ANALYZING MOMENTUM EFFECT IN HIGH AND LOW BOOK-TO-MARKET RATIO FIRMS WITH SPECIFIC REFERENCE TO INDIAN IT, BANKING AND PHARMACY FIRMS ABSTRACT

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1 ANALYZING MOMENTUM EFFECT IN HIGH AND LOW BOOK-TO-MARKET RATIO FIRMS WITH SPECIFIC REFERENCE TO INDIAN IT, BANKING AND PHARMACY FIRMS 1 Dr.Madhu Tyagi, Professor, School of Management Studies, Ignou, New Delhi 2 Venkatesh.C.K.,Assistant Professor, GFGC, Kadugudi, Bangalore Kiran.M.,Assistant Professor, GFGC, Bangaru Tirupathi ABSTRACT This paper tries to explore the Momentum Effect of High and Low Book-to-Market ratio firms with specific reference to Indian IT, Pharmacy and Banking Stocks. Previous Literature has documented that all High Book-to-Market ratio firms are financially distressed. It is also been proved that high Book-to-Market stocks are those with poor historical performance and have not received enough attention from the investors. In contrast to this, all Low Book-to-Market Ratio firms are financially strong and have received utmost investor attention. To prove this theory in the current research the researchers have developed this as a Problem Statement. Investors tend to be over optimistic about the future performance Low Book-to-Market Ratio stocks. Envisaging all these, the current research tries to prove all documented facts in the Indian context with specific reference to mentioned stocks. This paper records Momentum Effect of Selected Stocks in the Indian Context, for this purpose the data pertaining to Trading Volume, Market Capitalization and the Current Market Value of different stocks belonging to the mentioned sectors was chosen. Later, the stocks were bifurcated as the stocks with good fundamentals and bad fundamentals based on the F_Score and G_Score as proposed by Piotroski (2000) and Mohan Ram (2005) respectively. The goal of this paper is to show that investors can create a stronger value portfolio by using simple historical financial performance, along with trading volumes and Market Capitalization. Key words: Momentum, Trading Volume, Market Capitalization, Book to market ratio, Fundamental Score, Growth Score. Introduction The analysis of Technical Information such as, past returns and past trading volume in stock market anomaly is popularly known as Momentum Strategy. The momentum effect in which past winner stocks is going to win and past loser stocks are going to lose is a well known anomaly in 43

2 asset pricing. Jegadeesh and Titman (1993) showed that an investment strategy with long position of past winner stocks and short position in past loser stocks in the past three to twelve month generate significantly positive return in the ensuing three to twelve months. Rouwenhorst (1998) documented that the momentum effect is also observed in the European stock markets. Chui, Titman and Wei (2000) found the presence of the momentum effect in several Asian Stock Markets. The past trading volume, along with past returns has been envisaged to be associated with future returns. Lee and Swaminathan (2000) provided evidence to show that past trading volume predicts the persistence and magnitude of future price momentum effect. Moreover, they found reconciliation between the intermediate-term price momentum effect and longer term return reversals documented in the United States, equity markets. The past trading volume provides information about the timing of the return reversals was first documented by DeBondt and Thaler (1985). Barberis, Shleifer and Vishny (1998) argued that momentum arises because of investor s conservatism which leads to under reaction to news. The under reaction causes the prices to slowly adjust to information and thus momentum effect arises. They also argue that momentum effect arises because of delayed over reaction of the informed traders who over estimate their private signals about these stocks. Review of Literature Jegadeesh and Titman (1993), show that a strategy of purchasing the winning stocks and short selling the losing stocks generates positive returns over the three to twelve months holding periods. Their research also indicates that these returns are not compensation for excess risk. These positive momentum returns in the first 12 month holding period are followed by a price reversal in the months after the portfolio formation month. Mosknowitz and Grinblatt (1999) find that most of the momentum returns reported by Jegadeesh and Titman (1993) can be attributed to Industry Momentum returns. In addition they indicate that past industry returns predict the future industry returns. Rouwenhorst (1998) shows further support of the momentum strategy in twelve international markets, even after adjusting for risk. Conrad and Kaul (1998) confirm that momentum strategies are generally profitable at medium term holding periods, especially for the periods from 6 to 12 months. Marshall and Cahan (2005) apply the same 52 week high momentum strategy to stocks traded in the Australian Stock Exchange and their result show, the risk adjusted returns are significant. This stream of research can fundamentally be linked to the questions of whether technical trading rules can be successful if markets are efficient in processing information. The implied null hypothesis is that information about future prices is fully and correctly impounded in current stock prices, so that the knowledge of past returns and prices provides no information about future returns and prices. 44

3 Choe et.al. (2011) confirm the argument about the usefulness of technical trading strategies in equity market. They found that investment strategy under consideration does not make a difference for investment preference in Taiwan market. DeBondt and Thaler (1985) document long-term reversals at horizons from three to five years. They show that firms with poor performance in the past three to five years out perform those with good past performance over the next three to five years. Hong and Stein (1999) propose behavioral models that integrate the medium term momentum and long term reversals into one model where investors have cognitive bias or bounded rationality when extracting information from news. Jegadeesh and Titman (2001), indicates that stocks that perform the best over three to twelve month period tend to continue to perform well over the subsequent three to twelve months. Momentum trading strategies that exploit this phenomenon have been consistently profitable in the United States and in most developed markets. Similarly, stocks with high earnings momentum outperform stocks with low earnings momentum. This article reviews the evidence of price and earnings momentum and the potential explanations for the momentum effect. Statement of the Problem Most of research works concentrate on the foreign stock exchanges. The current research is on Indian stock market. Also the reviews from the study do not throw light on the comparison of high and low book to market ratio firms in any context. In the current research Momentum Effect is compared in between high and low book to market ratio firms. The problem statement established for this study is the critical comparison of high and low book to market ratio firms with specific objective of evaluating these firms envisaging momentum effect. Objectives of the study Evaluating Momentum Effect in high and low book-to-market ratio firms Measuring Co-Variance between Returns and Trading Volume of High and Low Score Firms Measuring Momentum Effect of High and Low Score Firms Evaluating the impact of past returns and past trading volume on Momentum Effect Data Sources and Methodology Data is sourced from secondary sources and various portals of Bombay Stock Exchange were considered for sourcing the data. The firms chosen for the research are Banking, Information Technology and Pharmacy which are listed in the Bombay Stock Exchange. At the first instance F_Score (Piotroski-2000) and G_Score (Mohan Ram-2005) was calculated for those firms having high and low book-to-market ratio. For high book-to-market ratio firms F_Score was calculated followed by a G_Score for the firms having low book-to-market ratio. These firms were ranked based on their scores, top ten and bottom ten firms were chosen for analyzing the Momentum Effect. A total of two hypotheses were set to evaluate the momentum effect. 45

4 The work is statistically validated using Descriptive Statistics, variation, Standard Deviation and Standardized Unexpected Earnings. Analysis and Interpretation As a part of measuring Momentum Effect a separate objective is set in this work of research. This section examines the momentum effect of firms with good and bad fundamentals, for the purpose of measuring momentum, past returns and past trading volumes are taken into consideration. Firms with high F_Score and G_Score are considered as the stocks having good fundamentals and stocks with low F_Score and G_Score are identified to be the stocks with bad fundamentals. Again the analysis is broadly categorized under two heads namely; a. Measuring Co-Variance between Returns and Trading Volume of High and Low Score Firms b. Measuring Momentum Effect of High and Low Score Firms Measuring Co-Variance between Returns and Trading Volume of High and Low Score Firms Hypothesis to Analyze Co-Variance between returns and trading volumes achieved by high and low score firms Ho: Firms with High F/G Score along with Low Co-Variance between returns and trading volume achieve lesser returns. H1: Firms with High F/G Score along with Low Co-Variance between returns and trading volume achieve higher returns. Table--01 showing Technical Information about past returns and past trading volumes of high/low F_Score Information Technology firms Market Value Number of Market Value Number of of High Trades High of Low Trades Low F_Score firms F_Score firms F_Score firms F_Score firms Average Standard Deviation Co-efficient of Variation

5 Table--02 showing Technical Information about past returns and past trading volumes of high/low F_Score banking firms Market Value Number of Market Value Number of of High Trades High of Low Trades Low F_Score firms F_Score firms F_Score firms F_Score firms Average Standard Deviation Co-efficient of Variation Table 03 showing Technical Information about past returns and past trading Volumes of high/low F_Score Pharmacy firms Market Value Number of Market Value Number of of High Trades High of Low Trades Low F_Score firms F_Score firms F_Score firms F_Score firms Average Standard Deviation Co-efficient of Variation From the above analysis there is an evidence to reject Null Hypothesis and accept the Alternate Hypothesis. Firms with High F_ Score along with Low Co-Variance between returns and trading volume are achieving higher returns. It is observed from analyses that, irrespective of the sector all the high score firms are achieving Low Variation which are associated with high Market Returns. It can be interpreted from the analysis, that all firms with low covariance are achieving higher returns at a consistent rate. All the firms with lower co-variance are achieving the returns consistently over a ten years period. It can be concluded that a low covariance between the past returns and the trading volume correspond to the winning stocks, viceversa is for losing stocks. The current finding is in consistent with the previous literature, Dr.Cheng-Few-Lee and Wei-Kang Shih 1 have also documented that the firms with lower co-variance between trading volume and returns have given consistent returns over a period of 15 years. 1 Cheng-Few Lee and Wei-Kang Shih (2009) Fundamental and Technical, and Combined information for separating winners from losers Review of Financial studies, 12(2),

6 Table--04 showing Technical Information about past returns and past trading volumes of high/low G_Score (Information Technology firms) Market Value of Number of Market Value Number of Trades High G_Score Trades High of low G_Score Low G_Score firms G_Score firms firms firms Average Standard Deviation Co-efficient of Variation Table 05 showing Technical Information about past returns and past trading volumes of high/low G_Score (Banking firms) Market Value of Number of Market Value Number of Trades High G_Score Trades High of low G_Score Low G_Score firms G_Score firms firms firms Average Standard Deviation Co-efficient of Variation Table--06 showing Technical Information about past returns and past trading volumes of high/low G_Score (Pharmacy firms) Market Value of Number of Market Value Number of Trades High G_Score Trades High of low G_Score Low G_Score firms G_Score firms firms firms Average Standard Deviation Co-efficient of Variation The above analysis relates to the Technical Information of high and low G_Score firms. It is been repeatedly observed that all high score firms are associated with low co-variance between returns and trade volumes, consistently these firms are giving good returns over a period of ten years. Therefore, here, in this analysis also null hypothesis is rejected by accepting the alternative hypothesis. It can again be concluded that a low co-variance between the past returns and the trading volume correspond to the winning stocks, vice-versa is for losing stocks. 48

7 The current finding is in consistent with the previous literature, Dr.Cheng-Few-Lee and Wei-Kang Shih 2 also have documented that the firms with lower co-variance between trading volume and returns have given consistent returns over a period of 15 years. Measuring Momentum Effect of High and Low Score Firms Hypothesis to analyze the Momentum Effect of High and Low Score Stocks Ho: High Score Stocks are documented to exhibit Weaker Momentum Effect than those of Low Score Stocks H1: High Score Stocks are documented to exhibit Stronger Momentum Effect than those of Low Score Stocks Table--07 showing Momentum Results of Top F_Score (Information Technology Firms) Mean Median Standard Deviation Variation Table--08 showing Momentum Results of Bottom F_Score (Information Technology Firms) Mean Median Standard Deviation Variation Table--09 showing Momentum Results of Top F_Score (Banking Firms) Mean Median Standard Deviation Variation Ibid Cheng-Few Lee and Wei-Kang Shih (2009) Fundamental and Technical, and Combined information for separating winners from losers Review of Financial studies, 12(2),

8 Table--10 showing Momentum Results of Bottom F_Score (Banking Firms) Mean Median Standard Deviation Variation Table--11 showing Momentum Results of Top F_Score (Pharmacy Firms) Mean Median Standard Deviation Variation Table--12 showing Momentum Results of Bottom F_Score (Pharmacy Firms) Mean Median Standard Deviation Variation Above six tables presents the results of Momentum Effect of High and Low F_Score firms. For the purpose of measuring Momentum Effect three variables are taken into consideration that is, Number of Shares, Number of Trades and Total Turn Over achieved. These three variables directly influence the Momentum of any stock. From the above results, there is an evidence to reject the null hypothesis. High score firms are exhibiting stronger Momentum Effect than that of the low score firms. For proving this hypothesis a comprehensive study is been done by separately presenting the results of the all the top and bottom F_Score firms pertaining to three sectors. The Mean Score of High F_Score firms are far ahead than the low score firms with respect to all the three variables, that is, Number of Shares, Number of trades and Total Turn Over. This indicates the rate at which High Score firms are getting traded in the market. The Variation is consistently lesser in case of High Score firms irrespective of the sector. This result indicates that across the sectors Momentum Effect is consistent and in case of Banking Sector High Score firms are showing greater Momentum than the Low Score firms. 50

9 Table-13 showing Momentum Results of High G_Score firms (Information Technology) Mean Median Standard Deviation Variation Table 14 showing Momentum Results of Low G_Score firms (Information Technology) Mean Median Standard Deviation Variation Table--15 showing Momentum Results of High G_Score firms (Banking) Mean Median Standard Deviation Variation Table--16 showing Momentum Results of Low G_Score firms (Banking) Mean Median Standard Deviation Variation Table--17 showing Momentum Results of High G_Score firms (Pharmacy) Mean Median Standard Deviation Variation

10 Table--18 showing Momentum Results of Low G_Score firms (Pharmacy) Mean Median Standard Deviation Variation The above tables represent the analysis of Momentum with respect to high and low G_Score firms. All the high score firms are repeatedly achieving higher Momentum than their counterparts the low score firms. It is been observed in the analysis that high G_Score firms are ahead of the rest of the firms when it comes to the analysis of Momentum. Grinblatt and Moskowitz (2004) 3 showed that Momentum Effect is more pronounced for firms with higher trading volumes and small firms with less institutional shareholders. Here, in this work institutional shareholders analysis is not been emphasized. The main focus is on measuring Momentum only. The results of this study is in consistent with the evidence as shown by Grinblatt and Moskowitz (2004), here in this study also firms with higher trading volumes are having higher Momentum than the firms with lower trading volumes. For this purpose the average trading volumes of High and Low F/G_Score firms are compared with one another and it is been observed that the average trading volumes of high G_Score firms are far ahead than the rest of the firms. Lakonishock, Shleifer and Vishny (1994) 4 argued from the behavioral aspects that high book-tomarket stocks are those with poor historical performance and have not received enough attention from the investors. To analyze this argument the total number of trades of these companies is compared. As it is evident from the results, high BM firms are having low trades with an average score of as compared to of low BM firms. This analysis clearly indicates the dominance of low BM firms in getting investor attention. Daniel and Titman (1999) 5 find that momentum profits are significantly higher when the strategy is implemented on growth (low book-to-market) stocks rather than value (high book-to-market) stocks. This result also is proven in this research work and for this purpose high and low bookto-market stocks were compared over a period of ten years. Average trading volumes of high G_Score firms are far ahead of rest of the firms. This finding is consistent with the evidence shown by Daniel and Titman. Calculation of Standardized Unexpected Earnings (SUE) To support the results presented above, in the current research, Standardized Unexpected Earnings calculations are been made. SUE is a renowned technique to measure the Momentum Effect of stocks. In the current research, this technique is used to present the Momentum Results 3 Grinblatt,M., and Moskowitz, T. (2004), Predicting Stock Price Movements from Past Returns: The Role of Consistency and Tax-Loss Selling. Journal of Financial Economics, 71(3), PP ibidlakonishok, J., Shleifer A., and Vishny, R.W., (1994), Contrarian Investment, Extrapolation, and Risk, The Journal of Finance, 49(5), Daniel and Titman (1999) Market Efficiency in an Irrational world Financial Analyst Journal, 55, PP

11 of high/low F/G_Score firms. It is observed from the Literature that the Practioners in the money management industry are aware about the Momentum Effect and they screen the stocks based on price momentum. Grinblatt, Titman and Wermers (1995) 6 and Chan, Jegadeesh (2000) 7 find that mutual funds tend to buy past winners and sell past losers. Also, Womack (1996) 8 reports that analysts generally recommend high momentum stocks than the low momentum stocks. Lee and Swaminathan (2000) examine the relation between momentum profits and turnover, and find that momentum is higher for stocks with greater turnover. A partial list of papers that investigate the relation between past earnings momentum and future returns are Jones and Litzenberger (1970), Latane and Jones (1979), Foster, Olsen and Shevlin (1984), Bernard and Thomas (1989), and Chan, Jegadeesh and Lakonishok (1996). These papers typically measure earnings momentum using Standardized Unexpected Earnings (SUE). SUE is calculated as below; SUE= QUARTERLY EARNINGS EXPECTED QUARTERLY EARNINGS/ QUARTERLY EARNINGS These papers use variations of time series models to determine earnings expectations. The papers assume that quarterly earnings follow a seasonal random walk with drift. In this work also the earnings expectations are determined using time series model. Table--19 showing SUE Results YEAR SUE High SUE Low SUE High SUE Low F_Score firms F_Score firms G_Score firms G_Score firms AVERAGE Grinblatt, Titman and Wermers (1995), Mutual Fund performance: An analysis of quarterly portfolio holdings, Journal of Business 62, PP Chen, Narasimhan Jegadeesh (2000), The value of active mutual fund management: An examination of the stockholdings and trades of fund mangers, Journal of Financial and Quantitative Analysis 35, PP Womack, Grinblatt, Mark (1996), Does Industry Explain Momentum? Journal of Finance 54, PP

12 Table above exhibits the results of SUE calculations over a period of ten years. It is evident from the results that all high score firms with good fundamentals are showing high Momentum than the low score firms with bad fundamentals. The above results are in consistent with Daniel and Titman (1999) 9 who found that Momentum is significantly higher in case of Growth Stocks (Low Book-to-Market Value Firms) rather than the Value Stocks (High Book-to- Market Value Firms). Growth Stocks are those stocks having High/Low G_Score and value stocks are those having High/Low F_Score. The Average SUE of high G_Score firms is 1.24 which is the highest among all the firms. Again, high F_Score firms are also exhibiting greater Momentum by having a positive Average SUE over a sample period of ten years. It is also proved in the current research that firms with bad fundamentals having low F/G_Score are failing to perform in the Momentum front also; these firms are achieving negative Average SUE of and respectively. To consolidate the results, it is concluded that all firms with good fundamentals are way ahead in generating momentum than the low score counterparts. Lee and Swaminathan (2000) 10 examine the relation between Momentum Profits and Turn Over and find that Momentum is higher for stocks with greater turn over. This finding of Lee and Swaminathan is also proved in the current research and for this purpose a comprehensive study was under taken to compare the returns generated by the firms having greater turn over and lesser turn over. It is concluded from the results that firms having greater turn over are generating more profits than the firms having lesser turn over. Summary The current research is specifically drawn to narrate the role of Momentum Effect in Technical Analysis, as Technical Analysis considers only past prices and trading volumes. The influence of Momentum Effect is considered to be more. For this purpose two hypotheses were established and this was tested using Descriptive Statistics and Variation, along with the calculation of SUE. All the high score firms were showing stronger Momentum than their counterparts the low score firms. Eventually all High and Low F/G_Score firms were compared with one another to understand the Momentum Effect and it is concluded from the analysis that all high G_Score firms are far ahead in terms of trading volumes and turn over. These results are in consistent with the evidence as shown by Grinblatt,M. and Moskowitz,T (2004) and Laknonishok,J. Shleifer A and Vishny, R.W(1994). From this analysis it is concluded that the overall Momentum Effect of high G_Score firms are far ahead of the rest of the firms chosen in this work of research. It is also observed in the process of analysis, that there exists a stronger momentum for firms with good financials. Therefore, this work of research considers F_Score and G_Score for measuring the financial characteristics of High and Low Book-to-Market firms. Using these results an investor can construct a portfolio to achieve better returns in the long run. 9 IBID Daniel and Titman (1999) Market Efficiency in an Irrational world Financial Analyst Journal, 55, PP Lee, Charles and Bhaskaran Swaminathan, 2000, Price Momentum and Trading Volume, Journal of Finance 55, PP

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