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Evidence and sources of momentum profits. A study on Indian stock market.

1. Introduction

The term "momentum" refers to the velocity of a price trend. It indicates whether a rising trend is accelerating or decelerating or whether prices are falling at a faster or slower pace. Momentum expresses the direction of the market, shows the amount of movement or pace of the market, and highlights turning points. The amount of price change and continuation of the trend are the basis for different indicators that have been developed to provide information on price, volume and trend analysis. Any anticipation of a change in momentum in the Index or for a particular stock is often a result of signals from technical indicators or changing fundamentals. Momentum trading is an investment strategy that aims to capitalize on the continuance of existing trends in the market. To participate in momentum investing, a trader longs an asset, showing an upward price trend, or short sells a security that has been in a downtrend. The basic concept is that once a trend is established, it is easier to continue in that direction than to negate the trend. It often describes the short term application of trading choice that accompany periods of high velocity of price change in the markets.

Momentum Trading is one of the most accepted investment styles, which enables a trader to identify potentially lucrative trading opportunities. Momentum trading is an investment style that lines up investors with securities reacting to sentiments. Momentum, whether in an individual security or the entire sector, is a resultant of either expected or actual earnings which is reflected in stock price movement and volatility. There are numerous circumstances which can lead to momentum generation. Any anticipation of a change in momentum in the markets or for a particular security is often a result of signals from technical indicators or changing fundamentals.

The focus for momentum trading is the reliability of the price trend segment being traded. Although the trend continues, traders organize trading strategies based on the current trend and assessment of whether the trend will continue over the estimated period or not. The focus is on any indication that a price reversal trend may occur. This may include trend line breaks or fundamental changes in the underlying. When forecasting a trend reversal, a trader expects falling markets to reverse and head higher and vice versa. Momentum trading essentially focuses on early identification of trading opportunities arising out of strong movement of the markets in one direction or another. The objective of the momentum trade is to continue with the trend, or carry any respective position, as long as momentum remains intact.

Recognizing the popularity of momentum as an investment strategy in developed markets like the USA and Europe, the present study attempts to establish momentum as an investment tool in emerging market like India. The study focuses on finding the evidence of momentum and identifying the factors contributing to momentum at the index level. Along with firm specific factors and macroeconomic factors 'Momentum Payoffs' is added as a fourth factor to test the Fama-French unconditional CAPM at index level. From an investor's perspective, the research will help investors to maximize the returns during both upside and downside market conditions.

2. Literature Review

Many of the articles, ranging from behavioral models to rational-expectation models, have attempted to offer an explanation for generation of momentum in stocks and indices. Macroeconomic factors like money supply, market liquidity, GDP growth, interest rates, exchange rates, institutional investment in stock market, etc, influence the stock price movements and help in creating momentum in stocks and indices. Similarly firms' specific factors like corporate earnings, dividend declaration, rights issue, bonus issue, etc, also have significant bearing on stocks and indices momentum. International factors like movement of stock indices of developed and emerging countries, foreign portfolio investments, international liquidity, interest rates, exchange rates, foreign trade turnover etc, also sway the domestic stock prices and indices movement and generate stock specific momentum and indices momentum.

Along with the macroeconomic and firm-specific factors, investors' behavioral pattern also plays a major role in generating momentum returns. Numerous studies have been carried out to provide the evidence and establish the factors generating momentum. However, the focus was primarily on developed markets like USA and Europe. The following sections highlights the major works carried out in the American and European markets with respect to Momentum trading phenomenon. Jegadeesh (1990) and Lehmann (1990) examined the performance of trading strategies based on one week to one month returns so as to investigate the existence of momentum as a reaction to information. Jegadeesh and Titman (1993) examined the performance of trading strategies in the form of winner and losers so as to articulate momentum. Jegadeesh and Titman (1993) reported that a zero cost momentum strategy of buying past winners and selling past losers generates significant average profits. Jegadeesh and Titman (1993) and Chan, Jegadeesh and Lokanishok (1996) mentioned that under-reaction of stock prices to information contained in past stock returns and past company earnings give rise to price momentum. Existence of a momentum effect in stock returns i.e. stocks that have outperformed (under-performed) the average stock return in the past few months tend to perform better (worse) than the average stock return over the subsequent few months. Similar evidence is also reported by Rouwenhorst (1998), for stocks traded on European Markets. Grundy and Martin (2001) also used the collective return as criterion for ranking stocks into winner and loser portfolios.

On the other hand, there have been contrasting interpretations about the potential causes of momentum effect. The first explanation given by Kahneman and Tversky (1982) and De Bondt and Thaler (1985) was based on a study about the market participants' overreaction or under reaction to information which reflected in the stock prices. Literatures have explained the consistency of 'Price Momentum' through investors' behaviors. Various researchers [Barberis et al (1998), Daniel et al (1998) and Hong et al (1999)] have put forward theoretical (behavioral) models of investors' behavior and suggested that price momentum is consistent with cognitive biases in the way investors interpret imperfect information. Behavioral models rely on psychological factors, such as representativeness, conservatism, overconfidence and self-attribution to explain the momentum profits. Investors' behaviors, preferences and market conditions, lead to over-reaction and under-reaction of news to low-weight, high-strength news (high-weight, low-strength news). Empirical validation of these models required identifying and characterizing news in terms of their "strength" and "weight". Hong and Stein (1999) argued that informed investors or the "news watchers" can generate momentum.

As per Conrad and Kaul (1998), the profitability of momentum strategies is due to cross-sectional variation in expected returns rather than predictable time-series variation in security returns. Kewei et al (2009) examined the role of investor attention in explaining the Price and Earnings Momentum strategies profits. Literatures mentioned that price momentum profits are higher among high volume stocks and in up markets, whereas earnings momentum profits are higher among low volume stocks and in down markets. Fama (1991) argued that the "abnormal" returns may not be reliable evidence against efficient market hypothesis if the equilibrium pricing model typically the static CAPM by Sharpe (1964), Lintner (1965), and Black (1972)--is incorrect. Asserting Fama's findings about the CAPM, Fama and French (1992) showcased that there doesn't seem to be any relation between average stock returns and the conventional Beta. To cast away the CAPM anomalies, Fama and French proposed three-factor asset pricing model that seems to describe adequately the average stock excess returns. However, one anomaly remains unresolved by Fama and French (1996) that is the three-factor model doesn't capture the short-term return continuation (or momentum) phenomenon. As Lakonishok et al (1996) put it, "in the absence of an explanation, the evidence on momentum stands out as a major unresolved puzzle. Wu (2001) focused on capturing the Return Momentum. The study shows that the incorporation of 'Conditioning Information' into an Asset-pricing model is one way to capture return momentum.

Chordia and Shivakumar (2002)'s Multi-Factor Model provided empirical evidence that associates momentum profits to business cycles and macroeconomic variables and showed that momentum is explainable by business cycle risk. The multi-factor model used a set of lagged macroeconomic variables like dividend yield, default spread, and yield on 3-month T-bills returns and term structure spread to predict one-month forward returns. Their study established a linkage between the Macroeconomic variables and the Industry returns. Both individual stock and industry momentum returns were attributed to common macroeconomic variables rather than industry specific or firm specific factors. However, Moskowitz and Grinblatt (1999) made contrary propositions when they evaluated momentum in industry returns and found that high momentum industries outperform low momentum industries in the six-months after portfolio formation. They concluded that the profitability of momentum strategy is primarily because of the momentum in the Industry factors. The reasoning in support of the theory was that after controlling the momentum across industries, there is no momentum in individual stock returns except when a past 12-month returns window is used to form the momentum portfolios. The findings of Moskowitz and Grinblatt (1999) were in contrast to the findings of Grundy-Martin (2001), which established that industry-based and individual-stock based momentum returns were separate phenomena. Biglova et al (2009) conducted a study to provide further insight into the stock return momentum phenomena by investigating the sources of momentum profits. The study applied statistical factor analysis to identify the most important variables significantly affecting momentum profits. The study also documented the periodic dynamics of momentum returns. The study was confined to US markets.

In the Indian context, Sehgal and Balakrishnan (2002) attempted to evaluate if there are any systematic patterns in stock returns for the Indian Equity Markets. The empirical findings suggested that there is a reversal in long term returns, once the short term effect has been controlled by maintaining a one year gap between portfolio formation period and the portfolio holding period. The study found that contrarian strategy based on long-term past returns provided moderate positive returns. Moreover, the continuation in short term returns and a momentum strategy based on it provided significant positive returns. The results were similar to those of developed markets like the USA. The empirical findings suggested that time-specific investment strategies based on the aforesaid patterns in stock returns provided extranormal returns. Rastogi et al (2009) conducted a study to verify both momentum and over-reaction phenomena in the Indian equity markets during the period 1996-2008. The study employed Jegadeesh and Titman's methodology to construct portfolios. It tried to account for the size effect by classifying data based on market capitalization. The results showed strong evidence for the existence of momentum, but weak evidence for over-reaction. Overreaction was present only for the midcap stocks in the Indian markets. The study supported the under-reaction hypothesis of behavioral finance researchers for the Indian equity markets. Ahmad and Khan (2012) conducted a study to examine the presence of momentum profit in the Indian Equity Market and tried to explain the sources of momentum profit employing both risk based and behavioral models. The study followed Jegadeesh and Titman's methodology for the construction of momentum portfolios. Idiosyncratic Risk, Idiosyncratic Volatility and Delay Measures were employed to test the behavioral models. The study found strong presence of momentum profits in India during the period 1995 to 2006. Risk based models like CAPM and Fama-French failed to account for the occurrence, whereas idiosyncratic risk showed a positive relation with momentum, lending support to behavioral factors as sources of momentum phenomenon.

Looking at the literature review and above discussion, there is a requirement of formal approach to study the evidence of momentum, factors influencing momentum and understanding the response of the momentum series to the change in factors. The paper focuses on the following objectives:

a) Empirical analysis to establish the relative importance of various domestic and international factors generating momentum at the Index level.

b) Test the Fama-French unconditional CAPM, with momentum as a fourth factor.

c) Testing of 'Momentum Clustering' in the generated momentum series to decide on any particular time period to enter or exit the momentum series to make momentum returns.

3. Empirical Design

3.1. Evidence and Factors Influencing Index Momentum

Based on the literature review, the study identifies that moving price trends of the assets generally indicate "momentum." The term "momentum" refers to the velocity of a price trend. This indicator measures whether a rising trend is accelerating or decelerating or whether prices are declining at a faster or slower pace. Accepting this argument, the study would carry out Simple Moving Average (SMA) and Exponential Moving Average (EMA) on 30Days cumulative Index Returns to generate momentum on respective series. The generated SMA and EMA series is an indicative pattern of momentum. The analysis would be confined to Index of National Stock Exchange of India. The momentum generated series would be regressed against various macroeconomic and firm specific factors. Based on the study conducted by Chordia & Shivakumar (2002), which examined and established the relative significance of the common or macroeconomic factors and the firm specific information as sources of momentum profits, the current study identifies following macroeconomic and firm specific factors in Indian context.

Macroeconomic Factors:

1. Terms Spread

2. Net Foreign Institutional Inflows

3. Index of Industrial Production

Firm Specific Factors:

1. Price to Book Ratio

2. Dividend Yield

3. Price to Earnings Ratio

The regression analysis is carried out on 30-days momentum returns where firm specific and macroeconomic factors will be the independent variables. It is expected that firm specific and macroeconomic variables would have lead-lag relationship with the momentum returns, realizing this the optimum lead-lag period would be decided using Akaike Information Criterion (AIC). Following 2 regression equations would be estimated

[] = [c.sub.i0] + [c.sub.i1][DIV.sub.t-1] + [c.sub.i2][PE.sub.t-1] + [c.sub.i3][PB.sub.t-1] + [c.sub.i4][IIP.sub.t-1] + [c.sub.i5]TermSpread + [c.sub.i6] NctFH + []

.... Equ(1)

In equation (1) [] 30-days momentum series and in the momentum series are estimated through SMA and EMA.

Using the methodology of Vector Autoregressive (VAR), the study will estimate the above mentioned equations. The methodology will also use the Impulse Response Function (IRF) to articulate the response of momentum to any change in the macroeconomic and firm-specific factors. The study would also estimate the ARCH effect so as to understand the 'Momentum Clustering' in the obtained momentum series.

3.2. Unconditional CAPM and Momentum Returns

Along with PE, PB and Div Yield, Momentum Payoffs will be added as a fourth factor to test the Fama-French unconditional CAPM at Index and individual Stock level.

[] - [R.sub.ft] = [alpha] + [c.sub.i1][DIV.sub.t-1] + [c.sub.i2][PE.sub.t-1] + [c.sub.i3][PB.sub.t-1] + [ci.sub.4][] + []

.... Equ(2)


[] - [R.sub.ft] : Excess returns of Index

[] : Momentum series on Index

[DIV.sub.t-1] : Dividend yield of Index

[PE.sub.t-1] : Price Earnings ratio of Index

[PB.sub.t-1] : Price to Book Value ratio of Index.

The above equation will be estimated on index for 30-Days cumulative returns, to test the significance of momentum parameter on pricing of capital asset.

3.3. Sample Size, Variables and Period of Study

Data Collection

Daily returns (Corporate action adjusted) of the CNX NIFTY 50 Index covering the period of April 2003 to March 2013 are collected. Firm specific factors will be collected from NSE, whereas various macroeconomic factors will be collected from Handbook of Statistics on Indian Economy, published by RBI.

Period of Study

This study period includes the stock market rally from 2003 to early 2008, the global financial meltdown of 2008-2009, and the subsequent recovery and the consolidation period beginning soon after that. So, the period signifies all the major ups and downs in the Indian equity markets over the last decade. The study comprises of the two phases which includes the boom phase of April 2003 to early 2008 and the volatile period till March 2013.

4. Results and Discussion

4.1. Generation of Momentum Series

Momentum in stock or index indicates the trends in the price movements. The fluctuating time series of index return need to be smoothened to arrive at momentum return. The study has used continuous trading for 30-days which covers about 12 settlement cycles. To generate momentum series (Figure 1a), the study has estimated Simple Moving Average using 12 settlement cycles.

To provide more importance to recent observations in the time series data, the study has used exponential moving average to arrive at smooth momentum series of the NIFTY returns (Figure 1b).

4.2. Factors Affecting Momentum

From the literature review, following macroeconomic and firm-specific factors that have an influence on momentum been identified.

Macroeconomic Factors:

1. Term Spread

2. Net Foreign Institutional Inflows

3. Index of Industrial Production

4. Net Banking Credit

Firm Specific Factors:

5. Price to Book Ratio

6. Dividend Yield

7. Price to Earnings Ratio

The above mentioned factors, along with the momentum series (individual EMA and SMA series), put through a VAR process to understand their interrelationship. By minimizing the AIC, the optimum lag length among the aforesaid variables has been identified (Table 1). With an optimum lag-length of 1, the VAR equation is estimated (Table 2).

The VAR equation (Table 2), with the dependent variable SMA Momentum Series, indicates the significance of momentum generating factors. Except Price-Book ratio, all other parameters in VAR equation (Table 2) are significant. The VAR estimate indicates SMA momentum series is influenced by Price-Earnings, Dividend Yield, Net FII inflows, Index of Industrial Production (IIP) and Terms Spread with one period lag. The momentum series is also influenced by its own momentum with one period lag. The negative coefficients of Price-Earnings Ratio, Dividend Yield, IIP and Terms Spread indicate a decline in the momentum returns which might be a result of investors' overreaction or negative sentiments associated with these variables. Similar result is also found when EMA is used as dependent variable in the VAR modeling (Table 3 and Table 4).

4.2.1. Impulse Response Function

To understand the response of the momentum series to the change in factors, the VAR system equation is given shock of one-sigma for each factor. The VAR impulse response function for each factor is estimated for a shock of one-sigma.

When one-sigma shock is given to the past momentum returns, the current momentum returns continuously decline till 12-months as shown in Figure 2a and Figure 2b. This indicates that the past earning momentum has negative feedback effect with the subsequent earnings momentum

With one-sigma shock in Price-Earnings, the momentum return decline till 6-months after which the trend reverses. This indicates, the decision of the momentum traders to stay invested is driven by Price-Earnings stability (Figure 3a and Figure 3b).

The momentum traders increase their long positions till the second month, with any shock in Price-Book ratio. From the second month onwards, the traders consolidate their positions till the fourth month, after which they sell-off their existing positions leading to a fall in momentum returns. This impulse response of momentum returns indicates overreaction of momentum traders during the initial phase and the effect of sentiment in the consolidation phase (Figure 4a and Figure 4b).

The impulse response shock in dividend leads to initial squaring off of existing positions by the momentum traders, which leads to a decline in momentum returns. The sell-off continues till one-quarter, after which the traders start rebuilding their position with an expectation of dividend declaration in the coming quarters (Figure 5a and Figure 5b).

With an impulse response shock in Net FII Inflows, momentum traders long their positions till the third month. The traders consolidate their positions till the sixth month, after which the market witnesses a sell-off in the existing positions leading to a gradual decline in momentum returns. The behavior of the momentum traders is initially driven by the expectation of stability of the FII Inflows and the declining phase of momentum returns might be influenced by the FII's profit booking (Figure 6a and Figure 6b).

The response of momentum returns to a change in IIP is erratic in nature, and is primarily driven by idiosyncratic behavior of momentum traders (Figure 7a and Figure 7b).

Terms Spread, the difference between long and short-term G-Sec rates, negatively influences the momentum returns till the second month, after which there is a gradual increase in the momentum returns. The momentum traders are highly sensitive to any increase in market interest rates during the initial phase and subsequently when the real sector absorbs the increase in the interest rates momentum reverses its trend (Figure 8a and Figure 8b).

4.2.2 Momentum Clustering Test

Momentum series (SMA, EMA) is derived from the time series returns of CNX NIFTY 50. Though the series indicates a continuous returns pattern, it might happen that in some periods there might be a continuous increase in returns or a continuous decline in returns. To understand the above pattern of returns, which is generally known as 'Momentum Clustering,' the study has conducted the ARCH Test on the SMA and EMA momentum series.

It is found that for both the series the null hypothesis of "having no ARCH effect" is accepted implying that Momentum Clustering is not observed in either series (Figure 9a, 9b). The implication of this finding is that, there is no particular time period to enter or exit from the momentum series to make momentum returns.

1.2.3 Fama-French Unconditional CAPM Test

In the Fama-French 3-Factor (FF) CAPM model, the overreaction or under-reaction of the investors' behavior could not be explained and remained a puzzle in the asset pricing. Recent literature have used momentum returns as a proxy to investors' sentiment and extended the FF 3-Factor model in resolving the momentum puzzle. The present study has included momentum as an additional factor to empirically test the Chordia and Shivakumar (2002) 4-Factor model in the Indian context.

From the test results, it is observed that along with the factors like Price-Earnings Ratio, Price-Book Ratio and Dividend Yield Ratio, the SMA and EMA momentum is also significant in deciding the pricing of the asset in the respective momentum series.

5. Conclusion

This paper discussed various methodologies in the Indian context to empirically establish and analyze the evidence of momentum, the factors generating momentum and the relative importance of momentum as a factor in the CAPM asset pricing model. The results conclude that the negative coefficients of Price-Earnings Ratio, Dividend Yield, IIP and Terms Spread indicate a decline in the momentum returns which might be a result of investors' overreaction or negative sentiments associated with these variables. Empirically it is observed that there is no Momentum Clustering in the NIFTY-50, implying that there is no particular time period to enter or exit from the momentum series to make momentum returns. It is also found that momentum is an inherent factor in pricing of the asset.


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Indian Institute of Technology Kharagpur


Indian Institute of Technology Kharagpur

Table 1 VAR Lag Test: SMA Momentum Series

Lags   loglik     p(LR)     AIC        BIC        HQC

1      -503.140             16.690 *   18.533 *   17.419 *
2      465.737    0.01023   17.036     20.492     18.404
3      -417.450   0.00006   17.058     22.125     19.063
4      -372.824   0.00039   17.188     23.868     19.832
5      -318.066   0.00000   17.016     25.309     20.298

Table 2 VAR Equation

Dependent Variable: SMA Momentum Series

                     Coefficient   Std.         t-ratio   p-value

Const                0.077         0.035        2.196     0.03183
SMA Momentum (-1)    0.849         0.088        9.583     <0.00001
Price-Earning(-1)    -0.003        0.001        -2.887    0.00533
Price-Book Ratio     0.002         0.003        0.623     0.53565
Dividend Yield(-1)   -0.019        0.0104       -1.843    0.07011
Net FII (-1)         2.30(e-07)    1.30(e-07)   1.786     0.07893
IIP_1                0.049         0.018        2.6762    0.00948
TrSpd_1              -0.004        0.002        -1.9318   0.05789

Adjusted R-squared 0.81
F(7, 63): 56.51395
P-value(F) : 9.37e-25
Durbin-Watson : 2.115561

Table 3 VAR Lag Test: EMA Momentum Series

lags   loglik     p(LR)     AIC        BIC        HQC

1      -503.430             16.699 *   18.542 *   17.428 *
2      469.253    0.035     17.141     20.597     18.509
3      -417.203   0.00001   17.050     22.118     19.056
4      -370.738   0.00015   17.126     23.806     19.769
5      -319.594   0.00001   17.062     25.354     20.343

Table 4 VAR Equation Dependent EMA Momentum Series

                     Coefficient   Std.         t-ratio   p-value

const                0.238         0.093        2.565     0.012
EMA Momentum (-1)    0.840         0.095        8.833     <0.00001
Price-Earning(-1)    -0.003        0.001        -2.963    0.004
Price-Book Ratio     0.002         0.003        0.716     0.476
Dividend Yield(-1)   -0.019        0.010        -1.877    0.065
Net FII (-1)         1.92(e-07)    1.22(e-07)   1.576     0.120
IIP_1                0.053         '0.019       2.812     0.006
TrSpd_1              -0.002        0.001        -1.844    0.069

Adjusted R-squared :0.79
F(7, 63): 51.34050
P-value(F): 1.21e-23
Durbin-Watson: 2.121264

Table 5 ARCH Effect Test: SMA Momentum Series

           Coefficient   Std. Error   t-ratio   p-value

alpha(0)   5.778e-05     3.260 e-05   1.772     0.082
alpha(1)   0.105         0.125        0.841     0.403
alpha(2)   0.115         0.125        0.919     0.362
alpha(3)   -0.000        0.126        -0.004    0.997
alpha(4)   0.038         0.125        0.304     0.762
alpha(5)   0.223         0.122        1.820     0.074

Null hypothesis: no ARCH effect is present
Test statistic: LM = 6.04912
with p-value = P(Chi-square(5) > 6.04912) = 0.301469

Table 6 ARCH Effect Test: EMA Momentum Series

           Coefficient   Std. Error   t-ratio   p-value

alpha(0)   5.530e-05     3.301e-05    1.675     0.099
alpha(1)   0.065         0.124        0.524     0.601
alpha(2)   0.072         0.123        0.588     0.559
alpha(3)   0.023         0.123        0.186     0.852
alpha(4)   0.114         0.123        0.930     0.355
alpha(5)   0.221         0.121        1.818     0.074

Null hypothesis: no ARCH effect is present
Test statistic: LM = 6.025
with p-value = P(Chi-square(5) > 6.025) = 0.303794

Table 7 VAR: Lag Test for SMA Momentum Series

Lags   loglik    p(LR)    AIC         BIC         HQC

1      253.997            -6.5881     -5.6089     -6.2001
2      351.803   0.0000   -8.7295 *   -6.9343 *   -8.0181 *
3      367.392   0.183    -8.4527     -5.8415     -7.418
4      389.086   0.0126   -8.3554     -4.9283     -6.997

Table 8 CAPM Test:
SMA Momentum Dependent Excess Market Return

                      Coefficient   Std. Error   t-ratio   p-value

const                 0.008         0.115        0.073     0.942
Price-Earning(-1)     0.017         0.004        3.565     0.000
Price-Earning (-2)    -0.017        0.005        -3.541    0.000
Price-Book (-1)       0.048         0.024        2.057     0.044
Price Book(-2)        -0.047        0.024        -1.950    0.055
Dividend Yield(-1)    -0.204        0.056        -3.643    0.000
Dividend Yield (-2)   0.205         0.048        4.261     0.000
Excess-Market (-1)    0.009         0.038        0.247     0.806
Excess-Market (-2)    0.010         0.043        0.250     0.803
SMA Momentum(-1)      1.322         0.466        2.837     0.006
SMA Momentum(-2)      -1.198        0.469        -2.556    0.013

Adjusted R-squared : 0.92
F(10, 59): 70.17426

Durbin-Watson: 2.356752

Table 9 VAR: Lag Test for EMA Momentum Series

Lags   loglik    p(LR)     AIC        BIC        HQC

1      253.955             -6.586     -5.607     -6.198
2      351.537   0.00000   -8.721 *   -6.926 *   -8.010 *
3      367.123   0.183     -8.444     -5.833     -7.410
4      387.596   0.023     -8.311     -4.884     -6.953

Table 10 CAPM Test:
EMA Momentum Dependent Excess Market Return

                      Coefficient   Std.     t-ratio   p-value

const                 -0.130        0.201    -0.647    0.520
Price-Earning(-1)     0.016         0.005    3.451     0.001
Price-Earning (-2)    -0.017        0.005    -3.460    0.001
Price-Book (-1)       0.050         0.023    2.164     0.034
Price Book(-2)        -0.048        0.0240   -2.039    0.0460
Dividend Yield(-1)    -0.204        0.055    -3.713    0.00046
Dividend Yield (-2)   0.205         0.045    4.170     0.00010
Excess-Market (-1)    0.011         0.039    0.299     0.767
Excess-Market (-2)    0.010         0.043    0.249     0.804
SMA Momentum(-1)      1.302         0.483    2.694     0.009
SMA Momentum(-2)      -1.166        0.480    -2.431    0.018

Adjusted R-squared: 0.91
F(10, 59): 72.47

P-value(F): 2.34e-29
Durbin-Watson: 2.36
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Author:Misra, Arun Kumar; Mohapatra, Sabyasachi
Publication:Economics, Management, and Financial Markets
Article Type:Report
Geographic Code:9INDI
Date:Sep 1, 2014
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