Empirical Evidence on the Predictability of Excess Stock Returns - Coursework Example

Evaluate the empirical evidence on the predictability of excess stock returns using technical analysis By Date Technical analysis is associated with changes in the market price and not the value of a commodity or company. Despite the exotic and fancy tools employed, technical analysis mainly studies demand and supply in the market in an attempt to discover the trend and direction that will be in future (Johnson, 2002). The paper will focus on analyzing how technical analysis can be utilized to evaluate the empirical evidence on the predictability of the excess stock returns as well as assess whether return predictability is a real test of market efficiency. Predicting the excess stock returns using technical analysis Technical analysis is an important tool strategy that aims at forecasting future prices done through studying trading volumes, numerous asset qualities and past movement of prices. Technical analysis works bests in situations where the market is ineffective. Stock market often follow predictability pattern of negative and positive returns. Analysis of the international markets has focused mainly on those markets that are developed as compared to those that are undeveloped. In the past, the efficient hypothesis of the market was widely accepted by economists, for instance, it was generally believed that stock markets were extremely efficient in reflecting individual stock information and relating to the stock exchange as a whole. The accepted view in the market was that in cases where information arose, the news was able to spread so fast and incorporated into security prices without delay (Fama, 1998). The unique experimental work of the notion of unpredictability in the stock prices focused on measures of short-run correlation between stock change directions. The approach supported the view that the stock market had no memory, and the past was not used to gauge the future. Psychologist and economists have found out that short term momentum is consistent with psychological feedback mechanisms. Perhaps the most frequently utilized variable in technical analysis, the moving average for stocks is the total selling price for the stocks over a certain period. Moving average data is often utilized to create charts that indicate whether the share prices are trending down or high. They can be used to follow up monthly, daily and weekly patterns. The numbers obtained daily are added to the average while the oldest numbers are removed. Technical analysis can also utilize relative strength to relate the price performance of a single stock against the entire market stock. Relative strength focuses on the past to predict the future trends in the market (Fama, 1998). The liquidity of an asset provides a great deal of the markets perceptions of information accuracy and how much such information has been embodied in prices. Market efficiency has been associated with risk-bearing activities of the makers. Predictability increases when the producers raise their capacity to bear the risk involved or identified. Therefore, the delay in adjustment to prices as well as market information inaccuracies can be absorbed partially into the market by the market makers with a capacity to do so (Asness et al., 2013). The existing empirical evidences suggest that liquidity can be related to stock returns. A relative illiquid security should carry a lower price in cases where the holder is for bearing the extra risk. Recent research in asset return predictability links market efficiency with market microstructure theories. Fama et al. (1998)’s findings indicated that dividend yields of stocks appear to be directly proportional to the interest rates. Additionally, the ability of the initial returns to predict a stock return may often reflect the adaptation of the stock market to the general economic condition. Dividends have remained quiet for the past decades resulting in less forecasting returns. Companies in the 20th century may be more likely to put in place the repurchase program rather than increasing their dividend. Therefore, the utilization of dividends may not be efficient compared to the past. The approach, however, is not practical and consistent with individual stocks. Investors who purchase a portfolio of individual stocks with raised yields in the market will not earn increased rates of return. One of the strongest results that investigators have realized is the tendency over extended periods of time for those markets that are considered small company stocks to generate returns that are larger as compared to the large companies. Technical analysis enables investors to produce excess risk-adjusted returns. Studies have also shown fewer gains in firms that deals with smaller stocks (Kim et al., 2011). Moreover, in most global markets, large stocks have been associated with high amount of returns. Investors preferred big business with more liquidity than the small ones that make it a challenge to liquidate significant shares. Survivorship bias has also affected small firms. Currently, computerized databases of businesses include only small businesses that were successful and not those that went bankrupt. Predictability can be since asset returns are correlated with individual time series as well as technical trading rules. Trading strategies that combine time series are technical analysis forecast are superior to either time series forecast or technical trading rules. The fact that time series forecast and technical trading rules are asymmetrical in the opposite direction during sell and buy period and determine the time to be in the market by focusing on the trends in the market stocks (Kim et al, 2011). The combine trading strategies give rise to higher returns during sell and buy periods. The return predictability from the combined strategies of trading deteriorates in recent years. Debates about stock returns have continued to be a hot arguable issue in financial studies. Even some authors, have pointed out that stock performance may be just snooping of data, it is that some economic and fiscal factors can explain much of the variation in the stock market return so as to create greater forecast power. One argument targeting return predictability is that, if the stock can be enhanced, then everyone will rush for the predictability profit. As a result making the idea vanish before investors can build their models. Critically evaluate whether return predictability is a real test of market efficiency Fama et al. (1998) introduced the event studies that explain the various ways in which stock prices is influenced by information. Numerous studies have focused on short-term returns. One of the advantages that are to the model is that the expected returns do not have a big impact on the inferences targeting abnormal returns. One of the assumptions that are often brought about by the studies mainly focus on a short performance windows is that any delay in the price response is often short lived. Examining returns over an extended period of time is however importance since stock prices often adjusts at a slower rate to information (Kim et al., 2011). Recent long-term return studies have suggested market inefficiency that is related to long-term overreaction or underreaction to information. Efficiency is essential and should never be . Efficient markets often generate event categories that give suggestions on the ability of the prices to overreact to information (Asness et al., 2013). Overreaction and underreaction are of the same frequency in a market that is considered efficient. Additionally, long-term return anomalies can be attributed to chance and may tend to disappear or become marginal when exposed to various models or when measured by different statistical approaches. One of the challenges that are when developing long term return studies is that they investigate a particular choice on the effectiveness of the market. Like any other model, market efficiency is a defective account of price formation. Past research has suggested that past winners and past returns tend to be future losers when stocks are in 3-5 year. Investors often give too much weight to the firm’s past performance and less weight to the fact that performance can mean-revert. Firms with high earnings ratios to price, book to market price and cash flow to price tend to experience poor past earning growth whereas firms with low book to market equity, drawing to cost and cash flow to price tend to experience healthy past earning growth. Since the market overreacts to recent growth, it is unprepared when the earnings growth rate reverts (De Bondt, 1989). Poor performers have high future returns whereas active past performers have low future returns. For an efficient market, the overreaction to the past earnings has to be recognized and corrected in the future. Businesses often list their stock in order to utilize the overreaction of the market to their previous strong performance. As long as the stock exchange is in place, the collective investor’s judgment will sometimes make mistakes. Predictable patterns, as well as pricing irregularities in the stock returns, can persist over a short period of time as well as appear over time (Malkiel, 2003). In addition, the market cannot be perfect and efficient, or there shall be no incentives aimed at uncovering professional information. Undoubtedly, with increasing sophistication, passage of time as well as empirical techniques, we shall document stock return development patterns. Return predictability is a real determinant of market efficiency since it enable the investors to plan based on technical analysis and thereby invest at the necessary time. References Asness, C., Moskowitz, T., Pedersen, L. (2013) Value and Momentum Everywhere, Journal of Finance, Vol. 68, No. 3, pp. 929-985 De Bondt, W., Thaler, R. (1989) Anomalies: A Mean-Reverting Walk Down Wall Street, Journal of Economic Perspectives, Vol. 3, No. 1, pp. 189-202 Fama, E. (1998) Market Efficiency, long-term returns, and behavioural finance, Journal of Financial Economics, Vol. 49, pp. 283-306 Johnson, T. (2002) Rational Momentum Effects, Journal of Finance, Vol. 57, No. 2, pp. 585-608 Kim, J., Shamsuddin, A., Lim, K., (2011) Stock Return Predictability and the Adaptive Markets Hypothesis: Evidence from Century-Long U.S. Data, Journal of Empirical Finance, Vol. 18, No. 5, pp. 868-879 Malkiel, B. (2003) The Efficient Market Hypothesis and its Critics, Journal of Economic Perspectives, Vol. 17, No. 1, pp. 59-82 Read More