Prediction from Technical Analysis- A Study of Indian Stock Market
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1 Prediction from Technical Analysis- A Study of Indian Stock Market Assistant Professor, Jannayak Chaudhary Devilal Institute of Business Management, Barnala Road,Sirsa. Abstract A lot of academicians and technical analyst are of the opinion that the markets can be predicted by using proper techniques. A lot of controversy always occurs as whether markets can be predicted or not. In order to judge the efficiency of the markets the researcher in this paper has tested the two most important techniques of technical analysis has used which are RSI (Relative strength index) and EMA (Exponential Moving Average). The data taken for the study is for five years starting from January, 2010 to December, Conclusion of the study claims that the markets can be predicted if proper and timely decisions are taken by using EMA and RSI. Key Words: - RSI, EMA, Brock t statistics, Alpha ratio, Sharpe ratio Introduction:- Technical Analysis specifically attracts the attention of economists as its successes cast doubt upon the Efficient Market Hypothesis (EMH) which states that market prices instantaneously and fully reflect all relevant information. The EMH maintains that publicly available information, such as past prices, should not assist traders in earning unusually high returns. Technical Analysis, on the other hand, suggests that economic fundamentals, such as interest rates and growth are not always the sole determinant of the exchange rate, rather it is driven away from its fair value by the traders irrational expectations of future exchange rate movements. A trading system is a systematic method for buying and selling financial instruments with a view to make money consistently. As such, not only do trading systems require that prices are predictable but also that the predictable component is financially exploitable. Technical analysis is an approach to predicting future price movements based on identifying patterns in prices, volume and other market statistics. Technical analysis usually proceeds by recording market activity in graphical form and then deducing the probable future trend from the pictured history. The premise is that prices exhibit various geometric regularities, which, once identified, inform the trader what is likely to happen next. This in turn allows the trader to run a profitable trading strategy. Technical analysis is the study of financial market action. The technician looks at price changes that occur on a day-to-day or week-to-week basis or over any other constant time period displayed in graphic form, called charts. Hence the name is chart analysis. A chartist analyzes price charts only, while the technical analyst studies technical indicators derived from price changes in addition to the price charts. Technical analysts examine the price action of the financial markets instead of the fundamental factors that (seem to) effect market prices. Technicians believe that even if all relevant information of a particular market or stock was available, you still cannot predict a precise market "response" to that information. There are so many factors interacting at any one time that it is easy for important ones to be ignored in favor of those which are considered as the "flavor of the day." The technical analyst believes that all the relevant market information is reflected (or discounted) in the price with the exception of shocking news such as natural disasters or acts of God. These factors, however, are discounted very quickly. 198
2 Watching financial markets, it becomes obvious that there are trends, momentum and patterns that repeat over time, not exactly the same way but similarly. Charts are self-similar as they show the same fractal structure (a fractal is a tiny pattern; self-similar means the overall pattern is made up of smaller versions of the same pattern) whether in stocks, commodities, currencies, bonds. A chart is a mirror of the mood of the crowd and not of the fundamental factors. Thus, technical analysis is the analysis of human mass psychology. Therefore, it is also called behavioral finance. The efficient-market hypothesis (EMH) contradicts the basic tenets of technical analysis by stating that past prices cannot be used to profitably predict future prices. Thus it holds that technical analysis cannot be effective. Economist Eugene Fama published the seminal paper on the EMH in the Journal of Finance in 1970, and said "In short, the evidence in support of the efficient markets model is extensive, and (somewhat uniquely in economics) contradictory evidence is sparse. Review of Literature:- Andrew (1991) tested for long-run memory that is robust to short-range dependence is developed. It is an extension of the "range over standard deviation" or R/S statistic, for which the relevant asymptotic sampling theory is derived via functional central limit theory. This test is applied to daily and monthly stock returns indexes over several time periods and, contrary toprevious findings, there is no evidence of long-range dependence in any of the indexes over any sample period or sub-period once short-range dependence is taken into account. Illustrative Monte Carlo experiments indicate that the modified R/S test has power against at least two specific models of long-run memory, suggested that stochastic models of short-range dependence may adequately capture the time series behavior of stock returns. Brock, Lakonishock and Lebaron (1992) tested two trading rules- Moving Average and Trading Range Break by utlising the Dow Jones Index from 1897 to This paper resulted strong support for Technical Strategies. The paper concluded that the return obtained from these strategies are not consistent with four popular null models : the random walk, the AR (1), the GARCH-M and the exponential GARCH and buy signals consistently generate higher return than sell signals and the returns following buy signals are less volatile than return following by sell signals. Blume, Easley, and Ohara (1994) investigated the information role of volume and its applicability for technical analysis. The authors developed a new equilibrium model in which aggregate supply is fixed and traders receive signals with differing quality. The paper shows that volume provides information on information quality that cannot be deducted from the price statistic. The paper showed how volume, information precision, and price movements relate, and demonstrate how sequences of volume and price can be information. The authors also showed that traders who use information contained in market statistics do better than traders, who do not. Technical analysis thus arises as a natural component agents learning process. As the analysis suggests, introducing volume unrelated to the underlying information structure would survey weakly the ability of uninformed traders to interpret market information accurately. Elton, Gruber, and Blake (1995) developed relative pricing (APT) models that were successful in explaining expected returns in the bond market. In this study authors employed publicly available bond returns indices (passive bond portfolios) as the independent variables utilized in fitting the equilibrium models. The appendix lists the bond indices used. These include indices of government bonds, corporate bonds, and mortgages. The sample period covered the period from February 1980 to 1992(155 monthly observations). Authors compared the four alternatives APT models, those that did not contain the fundamental expectation variables were rejected at the 5 percent level in favour of models that do contain those variables. The return indices were the most important variables in 199
3 explaining the time series of return. Authors utilized our fundamental relative pricing models to examine the performance of bond funds. Bond funds underperform the returns predicted by the relative pricing models by the amount of expenses on adverse, and the models using fundamental variables, do a better job than other models in accounting for the difference in performance between types of bonds funds. Wong, Manzurand Chew (2003) focused on the role of technical analysis in signaling the timing of stock market entry and exit. Test statistics are introduced to test the performance of the most established of the trend followers, the Moving Average, and the most frequently used counter-trend indicator, the Relative Strength Index. Using Singapore data, the results indicated that the indicators can be used to generate significantly positive return. It is found that member firms of Singapore Stock Exchange (SES) tend to enjoy substantial profits by applying technical indicators. Metghalchi, Gomez, Chen and Monsef (2005) tested three moving average technical trading rules for the S&P 500 stock index. Using daily data from 1954 to 2004, their results indicate that moving average rules did indeed had predictive power and could discern recurring-price patterns for the period up to mid-1980s. However, since mid-1980s, technical trading rules do not work and could not discern recurring-price patterns. Their results are consistent with market inefficiency from 1954 to 1984 and market efficiency from 1984 to present. Zhou and Zhu (2010) showed that the probability of a stock market drop of 50 percent from a high is about 90 percent over a 100-year period, based on the popular random walk model of stock prices. On 9 October 2007, the Dow Jones Industrial Average reached a high of 14,164.53; by 9 March 2009, it had dropped about 54 percent, to a low of 6, Former Fed chairman Alan Greenspan called this a once-in-a-century crisis. They concluded that with a broad market index and a more sophisticated asset pricing model that captures more risks in the economy, the probability rises to above 99 percent. They also cocluded that a market drop of 50 percent or more is very likely in longterm stock market investments, and investors should be prepared for it. Chiang, Ke,Liaoand Wang (2012) tested nine common trading strategies, including buy and hold (passive) and eight technical trading strategies (active). The results show that the Relative Strength Index (RSI) oscillator and parabolic strategies outperform the other technical trading strategies and all of the eight technical trading strategies beat the buy and hold strategy both before and after transaction costs. In addition, investing a portion of investors money in risky assets and a portion in risk free assets can help distinguish performance among the trading strategies. Research Objectives To use EMA and RSI as tools of Technical Analysis. To find out the validity of RSI and EMA in Indian stock market. Hypothesis Null Hypothesis (H 1 ): There is no significant difference between return calculated from Exponential Moving Average and Index Return. (R EMA <R INDEX ) Null Hypothesis (H 2 ): There is no significant difference between return calculated from Relative strength index and Index Return. (R RSI <R INDEX ) The hypothesis will be tested at 3 levels of significance which are 1%, 5% and 10%. Research Design The research design has been distinctive described to the objective of the study. The present study involves exploratory research design. 200
4 Sample Size and source of data In the present study data is taken only for Indian market form January, 2010 to December, The whole data of daily trade and prices of the markets is taken from the website:- Tools used The tool taken for analysis is RSI and EMA. To check the validity of EMA and RSI, Brock t- statistics is applied. Analysis and Interpretation:- Table I: Results of Technical Trading Rules for Whole Period Technique Index No. (Buy) No. (Sell) Long(B) Short(S) Long-Short(B-S) EMA NIFTY *** RSI NIFTY ** *,**,*** represents significant at 1%, 5%, 10% level respectively Source: Compiled by researcher on the basis of data. Table I represents the analysis of EMA and RSI for the whole period of study. The table shows that EMA as well as RSI is found to have significant presence in Indian market for the whole period of data. EMA and RSI both show to have significant presence in case of long strategy at 10% and 5% level of significance. But when judged for short strategy these techniques are not significant. In case of aggregate strategy too the result are not significant. So in short we can say that EMA and RSI have their prediction power in Indian market but mainly in case of long strategy. Table 2 represents the analysis of EMA and RSI for the whole period of study for risk and return attached with the techniques. When observed for the alpha and sharpe ratio in both cases EMA and RSI both have given the positive alpha and sharpe ratio. It means that using EMA and RSI one can have the positive returns as compared with the index return. Table-2 Risk Return Analysis Using RSI and EMA for whole period Technique Index No. of trades 1 Trade Repetition Time 2 (in days) Gross Returns (%) T C (%) 3 Net Returns (%) Sharpe 4,5 Aggregate CAGR Rank Aggregate Aggregate CAGR Rank EMA NIFTY RSI NIFTY Ratio (%) Alpha 6 ( Index Return) 1) Number of trades is reached as follows: e.g., buying X quantity on day one to be long and there after selling 2X quantity i.e. one quantity for becoming neutral and another quantity to be short by X quantity. 2) Trade Repetition Time is an average number of days between two consecutive trades and has direct bearing on the transaction cost. 3) T C (transaction cost): is estimated at 0.01 percent of average trade value (average of INDEX over years) numbers of trades. The transaction cost is usually variable between clients based on their volume of trade and almost nil for members of stock exchanges, where they buy a seat against one-time payment. Hence, transaction cost is being assumed. 4) Sharpe Ratio= (Net Returns - Index Return)/ Standard deviation, 5) Annual Standard
5 Deviation= SD of daily returns multiplied by square root of average numbers of days in a year for the panel to the given index. 6) Alpha Ratio= (Net Returns - Index Return) Conclusion:- At last it can be concluded that both techniques of technical analysis i.e. RSI and EMA can play the positive returns in Indian stock market. However as the transaction cost increases with the number of trades hence it may cut down the returns as earned by the investor. But if the investor can make a lot of trading then the transaction can be minimized as some of the brokering houses are charging a very nominal fee for a good volume of transactions. References:- Andrew W. Lo, 1991, Long-Term Memory in Stock Market Prices, Econometrica, Vol. 59, No. 5 (September, 1991), pp Brock, W., J. Lakonishock& B. Lebaron, 1992, Simple Technical Trading rules and the Stochastic Properties of Stock Returns, The Journal of Finance, Vol. 47, No. 5 ( Dec., 1992), pp Blueme, L., D. Easley & M. O Hara, 1994, Market statistics and Technical Analysis : The role of volume, Journal of Finance, Vol. 49, No. 1 (March 1994), pp Elton, Edvin J., Gruber, Martin J. and Blake, Christopher R.(1995), Fundamental Economic Variables, Expected Returns, and Bond Fund Performance, The Journal of Finance, Volume 2, Number 4, pp Wong, W.K., M. Manzur& B. K. Chew, 2003, How rewarding is technical analysis? Evidence from Singapore stock market, Applied Financial economics, 13 (2003), pp Metghalchi, M., X. G. Gomez, C. P. Chen & S. Monsef, 2005, Market Efficiency For S&P 500: , International Business & Economic Research Journal, Vol. 4, No. 7 (July 2005), pp Zhou, G. & Y. Zhu, 2010, Perspectives: Is the Recent Financial Crisis Really a 'Once-in-A- Century' Event?,Financial Analysts Journal, Vol. 66, No. 1, (2010). Chiang, Y.C., M.C. Ke, T. L. Liao & C. D. Wang, 2012, Are technical trading strategies still profitable? Evidence from Taiwan Stock Index Futures Market, Applied Financial Economics, (2012), pp
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