Antecedentes

Calendar patterns in stock returns are of different types and have been credited to a collection of factors notable among which are psychological or behavioural in nature (Malkiel, 2003). Explained below are the different types of calendar anomalies, but the emphasis is very much on the DOW and MOY being the most prominent effects and HOM effect as one of the earliest calendar effects. Although there seems to be no consensus on the reasons for these anomalies, some of the reasons for each calendar effect are also identified and discussed.

39 2.6.4.1 DOW/Weekend Effect

Common calendar patterns in stock returns have to do with weekdays. Weekdays’ effect involves the existence of higher returns than normal on certain days of the week, often in a recurring pattern over the year (Magnus, 2008). The DOW effect is the tendency for returns on stock to be abnormally greater on certain weekdays than on other days (Hassan et. al., 2015). It explains that the expected or standardised returns are different for all weekdays. For instance, the Friday anomaly compares the previous trading day’s closing price return; say Thursday to Friday’s closing price and similarly for the other days (Hansen & Lunde, 2003). According to the DOW effect, the returns on some days of the week are substantially different from the returns on other days of the week (Brooks & Persand, 2001). In other words, the distribution of security returns is not identical for all days of the week and it might vary based on the day (Rossi, 2007). Further, Pandey and Samanta (2016) state that the DOW effect is evidenced by notably different returns on certain days of the week, notably larger Friday returns and lower Monday returns. One well-known discovery among market participants and academics is the tendency of stock prices to fall on Mondays. Monday effect states that Monday returns are generally negative and lower than those on Tuesday through Friday (French, 1980). Monday effect is where the returns are significantly lower over the first trading day of the week (Yuan et al., 2006; Levy & Yagil, 2011; Floros & Tan, 2013). Results of the studies on DOW effect worldwide have generally indicated higher Friday and lower Monday returns, hence the use of weekend effect. Scholars (Dragan, Martin & Igor, 2012) have defined DOW effect to mean the same as weekend effect.

Weekend effect is otherwise known as the DOW effect (Dragan et al., 2013). It holds that securities displayed much lower returns over the period between Friday’s close and Monday’s close (Gibbons & Hess 1981, Mills & Coutts 1995, Al-Loughani& Chappell 2001). Weekend effect suggests that returns on Monday are significantly different from returns on Friday with the likelihood of security to display relatively high returns on last, compared to those on first days of the week (Phaisarn & Wichian, 2010 and Martin, 2011). Alagidede and Panagiotidis (2006) argue that the effect occurs where returns on

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Monday are appreciably lesser relative to other days of the week. Ideally, the returns on Monday should account for investment over 72 hours from Friday close till the opening on Monday, hence greater than the 24 hours returns expected for other days in the week (Dragan et. al., 2013). In other words, the anomaly presents a puzzle, as Monday returns cover three days, one would anticipate higher returns for other days in the week, as the longer period amounts to higher risk. It is safe to conclude that Monday effect, Friday effect and weekend effect are all subsets of or embedded in the DOW effect. There are several explanations for the DOW/weekend effect, ranging from investors’ psychology hypothesis, pattern of information flow and information release hypothesis, information processing hypothesis and settlement regime hypothesis.

Reasons for DOW/Weekend Effect

Amongst the several explanations for weekday effect, the primary one is the short selling14_{, as stated by Singal (2004). The author argues that this effect comes from}

unhedged short sellers that take a lot of risk. and this way they need to monitor their positions closely to avoid losses, which they cannot do in non-trading hours, therefore, they become highly exposed to risk as new information can arrive to the market and they cannot trade. This type of investor would want to close their positions before the end of the trading days, but because of the costs to do that, they would only close15

their positions on Fridays because the weekend is a period with more hours of non- trading, so they will have more risk if they left their positions open. Singal (2004) found evidence of the hypothesis, where the stocks with higher levels of short selling have stronger presence of this effect; additionally, this author states that this effect is more intense with institutional investors since individual investors do not execute short selling that often.

14_{A short position, is selling first and then buying later. The trader's expectation is that the price will drop;}

the price they sell at is higher than the price they buy it later, for profit.

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Investors’ psychology hypothesis may play a significant role in explaining DOW effect. Rossi (2007), Rystrom and Benson (1989) point out that investors may sometimes act irrationally; therefore, their economic decisions may be influenced by moods, emotions etcetera. In addition, if these moods differ across the days of the week it can very well produce differing degrees of optimism and pessimism across the days of the week, hence, differing returns to assets. Rystrom and Benson (1989) argue that if investors feel more pessimistic on Mondays than on other days of the week, they sell their securities and depress prices. In contrast, on Fridays, optimistic investors buy securities and create upward pressure on prices. In other words, Monday is seen as a bad day and investors are less positive. Hence, they will be more likely to sell and less likely to buy.

Pattern of information flow and information release hypotheses have also been used to explain DOW/weekend anomalies. Niederhoffer (1971) argues that stock markets react to both good and bad news headlines. According to Dyl and Maberly (1988), information flow over the weekend is the cause of weekend effect. Negative information flows on weekend days and the two non-trading days enable investors to absorb the information before reacting with trading activity. That is, the pattern of information flows, according to Damodaran (1989) and Lakonishok and Maberly (1990), who state that bad or unfavourable news tends to be released on Fridays or during the weekends and this leads to low demand or negative returns on Mondays. Consequently, Pettengill (2003) argues that investors would avoid purchasing securities on Mondays as a result of fear of the possible loss from trading with well-informed traders whose decision to sell might be based on bad information they have received during the weekend. Firms and government usually release bad news on weekends (Saturday and Sunday) and generally release good news between Monday and Friday. Hence, the bad news explains negative Monday returns while good news explains higher Friday returns (French, 1980; Rogalski, 1984; Damodaran, 1989; De Fusco, 1993).

Similarly, there is the information processing hypothesis, which postulates that while it is costly for all the investors to collect and analyse information, it is more costly for the

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investors to do so during weekday trading hours when they are engaged in other activities (Miller 1988; Lakonishok & Maberly, 1990). Therefore, weekends provide a convenient, low-cost opportunity for individual investors to reach investment decisions. Consequently, individual investors might be expected to be more active when markets reopen; although, they may put some buying orders through during other days of the week based on the recommendations of stockbrokers, for selling orders they rely on their own analysis. This causes the selling pressure to exceed the demand on Monday. On the other hand, the trading volume of institutional investors remains depressed on Monday morning. Osborne (1962) further explained that the decrease in trading activity of institutional investors is based on an industry-wide practice of using the early trading hours of Monday as an opportunity to plan strategy for the upcoming week. Simply put, individual investors make their financial planning during weekends and become more active on Mondays (mostly with selling orders) and institutional investors would make their planning on Mondays, thus, they would be less active in the market.

Another explanation for the negative weekend effect is that the delay between the trade date and the settlement date creates an interest-free loan until settlement. Friday buyers get two extra days of free credit, creating an incentive to buy on Fridays and pushing Friday prices up. The decline over the weekend reflects the elimination of this incentive. This hypothesis is supported by the intra-week behaviour of volume and returns: Friday is the day with the greatest volume and the most positive stock returns. Gibbons and Hess (1981), Lakonishok and Levi (1982) report that the waiting period before the cash settlement for an asset can result in an increase in asset return on certain days owing to the additional credit arising out of the two weekend days. Overall, Lukas (2012) submits that there has been no convincing justification than the psychological cause of DOW effect.

2.6.4.2 MOY/January Effect

When stock returns on a particular month are higher than other months of the year, the result is the MOY effect (Olowe, 2010; Oba, 2014). This is described by Rahele, Fereydoun and Mohammad (2013) as monthly effect, which holds that the average

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return for stock depends upon the month of the year. Large numbers of empirical results on this effect have indicated the presence of higher returns in January than other months of the year, hence, the name January effect. The effect is the likelihood that security returns in January are larger than or exceed those of other months of the year (Alagidede & Panagiotidis, 2006; Aylin, 2014). January effect is the foremost and the most important calendar anomaly because January has an important implication in predicting the movement of the stock market for the rest of the calendar year (Haugen & Jorion, 1996; Rossi, 2015). Jayen (2016) showed that mean raw returns of January month are relatively greater than mean returns of the remaining 11 months of the year. The MOY and January effects have also been used interchangeably with the turn of the year (TOY) effect in the literature to explain the possibility of estimated returns being larger in the January month. This is particularly so in the first few trading days of the month than the returns obtainable in other months of the year (Rozeff & Kinney, 1976; Keim, 1983; Gultekin & Gultekin, 1983 and Alagidede 2013). The TOY effect refers to the anomaly, which causes the stock prices to rise between 31st December and the end of the first week of January (Ana, Luís & José, 2015). Thus, the effect considers the last trading day of the previous year to the fifth trading day of the new year. The effect is characterised by an upsurge in purchase of stock by year ending at a lower price, for sales in January to generate profit from the price differences (Karadžić & Vulić, 2011).

Reasons for MOY/January/TOM

One of the explanations provided for the January effect is rooted in tax-loss-selling hypothesis. This hypothesis states that the investors put up for sale shares that perform poorly at the closing stages of the tax year in order to realise capital losses. This is done to make up for profits on other shares and in so doing cut investor’s tax liability. Given that most countries have December as the tax year end, tax-loss-selling leads to a decrease in prices towards the end of the year. As soon as investors begin acquiring stocks again in January, there would be a price increase and the January effect occurs (Branch, 1977; Dyl, 1977; Aylin, 2014; Márcio, 2015). In other words, as most investors

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sell securities towards the end of the year, the pressure leads to a fall in prices at year ending. In January, when this downward pressure is relieved, securities rise back up to their equilibrium values, thereby creating higher returns. That is as investors invest their money in January, the pressure on the prices leads to a rise thereby generating higher returns (Elton & Gruber, 1995). Since taxation of capital gains is common in all developed countries, Africa can act as a counter example because capital gains are usually free of taxes. Hence, tax-motivated selling may not be observable on the African Stock Exchanges. Turn of the year or January effect effects could also be explained by liquidity hypothesis. The liquidity hypothesis of Ogden (1990) postulates that individual investors receive additional cash via holiday bonuses and annual salary benefits at year ending and plow this money into the stock market, leading to an increase in demand, prices and stock price changes at the turn of the year.

Another well-known explanation for the January effect is the window dressing hypothesis developed by Lakonishok, Shleifer, Thaler and Vishny (1991). This theory states that institutional managers, who are evaluated based on their performance, sell poorly performing stocks at the end of the year to make their portfolios look safe and successful. Then, in January, after the year-end evaluations, they buy back the loser stocks. Because of these window dressing actions, prices go down in December, which causes the December returns to be low; and up in January, which causes the January returns to be high. This means that at the end of the year many professional fund managers decide to sell those stocks that have performed badly during the year in order to avoid their existence in annual reports. At the beginning of the year, managers buy a lot of stocks that have performed extremely well in order to make their funds attractive for investors (Sharpe, Alexander & Bailey, 1999).

Apart from the window dressing hypothesis, Merton (1987) formulated investor recognition of new information hypothesis, which was studied by Chen and Singal (2004). In line with this hypothesis, investors are inclined to acquire more stocks once companies release new information, for the reason that this kind of information boosts their consciousness. Because new information is normally released at the start of the

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year, investors are persuaded to put more buy orders in this time and, consequently, the stock returns in January months are drastically bigger. The investor recognition hypothesis holds that the frequency at which stocks are bought and sold is higher in January than in December for the reason that dealers will delay investments until the beginning of new year, when many companies will make new information public (Aylin, 2014).

A risk-based argument for the January effect was offered by Rogalski and Tinic (1986). They opine that nearly all investigations on seasonality in stock returns erroneously presumed that risk remains unchanged all through the year. It was argued that the stock returns in January are greater than in any other month, as is the risk. Investors, therefore, need superior rates of return in January to pay them compensation for the bigger risk assumed in this month. Hence, the January effect is not a valid anomaly, other than a matter of risk measurement. Earlier studies on the risk-based explanation of the January effect such as Tinic and West (1984, 1986), Keim and Stambaugh (1986), Hillion and Sirri (1987) and Chang and Pinegar (1988) offered more proof to support the risk-based argument. However, later studies have not always established this hypothesis (Seyhun, 1993; Sun & Tong, 2010). In essence, there has been no convincing justification than the psychological cause of MOY effect (Malkiel, 2003 and Lukas, 2012).

2.6.4.3 Turn of the Month and Intra-month Effects

Another anomaly, which has been discovered in the literature, is that turn of the month (TOM) has a significantly higher return compared to the rest days. This is called TOM effect and it is particularly strong (Urquhart, 2013). Karadžić and Vulić (2011) view TOM effect as the tendency for stock prices to rise in the last two days and the first three days of each month, while Urquhart (2013) used TOM to describe the presence of particularly high returns in the last day of a month and the first three days of the following month. It simply refers to the patterns of stock returns on the last days and the first days of a given month (Muhammad et al., 2013). On the other hand, Pandey and Samanta (2016) postulate that the TOM effect means that returns are higher over the

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first fortnight of the month. His own definition is similar to semi-monthly, HOM or intra- month effect. Intra-month effect shows the changes in return within a month as the days elapse and reflects the tendency of market to generate higher returns on the early days than the rest of the month. Mainly, the intra-month effects involve the existence of positive/higher returns only in the first half of the month (Martin, 2011).

Reason for TOM/HOM

Liquidity hypothesis has been identified as the possible cause of this calendar effect. Liquidity hypothesis is associated with Ogden’s (1990) study, which holds that most investors have access to cash receipt at the end of the month and become liquid. This liquidity encourages them to invest more in shares, thereby creating an increase in demand, which in turn leads to a rise in prices and hence, higher returns at the turn of the month (Márcio, 2015). The rise in cash flow at the turn of the month and year can explain the so-called January, turn of the year, MOY and turn of the month anomalies. Others have attributed it to macroeconomic news announcement.

2.6.4.4 Other Calendar Effects

Apart from the groups of calendar anomalies described above, which are the focus of this study, there are other types that include the holiday effect, lunar effect and Halloween effect. Holiday effect refers to the tendency of the market to generate higher returns on any day that precedes a holiday (Lakonishok & Smidt, 1988; Martin, 2011; Brishan, 2012; Pandey & Samanta, 2016). Holiday effect can be explained by the investor’s psychology hypothesis. This hypothesis states that investors tend to buy shares before holidays because of ‘high spirits’ and ‘holiday euphoria’ (Brockman & Michayluk 1998; Vergin & McGinnis 1999; Marrett & Worthington, 2006). Further, a lunar effect is synonymous to moon effect. It is a situation whereby the average returns around the new moon are higher than the mean returns around the full moon (Yuan, Zheng & Zhu, 2006; Nur, Zuraidah & Carolyn, 2014). According to Dichev and Janes (2001), strong lunar cycle effects in stock returns are usually indicated by higher returns in the 15 days around the new moon dates, than the returns in the 15 days around the

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full moon dates. It is believed that the moon has a natural power and tends to influence investors’ psychology and human decision (Levy & Yagil, 2011). This affects investors’