Evaluation of Returns Predictability - Coursework Example

Evaluation of Returns Predictability Evaluation of Returns Predictability Regarding the stock market, excess of returns is also referred to as abnormal rate of return. Fama (1998) explains it as investment returns from a security or portfolio that exceeds a benchmark or an index that has a similar level of risk. In other words, it is a portfolio’s return that is not explained by the overall rate of return in the market. Technical analysis is a methodology used in security analysis that uses statistics, such as prices and volume that have been generated by the market in the past. Specifically, Park and Irwin (2004) suggest that this method tries to predict the direction of prices by studying past market data and specifically that of price and volume of securities. This paper has two sections; in the first section it discusses empirical evidence on the predictability of excess of returns by the technical analysis method, the second section is devoted to evaluating whether return predictability is a good test for market efficiency. Part A: Empirical Evidence of Technical Analysis on the Predictability of Excess of Return Gustafsson (2012) conducted a research on how the stock market performed relatively to the predictions by the technical analysis method in the Swedish stock market. According to technical analysis assumed that successive returns were related. In other words, positive returns in a given time period would be followed by equally positive returns in the same period of the subsequent year. On the basis of the above assumption, Gustafsson (2012) wanted to test the hypothesis that successes in the stock market were independent. Among other tests, the research summarized results from various trading rules to test whether a technical analysis would have been used to accurately predict recurring price patterns and hence returns. The research found out that the comparison between 2001-12-28 and 2011-12-30, the average daily return was 0.0184% for the buy and hold strategy, with a standard deviation of 0.016 and the total number of trading days was 2517. With this statistic, the t-statistic for the buy and hold strategy was 0.58. This study made reached some interesting conclusions from the data. Interestingly, it found out that the introduction of RSI and the RSIstoch that indicates the directional strength of price changes had an impact on the return predictability. Further, the findings lend to the support of the assumption that technical analysis can be used to predict future price movements. This is because; average daily buy-day returns were significantly larger than average daily-sell returns. As such, it meant that the technical trading rules also had a predictive power over future returns movement. Moreover, the research found out that technical analysis predictability of returns was not only associated with excess returns but also with a lower risk. This conclusion of associating the predictive power of technical analysis was based on the findings that were consistent with the asymmetric volatility phenomenon. Conversely, the asymmetric volatility phenomenon suggests that volatility following negative returns tends to be high while it is low following high returns. Kim, Shamsuddin and Lim (2011) used the technical analysis method for a long century data to examine the predictability of the U.S stock market. They found out that the technical analysis ability to predict excess returns was dependent on market conditions. As a result, the return predictability tends to be statistically insignificant during market crashes; however, with a high degree of uncertainty in the market the return predictability is statistically significant. In this case, in times of economic and political crises returns have been highly predictable. Contrary, during economic bubbles the excess return predictability has been small with a high degree of uncertainty. However, even though this study showed that technical analysis was associated with excess returns, it was hard to judge from it whether technical analysis contributed to the excess returns. In contrast to the above findings, Fong and Young (2005) successfully explained the most trading profits observed by technical signals. This research aimed at determining whether technical trading rules based on the technical analysis contributed to the associated daily returns in the period of expanding market. Contrary to the above research based on past data, the analysis was done in a real time fashion. According to the Fong and Young findings, since the technical analysis method used uses stock prices whose behaviour is random and volatile, the technical analysis method manipulates simple moving averages for the sake of prediction of daily returns. Moreover, they observed that the weekly market was more efficient because there is little information for traders as compared to the daily market. Further, they considered the weekly market to be more volatile due to behavioural aspects of traders who were overconfident of there for forecasting abilities before. In conclusion, Fong and Young assert that technical rules and technical analysis led to incorrect forecasts thus causing unpredictable volatility and, therefore, zero or even negative returns. In order to examine the general attitude concerning the issue of technical analysis profitability, Park and Irwin (2004) evaluated a wide range of data in more than 130 works that had tested trading technical rules. They selected studies ranging from 1960 and 2004 that had examined the issue. According to the findings of this study, majority of the studies that had been done before indicated that technical analysis strategies had been used widely throughout the world by practitioners in the stock and foreign exchange markets. Specifically, their study claimed that at least, 30-40 % of traders consider technical analysis an important instrument for predicting price movements. However, examinations of modern data supported that technical strategies made profits on markets of speculation at least until 1990s. In particular, of the 92 modern works examined, 58 indicated positive annual returns, 10 revealed negative returns, 24 mixed results while the others were outliers. Researchers have criticized this already published empirical evidence claiming that it does not involve risk estimation and transaction costs. These omitted factors affect profits considerably and should have been tested before making conclusions from the evidence. Generally, many studies have offered empirical evidence for positive returns and are appreciated in the scientific society while a few empirically proved works suggest that technical analysis indeed offers excess returns. In conclusion, there are a lot of controversies concerning the working of the technical analysis. For one, often methods vary greatly while, on the other hand, different technical analysts sometimes make contradictory forecasts based on the same data. Academic appraisals have claimed that technical analysis has little predictive power while experience from investors affirms that it is indeed powerful in predicting excess returns. Part B: Return Predictability and the Efficiency of the Stock Market Generally, investors put money into the stock market so that they can generate a return. Investors using predictive skills not only try to make a profitable return but also to outperform the market. The market efficiency concept requires that no investor should have access to information that everyone else in the market does not have to give them an advantage in predicting return. Basically, the price of stock, like that of other goods and services, is very sensitive to information. Moreover, the price reflects the information that the market has concerning some stock. Consequently,for efficient markets prices become less predictable and random such that a planned approach to investment cannot successes. The efficient market hypothesis presented by Eugine Fama in 1970 suggests that at any given time price mirrored all the available information concerning a particular market or stock (Malkiel, 2003). As such, since prices are very sensitive to any information in the market, such information should not be limited to certain parties in the market. In fact, De Bondt and Thaler (1989) observe that any information about politics, economic and social activities combined with how investors perceive this information is normally reflected in the stock price. Indeed, such information should be availed to all market participants so that none has the ability to out profit the other. The ‘random walk of prices that is common in the efficient market hypothesis causes any investment strategy made to consistently beat the market to fail. In addition, Fama (1998) differentiates three forms of market efficiency based on predictability. One is the weak form which claims that the price reflects all the information such that current price changes are not predictable based on past prices. In this case, markets in which past prices can accurately predict the future prices of stock are inefficient. In the second case of a semi-strong form, prices of assets changes to reflect all the publicly available information as well as past prices and hence making it more difficult to predict as compared to the first case. Finally, there is the strong form in which the price reflects information fully so that no investor or even a group of investors have any monopolistic access to some information. This last form the market is unpredictable and regarded most efficient. In addition, the adaptive market hypothesis attempts to reconcile efficient market hypothesis with behavioural economics through the application of evolution to financial interactions. The principles assimilated are competition, adaptation and natural selection (Kim, Shamsuddin & Lim 2011). Through the use of behavioural finance which predicts investor behaviours such as loss aversion, overconfidence and overreaction, the adaptive markets hypothesis to explain anomalies in the efficient market hypothesis. Suggestively, since humans make their predictions on a trial and error method, the ability to predict the market has no relation to market efficiency and prediction of a return is based on chance. Lastly, the rational momentum effects theory tries to unite the efficient markets theory with the adaptive markets system. In essence, Johnson (2002) asserts that momentum effects in the stock market need not to imply irrational behaviour in investors, market frictions or heterogeneous information. Illustratively, a firm with standard pricing can normally produce such effects when the growth of dividend varies over time (Asness, Moskowitz & Pedersen 2013). This model can be enhanced to match with the shocks that occur episodically as the market grows and this explains the documented empirical research for market abnormalities. Therefore, the predictive power cannot tell us accurately how the market is efficient. In conclusion, it is often argued that the predictability of returns is a good indicator of the efficiency of the market. In this regard, if the stock market is efficient then it should be difficult to predict returns. This argument implies that none of the variables in the stock market, when run in a regression, should be statistically significant. In addition, some researchers have gone as far as arguing that stock market efficiency is equal to the non-predictability of the returns. However, these conclusions make it difficult to learn how the stock market normally operates. Consequently, some researchers feel that the issue of market efficiency should be defined as a separate concept from predictability. Further, these researchers argue that stock returns can be unpredictable only if the efficiency is combined with the concept of risk neutrality. References Asness, C., Moskowitz, T. and Pedersen, L. (2013). Value and Momentum Everywhere. The Journal of Finance, 68(3), pp.929-985 Asness, C., Moskowitz, T. and Pedersen, L. (2013). Value and Momentum Everywhere. The Journal of Finance, 68(3), pp.929-985. De Bondt, W. F., & Thaler, R. H. (1989). Anomalies: A mean-reverting walk down Wall Street. The Journal of Economic Perspectives, 189-202. Fong, W. and Yong, L. (2005). Chasing trends: recursive moving average trading rules and internet stocks. Journal of Empirical Finance, 12(1), pp.43-76. Gustafsson, D. (2012). The Validity of Technical Analysis for the Swedish Stock Exchange: Evidence from random walk tests and back testing analysis. Johnson, T. (2002). Rational Momentum Effects. J Finance, 57(2), pp.585-608. Kim, J., Shamsuddin, A. and Lim, K. (2011). Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data. Journal of Empirical Finance, 18(5), pp.868-879 Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of economic perspectives, 59-82. Park, C. H., & Irwin, S. H. (2004). The profitability of technical analysis: A review Read More