, the long-term
speculators who had expected obtaining significant gains and earning huge profits would
incur much more losses by purchasing LEAPS calls instead of common stocks.
Therefore this strategy is highly risky to involve in, because speculators cannot exactly
ensure the doom or gloom of the market. The market sometimes follows some irrational
patterns which cannot be predicted through the existing technical market analysis. So, it is
suggested to the risk-averse investors or risk-averse long-term speculators with low level of
risk tolerance to avoid this strategy and not replace LEAPS calls with common stocks in their
portfolio of investment. In other words, it would be better for risk-adverse speculators to put
their money into common stocks rather than LEAPS calls to avoid the risk of making their
funds worthless in the market downturn.
However, it would be recommended for that group of investors or long-term speculators
who are ready to take higher level of risk while making investments and are risk-seekers to
hold the portfolios of LEAPS calls with HBTM ratio in order to enjoy from favorable market
movement in the future. Since the portfolio of LEAPS calls with HBTM ratio is less volatile
than the portfolio of LEAPS calls with LBTM ratio and it generates smaller negative rates of
return when the market turns downward. At the same time, the portfolio of LEAPS calls with
HBTM is more volatile than the portfolio of stocks and it can earn higher rates of returns
relative to a stock portfolio when the market turns upward. Therefore, risk seekers can bet on
these portfolios and earn higher profit when the market moves up.
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
26
A. Limitation of the Study
As with all research, the current study has certain limitations. The major constraint of this
study is that we could not investigate both market upturn and market downturn
simultaneously within the period of the study to get a comprehensive and conclusive result
from applying the strategy of "Buying In-The-Money LEAPS Calls vs. Purchasing Stocks" in
both patterns of the market. Based on the behavior of the US stock market during the
investigation period, the study is only limited to the market downturn or the bearish market,
nor bullish one.
Data constraint is another limitation of the study. The primary data on LEAPS is not
distinctively available on Bloomberg and many other financial data providers. Although the
financial databases provide data on options, they do not differentiate the LEAPS from
standard or short-term options. The fact makes it impossible to distinguish between LEAPS
and short-term options and consequently find the required data on LEAPS calls. The only
database possessing the historical data on LEAPS in a classified manner is the historical
option database of CBOE. However, this database also does not provide customized data on
LEAPS to customers for different requirements. The historical data on LEAPS are offered to
all individuals in a uniform format. As it is not certain that the data is extracting would be
exactly the ones can be used in the study, the matter makes working with this database and
extracting data from it more challenging. The database provides the trading data associated
with LEAPS for each symbol but it does not offer any information about the issuance date,
expiry date and strike prices of LEAPS before purchasing the data. As the data cannot be
customized before purchasing, the risk of collecting wrong data and spending money in vain
is high.
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
27
Moreover, gathering and processing data from the historical option database of CBOE is
very expensive and has caused budgetary constraints for the study. Considering all the
challenges of dealing with this database and associated costs of collecting the required
primary data, we could not extend the duration of the study more than three years.
Another limitation of this study is ignoring transaction costs for the calculation of rates of
return. As the transaction cost has only happened one time in the period of study for rolling the
LEAPS Calls over and the strategy of buy and hold is applied, so transaction costs are not taken
into consideration in this study. However for any extension of the study using longer period of
time, for instance 5-10 years, the transaction costs should be included into the calculations.
B. Suggestions for further research
Areas that this study is not able to explore may provide fruitful avenues for future
researches. This study is the first step in investigating the integrity of replacing LEAPS calls
with stocks in practice. As this study could only capture the market downturn, further studies
can accomplish the results of this study by providing a comprehensive insight from both
market upturn and market downturn. So, it is highly recommended to repeat the similar study
in another time frame and also repeat it within a longer period of time. Then, through
comparing the results of this study with those of new researches, we can ensure about the
precision of adopting or preventing this investment strategy in practice rather than in theory.
C. Implications
The results of this study will give the investors, long-term speculators and funds managers a
practical insight about the performance of LEAPS calls against common stocks and the
possible gains or losses that they will likely experience in the financial markets. The findings
of this study show that investors or long-term speculators have to be very careful about
trading LEAPS calls and replacing them with common stocks. Although Roth, 1994; CBOE,
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
28
2001; McMillan, 2002; OIC, 2008 suggest buying equity LEAPS calls rather than common
stocks as a good substitutes for the underlying common stocks in the market and Thomsett
(2009) call it wise and a more conservative approach to buy LEAPS calls as an alternative to
simply buying common stock in a volatile stock market, this study empirically represents
opposite results. If investors or long-term speculators buy LEAPS calls in the situations when
the market suddenly moves downwards, they will incur lots of losses.
Also against the claims of many financial advisors and option specialists (Finnegan,
1977; McMillan, 2002; Taylor, 2008; Rahemtulla, 2009, Zigler, 2010) as well as that of the
CBOE about the ability to earn higher returns and lower risk relative to the underlying stocks
by constructing the portfolio of investment with LEAPS calls rather than stocks, the LEAPS
call portfolios can generate lower average rates of returns in the market. Thus, the long-term
expiration of them could not afford a conservative nature to them and they are still high risky
securities to be traded.
REFERENCES
Allaire, M., and Kearney, M., 2002, Understanding LEAPS (McGraw Hill, USA), 7-33 & 237- 251.
Apostolou, N., and Apostolou, B., 2005, Keys to investing in options and futures (Barron’s educational series,
NY), 4th ed, 60-70, 90-93.
Bakshi, G., Cao, C., and Chen, Z., 2000, Pricing and hedging long-term options, Journal of Econometrics, 94,
277 – 318.
Barber, Brad M., Yi-Tsung Lee, Yu-Jane Liu, and Terrance Odean (2004) “Do individual day traders make
money? Evidence from Taiwan.” Working Paper.
Barber, B. M., Lehavy R., and Trueman B., 2007, Comparing the stock recommendation performance of
investment banks and independent research firms, Journal of Financial Economics, 490-517.
Bauer, R., Cosemans, M. and Eichholtz, P., 2008, Option trading and individual investor performance, Journal
of banking and finance, Vol. 33, 731-746.
Black, F. and Scholes, M., 1973, The pricing of options and corporate liabilities, The Journal of political
Economy, vol. 81, 637-654.
Bharadwaj, Anu and Wiggins, James B . (2001). Box Spread and Put-Call Parity Tests for the S&P 500 Index
LEAPS Market. The Journal of Derivatives. Vol. 8, No. 4: pp. 62-71.
Blanchet-Scalliet C., Jeanblanc M., 2001. Hazard rate for credit risk and hedging defaultable contingent claims,
to appear in Finance and Stochastics,.
Bodi, Z., Kane, A. and Marcus, j. A., 2009, Investment (McGraw Hill, New York), 680-682, 681-682, 716-721,
126-148, 325.
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
29
Bollerslev, T. and Mikkelsen, H., 1999, Long-term equity anticipation securities and stock market volatility
dynamics, Journal of Econometrics, 92, 75 – 99.
Changyun, Wang. (2003). The Behavior and Performance of Major Types of Futures Traders. The journal of
future markets, Vol. 23, no.1. pp. 1-31.
Chapman, Zaneta. (2010). Risk, Return and Credit. (Doctoral dissertation). Retrieved from ProQuest
Dissertations and Theses. (Accession Order No. AAT 3390466) Clark, Jeffrey A., Doran, James S. and
Delisle, Dusak, Katherine. (1973). Future Trading and Investor Returns: An Investigation of Commodity Market
Risk Premium. Journal of Political Economy. Vol. 81, No. 6. pp. 1387-1406.
Coval, J. D. and Shumway, T., 2001, Expected options returns, The journal of finance, vol. 56, 983-1009.
Crouhy, M. , Galai, D. and Mark, R., 2002, Book review of risk management (McGraw Hill, New York).
Fama, F. E. and MacBerth, D. J., 1973, Risk, return, and Equilibrium : Empirical tests, The Journal of political
Economy, Vol. 81, No. 3, 607-636.
Fama, Eugene F.; French, Kenneth R. (1993). "Common Risk Factors in the Returns on Stocks and Bonds".
Journal of Financial Economics 33 (1): 3–56.
Fama, E. F., and French, K. R., 1995, Size and book-to-market factors in earnings and returns, Journal of
Finance, 50, 131-156.
Farhi, M. and Borghi, R. A., 2009, Operations with Financial Derivatives of Corporations from Emerging
Economies, Journal of Estudos Avancados, Vol 23, No. 66.
Finnegan, J. P., 1977, Options, LEAPS can strengthen your portfolio: effective use of options can help protect
and grow your portfolio Long-Term Equity Anticipation Securities, retrieved on April 2, 2010, from the
World Wide Web:
http://findarticles.com/p/articles/mi_m0JQR/is_4_12/ai_30546422/?tag=content;col1.
Fisher, L. and Lorie H., 1968, Rates of return on investments in common stock, the year-by-year record, 1926-
65, The Journal of Business, Vol. 41, No. 3, 291-316.
Gurusaour, S. ,2009, Capital Markets (Tata McGrow Hill, New Delhi), 2nd ed., 109-111.
Hieronymus, Thomas A. (1977) Economics of Futures Trading for commercial and personal profit, 2 ed. , New
York: commodity Research Bureau. Chapter 13.
Hodrick J. R. and Srivastava S. (1984) . An Investigation of risk and Return in Forward Foreign Exchange.
Journal of International Finance .Vol. 3, pp. 5-29.
Holland, L. C., and Wingender, Jr., J., 1997, The price effect of the introduction of LEAPS, Financial Review,
32(2), 373 – 389.
Ibbotson, R. G. and Sinquefield, R. A., 1976, Stocks, Bonds, Bills, and Inflation: Year-by-Year Historical
Returns (1926-1974), The Journal of Business, Vol. 49, No. 1 (Jan., 1976), 11-47.
Israelsen, C., 2004, A refinement to the Sharpe Ratio and information ratio, Journal of Asset Management.
JarrowR. A. and Turnbul S. M., 1995. Pricing Derivatives on Financial Securities Subject to Credit Risk. The
Journal of Finance, Vol. 50, No. 1. (Mar., 1995), pp. 53-85.
Jensen, Michael C. (1968). The Performance of Mutual Funds in the Period 1945-1964. Journal of Finance, pp.
389-416.
Kolb, R. W. and Overdahl, J. A., 2007, Futures, options and swaps (Blackwell Publishing, USA), 5th ed., 332-
333.
Kumar, s., s. , 2007, financial derivatives (PHI learning, Delhi), 14-15.
Lasher W. , 2007, Practical financial management ( Thomson, USA), 5th ed., 8-10.
Lotz, C.N. (1999) Energy and water balance in the lesser doublecollared sunbird, Nectarinia chalybea. PhD
Thesis, University of Cape Town, Cape Town.
Lapan, H. E., Moschini, G.C. and Hanson, S. D. , 1991, Production Hedging and Speculative Decisions with
options and Future Markets, American Journal of Agricultural Economics, (February), vol. 73, 66-74.
Lundstrum, L. L, and Walker, H. C., 2005, Costly Trading, Managerial Ouropia, and Long-Term Investment,
Social science research network, 16 (September).
Markowitz, H.M. ,1952, Portfolio Selection, The Journal of Finance 7 (1): 77–91.
Mayers, David and Clifford W. Smith, 1982. On the Corporate Demand for Insurance, Journal of Business, Vol.
55, pp. 281-296.
McMillan G. L., 2002, Options as a strategic investment (New York Institute of Finance:USA), 382, 375- 385.
Merton, C. R., Scholes, S. O. and Gladstein, L. M., 1978, The returns and Risk of Alternative Call Option
Portfolio Investment Strategies, Journal of business, Vol.51, No.2.
OIC (The option industry council), 2008, Long-Term Equity Anticipation Securities (OIC publication, USA).
Equity LEAPS Calls vs. Stocks: An Empirical Study for Long-Term Speculation
30
Pilotte, E. A. and Sterbenz, F, P. , 2006, Sharpe and Treynor Ratios on Treasury Bonds, Journal of Business,
2006, vol 79, no.1.
Rahemtulla, K., 2009, LEAPS vs. Stocks: An Investment Vehicle Throwdown, Retrieved on September 3, 2010,
from the World Wide Web: http://www.investmentu.com/2009/September/leaps-vs-stocks.html.
Ross, Ray L. (1975) “Financial consequences of trading commodity futures contracts.” Illinois Agricultural
Economics pp. 27–31.
Roth, H., 1994, LEAPS: what they are and how to use them for profit and protection (McGraw Hill, USA), 48-
52.
Sharpe, W. F., 1966, Mutual Fund Performance, Journal of Business, Vol.39, No.1: 119–138.
Sharpe and William F. ,1994, The Sharpe Ratio, Journal of Portfolio Management (fall) 21 (1): 49-58.
Sears, R. S. and Trennepohl, L. G., 1982, Measuring Portfolio Risk in Options, The Journal of Financial and
Quantitative Analysis, Vol. 17, No. 3, 391-409.
Smith Jr, C, and R Stulz, 1985, The determinants of firms' hedging policies, Journal of Financial and
Quantitative Analysis 20, pages 391–405.
Statman M.,1987, How many stocks make a diversified portfolio, Journal of Financial and Quantitative
Analysis, vol. 22, NO. 3.
Steward, Blair (1949) “An analysis of speculative trading in grain futures.” In “Technical Bulletin No. 1001” ,
Washington, D.C.: U.S. Department of Agriculture.
Taylor, J., 2008, Create a LEAPS portfolio, retrieved on August 20, 2010, from the World Wide Web
http://leapsinvestor.com/basic-strategies/create-a-leaps-portfolio/.
Teweles, Richard J., and Frank J. Jones (1987) The Futures Game , McGraw-Hill.
Thomsett, M. C. , 2009, Getting started in options (John Wiley & Sons: USA), 8th ed., 241-248.
Weiyu Guo, 2003, Some evidence in trading and pricing of equity LEAPS, International Review of Economics
and Finance, 424-426.
Zigler, B., 2010, LEAPS vs. Gold Stocks, Retrieved on February, 15, 2010, from the World Wide Web:
http://seekingalpha.com/article/187361-leaps-vs-gold-stocks.
Wang, G. Y. and Yang Y. T., 2007, Portfolio diversification and risk reduction: Evidence from Taiwan stock
mutual funds, Dept. of Int. Bus., Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung, Taiwan .
Warner B. Jerold, 1977. Bankruptcy costs: some evidence. The journal of finance, vol. 32, pp. 337-347.
Wilmott, P., Howison, S., and Dewynne, J., 1997, The mathematics of financial derivatives: a student
introduction, Cambridge University Press.
Xiaoyan Ni Sophie, 2007, Stock option return: A puzzle, Hong Kong University of science and technology.