Momentum Effect in the Oman Stock Market Over the Period of 2005-2018

Abstract

The purpose of this paper is to investigate the profitability of the momentum effects on the Oman Stock Market (OSM). This study uses the monthly returns of all stocks listed on the OSM, with a total of 107 companies used in the study for the period from 2005 to 2018. According to the methodology developed by Jegadeesh and Titman (1993), this study builds momentum portfolios based on various sizes. Moreover, the January effect is also examined to recognize if this effect is related to the momentum effect. The results find that there is evidence of momentum returns and these returns are statistically and economically significant. The sub-periods confirmed the profitability of the momentum strategy. This paper shows that momentum returns are evident at different sizes; big, medium, and small-sized portfolios. Besides, the result shows that the classic January effect does not play an important role in the momentum returns. Thus, the implication is that the momentum should not take into account the annual, seasonal, and size returns. The capital asset pricing model (CAPM) or the three-factor model cannot explain momentum returns generated by individual stocks in the Oman Stock Market. These results are useful to academia and investors alike.

1. Introduction

Momentum is the rate of change of returns of the stock or the index. If the rate of change of returns is high, then the momentum is considered high and if the rate of change of returns is low, the momentum is considered low. The effect of momentum remains a controversial subject for researchers and challenges the efficient market hypothesis (EMH). Jegadeesh and Titman (1993) documented the momentum effect, which means short-term winners will outperform short-term losers. Rouwenhorst (1998) asserted that the momentum effect is the persistence of price. Jegadeesh and Titman (1993) and Chan et al. (1996) examined whether the predictability of future returns from past returns is due to the market’s underreaction to information, in particular to past earnings news. Past return and past earnings surprise each predict large drifts in future returns after controlling for the other. Chan et al. (2000) examined the profitability of momentum strategies implemented on international stock market indices. They showed that momentum profits arise mainly from time-series predictability in stock market indices—very little profit comes from predictability in the currency markets.

Using Middle East data for the period 2008–2013, Ejaz and Polak (2014) examined the existence of momentum effect in six countries (1) the United Arab Emirates (2) Egypt (3) Jordan (4) Morocco (5) Oman (6) Saudi Arabia. The objective of the paper was to find short-term momentum effect in stock markets of the Middle East and to examine whether short-term momentum profits can be explained by the risk-based CAPM model. Seven major stock markets from the Middle East were selected. Short-term momentum effect was found in all seven stock markets and CAPM does not adequately explain the short-term momentum profits but momentum portfolio returns are statistically significant. This paper is the first attempt to bring major stock markets of the Middle East together and examine them for the short-term momentum effect phenomenon. Future research should include more stock markets to have a better understanding of Middle Eastern stock markets.

The current study extends the previous study by applying the CAPM and three-factor model to attempt in explaining the momentum profits on the Omani stock market. The question posed in the current study has significant implications in academia and to investors. Furthermore, this study extends the previous study by using a large sample that consists of all firms listed on the Omani stock market covering a longer period from November 2005 to December 2018.

This study was carried out for different reasons. First, some of the existing literature found that momentum returns are related to the January effect (Yao, 2012), while others such as Fama and French (2008), Alhenawi (2015), and Gharaibeh (2015) showed that momentum returns are attributed to the size effect. It is useful to know if the momentum is existence on different size levels and if it is attributed to January or size effects. Thus, searching for further evidence is still required to support the current literature. Second, Smith (2007) indicated that although the Omani stock market has a foreign investment limit of 49% for non-GCC nationals, it is one of the more-open Arab equity markets. Assaf (2003) documented that listed companies are open to all foreign participants in Oman. This openness to foreign investors motivates us to study this market to achieve abnormal returns.

The research questions in this study are as follows: Is there a momentum effect at the stock level in the Oman stock market for 2005–2018? Is there a momentum effect on the small, medium, and big size level of the Oman stock market? Is the momentum is attributed to January or side effects? Can the CAPM and three-factor model explain the momentum returns on the Oman stock market?

This paper also investigates the implications of the January effect of a crucial paper in a momentum strategy. The paper on return momentum by Yao (2012) found that momentum is entirely attributed to the January effect. Inconsistent with Yao’s (2012) finding, the analysis in this study shows that the behavior of January profits is not important in the momentum returns, which leads to different conclusions from the previous study. The other motivation behind the current study is the need to test whether there is a momentum effect on different size levels on the Omani stock market. This study shows that the momentum effect exists at different size levels. Alhenawi (2015) showed that the momentum effect is stronger in big firms, while Fama and French (2008) showed that the momentum effect is only evident in a micro-size and small size portfolio. This paper contradicts the previous results and shows that the momentum strategy is pronounced in large, medium, and small firms. This result helps investors in the Oman stock market to invest and make profits from the momentum strategy regardless of company size. This study will contribute to the existing literature in momentum, especially in the Arab stock market such as Oman. Finally, this paper reveals that the CAPM and the Fama-French three-factor model fail to explain the nature of momentum returns and this result is consistent with some previous studies such as Fama and French (2008).

2. Literature Review

Jegadeesh and Titman (1993) is the first study to document momentum strategies. Using the profitability of the momentum effect, they show that short-term performance can be used to predict future returns. They documented that strategies that buy stocks that have performed well in the past and sell stocks that have performed poorly in the past generate significant positive returns over three- to twelve-month holding periods. The authors find that the profitability of these strategies is not due to their systematic risk or to delayed stock price reactions to common factors. However, part of the abnormal returns generated in the first year after portfolio formation dissipates in the following two years. A similar pattern of returns around the earnings announcements of past winners and losers is also documented. They concluded that the momentum strategy is due to the under-reaction to firmspecific news and investors are conservative. Therefore, they are slow to change their beliefs.

Fama and French (2008) proposed that the momentum effect should take place as compensation for high risks. Thus, by applying the Fama-French three-factor model, they fail to find a risk-based explanation for momentum profits. The anomalous returns associated with net stock issues, accruals, and momentum are pervasive; they show up in all size groups (micro, small, and big) in cross‐section regressions, and they are also strong in sorts, at least in the extremes. The asset growth and profitability anomalies are less robust. There is an asset growth anomaly in average returns on microcaps and small stocks, but it is absent for big stocks. Among profitable firms, higher profitability tends to be associated with abnormally high returns, but there is little evidence that unprofitable firms have unusually low returns. However, the current study uses both the CAPM and the Fama-French three-factor model because the author believes that what may be bad practice for these models to explain momentum returns within developed countries may be very good to explain the nature of momentum returns in developing countries.

Contrary to the results of Jegadeesh and Titman (1993), Fama and French (1996) and Yao (2012) suggested that the January effect is necessary to further clarify the momentum effect. Yao (2012) used the US stock data from 1926 to 2009 and revealed that the superior performance of the momentum depends on the January effect in the cross-section of returns. He re-examined the apparent success of two prominent stock trading strategies: long-term contrarian and intermediate-term momentum. The paper demonstrated that long-term contrarian is entirely attributable to the classic January size effect, rather than to investor overreaction. Further, the paper also resolves the Novy-Marx (2011) concern about whether return autocorrelation “is real momentum” by demonstrating that the superior performance of intermediate-term momentum is due to strong January seasonality in the cross-section of returns. The implications are that long-term contrarian must be considered largely illusory, and intermediate-term momentum must take account of annual seasonality in returns.

Narayan and Phan (2017) estimated momentum profits for a large portfolio of Islamic stocks, control for stock characteristics and the state-of-the-market, explore seasonal patterns, and examine the determinants of profits. They discovered ample evidence that momentum strategies work for Islamic stocks, but are stock characteristic-dependent, that up and down phases of the market offer different profits, and that there is a January effect on profits. They also found that the market risk factors – namely, excess market returns, value, size, and betting-against-beta factors – and macroeconomic risk factors do explain profits. They concluded that the profitability of Islamic stocks is merely compensation for risks and is not due to mispricing.

Alhenawi (2015) used a sample of firms listed in the NYSE, AMEX, and NASDAQ between January 1963 and December 2012 to analyze the interaction between size effect and momentum effect in cross‐sectional stock returns. Furthermore, this paper focused on the evolution of this interaction through different market states. He reported a significant shift in stock returns structure during the rising markets of the 1990s and the 2000s. First, momentum had absorbed the size effect. Second, the momentum effect had become stronger in larger, not smaller, firms. These patterns are indicative of a strong interaction between the two effects. Conceivably, in up markets, firms grow fast, and thus, the size and momentum effects stem from a common economic phenomenon: growth. The findings were robust to variations in the length of the formation period and the use of residual return (instead of total return) to rank stocks. However, the study by Alhenawi (2015) is not consistent with Fama and French (1998) who found that there is an anomaly in asset growth in the average return on micro and small stocks, but it is absent from big stocks.

Cakici et al. (2013) examined the value and momentum effects in 18 emerging stock markets. Using stock level data from January 1990 to December 2011, we find strong evidence for the value effect in all emerging markets and the momentum effect for all but Eastern Europe. We investigate size patterns in value and momentum. After forming portfolios sorted on size and book-to-market ratio, as well as the size and lagged momentum, we use three well-known factor models to explain the returns for these portfolios based on factors constructed using local, U.S., and aggregate global developed stock markets data. Local factors perform much better, suggesting emerging market segmentation.

While numerous studies have provided evidence of the momentum effect, many studies have provided evidence suggesting that momentum returns are not present in many emerging markets. By looking at the CAPM model for risk analysis, Khan (2016) examined the momentum effect of 83 firms on the Karachi stock exchange (KSE) from 2007 to 2014. The returns of the winner portfolio were positive only in 1 out of 16 strategies while the returns of zero cost portfolios were positive in four out of 16 strategies. Moreover, a diminishing trend in losses stated in 14 strategies was observed. His analysis confirmed that the loser portfolio is solitarily producing a profit of zero cost portfolios. In all momentum strategies, the value of beta and alpha confirmed that returns can be boosted by taking a short position in the loser’s portfolio with regard to the winner portfolio and it also confirmed that there is no need to take more excessive risk. This study concluded that winner and winner minus loser’s portfolio firms of KSE do not follow the momentum effect while loser’s portfolio firms of KSE follow the momentum effect. This study concluded and found a low and significant momentum effect at Karachi stock exchange and these results are consistent with Habib and Mohsin (2012) Griffin et al. (2003), and Gharaibeh (2015) who found that although there are no momentum profits at the level of firms in the Oman Stock Exchange (OSE). This study shows sufficient evidence of the momentum for large-sized portfolios. The CAPM and Fama-French three-factor model cannot explain large-sized momentum returns.

Mobarek et al. (2008) sought evidence on whether the return series on Bangladesh’s Dhaka Stock Exchange (DSE) is independent and follows the random walk model. The study focused on assessing if the DSE deviates from idealized efficiency. The sample primarily includes all the listed companies on the DSE daily price index over the period 1988 to 2000. The results provided evidence that the security returns do not follow the random walk model and the significant auto-correlation coefficient at different lags reject the null hypothesis of weak-form efficiency. The results are consistent with observations in different sub-samples without outlier and for individual securities. This anomaly with the efficient market hypothesis supports the thought that the market does not respond to new information instantaneously. This may be due to a delay in the dissemination of new price-sensitive information or biases (under or overreaction) in the response of market participants to such information. It may also be for the momentum effect related to herding in particular ‘positive feedback trading’ or ‘trend following’ the trading strategy by the average investors.

Zaremba (2018) investigated the momentum effect in country-level anomalies in global equity markets. By using a sample of 78 countries for the period from 1995 to 2015, they tested a set of potential 40 cross-sectional inter-market anomalies, some of which had never been examined before. Based on the findings, according to which half of these return patterns serve as reliable and robust sources of returns, they provided convincing evidence that the anomalies with good performance over the past 6–12 months tend to outperform in the future. Furthermore, returns on individual country-level strategies are weakly correlated. Consequently, developing a portfolio consisting of past top-performing strategies may constitute a valuable approach for international investors.

Zaremba et al. (2019) developed a novel model for the success of a start-up company based on the first passage time of a Brownian motion. To test the performance of their model, they used it to build a portfolio of companies where the goal is to maximize the probability of having at least one company achieve an exit (IPO or acquisition), which they referred to as winning. They framed the construction of a picking winners portfolio as a combinatorial optimization problem and showed that a greedy solution has strong performance guarantees. They apply the picking winners framework to the problem of choosing a portfolio of startup companies. Using the model for the exit probabilities, they could construct sample portfolios that achieve exit rates as high as 60%, which is nearly double that of top venture capital firms.

Boussaidi and Dridi (2020) examined two controversial explanations for the momentum in the Tunisian stock market: the risk hypothesis and the underreaction hypothesis. To address the risk issue, the five-factor model of Fama and French (2015) was used to estimate the momentum profits. They found strong evidence of risk-adjusted momentum profits indicating that the risk cannot explain the momentum effect. To test the underreaction hypothesis, an event study was performed to track the market reaction to the information content of earnings before, on, and after the earnings announcement. They found that good earnings news is followed by positive abnormal returns; while bad earnings news is followed by negative abnormal returns over 12 months after the announcement date. Consistent with the underreaction hypothesis, these findings indicated that the market slowly adjusts in the same direction to the unexpected earnings. To control for this effect, they extended the five-factor model to include a factor based on unexpected earnings. They found that the momentum profits are captured by a zero-investment portfolio that is short on the portfolio with the lowest unexpected earnings and long on the portfolio with the highest unexpected earnings.

The remainder of this paper is structured as follows: Section 2 provides the data and the empirical methodology used in the current paper. Section 3 discusses the empirical findings and the robustness check. Section 4 concludes this paper with a summary of the results and discussion.

3. Methodological Approach

Oman stock market (Muscat Securities Market) was established on 21 June 1988. It holds 60% of its capital in banks, brokerage companies and invests the remaining stocks of its capital. The sample includes the monthly prices, firm size, and firm book-to-market ratio for all the stocks listed on the Oman Stock Market (OSM) over the period from November 2005 to December 2018. For the market index, the price of Morgan Stanley Capital International (MSCI) Oman market index is taken. All previous data is downloaded from Datastream. A total of 107 stocks are employed in the study with observation ranges from a minimum of 70 to a maximum of 164.

Table 1 details the summary statistics for each of the 107 firms included in the sample. Notably, there is a huge variation in the average and standard deviation of returns. The average monthly returns for firms range from –1.03 to 11.3, with a grand average monthly return of 1.39 and an average standard deviation of 14.34 for all firms. National Finance, Dhofar Fisheries & Food Industry, Flexible Indl. Packages have the highest monthly averages (over 5 per month). In contrast, Al Oula has the lowest average (under –1 per month). Concerning the distribution of returns seems to a wide range of kurtosis and skewness values.

Table 1: Descriptive statistics of firm stock returns in Oman

The objective of this study is to examine whether the momentum effect is related to January and size effects on the Omani stock market and to investigate whether the momentum profits can be explained by the CAPM and three-factor model. A description of the strategy is provided next. The research questions in this study are as follows: Is there a momentum effect at the stock level in the Oman stock market for 2005-2018? Is there a momentum effect on the small, medium, and big size level of the Oman stock market? Is the momentum is attributed to January or side effects? Can the CAPM and three-factor model explain the momentum returns on the Oman stock market?

To examine these previous research questions and to test for the existence of a momentum effect and whether the short-winner (SW) on the small, medium, big level size of momentum strategies outperform the short-loser (SL) on the small, medium, and big level size of momentum strategies, as well as to test whether momentum strategies are attributed to January or side effects, the following hypotheses are tested.

H1: SW portfolios outperform SL portfolios over their holding period.

H2: Portfolios of SW on the small, medium, and big size outperform portfolios of SL on the small, medium, and big size over their holding period.

H3: Momentum returns are attributed to January or size effect.

H4: Momentum returns can be explained by the Capital Asset Pricing Model (CAPM) and the Fama and French three-factor model.

This table details descriptive statistics for the data of all firm stock returns in Oman obtained from DataStream. Av. in column two indicates the average monthly returns; S.D. in column three refers to the standard deviation of monthly returns. Kurtosis and skewness indicate measures of normal distribution.

3.1. The Momentum Strategy

Following the methodology developed by Jegadeesh and Titman (1993), this study performs the construction of the momentum portfolios. At the beginning of every month, stocks are sorted based on the past J-month returns, for J = 3, 6, 9, and 12 months. For a given J, 25% of firms that have the highest past J-month returns represent the short-term winner (SW) portfolio, whereas 25% of firms that have the lowest past J-month returns indicate the short-term loser (SL) portfolio. There are about 26 firms in each winner and loser portfolio. Concerning the momentum strategy, short-term winners over the previous 3, 6, 9, and 12 months should persist to outperform short-term losers over the subsequent 3, 6, 9, and 12 months. So, the strategy of momentum longs the SW portfolio and shorts the SL portfolio to construct the SW-SL momentum arbitrage portfolio. Portfolios are held for the K-month holding period and K = 1, 3, 6, 9, and 12 months.

3.2. Momentum Strategy Based on Different Size

To form the momentum strategy based on different sizes, this paper divided the Oman firm sample into three groups: small, medium, and big size portfolios including 33%, 34%, and 33%, respectively. Small, middle, and big-sized portfolios contain 33, 34, and 33 of Oman firm sample respectively. Then, 4 momentum portfolios were classified for each size in the same way as defined in section 3.1 (the momentum strategy). Therefore, there are about 8 firms in each winner and loser portfolio.

4. Conducting Research and Results

Section 4.1 provides the results of the momentum strategy for Omani stock returns. Section 4.2 displays the profitability of momentum strategy based on size to check whether momentum returns are driven by a given size class, while Section 4.3 presents robustness checks for the momentum strategy based on the sub-period analysis. Section 4.4 shows the January effect on momentum returns. Finally, section 4.5 introduces risk-adjustment regressions.

4.1. The Momentum Strategy

The results in Table 2 refer to the momentum strategy profits (SW-SL) which is statistically significant for the overall K-month holding period. For example, for the 6-month formation period case with a 6-month holding period, the past short-term winner generates an average of 5.46 per month while the short-term loser generates an average of –1.79 per month over the same period. The difference between the average monthly returns of the short-term winner (SW) portfolio and the short-term loser (SL) portfolio is large at 7.25 per month (t-stat 11.57).

Table 2: Profitability of momentum strategy

Overall, the results in Table 2 provide strong evidence of profitability in the momentum strategy during all holding periods. This finding confirms the finding of Ejaz and Polak (2014) who found strong evidence of the momentum effect on the Omani stock market. To check whether the previous momentum results documented in Table 2 are driven by a given size class, Table 3 provides momentum returns based on size by dividing the Omani firm sample into three groups; small, medium, and big-sized portfolios. To conserve space, the strategy based on J = 6 months is presented.

Table 3: Profitability of Momentum Strategy Based on Different Sizes

This table provides the average monthly returns of the selling, buying, and arbitrage portfolios of the momentum strategy. Following the Jegadeesh and Titman (1993) methodology, portfolios are classified where every month t, and the firm stock returns in Oman are sorted based on the compound return due to past J = 3, 6, 9, and 12 formation months. The largest 25 are firm returns with the largest past returns and classified in the short-term winner SW portfolio, while the lowest 25 are firm returns with the lowest past returns and classified in the short-term loser SL portfolio. All previous portfolios are equally weighted. SW-SL represents the momentum strategy based on buying the winner portfolio and selling the loser portfolio. These portfolios are held for K = 1, 3, 6, 9, and 12-month. The simple t-statistics are produced in parentheses.

4.2. Momentum Profits Based on Size

The results in Panel A, B, and C in Table 3 show large and significant profits for all three groups - small, medium, and big-sized portfolios. For example, the momentum strategy with a six-month holding period (K = 6) generates a significant profits for small, medium and big-sized portfolios of 5.99% (t-stat 7.31), 7.15% (t-stat 8.6) and 5.77% (t-stat 11.81), respectively. The results in Panel A, Panel B, and Panel C in Table 3 demonstrate that there is strong evidence of momentum effects that are not driven by a particular size class, unlike previous studies such as O’Brien et al. (2010), Alhenawi (2015), and Gharaibeh (2015), who found that the momentum effect exists only in a large-sized portfolio, while Fama and French (2008) showed that the momentum effect is only evident in a micro-size and small size portfolio.

This table provides the average monthly returns of the selling, buying, and arbitrage portfolios of the momentum strategy. The Oman firm sample is divided into three groups based on their sizes; small, middle, and big. Small, middle, and big-sized portfolios contain 33, 34, and 33 of Oman firm sample. The way these portfolios are constructed is defined in Table 2.

The post-formation behavior of the momentum strategy returns is also demonstrated in Figure. 1. Figure 1 illustrates the sub-period post-holding cumulative monthly profits of the momentum strategy with K = 1. It indicates an increase across the first 5 months then it begins declining for the whole period. However, the strategy keeps providing large cumulative profits.

Figure 1: Cumulative Returns of the Momentum Strategy​​​​​​​

Figure 1 is done by the researcher on an excel program. This figure demonstrates the cumulative raw profits of the momentum strategy for the non-overlapping holding period K = 1 month, for the firm stock returns of Oman for 60 months following the beginning of the holding period.

4.3. Sub-period Analysis

In this section, we test the stability of the momentum strategy over sub-periods by examining the momentum profits in two sub-periods for equal size. The first sub-period extends from April 2006 to June 2012. The second sub-period extends from July 2012 to December 2018. Table 3 provides the profitability of the momentum strategy in these two sub-periods. To conserve space, only the strategy based on J = 6 months is presented.

This table provides the average monthly returns in percentages of the buying, selling, and arbitrage portfolios of the momentum strategy in sub-periods. Panel A shows the results for the first Sub-period (April 2006 to June 2012), while Panel B provides the results for the second Sub-period (July 2012 to December 2018). The way these portfolios are constructed is defined in Table 2.

Figure 2 illustrates the sub-period post-holding cumulative monthly profits of the momentum strategy with K = 1. It refers to roughly similar post-holding period behavior in the two sub-periods. While both charts largely increase across the first 5 months then begin declining for the whole period, the two sub-periods keep providing large cumulative profits.

Figure 2: Cumulative returns of the momentum strategy in sub-periods​​​​​​​

Figure 2 is done by the researcher on an excel program. This figure represents the cumulative returns of the momentum strategy for the first and second sub-periods. The graph demonstrates the cumulative raw profits of the momentum strategy for the non-overlapping holding period K = 1 month, for the firm stock returns of Oman for 36 months following the beginning of the holding period.

4.4. January Effect in Momentum Profits

This paper considers seasonal effects. The question of whether momentum returns are characterized by seasonal effects is motivated by the work of Jegadeesh and Titman (1993) and Sias (2007) amongst others, who reveal ample evidence of seasonal effects in momentum profits and these results are consistent with the earlier literature by Conrad and Kaul (1998) and Chordia and Shivakumar (2002). They show that Winners outperform Losers in all months except January. The findings of Jegadeesh and Titman (1993) showed that January is evident when Losers outperform Winners. These results are also confirmed by those in Sias (2007). The results in the current study are inconsistent with this. This paper finds that over the period from November 2005 to December 2018, the Oman stock portfolio produces January profits of 12.20 per month (t-stat 2.46), and over the non-January months, the portfolio provides a profit of 10.59 (t-stat 11.71). Therefore, the momentum profits are existence in both January and non-January month.

Table 4: Profitability of Momentum Strategy in Sub-Periods​​​​​​​

This table provides further details on the average monthly momentum portfolio returns in percentages of the buying, selling, and arbitrage portfolios. The first column indicates the overall, January and non-January average monthly returns.

This table demonstrates the CAPM and Fama-French three-factor regression results for monthly profits of J = 6 and K = 6 holding period for the momentum strategy. These portfolios are defined in Table 2. The CAPM regression model is as follows:

\(R_{p t}=\alpha_{p}+\beta_{p} R_{n d}+\varepsilon_{p t},\)

The Fama-French three-factor regression model is as follows:

\(R_{p t}=\alpha_{p}+\beta_{p} R_{m t}+s_{s m b} \mathrm{SMB}_{\mathrm{t}}+h_{h m l} \mathrm{HML}_{\mathrm{t}}+\varepsilon_{p t},,\)

Where R_pt is the portfolio’s return, R_mt is the return on the market, SMB_t is the Fama-French size factor, and HMLt is the Fama-French book-to-market factor. The t-statistics is presented in parentheses corrected for heteroskedasticity using the White (1980) test.

4.5. Risk-adjusted Momentum Profits

A reward for bearing risk should be taken into consideration to examine whether these strategies provide abnormal profits - the profits of the pure, early-stage, and late-stage momentum strategies are risk-adjusted using both the CAPM model and Fama-French three-factor model. The CAPM model includes a market factor as follows:

\(R_{p t}=\alpha_{p}+\beta_{p}\left(R_{m t}\right)+\varepsilon_{p t}\)     (1)

Where the dependent variable R_pt is the monthly return of the strategy portfolio p, R_pt is the monthly return of portfolio p at time t, for the explanatory independent variables, R_pt denotes the value-weighted Oman index’s monthly return for month t. The monthly market returns extending from January 2004 until April 2014 is downloaded from Datastream.

Table 5: Seasonal Momentum Profits​​​​​​​

The three-factor regression is as follows:

\(R_{p t}=\alpha_{p}+\beta_{p}\left(R_{m t}\right)+\beta_{a m b} \mathrm{SMB}+\beta_{h m l} \mathrm{HML}+\varepsilon_{p t}\)     (2)

Where R_mt indicates the portfolio’s return, R_mt indicates the market variable represented by the return on the MSCI Oman market index, and SMBt is the Fama-French size factor, and HMLt is the Fama-French book-to-market factor. The monthly return for each holding period arises from employing the Jegadeesh and Titman (1993) model overlapping portfolio methodology. The t-statistics is presented in parentheses corrected for heteroskedasticity using the White (1980) test.

The coefficients β_p β_smb and β_hml are the regression loading corresponding to the market return, size, and book-to-market factors of the model, while the alpha α_p (or simply alpha) is the risk-adjusted abnormal returns of the portfolios over the estimation period. Abnormal profits are evident when alpha is statistically significant. The White test is used in the current section to be regression coefficient t-values corrected for heteroskedasticity.

Table 6 presents the estimated regression coefficients and the associated tvalues for the long, short, and arbitrage portfolios denoted by SW-SL for the momentum strategy with six-month holding periods (K = 6). The alphas of the momentum zero-cost portfolios (SW-SL) in the two models are big (0.112 and 0.104 per month) and they are statistically significant (tstat 12.30, and 10.75, respectively). The long and short sides of these strategies generate significant abnormal returns. Clearly, both the CAPM and Fama-French three-factor model cannot explain these abnormal profits.

Table 6: Risk-Adjusted Momentum Profits​​​​​​​

5. Research Limitations

The non-parametric momentum strategy result in this paper is that no consideration is taken of trading costs. Trading costs are ignored because the purpose of this study is not to suggest long-short momentum strategies for investors to use but rather to use abnormal long-short profitability as evidence of the presence of momentum effect more generally.

6. Conclusions

This paper aims to examine the existence of the momentum effect and its relationship to January and size effects. Besides, the current study extends the previous study by applying the CAPM and three-factor model to explain the momentum returns on the Omani stock market for the period 2005 to 2018. The current study confirms that there is strong evidence of momentum returns on the Omani stock market. The result of this study shows that the momentum profits in two sub-periods of equal size are statistically significant. The existence of the momentum effect violates the efficient market hypothesis.

To understand more about momentum profits, this paper investigates momentum profit portfolios in seasonal and size effects. By investigating the effects of January and size, this paper addresses the subject raised recently by Yao (2012), who argued that momentum returns have risen since the January effect. Contrary to the previous study, the current study states that the performance of the momentum portfolio is not due to the January effect. Besides, O’Brien et al. (2010), Alhenawi (2015), and Gharaibeh (2015) found that the momentum effect exists only in a large-sized portfolio, while Fama and French (2008) showed that the momentum effect is only evident in a micro-size and small size portfolio. This paper showed that the momentum effect exists in different sizes, large, medium, and small-sized portfolios. These results help investors in the Oman stock market to invest and make profits from the momentum strategy regardless of January and company size. The findings in this paper provide new insights related to both professional practitioners and academic researchers.

This paper also attempts to understand the source of the momentum effect. The results show that the CAPM and Fama-French three-factor model cannot explain momentum returns on the Oman stock market. This result is consistent with previous studies such as Fama and French (2008). Therefore, to understand the momentum returns in the Oman stock market, this study recommends employing the Fama and French five-factor model in future research. In general, the presence of momentum profits opens up new opportunities for investors and researchers using the momentum strategy to achieve abnormal returns.