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A STUDY ON THE EFFECTS OF SHORT TERM MOMENTUM ON STOCK AND INDUSTRY RETURN IN IRAN'S STOCK EXCHANGE
© Mahmoud Moeinadin, Forough Heyrani, Alireza Dehghanizadeh Bagdadabad, Mohammad Mirmohammadi Sadrabadi
Department of Accounting, Yazd Branch, Islamic Azad University, Yazd, Iran
One phenomenon challenging world's financial markets is the abnormal short term momentum pattern. So the presentstudy's aim was to examine the effects of 6-month and 12-month short term momentum pattern on stock and industry return in Iran's stock exchange. In this study, the sample consisted 50 selected companies in stock exchange during 2008-2013. To examine industry momentum, the sample was chosen among outstanding active industries in stock exchange. The investigated models include three-factor model of Fama and French and four-factor Carhart model. The results of using SPSS and EVIEW software showed that abnormal and excess returns on market is revealed using 6-month and 12-month momentum pattern and there is a significant relation between company size, value element and market element, and excess 6 and 12-month momentum. Also the results showed that there is no significant relation between excess momentum return and industry momentum in Iran's stock exchange.
Keywords: short term momentum pattern, stock return, industry return, size element, market element, value element, industry momentum, three-factor model, four-factor model.
Introduction
Most of investors entering to the market are seeking to use an approach or a pattern to gain excess return in market and gain victories against market, while according to assumptions of market Kara hypothesis stating that due to the fact that no process exists in price and return of market, this matter is impossible and every one can benefit to the extent that confronts risks (Fadainejad and Sadeghi, 2006). So much evidence in financial researches is Iran and all over the world shows that due to the existence of continuous return, we can increase the gain of investments by tracing the historical track and investing the observed process in general stock and taking into practice the suitable investment approach with the given temporal scale such as momentum pattern (Eslamibigdeli, et.al. 2010). Behavioral finance hypothesis is presented by Dash and Mahakuk (2012). One of the components of behavioral finance hypothesis is momentum strategy and contrarian strategy which now are generally used in world financial markets. In other words we can increase the return of investment by using suitable investment approach with the appropriate given temporal horizon for investment (Shirazipour, et.al, 2012). Momentum pattern showed that a stock operating better in past times
(past winners) should be sold out to a stock operating worse at past times (past losers). But in contrary, inverse investment indicates that the past winner stock should be sold and the past loser stock should be purchased. The results of several researches shows that in very short time spans (from 1 day to 3 months) and long term spans (3 to 5 years) we can gain the excess return to market without taking more risks by using inverse strategy in short term time spans (3 to 12 months) and momentum strategy (Jegadeesh and Titman, 1993). Segal and Jain (2012) showed that momentum profit exists in lots of financial markets of the world and this profit is not to be explained merely by the elements of three-factor model of Fama French and four factor Carhart model. But about 66 % of this profit is laid on optional industry factor return (that is the average of return in outstanding companies in every industry). According to the fact that industry option can have an important role in gaining excess return per market and that confirming short term momentum pattern is a crucial challenge against efficient market hypothesis, so the present study seeks to examine the effects of momentum pattern on the return of various companies stock and the combination of optional industries and seeks to answer this question: is there any short-term momentum pattern on stock return and industry in Iran's stock exchange?
Short term momentum strategy (pattern)
This strategy named as velocity and partial power, is one of the exceptions of financial market which is ignored in three-factor Fama and French model (Jega-deesh and Titman, 1993). This investment pattern is composed of motion and investment in market orientation which is presented first by Jegadeesh and Tit-man (1993). Profitability of this investment model in short term span (that is 3 to 12 months) is proved in lots of world financial markets (Jegadeesh and Titman, 2001). One of other assumptions of this model is that it states the positive or negative return of stocks in past times is going to be continued up to a certain time in future. In other words, the momentum showed a positive self correlation in capital long term and short term return (Fadainejad and Sadeghi, 2006). Considering the theoretical bases mentioned, we can design the first research hypothesis as below:
H1: using short term momentum pattern results in gaining excess return in stock and industry in Iran's stock exchange.
H1.1: using short term momentum pattern results in gaining excess return in stock and industry in Iran's stock exchange in forming and maintaining portfolio status in 6-month time span.
H1.2: using short term momentum pattern results in gaining excess return in stock and industry in Iran's stock exchange in forming and maintaining portfolio status in 1-year time span.
Fama and French three-factor model
Fama and French investigated the shared role of beta and companies feature changes on average time section returns of US stock companies in a research in
1992 and found out that two variables of official value to market (B/M) and company size has an important role in describing the related returns. They also showed that the average returns of small companies in comparison to the average returns of big companies with the same Beta level are higher. This matter was a crucial challenge against (CAPM) which stated that in case of equal Bata, we have equal returns too (Fama and French, 1992). Fama and French in 1993 extended their current research and presented three-factor model according to conventional pattern CAMP. The factors of this model includes: 1. Market factor 2. Size of company (SMB), and 3. Official value factor to market or HML. This model could clearly demonstrate the return changes and explain nearly all the contractions such as the proportion of profit to price, sale growth proportion and so on (Fama and French, 1993). The only exception that the pattern could explain and justify was Jegadeesh and Titman momentum pattern (Jegadeesh and Titman, 1993). Of course Fama and French induct that irregularities of market is accidental and excessive reaction is as common as under stated reactions (Fama and French, 1993). According to this pattern we can present the below hypotheses:
H2: there is a significant relation between the return of momentum strategy and return of market factor in Iran's stock exchange.
H2.1: there is a significant relation between the return of momentum strategy and return of market factor in Iran's stock exchange informing and maintaining portfolio status in 6-month time span.
H2.2: there is a significant relation between the return of momentum strategy and return of market factor in Iran's stock exchange in forming and maintaining portfolio status in 1-year time span.
H3: there is a significant relation between the return of momentum strategy and return of company size factor in Iran's stock exchange.
H3.1: there is a significant relation between the return of momentum strategy and return of company size factor in Iran's stock exchange informing and maintaining portfolio status in 6-month time span.
H3.2: there is a significant relation between the return of momentum strategy and return of company size factor in Iran's stock exchange informing and maintaining portfolio status in 1-year time span.
H4: there is a significant relation between the return of momentum strategy and return of value factor in Iran's stock exchange.
H4.1: there is a significant relation between the return of momentum strategy and return of value factor in Iran's stock exchange informing and maintaining portfolio status in 6-month time span.
H4.2: there is a significant relation between the return of momentum strategy and return of value factor in Iran's stock exchange informing and maintaining portfolio status in 1-year time span.
Four-factor model
To consider the effect of momentum factor, Carhart (1997) introduced the risk related to momentum factor (WML) and created four-factor model adding this factor to three-factor model of Fama and French in which momentum was calculated by the difference of winner portfolio return (30 % of stock with high return) from the loser portfolio return (30 % of stock with low return) and used this model to explain the returns of investment banks. This new four-factor model in comparison to three-factor model of Fama and French model (CAPM) could extensively reduce the ranked pricing error according to 1-year periods. In fact so many researches examined the validity of this pattern and confirmed its efficiency (Sadeghi et al, 2013). So we can say:
H5: there is a significant relation between the return of momentum strategy and return of industry factor in Iran's stock exchange.
H5.1: there is a significant relation between the return of momentum strategy and return of industry factor in Iran's stock exchange informing and maintaining portfolio status in 6-month time span.
H5.2: there is a significant relation between the return of momentum strategy and return of industry factor in Iran's stock exchange informing and maintaining portfolio status in 1-year time span.
Background
Lewellen (2000) examining the role of optional industry factors, official value proportion to market and company size variable in momentum in accepted companies' stock in AMEX, NYSE and NASDAQ resulted that the returned obtained from momentum strategy cannot be related to a specific company and no relation was found between industry return factor and return of this strategy. The results of this research in fact strongly confirmed their previous findings of research in 1993. Also they found out that output of momentum portfolio (past winners minus past losers) for the first 12 months after building portfolio is positive and after that the output will be negative. Kaminsky and et al (2002) in a study on performed strategies by shared investment banks in developing countries found that foreigner investors in developing countries of South America such as Mexico, Brazil, and some of emerging markets of Asia in lots of cases have inclinations to use momentum strategies. So it became clear that shared investment banks automatically began to buy the winner's share and sell the loser's share. They confirmed significant excess gain with level of 10 % after choosing past winners' portfolio.
They claimed, in contradiction to other previous researches and literature that the resulted momentum gains in monetary market is due to excessive reactions not few reactions in market and they found out that the resulted momentum gain in monetary market is more related to trivial moneys that have high transaction prices in market. In a research carried out by Moskowitz et al (2012) investigating 24 types of different goods being transacted in France monetary market, they
found out that momentum strategy can be implemented in some future transactions and derivatives and monetary tools (specially in predecessor contractions). They found that this strategy's profitability is pertaining to 1-year investment periods. Dash and Mahakud (2012) used three and four factor patterns of Fama and French and Carhart and invented a new model to measure the risk of investors' inclinations. They claim that unreasonable and illogical behavior of lots of investors causes phenomena such as momentum and short term continual of share gain.
Methodology
The present study is functional in terms of purpose, quantitative in terms of implementation and also correlative in terms of investigating the relation between variables.
Data analysis
To achieve the main purpose of the study, we examined the relation between variables risk, size factor, value factor, industry factor or short time momentum strategy and to do so, we chose one of the most appropriate regression models which moves us significantly toward our purpose. First according to one sample t-test, we determined the status of significance of relation between means of variables, and to examine the normality of variables we use Jarko test, to examine errors and model self correlation we used Watson's model. Then to investigate the relation between the variables we used multi variable linear regression pattern.
Research Findings
To calculate the data related to research variables we used EXCEL software and to examine hypotheses and to analyze data obtained, we used correlation approach and simultaneously multi and one variable regression with help of EVIEWS software. At first, to identify the function of fixed effects model in comparison to data combination we used Chau test. The results show that significance level for all hypotheses was below 5 %. In the next stage to indicate the usage of fixed effects model in comparison to accidental effects model we used Hussmann model. According to the results of Hussmann test, the most appropriate approach to estimate the parameters and to examine the first, third and fourth hypothesis is accidental effects model and fixed effects model for the second hypothesis.
H1 examination
The first hypothesis is about the usage of momentum strategy to create positive return and is examined by t-test. To do so, the difference of redundant portfolio of winner (RPW) and redundant portfolio of loser (RPL) are compared to zero and if the average is not zero we can say that we can gain excess return in Iran using momentum strategy. It's worthy to say that this test is taken in 6 and 12 month time spans.
The results of examination of hypothesis are summarized in table 1 and 2:
Table 1
One sample items
Frequency mean SD S error
Rpw-Rpl 6 50 0.88 1.830082 0.365362
Rpw-Rpl 12 50 0.6925 2.525799 0.358354
Table 2
One sample t-test for comparison of distribution average with zero
variable Test value=0
T value Degree of freedom Sig Mean difference 95 % confidence difference
Low limit High limit
Rpw-Rpl 12 2.046 49 0.025 0.10553110 0.01597521 1.45010554
Rpw-Rpl 6 2.455 49 0.017 0.27024120 0.16582149 1.62799729
According to the fact that the significance level of each three test is below 0.05, we can say that above variables (RPW and RPL in 6 an 12 month time spans) are significantly distant from zero and according to positive difference of means we can say that the average of two variables (RPW and RPL in 6 an 12 month time spans) are significantly positive. The results show that the mean of return portfolio of winner in time spans mentioned is higher than the mean of return portfolio of loser.
H1.2 examination
The results of this test are presented in table 3. As you can see, market value (p-value < 5 %) has positive significant effect on momentum return. According to item F, regression model is significance and according to modulated determinant coefficient, these variables can explain 29.6 % of the changes in momentum return. Watson item is among 1.5 and 2.5 so we can conclude that there is no self correlation problem between variables.
Table 3
The results of one-variable regression between momentum return and market factor momentum in 6-month portfolio
R MOM = A+ B. RM
variable symbol name coefficient T - value Sig. level
Dependent variable R MOM Momentum return - - -
constant variable a Alpha -1.774 -1.648 0.025
Independent variable RM Market factor *0.380 2.41 0.002
Watson 1.856 - -
F value 3.742 - 0.003
Adjusted R Square Modulated determinant coefficient 0.296 - -
*: significance level is 0.05.
H2.2 examination
The results of this test are presented in table4. As you can see, market value (p-value < 5 %) has positive significant effect on momentum return. According to item F, regression model is significance and according to modulated determinant coefficient, these variables can explain 44.5 % of the changes in momentum return. Watson item is among 1.5 and 2.5 so we can conclude that there is no self correlation problem between variables.
Table 4
The results of one-variable regression between momentum return and market factor momentum in 1-year portfolio
R MOM = A+ B. RM
variable symbol name coefficient T - value Sig. level
Dependent variable R MOM Momentum return - - -
constant variable a Alpha 1.765 1.544 0.002
Independent variable RM Market factor *0.357 1.68 0.03
Watson 1.775 - -
F value 14.002 - 0.003
Adjusted R Square Modulated determinant coefficient 0.445 - -
*: significance level is 0.05.
H3.1 examination
The results of this test are presented in table 5. As you can see, size value (p-value < 5 %) has positive significant effect on momentum return. According to item F, regression model is significance and according to modulated determinant coefficient, these variables can explain 39.1 % of the changes in momentum return. Watson item is among 1.5 and 2.5 so we can conclude that there is no self correlation problem between variables.
Table 5
The results of one-variable regression between momentum return and size factor momentum in 6-month portfolio
R MOM = A+ B. SIZE
variable symbol name coefficient T - value Sig. level
Dependent variable R MOM Momentum return - - -
constant variable a Alpha 1.445 1.365 0.000
Independent variable size size factor *0.354 2.223 0.000
Watson 1.894 - -
F value 5.517 - 0.01
Adjusted R Square Modulated determinant coefficient 0.391 - -
*: significance level is 0.05.
H3.2 examination
The results of this test are presented in table 6. As you can see, size value (p-value < 5 %) has positive significant effect on momentum return. According to
item F, regression model is significance and according to modulated determinant coefficient, these variables can explain 3.7 % of the changes in momentum return. Watson item is among 1.5 and 2.5 so we can conclude that there is no self correlation problem between variables.
Table 6
The results of one-variable regression between momentum* return and size factor momentum in 1-year portfolio
R MOM = A+ B. SIZE
variable symbol name coefficient T - value Sig. level
Dependent variable R MOM Momentum return - - -
constant variable a Alpha 1.545 1.405 0.050
Independent variable size size factor *0.514 1.87 0.004
Watson 1.921 - -
F value 6.950 - 0.001
Adjusted R Square Modulated determinant coefficient 0.37 - -
*: significance level is 0.05.
H4.1 examination
The results of this test are presented in table 7. As you can see, value factor (p-value < 5 %) has positive significant effect on momentum return. According to item F, regression model is significance and according to modulated determinant coefficient, these variables can explain 1.6 % of the changes in momentum return. Watson item is among 1.5 and 2.5 so we can conclude that there is no self correlation problem between variables.
Table 7
The results of one-variable regression between momentums return and value factor momentum in 6-month portfolio
R MOM = A+ B. HML
variable symbol name coefficient T - value Sig. level
Dependent variable R MOM Momentum return - - -
constant variable a Alpha 1.545 1.405 0.050
Independent variable HML value factor 52.22 2.69 0.001
Watson 1.94 - -
F value 2.63 - 0.001
Adjusted R Square Modulated determinant coefficient 0.16 - -
*: significance level is 0.05.
H4.2 examination
The results of this test are presented in table 8. As you can see, value factor (p-value < 5 %) has no significant effect on momentum return. According to item F, regression model is significance and according to modulated determinant coefficient. Watson item is among 1.5 and 2.5 so we can conclude that there is no self correlation problem between variables.
Table 8
The results of one-variable regression between momentum* return and value factor momentum in 1-year portfolio
R MOM = A+ B. HML
variable symbol name coefficient T - value Sig. level
Dependent variable R MOM Momentum return - - -
constant variable a Alpha 5.41 1.727 0.02
Independent variable HML value factor 0.157 1.92 0.061
Watson 1.828 - -
F value 1.918 - 0.002
Adjusted R Square Modulated determinant coefficient 0.036 - -
*: significance level is 0.05.
H5.1 examination
The results of this test are presented in table9. As you can see, industry factor (p-value < 5 %) no significant effect on momentum return. According to item F, regression model is significance. Watson item is among 1.5 and 2.5 so we can conclude that there is no self correlation problem between variables.
Table 9
The results of one-variable regression between momentums return and industry factor momentum in 6-month portfolio
R MOM = A+ B. IND
variable symbol name coefficient T - value Sig. level
Dependent variable R MOM Momentum return - - -
constant variable a Alpha 0.287 1.331 0.126
Independent variable IND industry factor 0.018 1.558 0.076
Watson 1.803 - -
F value 1.53 - 0.0025
Adjusted R Square Modulated determinant coefficient 0.024 - -
*: significance level is 0.05.
H5.2 examination
The results of this test are presented in table 10. As you can see, industry factor (p-value < 5 %) no significant effect on momentum return. According to item F, regression model is significance. Watson item is among 1.5 and 2.5 so we can conclude that there is no self correlation problem between variables.
Table 10
The results of one-variable regression between momentums return and industry factor momentum in 6-month portfolio
R MOM = A+ B. IND
variable symbol name coefficient T - value Sig. level
Dependent variable R MOM Momentum return - - -
constant variable a Alpha 8.433 1.363 0.121
Independent variable IND industry factor -0.199 -0.416 0.59
Watson 1.803 - -
F value 1.769 - 0.038
Adjusted R Square Modulated determinant coefficient 0.034 - -
*: significance level is 0.05.
Conclusion and discussion
About excess return on market of momentum portfolios of winner in purchasing and maintaining status and selling portfolio of loser in 6-month span it shows that the average of winner return portfolio in maintaining term is higher than the average of loser and this means that positive (negative) returns of successful (failed) stocks in the last 6-month span was continued and using momentum pattern in 6 month brings about abnormal profits. The results are consistent with the results of Jegadeesh and Titman (2001, 1993), Chan et al (1996), Lee and Swaminathan (2000), Kaminsky et al (2002), George and Hwang (2004) and Segal and Jain (2012).In relation to excess gain on momentum portfolio markets in buying and saving winner's portfolio and selling loser's portfolio in 12-month period similar results have been obtained and it was cleared that using 12-month pattern also has caused abnormal excess gain on market portfolio in Iran's stock exchange. These results are consistent with those of Jegadeesh and Titman (1993, 1999, 2001), Chan et al (1996), Sehgal and Balakrich-nan (2002), Kaminsky et al (2002), George and Hwang (2004), Burnside and et al (2011), and Segal and Jain (2012). The results showed that in an investigation on the significant relation between market factor and momentum excess gain in 6-month investment period, explains about 19.7 percent of momentum gain changes in mentioned period of market factor. The result is consistent with those of Fama and French (1992, 1993), Jegadeesh and Titman (1993, 1999, 2001), Verma and et al (2011) and Segal and Jain (2012). Also in an investigation of significant relation between market factor and momentum excess gain in 12-month investment period, it was cleared that about 44.6 percent of momentum gain changes in mentioned period of market factor is explained. So we can understand that the longer the term of investment, the larger the explaining power of Pand conventional pricing models. In contrast profitability of momentum will decrease. The results of this research is consistent with those of Fama and French (1992, 1993), Jegadeesh and Titman (1993, 1999, 2001), Sehgal and Balakrichnan (2002) and Verma and et al (2011).
The results of examination on significant relation between industry momentum factor and momentum excess gain in 6-month investment period shows that in contrast to many researches done in foreign countries, there is no significant statistical relation between short term momentum pattern excess gain and industry factor. So we can conclude that buying the shares of high gain industries and sale of shares of low gain industries and maintaining them in 6-month stock exchange period in Iran can not provide excess and abnormal gain. So, careful selection of individual share type is much more important than care given into selection of industry type in Iran's monetary market. But we should not ignore the damaging role of severe fluctuations imposed in industries' gain resulted from different factors such as broadcasted news in market and the effect of foreign economy on market, because this is one of major factors of industry momentum factor's inefficiency. The results of this examination which was tested for the first time in Iran was consistent with those of Grandly and Martin (2001), Chordia and Shivkumar (2002), Lewellen (2002) and Nijman (2004).
Also according to increase of investment period to 12-months, no specific effect is observed in creating momentum excess gain and there is no significant relation between excess gain resulted from one-year momentum pattern and buying share related to successful industries (portfolio K1 or 20 % of whole industries with the highest gain) and selling shares related to low gain and unsuccessful (portfolio K2 or 20 % of whole industries with the lowest gain) and momentum profitability. The results of this examination which is tested for the first time in Iran was consistent with those by Grundy and Martin (2001), Chordia and Shivkumar (2002), Lewellen (2002) and Nijman (2004).
Functional suggestions
According to the fact that forming portfolio using momentum pattern causes excess positive return, so using this investment model in 6-month and 12-month investments and for the portfolio we suggest purchase and sale of stocks to investors and investment managers. According to new approaches of investments for participants in market and portfolio managers we suggest that before any action, pay more attention to the past returns of companies. Also considering the realities resulted in this research, we suggest to investors that pay attention wisely to severe effects of company size on excess return and at the time of entering to market pay more attention to smaller companies, because the data of these companies are less publicly observable and according to high risk and return, there may be some returns hidden in these companies which is not clear for investors. Generally, according to the results of the present study for predicting excess return of investment strategies, Fama and French model has stronger distributive powers in comparison to four-factor model.
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ПРОБЛЕМЫ РАЗВИТИЯ МАЛОГО БИЗНЕСА В УСЛОВИЯХ ИННОВАЦИОННОГО РОСТА
© Беликова И.П.*
Ставропольский государственный аграрный университет, г. Ставрополь
В статье исследованы особенности развития малого бизнеса в региональном комплексе, уточнены сдерживающие факторы развития инновационной деятельности в сфере малого предпринимательства.
Ключевые слова: инновационное предпринимательство, функции бизнеса, развитие малого предпринимательства, деловой климат, конкуренция.
* Профессор кафедры Менеджмента, доктор экономических наук.
