Return Predictability as a Good Test of Market Efficiency - Coursework Example

Evaluate the Empirical Evidence on the Predictability of Excess Stock Returns Using Technical Analysis Table of Contents Part A 3 Thorough Evaluationof the Empirical Evidences on the Predictability of Excess Stock Returns Using Technical Analysis 3 Part B 6 Critical Evaluation of Determining Whether Return Predictability Is a Good Test of Market Efficiency 6 6 References 9 Part A Thorough Evaluation of the Empirical Evidences on the Predictability of Excess Stock Returns Using Technical Analysis In stock market, it is important for the investors to select stocks with utmost care to attain good return. Thus, in this context, there lays the requirement of forming a reliable tool, which evaluates the performance of the stocks and helps in identifying the performance of the same for yielding better returns. Investors usually study the market trends by analysing the historical data for maximising profit. However, the modern stock market is more complex in nature, hence requires effective method for selecting stocks with better return. In this regard, technical analysis is being used as a sophisticated tool for investment. Technical analysis is identified to be a different approach, as it focuses on the movement of the prices of stocks (Fang & Xu, 2000). It is to be affirmed that technical analysis studies only the supply and demand of the market trends for making better future prediction and attaining greater return from stocks. Technical analysis is regarded as an approach wherein the prices of the stocks are analysed based on altering attitude of the investors due to various factors such as economy, political and monetary forces. This approach also considers the concept of behavioural finances for predictability of stock return trends. The normal perception of the investors is that with the increase in demand, the price of the stock rises and vice versa. In this context, the ability to predict the future trends of stock would help the investors to gain maximum profit. The technical analysis tool used by the investors is fundamentally based upon three dimensions including “Market action discounts everything”, “Prices move in trends” and “History repeats itself” (Larsen, 2010). All these dimensions help in predicting a proper future price and the change in the demand and supply of stocks with return. Moreover, for the prediction to be accurate, technical analysis is being used wherein the correlation amid the companies and markets is analysed to derive future prices. In other words, this form of analysis helps in forecasting the price of the securities. Specially mentioning, the effectiveness of this method is not yet justified depending on various factors. However, for the investors, the technical analysis is deemed to be effective for deriving better returns. It is worth mentioning that several hedge funds along with investment banking use technical trading strategies for reaping several significant benefits (Kim & et. al., 2011). The investors mainly focus upon profit maximisation by taking fewer risks. In this context, researchers often regarded the tool of technical analysis to be much effective. On the contrary, it is argued that technical analysis for predicting stock price and deriving satisfied returns is not efficient enough. There are various opinions about the use of the technical analysis wherein it is suggested that technical trading rules are important for predicting better changes in the market (Kim & et. al., 2011). From the empirical perspective, it is to be stated that due to the forecasting ability of the analysis even in the nonexistence of transaction costs, the investors can trade effectively by buying and holding the stocks. According to Fang & Xu (2002), evidences witnessed regarding the expectedness of asset return using technical tools. Notably, the technical analysis method uses the auto regression method for predicting excess stock returns, wherein the primary focus is levied on double cross trading strategies. In this regard, the two moving average are considered and at the intersection point, the trading signals are being created. This is a sophisticated method, which helps in providing accurate predictability for the investors. The technical trading rule states the fact that the trend of stocks towards one direction stays for long. The advantage for this form of analysis is that it is able to provide a greater insight about the structure of asset price, predicting the excessive stock returns in an efficient manner. Contextually, technical analysis requires a strong understanding about the market and the trading systems. It is often regarded as an umbrella phrase for innumerable indicators. Various technical indicators have relevance with the predictability of excess stocks. These indicators include Technical theory, which comprises Dow Theory and Gann Line among others. These particular theories assist in acquiring a better idea about the market trends and the performances of stocks (Fama, 1998). Technical charts are regarded as the base of analysis for obtaining a brief overview of the prices, which indicates the bullish and bearish pattern of stock market (Jackson, 2007). This implies that indicators of technical analysis help in predicting excess returns. However, it is argued that excess return can be measured by using ‘series of stock indices’. This might hamper the predictability of the analysis due to the calculation of series of data within a specified timeframe. According to Bessembinder & Chan (1997), it is observed that frequent transaction is required for technical trading, hence excess return is not seen, if the transaction costs are being considered. From the past analysis, it can be ascertained that technical analysis is gaining its importance in the trading industry; however, the opinion of the academics relating to the above context is still different. It is strongly believed that technical analysis is effective, as it considers the historical and the present data for future prediction. Moreover, this strategy cannot help in earning excess return for investors. The volatility of the market in future is another reason, which reduces chances of predicting excess stock returns. There are various views about the significance of technical analysis predictability for excess return stocks (Brock & et. al., 1991). Various empirical studies relating to technical analysis profitability have been analysed wherein it is witnessed that different trading system and transaction costs are duly considered for making better prediction about the excess returns. Hence, providing trend line technique helps in analysing the stock market. Use of the moving average method for the calculation of price trend and movement is also important to consider for deriving positive results (Jackson, 2007). Thus, it can be comprehended that technical analysis method evaluates the performance of securities with regards to the market activity. Part B Critical Evaluation of Determining Whether Return Predictability Is a Good Test of Market Efficiency Efficiency is a term, which describes a market with relevant information, relating to pricing of assets. In other words, an efficient market is the one wherein the “security prices fully reflect all available information” (Kothari, n.d.). Security prices are deemed to be quite important for the investors and others as well. The price of shares is prejudiced by the financial information. In this respect, it can be stated that market efficiency helps in judging the accounting principles. Due to the efficiency market, the test cross-sectional is used for predicting the returns. This method is effective in predicting market concerning returns (Fama, 1998). Specially mentioning, Efficient Market Hypothesis is a market, which provides information about the price of shares. Hence, in this market, it is not possible to gain an average market return. In other words, it can be stated that returns in an efficient market are not predictable. However, efficient market can be further divided into strong, weak form and semi form. This implies that as the price and information is linked with shares, no investor will be able to acquire abnormal profit. In this context, if the information provided is weak and is limited to past records, the predictability will be not accurate and hence the investors might get affected. Hence, the return prediction cannot be made effectively. There are counter arguments regarding the behaviour of the investors, which eventually makes the prediction of returns to be highly effective. It is a proven fact that the predictability of market and returns on share is constant and thus changes of the same over the time depend on various factors. Several researches have been conducted regarding the return predictability and market efficiency. In 1960’s, the support with regards to market efficiency was identified to be mixed outcome and in 1970’s, the outcome was challenging. Finally, in 2007, no evidence in respect to profit was witnessed (Kim & et. al., 2009; Dimson & Mussavian, 2000). Hence, based on the above context, it can be stated that return and stock market becomes less effective with time. Contextually, to determine the return predictability and identify the non linear dependence of stock returns, automatic variance ratio test is duly taken into concern. With regards to prediction of stocks, various elements are analysed, however, during the crash in market, no excessive stock returns are predicted. Furthermore, it is evident that during political turmoil, the return on stocks is highly predictable. For the prediction to be effective, automatic variance ratio test is conducted to derive predictability along with its direction. Hence, from the analysis, it can be stated that the return prediction depends on various aspects such as market efficiency and different tools used for analysis. Various arguments and counter arguments are evident with respect to stock returns prediction and good test of market efficiency. From the evaluation, it is clear that prediction depends on various conditions and with the changing market situations, the degree of return prediction varies considerably (Kim & et. al., 2009). Thus, based on the above analysis, it can be affirmed that return predictability is identified to be a good test of market efficiency. References Bessembinder, H. & Chan, K., 1997. Market Efficiency and The Returns To Technical Analysis. Department of Finance, pp. 1-22. Brock, W. A. & et. al., 1991. Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. University of Wisconsin, pp. 1-45. Dimson, E. & Mussavian, M., 2000. Market Efficiency. The Current State Of Business Disciplines, Vol. 3, pp. 959-970. Fang, Y. & Xu, D., 2000. The Predictability of Asset Returns: An Approach Combining Technical Analysis and Time Series Forecasts. International Journal of Forecasting, Vol. 1, pp. 1-19. Fama, E. F., 1998. Market Efficiency, Long-Term Returns, and Behavioral Finance. Journal of Financial Economics, Vol. 49, pp. 283-306. Jackson, W. T. S., 2007. An Empirical Investigation of Technical Analysis in Fixed· Income Markets. Thesis, pp. 1-267. Kim, J. H. & et. al., 2009. Stock Return Predictability and the Adaptive Markets Hypothesis: Evidence from Century Long U.S. Data. The University of Newcastle, pp. 1-24. Kim, J. H. & et. al., 2011. Stock Return Predictability and the Adaptive Markets Hypothesis: Evidence from Century Long U.S. Data. Journal of Empirical Finance, Vol. 18, pp. 868-879. Kothari, S. P., No Date. Capital Markets Research in Accounting. Sloan School of Management, pp. 2-144. Larsen, J. I., 2010. Predicting Stock Prices Using Technical Analysis and Machine Learning. Master of Science in Computer Science, pp. 1-81. Read More