It has been noticed over the recent years that the testing for market anomalies in stock returns has become an active field of research in empirical finance, and it has been very subsequently gained enormous attention from the academic journals but also in the financial press. Amongst the commonly known anomalies are the size effect, the January effect and the day-of-the week effect. Let’s talk the road these three anomalies likewise -The day of the week effect is a phenomenon which constitutes of a form of anomaly of the efficient capital markets theory. According to this phenomenon, the average daily return of the market is not the same for all days within a week, as we would expect on the basis of the efficient market theory. Studies that have taken place in the past very adequately suggest that existence of the day of the week effect not only in the USA and other developed markets but apparently is also emerging in international markets such as Malaysia, Hong Kong, Turkey.
As far as the western economy is concerned U.S.A., U.K., Canada a lot of empirical results have shown that on Mondays normally the market has statistically significant negative returns, but as if we compare it with Fridays statistically significant positive returns are due for sure. Where as we talk about markets in different countries such as Japan, Australia, Singapore, Turkey and France the highest negative returns appear on Tuesdays i.e. not much effect as compared to Mondays and Fridays subsequently. Hence it will be the most satisfactory explanation that has been given for the negative returns on Mondays is that usually the most unfavorable news which normally appears during the weekends. The uniformity in unfavorable news influences the majority of the investors with a negatively which ultimately causes them to sell on the following Mondays. The most satisfactory explanation that has been given for Tuesday’s negative returns is the total turnover on a Monday and Friday in the western markets, as a result the major emphases of the weekend affecting the entire US market which rather influences a negatively over the market scenario.
While the markets in Spain intend to explain an entire different story i.e. the Spanish stock market have revealed that there is not a single day of the week which gets effected , [Santemases (1986), Pena (1995) and Gardeazabal and Regulez (2002)]. Solnik and Bousquet (1990) primarily been focused on the period 1978- 1987 and as examined by the CAC Index of Paris Bourse the results emerged with a very extraordinary conclusion and resulting persistent negative mean returns on Tuesday. Although it wondered that whether the settlement procedure could explain the pattern of daily returns as observed in the previous studies of the Paris Bourse. It was also been crosschecked by Dubois and Louvet who later re-examined the day of the week effect over the French stock market as compared and contrasted by the rest of the market places as in compared to the developed nations such as the US and UK. The studies came with a remarkable change i.e. along with other markets such as the US, UK, German, Japan, Australian and Swiss markets, during the period 1969-1992 using the standard statistical approaches using them and moving averages. They observed that Wednesdays presented the highest return while the day with the lowest (negative) return was Monday for all the above markets excluding the markets of Japanese and Australian. Hence the null hypothesis of the equality when actually implemented the mean returns of all days of the week were future been rejected by even 1% of its confidence level.
As a result an increasing internationalization of US, UK and other developed market economies has given the investor additional choices while considering there respective portfolios. Here by it is no longer obliged to focus attention on the financial markets where the assets of there own country are listed in the stock market but instead may look towards other investment horizons whose markets offer opportunities to obtain greater results with respect to profit and its subsequent arising risks within the market. This scenery is future been characterized by significant relaxation of national barriers, thus allowing the entrance of foreign capital, and its repercussions can very easily be seen in the considerable increase of international capital flows (see Climent and Meneu, 1999). Torrero (1999) then also recognized the same occurrences and provided his comment that institutional investors are causing a great internationalization in investments as a result its very obvious that the growing influence of international investments in their portfolios. Rather it is also necessary to remember that investment opportunities in international markets depend on the degree of integration or segmentation of markets, eventually the increasing international nature of these economies is quite evident. In this respect, Jacquillat and Solnik (1978) stated that the advantages that are derived from international diversification resulting in creating freeness amongst the distinct national economy and the price behavior of the securities. Hence if in case the market is been very highly integrated where as the opportunities of receiving profits from an international portfolio are comparatively not so high. The presence of anomalies in international financial market very clearly signifies that – lack of integration among these markets exists; hence investment opportunities derived from different behaviors in the generation of returns are also reasonably available. Various studies have designed and developed on relative anomalies in the seasonality of distinct financial markets of developed countries and as an explanation as in case why there is an absence of integration between the international financial markets when compared with the developed nations. Hence potentially accelerated use of daily data has led to an enormous studies in the financial literature and which future concludes that – there has been a rather more monotonous change been noticed as in market when compared to a bank holiday weekend to that of a normal weekend, hereby this topic has offered various justifications for these anomalies: the absence of negotiations during the weekends, Monday’s availability of information regarding the overall responses to generated information as and when compared to the non-listing days.
Days of the week effect
This variation in the seasonality has been the subject of different studies which eventfully detected ample evidences of abnormal distributions based upon the various days of the week. The pioneering of work considered to be one of its kind was carried out using data from the developed nations markets such as United States and the UK primarily. Here are the different authors who primarily made there important contributions: Osborne (1962), Cross (1973), French (1980), Gibbons and Hess (1981), Lakonishok and Levi (1982), Keim and Stambaugh (1984) and Rogalski (1984). This effect has also been minutely examined in security markets under an international setting with works conducted by Jaffe and Westerfield (1985a), (1985b), Aggarwal and Rivoli (1989), Solnik and Bousquet (1990), Chang, Pinegar and Ravichandran (1993), Athanassakos and Robinson (1994), Corredor and Santamaría (1996), Dubois and Louvet (1996) and Kyimaz and Berument (2001). A very fresh circulated article by Pettengill (2003) very clearly depicts that the day-of-the-week and its subsequent effect in stock markets which dates straight back to 1930s. Another researcher has done his in-depth analyses i.e. Fields (1931) who has very clearly experimented and re examined that the effect is very consistently noticed positively on a Saturday rather that as we compare and evaluate on a Monday , what other researchers are trying to prove that – weekends prove to be more worthy rather compared to a Monday, where as Cross (1973) finds that S&P’s returns during 1953-55 in the US markets were open on Saturdays every month before 1946 and apart from the summer months before 1953. Hence in totality 70% market performance is significantly negative on Mondays (-0.18%) and positive on Friday (0.12%). Whereas French (1980) examines the daily seasonal of S&P500 from the period of 1953-1977 and finds that statistically and significantly that market behaviour is very much negative on a Monday.
Again a study been conducted by Gibbons and Hess (1981) examine S&P500, CRSP value and equally-weighted the index and 30 individual stocks of DJIA and documented that Monday’s return are significantly and strategically negative not only for stock indices but also for the point of view of individual stock. In the same manner Keim and Stambaugh (1984) examine the day to day of the week’s effect for a very long period i.e.from (1928-1982) and find statistically significant negative Monday returns and positive returns on the last trading of the week for S&P composite index. Studies for Negative Monday returns are also documented by Lakonishok and Smidt (1988) for DJIA and subsequently by Lakonishok and Maberly (1990) for NYSE. Where Rogalski (1984) examines the negative Monday effect in terms of trading which actually started from Monday close and finished on Friday close, and non-trading (Friday close to Monday open) day returns and documents that irrespective of firm sizes the day-of-the-week effect occurs during non-trading period. Working on the theory of intra-day returns, similar findings (negative overnight weekend returns) were also documented by Smirlock and Starks (1986) for DJIA (1963-1983); Dyl and Maberly (1986) for S&P500 futures; Harris (1986) and Jain and Joh (1988) for NYSE; Becker and Finnerty (1993) for UK and Japanese index futures and Chow, Hsiao and Solt (1997) for S&P500. Where as Harris (1986) finds evidences of size effect in weekend returns. Siegel (1998) provides the longest time frame (112 years) of investigation on the day-of-the-week effect for DJIA. From the year of 1885 to 1997, he reports that the average daily return has been.024%, while the average returns for Mondays has been at minus 0.110% which in itself explains the story. They find negative Monday returns in all sub-periods: -0.087%, -0.211% and -0.089% during 1885-1925, 1926-1945 and 1946-1997 respectively. He then future documented that if Monday returns, on average, were equal to the other days of the week, the DJIA index would have been approximately twice to that of its current level (year end 1997). Recent studies document a shift (higher and positive Monday returns) in the day of- the-week effect especially for the larger firms. Connolly (1989) shows some degree of Monday shifts for the US markets during mid-1970s. Kamara (1997) examines S&P500 on the small-cap index of the NYSE during 1962-1993 and finds that after the introduction of S&P future contracts in April 1982, the Monday effect has actually declined significantly for large stocks usually hold by institutional investors. Brusa, Liu and Schulman (2000) find a reverse weekend effect for S&P500, NYSE, DJIA and CRSP value-weighted indexes during 1990-1994 (dominated by large-cap portfolios). However, negative Monday effect still dominates for small-firms. But where as it still remains a open question weather any anomaly existed in the market, the investors can take advantage of the same and adjust their buying and selling strategies in such a manner that would increase their returns with timing the market. The day of the week effect in Indian market was examined by many researchers (Chaudhury (1991), Poshakwala (1996), Goswami and Anshuman (2000),
All combined together Choudhry (2000), Bhattacharya, Sarkar and Mukhopadhyay (2003). The relevant study of all except Choudhry (2000) and Bhattacharya (2003) have been based on data of mid-1980s and mid-1990s and all these studies have used conventional methods like serial autocorrelation tests and or fitting an OLS. Then Choudhry future (2000) examined seasonality of returns and volatility under a unified framework but the study had a misspecification issue with regard to conditional mean. Bhattacharya (2003) used GARCH framework by incorporating the lagged returns (BSE 100) as explanatory variables in the conditional mean. They have used reporting and non-reporting weeks to study the day of the week effect. All these studies have used to analyze the end of day data. The availability of high frequency data from NSE has opened up many avenues of research that helps us to look closer into the market activities. Where now a days the study normally finds out the week effect on India equity market using high frequency data. This study is different in two aspects: (1) it uses the high frequency data to study the day of the week effect and for the same we have to calculate the 1-minute returns and then aggregate the same for the day to get the daily returns. This process is primarily been done to understand the market dynamic observed during the whole day and to conduct a study on a micro level analyses. The closing value which is generally available is the average of last 30 minutes of trade and may not suitably bring out the dynamics of the market and most of the information that happens during the day is not absorbed in the last 30 minutes of trades; (2) the study also does a comparative analysis using the closing values to understand if any additional valuable information can be obtained from high frequency data.
The Weekend or Monday effect is described as the tendency for Monday security returns to be low (or negative) compared to other days of the week. This evidence is also observed before or after the holidays. The weekend effect is theoretically interesting because of its deviations from market efficiency and practically appealing if smart investors can exploit it to earn excess returns. Several studies depicts t that the autocorrelations are greatest over the weekend (Friday-Monday) than over other pairs of days. Assuming this effect still exists (and it may not) it would suggest a profitable trading strategy, namely investing in the US market on Fridays when the market appeared likely to close high (based on one of the indices that is available during the day, and hence available just before the market closes) and being out of the market on other days or being in the market on all days except Mondays. Because of spreads and commissions the trading strategy might not be profitable when applied to buying individual stocks. However, if implemented through mutual funds families or retirement funds or annuities that permit switching from one fund to another with no charge, above market returns (returns that are above market on a risk adjusted basis) may be possible.
Hence the overall intuition behind the weekend predictability and profitable trading opportunities is discussed as follows- For instance Japanese index provides positive Friday returns and negative Monday returns then the US-based Japan mutual funds will also exhibit negative Monday and positive Friday returns because the underlying shares of the US based Japan mutual funds are finally based on the considerable behavior and performance of the Japanese index. The US investors can form a simple trading strategy: sell the US-based Japan mutual funds on Friday and shift investment to a money market fund to avoid negative Monday returns from Japan funds. The US-investors may also use a complex trading strategy by shifting their investment out of risky Japan funds and into money market funds on days(s) of the week when mean returns for a major Japan index historically exhibit negative returns. Hence by the application of this model no investment cost is been beared by investers which costs to exchange one mutual fund for the other fund within fund families, retirement accounts or variable annuities (although some mutual funds impose restrictions on frequent exchanges, these time the dependent trades have a vital role to play and can actually save retirement accounts without transactions costs).
Mutual funds are gathered assets from investors’ point of view and collectively invest those assets in stocks in the form of bonds or money market funds. Therefore the effect that produces the predictability in the stock prices would also produce predictability in the NAVs of a mutual fund or retirement annuity. If an investor of stock market finds a rule that predicts that stock’s closing prices will rise by a significant amount from Thursday to Friday, he will buy stocks at Thursday’s closing price and will sell stocks at Friday’s closing price. This will provide us with some substantial profits. However many investors will find out this profitable trading rule because of weak form of market efficiency, Then its very much obvious that they will also execute it and eliminate the profit by bidding the Thursday’s closing price up (demand pressure) and asking the Friday’s closing price down (supply pressure). Hence any effort to exploit predictabilities and probabilities by trading individual stocks tends to eliminate the predictability and should quickly reduce them to the level where the remaining trading profits are offset by transactions costs. Similar to individual stock investor, an investor of mutual fund might also be able to make profits by buying the mutual fund, or retirement annuity at Thursday’s NAV and selling it at Friday’s NAV. Nut precaution should also be taken that trading strategy would be unlikely to eliminate the effect observed in stocks if the trading was done in mutual funds or retirement annuities.
Ultimately the fund manager does not have the influx of funds until after Thursday 4PM ET when the funds’ NAV’s are computed and future calculated when the trades are actually done. Furture to that if there is no action been taken by the fund manager until the new funds are received, the effect may continue because no action is taken to affect stock prices. Providing an alternative to if the fund manager responds to the influx of new funds by buying more stocks immediately then here are consequences that may arise – (1) if fund manger’s buying is too little to affect the prices, the effect will continue or (2) if fund manager’s buying is large enough to affect prices, it will increase the closing Friday prices of the underlying stocks of the mutual funds, and eventually fund’s NAV will be even higher on Friday. The trading actually accelerates the happening price change, especially for specialized mutual funds and sectors where in the funds where there is little scope for diversification. Hence on a day-to-day transactional level it could persist even if they were known and Miller, Prather and Mazumder (2003) show exploitable patterns in mutual fund returns.
Within Month Effect
The over all result was noticed within Month Effects allowing returns to vary as each month progresses. The evidence (e.g. Ariel, 1987, Ogden, 1990, Peterson, 1990, Kohers and Patel, 1999) that the returns are higher near the beginning of the month and much lower at other times. Various reasons have been suggested, including liquidity (Ogden, 1990) and earnings announcements (Peterson, 1990). Since now the various effect has been noticed through the way within-month effect is measured in the literature, with Ariel (1987) distinguished between the first half and second half of the month, and later Kohers and Patel (1999) considered each third of the month, here we initially introduce difference in zero-one dummy variables for each working day of the month, before and after simplifying the entire model. As we have very well analyised and understood that turn-of-the-month is defined in the beginning with the last trading day of the month and ending has b later been defined with the third trading day of that of the following mont Lakonishok and Smidt L&S (1988) appear to be the first to have reported a turn-of-the month seasonal in equity returns. Research and methodologies adopted by Dow Jones Industrial Average (DJIA) have very well figured out that the on average, the four days at the turn-of-the-month account for all of the positive return to the DJIA over the period of 1897-1986 has been although of a vital change. Redefining the same more accurately and specifically- over the 90-year period, the average cumulative return on the four-day turn of the-month has been 0.473%, whereas the average cumulative return over the full month is 0.349%, This very well indicated us how returns have been and the overall influence was surely negative over the remaining days of the month. Taking into consideration the relatively small sample which was later designed and developed by the DJIA, which also includes only 12 to 30 stocks over the period considered, and future concluded that ultimately the effect is not just their primary concern, L&S. Then future Schwert (2003) has again noted that return patterns that appear during a particular time has been fluctuating once they have been discovered or once when they are in scrutiny which eventually turns out not to be a negative model against the one which existed at the first place. It has Rather been pressed with admonition, studies put into it gives us vibrant results i.e. the L&S study performed in 1986 came out with a very obvious starting point for our analysis i.e. in the 19th year period that has been transpired since the end of the period they have been examining. The return over the period of 1987-2005 is remarkably similar to the pattern over the earlier time frame. During this post-1986 period, the average daily value-weighted as (VW) excess market return over the four-day turn-of-the-month interval is 0.14%. Whereas in comparison, the average daily VW excess market returned over the other 16-trading days of the month is -0.01%. With equal-weighted (EW) excess market returns, the average daily turn-of-the-month return is 0.24%. In comparison, the average daily EW excess market return over the other 16-trading days of the month is 0.04%. Hence resulting the entire period of 1987-2005, eventully the turn-of-the-month effect is pronounced and that too results indicating the difference very much statistically significant. In addition to this is the changes especially with VW returns, virtually all of the excess market return over the 19-year interval accrued during the four-day turn-of-the-month such as that of investors receiving very little or absolutely no reward at all for bearing market risk over the 16 trading days of the month. The effect occurs at turns-of-the-month that coincides with turns-of-the-year, but the effect keeps on happening over the other months. Likewise, the turn of-the-month effect is not concentrated at calendar-year but focusing at the quarter and the way it eventually ends. With the useage of standard deviation of return for measuring risk, we find that risk is no higher during the four turn-of-the-month and days than over the other 16 trading days of the month. Hence higher risk does not appear to explain the turn-of-the-month effect. In a related analysis, we ask whether the size i.e. from book-to-market and momentum factors identified by Fama and French (1993) and Carhart (1997) exhibit a turn-of-the-month pattern. The turn-of-the-month pattern is not due to a change in risk or to a change in the risk premium at the turn-of-the-month, but would rather due to a more fundamental shift in the economical activities of various countries. The Fixed income securities do not evidence or consistent in turn of-the-month pattern. Where by systematic month-end shift in interest rates does not appear to explain the turn-of-the-month pattern in the equity returns. The turn-of-the-month effect occurs in 30 of them. The effect is apparently not due to a factor unique to the U.S. market structure. Later Ogden (1990) future proposed that the turn-of-the-month effect is due to“regularity in payment” especially with the USA. The idea is that investors receive a series of compensation from there respective employment in the form of dividends as a part of profits and interest at month-ends. Hence when it comes to Investors it’s already presumed that they will invest those funds immediately. By doing this the equity prices are pushed up.
Ogden examines daily CRSP market returns over the period 1969-1986 and finds that the turn-of-the-month effect is concentrated in months with a very tight monetary policy which is measured by the spread between the Fed funds rate and the conclusion of the 30 day billing cycle. He then also concluded that these results support in making the payments regular as the explanation of the turn-of-the-month effect. As a more direct test of the payday hypothesis, we examine net fund flows to a set of mutual funds tracked by Trim Tabs over the period starting from February 1998-December 2005. Our presumption is that, if the turn-of-the-month pattern is due to a net demand by individuals for equities at the turn-of-the-month, this will show up as a monthly pattern in net flows to that of the equity mutual funds. A further test of the payday hypothesis concludes where as we consider daily aggregate NYSE into trading volume over the period 1926-2005. Additionally, the preponderance of a turn-of-the-month effect in other countries would argue against the payday hypothesis. What explains the peculiar, long-lived and, apparently, global turn-of-the-month effect in equity returns? There are currently three extant explanations of security returns. The first is factor models of asset pricing. These include such models as the classic capital asset pricing model of Lintner (1965) and Sharpe (1964). Two prominent factors in such models are the risk free rate and volatility of return. Thus, turn-of-the-month returns do not seem to be related to these pricing factors. The second class of models comprises of the characteristic and models of asset pricing. According to characteristic models returns are related to security characteristics. Two identified effect is not related to either of these characteristics. Thus, these security characteristics do not appear to be able to explain turn-of-the-month returns either. The third class of models relies on irrational investors. These are labeled behavioral models of asset pricing. These include such models as Hong and Stein (1999) and Daniel, Hirshleifer and Subrahmanyam (1998). Under these models, investors repeat errors in assessing security payoffs. Such a model might be able to explain the turn-of-the-month effect in equity returns, but an explanation does not appear to lie in extant models. The turn-of-the-month effect in equity returns presents a challenge to extant asset pricing models. Historically, seasonality’s in the asset returns have been labeled as “anomalies.” At some point of time a persistent anomaly becomes the norm and where in as The turn-of-the-month effect in equity returns appears to have persisted for over 100 years, or for as long as we have been reliable daily data to inspect it. Perhaps it is the norm.
The analyses of turn-of-month effect begin with studies that report that small-cap stocks significantly outperform large-cap stocks (Banz (1981), Basu (1977), Chan, Chen, and Hsieh (1985), Reinganum (1981)). Perhaps small-cap stocks outperform large-cap stocks primarily at turns-of-the-month and the small-cap premium is the same as the turn-of-the-month effect. Examinations of stocks sorted by price and by whether the turn-of-the-month coincides with the turn-of-the-year are motivated by studies that have shown that stocks in general perform well after the turn-of-the-year and that this superior performance is concentrated among low price stocks (Jones, Lee and Apenbrink (1991), Conrad and Kaul (1993), Ball, Kothari and Shanken (1995), Baytas and Cakici (1999)). The analysis of calendar quarter-ends is motivated by studies that report exceptional performance by mutual funds at the turn-of-the-quarter and attribute this to last minute end-of-the-quarter trades that are designed to drive up prices and improve reported mark-to-market fund performance (Bernhardt and Davies (2005), Carhart, Kaniel, Musto and Reed (2002)). The analysis of volatility of returns is motivated by traditional asset pricing theory that posits a positive relation between risk and return where risk is measured by standard deviation of return.
The turn-of-the-month effect occurs in both the small- and large-cap of stocks, but it is more pronounced and prominent in the small-cap portfolio. Keeping larg stocks the average daily turn-of-the-month return is 0.15%, while the average return over all other days is 0.01%. The difference between the two is significant with a t-statistic of 7.81. With small-cap stocks, the mean turn-of-the-month return is 0.25%, while the mean return for all other days is 0.03%. This difference also is highly statistically significant with a t-statistic of 8.54. We do not replicate this analysis using an EW index because stocks within each domiclie have similar market values such that the VW returns are essentially EW returns. This analysis demonstrates that the turn-of-the-month effect is not just a variation of the high returns historically earned by small-cap stocks. Regardless of market capitalization, U.S. equities earn the bulk of their returns over the four days beginning one day prior to and ending three days after the end of the month.
The turn-of-the-month effect occurs among both high- and low-price stocks and with both VW and EW indices. Furthermore, given the correlation between stock price and total market capitalization, it is perhaps not surprising that the effect is more pronounced and perpetual among low price stocks. Nevertheless, the effect is also strong among high-price stocks. For example, with VW returns, the mean daily turn-of-the-month return for high-price stocks is 0.19%, while the VW return for all other days is 0.04%. The t-statistic for the difference between the two is 8.22. For low-price stocks, the mean VW turn-of-the-month return is 0.27%, while the mean return over all other days is 0.03%. This difference, too, is highly statistically significant ( statistic = 7.53). As shown in panels B.3 and B.4, with EW returns the results for high- and low-price stocks are quite similar to those calculated with VW returns in panels A.3 and A.4. Hence a very clear conclusion can be drawn i.e. the turn-of-the-month effect is different from the low-price effect examined and experimented in other part of Europe and elsewhere. If anything, the low-price effect may actually be a turn-of-the month effect. Once the turn-of-the-month effect is accounted for there may be no low-price effect at the turn-of-the-year.
The turn-of-the-month effect can very clearly be seen and noticed in -December-January turns-of-the month. Lets consider For example, with VW returns, the average daily turn-of-the-month return for all non-December-January turns is 0.15%, while the mean daily return for all other days of these months is 0.00%. The t-statistic for the difference is 7.86. The results with EW returns are quite similar. Given that most turns-of-the-month are non-December-January turns, it is perhaps not surprising that these results are similar to those for the overall sample. Clearly, the turn-of-the-month effect is not just due to unusual returns at the turn-of-the-year. Even though there is a distinct turn-of-the-month effect at the January-December turn, the magnitude of the effect is different from non-January-December turns. First, consider the VW returns in panel A.6. For the December-January turn-of-the-month, the mean daily return is 0.23%. For all other days of these months, the mean return is 0.10%. The t-statistic for the difference is only 1.87. Thus, in general, returns during December and January are high, but they are even higher at the turn-of-the-month. These high returns are reflective of the well known high January returns that have been documented previously (Rozeff and Kinney (1976), Roll (1983), Chan (1986), Haugen and Lakonishok (1988)). High January returns have historically been concentrated among low-cap stocks. This factor is manifest in the EW returns of panel B.6. With EW returns, the mean return for the December-January turn-of-the-month is 0.81%, while it is 0.20% over the other days of these months. The t-statistic for the difference is 7.41. (As an aside, it is interesting to note that a major component of the high turn-of-the-year effect occurs on day -1 with an extraordinarily high mean EW return of 1.06% over the 1926-2005 interval).
This effect actually allows the mean returns to be different on the day before a holiday and the day after effect made a vital change. The evidence for holiday effects (e.g. Pettengill, 1989, Ariel 1990, Vergin and McGinnis, 1999) is that higher the normal returns occur before a holiday, because of increased activity overall around, and lower returns after the holiday. Holiday effects can be represented by zero-one dummy variables for the day before and the day after a holiday. The mean stock returns on the first trading day after a holiday is relatively low as studies incorporated by (French, 1980; Lakonishok & Smidt, 1988). But In contrast, the mean return on the last trading day before a holiday tends to be unusually high (Ariel, 1990). In the U.S., 35 percent of the market advances in the years 1963-1982 occurred on the last trading days before holidays (Jacobs & Levy, 1988). The holiday effect seems closely related to the weekend effect. The similarity is not only in the low return after a weekend or holiday. Trading days before holidays and weekends also behave qualitatively the same. Keim and Stambaugh (1984) report that in the 1953-82 periods the average return of U.S. stocks on Fridays was 0.092 percent, which is large relative to the average daily stock return of 0.025 percent during that period.
The entire data that was used for analysis the seasonality was been captured from the U.S. database. This led researchers a reason to worry because the anomalies are artifacts of extensive U.S. “data mining.” Hence in order to safe guard against this false-positive alternative, other stock markets besides the U.S. were examined. Studying equity markets outside the U.S. seems prudent also because an international comparison might aid in formulating hypotheses about the origin of the seasonal effects. For example, if it is found that a particular seasonal is missing in some market, the source of this anomaly should relate to the unique features of that market.
Studies of international stock markets such as Jaffe and Westerfield (1985) generally confirm the U.S. findings. However, occasionally international markets exhibit different behavior than the United States. For example, Cadsby and Ratner (1992) do not find pre-holiday effects in any of the European countries; Lee, Pettit and Swankoski (1990) do not find lower post-holiday stock returns in Japan and Taiwan; and Kato (1990) and Ziemba (1991) find higher returns on the last five to seven days of the month in Japan.
It has been observed that calendar anomalies are often cited as evidence in contrary to market efficiency because by the arbitrage rate it should rather rule out the possibility of psychological or institutional factors would systematically affect asset prices. Hence this S&P 500 Index can future be classified from 1990 – 2002 into two categories: – (A) The trading days in which macroeconomic announced the occurrence and all other days. If investor psychology or market institutions were the cause of calendar anomalies, one would expect their impact to be most pronounced on the days in which no macroeconomic announcements were made. Hence by the implementation of the important of macroeconomic data can be incorporated into asset price valuation, one would expect that subtle influences like psychology or institutions would be most noticeable. However, the six calendar anomalies – the turn-of-the-month, monthly, rainfall, temperature, holiday and lunar effects – found in the S&P 500 Index from 1990 – 2002 do not exist on the roughly 2/3 of the days in which macroeconomic announcements were not made but All six of the calendar anomalies are due to unusual returns that occurred on macroeconomic-announcement days, which implies that the anomalies are generated by market responses to macroeconomic data and not institutions or psychology. The observations can also call into question the conclusion that the calendar anomalies are simply not just a manifestation of data mining, but eventually are the evidence that suggested that the calendar anomalies arise because of fundamental information affecting asset prices, and which is occurring due to uneven distribution across time.
Investors that are interested in including international markets in their portfolio need to know if these markets are integrated or not. We pursued the answer to this question by studying possible seasonality in international markets. Our analysis focused on an empirical comparison of the day of the week effect in the major European markets from July 1977 to March 2004, and included not only returns but volatility as well.
To begin with it has been very well examined and that most of the European markets/countries actually do not reflect a day of the week effect since the results for each day do not differ significantly from the other days of the week. The returns in these markets are based on representative index and with relevant independent concerning which day of the week the return is calculated on. Whereas the seasonal effect can be observed on Mondays as far as the market condition in the French and Swedish market is concerned. The Swedish markets also reflect a very significantly higher return on Fridays as opposed to the remaining days of the week. With respect to the existence of abnormal volatility in the equation of conditional variance in the European markets, the following can be observed. A day of the week effect is present in all of the financial markets except in Portugal and the Czech Republic, where a similar model is applied all over. But as we don’t live in a perfect world, Exceptions are found in France and the Czech Republic, using an asymmetric T-ARCH model. Nevertheless, this effect does not agree with other analyzed financial markets in eupore and in the USA. However if we introduce a parameter which accounts for different behaviour in the volatility of the stock market indexes, then continuity in the day of the week effect becomes evident, differentiating the rise and fall of prices. Its presence can be explained to that of the GARCH model because the statistical significance of the day of the week in the symmetric model in some cases could have been affected by asymmetric effects that were considered in the structure of the variance in the model. The Seasonality conditional volatility in specific markets follows a similar behavior pattern which is actually an independent type of model that is being used. Mondays and Thursdays are more uncertain than on Wednesdays, while the Wednesday measure is lower than that of Tuesdays and Fridays. Even though initially there does not seem to be a day of the week effect that yields from different European markets, an analysis of the conditional variance verifies that the extreme shifts observed in the major stock markets of each country indicate the absence of complete integration among all markets. This finding came out to be of a great significance i.e. data can be useful for an investor who is looking for investment instrument opportunities based on the change in volatility of these financial markets during specific days of the week.
Hence the compelling part of evidence is over the calendar anomalies i.e. there existence is in many international markets i.e. not just bounded to the United States. Amongst the four of the Asian markets examined above, there were unusually high returns on the days following the release of macroeconomic data in the U.S. But Further for the fall and turn-of-the-month calendar anomalies, which exist in most of the Asian markets, the evidence shows that returns on days following macroeconomic announcements in the United States create the calendar anomalies in six of the eight cases. Given that returns on macroeconomic-announcement days drive the calendar anomalies in the S&P 500 Index from 1990 – 2002, and that U.S. macroeconomic announcements created unusual returns in the Asian markets during this period, hence there should be no hesitation in accepting the fact that calendar anomalies that exist in the U.S. also exist abroad in various countries.
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