Volume.II Issue.6 June 2011 .0/msohtmlclip1/01/clip_image001.gif”>.0/msohtmlclip1/01/clip_image002.gif”>.0/msohtmlclip1/01/clip_image003.gif”>.0/msohtmlclip1/01/clip_image004.gif”> Contents Testing for Seasonality in Option and Calendar Month: An Empirical Investigation on the US Major Index Components 590 – 608 Rafiqul Bhuyan Determinants Influencing the Seasoned Equity Offerings: Private Placements vs Rights Issue 609 – 621 Norhanim Dewa & Izani Ibrahim Effect on Security Prices and Volatility from Cross Listing within the GCC Markets 622 – 630 Dr. Abraham Abraham Cash Flow-Investment Sensitivity for Manufacturing Firms in America Japan and Taiwan 631 – 641 Feng-Li Lin & Jui-Ying HungDeterminants of the ‘Decision to Finance’ in Micro Finance Institutions Prof. Fedhila Hassouna & Dr. Mehdi Mejdoub Cost of equity in emerging markets. Evidence from Romanian listed companiesCostin Ciora Corporate Events’ Effect on Stock Returns: Evidence from Athens Stock Exchange 692 – 715 Aristeidis Samitas Dimitris Kenourgios & Ioannis Tsakalos .0/msohtmlclip1/01/clip_image005.gif”>.0/msohtmlclip1/01/clip_image006.gif”>.0/msohtmlclip1/01/clip_image007.gif”>.0/msohtmlclip1/01/clip_image008.gif”> www.irjaf.com .0/msohtmlclip1/01/clip_image009.gif”>.0/msohtmlclip1/01/clip_image010.gif”>.0/msohtmlclip1/01/clip_image003.gif”> International Research Journal of Applied Finance ISSN 2229 – 6891Vol – II Issue – 6 June 2011 Call for Papers International Research Journal of Applied Finance (IRJAF) is a double blind peer-reviewed open access online journal. Every month a copy of IRJAF reaches faculty members in the areas of accounting finance and economics working in 70% of AACSB accredited Business Schools across the world. 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Click here to submit your paper: http://irjaf.com/Submission.html 589 International Research Journal of Applied Finance ISSN 2229 – 6891Vol – II Issue – 6 June 2011 Testing for Seasonality in Option and Calendar Month: An Empirical Investigation on the US Major Index Components Rafiqul Bhuyan PhD Associate Professor of Finance Dept. of Accounting & Finance College of Business & Public Administration California State University San Bernardino CA 92407 [email protected] 590 International Research Journal of Applied Finance ISSN 2229 – 6891Vol – II Issue – 6 June 2011 Abstract Using all securities from Dow 30 S&P 100 and S&P 500 indices respectively we show that Option month based monthly returns and volatilities are different from those of calendar month. These differences may explain the worthiness of options contracts on call and put options when they expire. We conclude the option expiration effect may explain the differences in return in option month compared to the calendar month. Our result contributes to the existing literature by offering evidence of return differences in option month what support the option expiration effect. When security returns are analyzed based on calendar month and option month to test for seasonality our results support the findings of the existing literature in terms of the calendar month. Our findings contribute to the literature by adding the result that when security returns are analyzed based on the option month; it also shows the seasonal pattern. Our both results could be the added evidence against the weak-form market efficiency. I. Introduction Seasonality in financial market is widely investigated in finance literature. It is addressed by investigating the abnormal returns in the month of January day of the week tax loss selling effect among other effects. When analyzing the January effect it is the calendar month January effect addressed in the literature. However there remains to be seen the effect of option month in return pattern. Option month’s beginning and ending dates are from two calendar months. The third Friday of a month marks the ending day of the option month and the Monday after the third Friday marks the First day of the option month. Trillions of dollar transaction takes place in option markets to profit from the movement of the underlying assets. That makes the option month a special case and event for stock market. The intent of this current research is to analyze the option month effect whether it adds as an additional anomaly in financial market. II. Literature Review The January effect has been widely studied to see if a profitable investment strategy exists. The key explanations for the January effect are: the year-end tax-loss-selling hypothesis (e.g. Branch (1977) Dyl (1977) and Schultz (1985)); the window-dressing hypothesis (e.g. Haugen and Lakonishok (1988) and Ritter and Chopra (1989)); turn-of- the-year ‘liquidity’ hypothesis (e.g. Ogden (1990)); accounting information hypothesis (e.g. Rozeff and Kinney (1976)) and bid-ask spread (e.g. Keim (1989)). However Bhardwaj and Brooks (1992) conclude that for typical investors the January anomaly of low-price stocks outperforming high-price stocks cannot be used to earn abnormal returns. Mills and Coutts (1995) report that even if calendar effects are persistent in their occurrence and magnitude the costs of implementing trading rules is prohibitive. Draper and Paudyal (1997) find that although it appears to be feasible to earn a high nominal return by trading on seasonality it does not appear to be feasible to earn excess returns after allowance for transaction costs. Booth and Keim (2000) also conclude that the January effect is ‘alive’ but difficult to capture. On the other hand Ko (1998) gives some favorable evidence on the economic exploitation of seasonalities. Specifically he investigates the effects of international diversification on the stock market monthly seasonality from an economic point of view. He finds that the strategy using monthly seasonality outperforms a buy-and-hold strategy. De Bondt Thaler and Bernstein (1985) found that investors over-reacted to unexpected news. Stocks that performed well in the previous periods (winners) and stocks that performed poorly in the 591 International Research Journal of Applied Finance ISSN 2229 – 6891Vol – II Issue – 6 June 2011 previous periods (losers) both tended to revert back to their mean value in the subsequent periods. In a psychological study Kahneman and Tversky (1982) document individuals over-reacting to new information whether good or bad. If over-reaction behavior occurs profitable contrarian trading strategies buying past losers and selling past winners can be formed. Smirlock and Starks (1986) report the negative Monday effect in stock returns has been “moving up” in time. Johnston Kracaw and McConnell find similar results (for GNMAs this effect occurs after December 1984. For T-bonds the negative Wednesday occurs before January 1981) of Gay and Kim (1987) and Chang and Kim (1988) who document the disappearance of Monday effects in the commodities futures index. III. Data and Methodology The data in this research is taken from PC QUOTES for stocks of three US indices: Dow Jones Industrial Average S&P 100 and S&P 500 respectively. Stocks of each of these indices are analyzed from the period beginning 1970 to the end of 2001. We sort the stock price data for each stock based on calendar month and option months and estimate two different monthly returns and standard deviations respectively. We then organize times series of monthly returns and standard deviation of each stock according to month. For example we pool all monthly returns of January (only) from 1970 to 2001 and averaged over this period to estimate mean January return for a stock. Similarly mean monthly return for all other months are estimated. This process is followed to estimate mean monthly return for both calendar month and option month. Then a cross section of all stocks’ monthly returns are pooled together to conduct different econometric analysis. Once the data are processed and pooled we first test for the equality of mean return and volatility for calendar month and option month i.e. for return if the mean calendar month January returns is equal to mean option month January return and so on. Our hypothesis is that there is no difference between the return and volatilities of these two types of months. Second we also test if there is any seasonality in monthly return. We conduct the seasonality test on both calendar month and option month returns. Our test hypothesis would be that there is no difference between returns in different option months. Using the F test we investigate if the seasonality persists in return pattern of the option months. If the calendar month and option month returns are not the same then the second issue is of our importance. Third if seasonality exists in options month then we investigate if one can capture abnormal returns from the seasonality in options month by applying some trading rules. IV. Econometric Analysis: We propose that the mean return of the calendar month return and option month return are equal. So our formal hypothesis is: H0:µOM = µCM (1) Ha:µOM ? µCM Here µOM indicates the mean option month return andµCM indicates the calendar month mean return. This hypothesis is tested under two different circumstances: when the variance of calendar month and option month are same and when they are different. Similarly the equality 592 International Research Journal of Applied Finance ISSN 2229 – 6891Vol – II Issue – 6 June 2011 of monthly variance is tested to see if the variance based on option month is equal to that of calendar month. So our test hypothesis is of the following form: H0:?OM2 =?CM2 (2) Ha:?OM2 ? ?CM2 Next we investigate the seasonality in monthly returns on both calendar month return and on option month return. The regression equation that addresses this issue is as follows: Rt =? ?1D1t ?2 D2t ?3 D3t ?4 D4t ??????? ?11D11t ?t (3) The dependent variable Rt is the stock’s monthly return at time t ?t is the white noise error term. ? the constant in the right hand side of the equation identifies the monthly return for the month of January. The seasonal dummy variables are defined by the D1t ……… D12 t where 1 for the ith month Di t = otherwise and month begins from the second month ( February) of the 0 year and hence i = 2 —— 12 indicates the difference in return between January and the ith month of the year. V. Results: V.1 Summary Statistics: We calculate returns on calendar month and option month and is presented in Table 1. Table 1A shows the calendar month based monthly returns for the 30 DOW components from period 1970-2001. Results are pooled by the month. It is shown in the table that on an average the DOW components offer the lowest return of -0.686% in the month of September and highest return of 2.89% in the month of January. Please insert Table 1A and 1B about here Looking at the results one would presume that historically September and August are the two worst months for DOW components and January December and November are the best months. In Table 1B it is observed that option period average monthly returns offer different returns. The worst average return for the DOW components comes in October option month with -0.118% and the highest average return comes in January option month with 2.602%. Historically October and September offer the worst returns and January November December and February offer the best returns. Table 2A offers the summary statistics of historical returns for S&P 100 components based on the calendar month. Results show that S&P 100 components offer worst return in the month of April with average return of -3.747% and best return in the month of January with average return of 3.268%. Please insert Table 2A and 2B about here Table 2B shows the summary statistics of historical returns for S&P 100 components based on the option month. On average S&P 100 components offer worst return in the option month of October with average return of -0.051% and best return in the option month of January with average return of 2.823%. 593 International Research Journal of Applied Finance ISSN 2229 – 6891Vol – II Issue – 6 June 2011 Table 3A presents the summary results of historical average returns for S&P 500 components based on calendar month. It shows that April calendar month offers the worst average ret urn of – 6.937% and January calendar month offers the highest average return of 3.449%. Please Insert Table 3A and 3B about here When data are arranged based on option months the summary results are shown in Table 3B. It shows that the lowest return comes in the October option month with average return of 0.102% and the highest return comes in the option month of January with average return of 4.078%. V.2 Results for Mean-Variance Equality Test Table 4-6 shows the test results on S&P 100 components. When the mean returns based on calendar month and option months are tested they are done under two different assumptions: once it is tested assuming the variances (based on calendar month and option month respectively) are equal and next when assumed that variances are not equal. Please Insert Table 4 5 and 6 about here In both cases our test results show that the null hypothesis is rejected implying that the mean return based on calendar month and that of option month are different. Next we conduct a variance equality test and result indicates that even variance calculated based on calendar month and on option month is different. We conduct the similar tests on the DOW 30 components. Table 7 8 and 9 show the test results. When assumed that the variance calculated based on calendar month and option month are equal our mean equality test indicate the similar result that we find in S&P 100 components: the means are not equal. Please Insert Table 7 8 and 9 About Here Also when assumed the variances are not equal the mean returns turn out to be different. The tests for variance equality also show that the variances are not equal either. Please Insert Table 10 11 and 12 About Here Finally we conduct the similar tests on S&P 500 components. The test results are identical to those of S&P 100 and DOW 30 Components. The main conclusion we can draw from our findings is that monthly returns show a pattern when they are estimated based on the option period. Outstanding options contracts whose value depend on the third Friday’s closing price whether they are worthless or not and hence exercised or not may have some impact on the closing price of the Third Friday and that may make the monthly return and variances to be different from calendar month. V. 3 Results for Seasonality Test Seasonality tests are done both on calendar month return and on option month returns. Table 13 shows the seasonality test results conducted on S&P 500 components. Please Insert Table 13 and 14 About Here Results indicate that there is a seasonal pattern exists in the calendar month returns. Table 14 shows the seasonality test results of option month returns. It is quite clear that there is a seasonal pattern in return structure of the option month as well. One difference we like to include is the T statistics for the month of December. Calendar month show that it is insignificant where as the Option month based T statistics shows that it is significant. 594
