Technical Analysis of Predictability of Excess Stock Returns - Case Study Example

Empirical Evidence on the Predictability of Excess Stock Returns Using Technical Analysis + University Name City, State Date Part A To study the empirical evidence on the Befall of Plentifulness stock returns, one must be driven by the change in the implication of market hypothesis and the changing market conditions. Extra returns are said to be an investment return from a portfolio that overlaps an index with similar risk levels (Phillips & Yu, 2011.p.455-463). Predictability of surplus stock returns is always associated with the highest degree of uncertainty. Through political and economic crises, stock predictability is highly predictable with less uncertainty while during market crashes no excess stock returns are witnessed. It has been noted that the best stock robust predictor in developed markets is the empirical evidence. It is also believed that no consistent evidence of predictability is noted when considering dividend price predictors and earnings (Phillips & Yu 2011). The knowledge of empirical evidence on the predictability of excess stock returns by variables has been the subject to the constant updates over a long period. The measurement of return predictability is done with various methods of data and statistical analysis. The methods vary depending on their eligibility and efficiency on empirical evidence and predictability (Cooper, Dimitrov & Rau, 2005). The measurement scheme that is used to calculate the excess stock return predictability can be invalid in case the variables are persistent and innovations are correlated with the returns; hence, the best choice of measurement method should be highly considered (Frankel & Froot, 1990). One of the best measurements of returns is the generalized spectral test that has the capabilities of detecting linear dependency and autocorrelation. In this measurement method, the return predictability is always ignored at the 5% level. This method also has a perfect power against variety of dependent alternatives. It is equally important as it best explains the power of empirical functions. There is also the use of test statistics in predicting the degree return of predictability, in this measurement formula, the faith of an interval is used to measure the level of uncertainty linked with return predictability. So to make this all processes very simple to understand you only report the outcome using a five-year door length for weekly data and a two-year door length for daily data (Frankel & Froot 1990). The other method that can also be used to carry out measurement of excess stock returns is an automatic portmanteau test. This measurement is considered to be the cheapest method because of its asymptotic bull distribution, which is a chi-square with a single degree of freedom. At the same time, the researcher must not specify the autocorrelation tested because this test automatically selects this number (Asness, Moskowitz & Pedersen, 2013). Another best method that is extremely used for the measurement of the excess stock returns is the automatic variance ratio test. In this measurement, it was noted that it showed serious size distortion in small samples by the use of wild bootstrapped whereby the variance ratio test does not show the size distortion making it be realer as compared with its competitors. This method has extremely been used to test poor form efficiency of capital markets. The test statistics in this measurement method can always be written as the weighted sum of autocorrelation of redundant stock returns. In this method, in case the remaining stock returns are subjects to unknown heteroskedasticity, the statistical inference can be correct (Escanciano & Lobato, 2009.p.). The Empirical Evidence The primary source of empirical evidence is senses, and other evidences can also be testimony are memory among others, while some sensory experiences are considered to be indirect or secondary. One of the empirical results is the further analysis and robustness checks. In this result, narrative text is analyzed afterward a first-guess set of an object is identified, into three: the control object, boundary object, and the entity objects. All these objects played different roles. This analysis helps in ensuring that the case text is always correct and changes the nature of the text. This analysis helps to fill the role of preliminary design (Frankel & Froot 1990). Another empirical result is the return predictability and market conditions. This is where you are supposed to note the autocorrelations between returns on large and little firms after the time of market gains or losses. It, therefore, suggests that market decline unfailingly resulted in a delay in the registration of market-wide information in stock tiny stock prices. It also indicates that higher predictability market returns are consistently experienced during the political and economic crises (Malkiel, 2003). It will moreover be noted that low predictability is always experienced during bubble times. Time-varying return predictability is another empirical return, which relate trading frequency and the firm size. In this empirical evidence, it is argued that stock return predictability is always varying with time. It has been noticed that technological improvement, political and economic crisis contributed largely to varying return predictability. Subsequently, it can be argued that stock return predictability is linked to the business cycle, and it is also the time-varying. The strength of the predictive relationship is linked to coefficients, which are regressed on the industrial production growth and recessions (Frankel & Froot 1990). Lastly, another empirical evidence is noted using adaptive markets hypothesis, which monitors the behavior of finance and data snooping. Here is where the high return predictability is constantly experienced during the political and economic crises. It should also be noted that the weak forms of market efficiency are invariably dependent upon the market when the research is always being carried out (Parker & Julliard 2005). It is also believed that many people have a firm belief in the technical analysis because it gives out the selective thinking in addition to eliminating the case of biases. This method also enables people to form a communal reinforcement. It is also considered the best method because it is self-fulfilling (Fama, 1998). Hence, any publication bias should not affect the performance of mechanical analysis. Finally, it can be noted that computerized analysis works best when it comes to intermediate future markets, currency markets and worst when it comes to stock markets (Escanciano & Lobato 2009.p.140-149). Part B Critical evaluation of whether return predictability is a good test of market efficiency Returned predictability normally varies with various changes that do occur in the market conditions. It is neither indicated whether changes that do occur outside the market are specific nor are they refutable predictions about the relation between return predictability and market conditions. Regressing the monthly measure of return predictability is normally given the lack of a well-structured model against a range of dummy variables for the conditions of the market and the economic fundamentals (Kim, Shamsuddin & Lim, 2011). The main findings from this analysis are based on the return predictability that is noted to be a very high context dependent and dynamic. Changing market conditions do not affect the return predictability is because bubbles and market crashes represent extreme events that do change market ecologies. According to Johnson (2002) the stock market is also said to become inefficient depending on the current market conditions, but at times it may be very efficient when it comes to normal times of market conditions. It has also come to the realization that short time profits normally depend on the state of the market, even though, this is drawn from up and down market states weighed by lagged market returns (Escanciano & Lobato 2009). Return predictability is always the reflection that investors may tend to overreact or under react to news about standpoints of behavioral finance. These discretions are said to be very strong during political and economic crises; hence, linked to higher return predictability. Finally, the stock market responses to macroeconomics news do vary over the business cycle making many investors overreact to such down market news. The investors should learn to cope with such market changes to avoid overreactions that may result in poor performance of their firms, in terms of profit and quality output. It should be noted that, predictable patterns in stock returns may seem and even persist for short periods but then the collective judgment of investors will still make mistakes. It has also been noted that reports on the evidence of empirical stock always indicated the humble levels of local predictability only during the certain time, while those that are unpredictable occur most of the time. At the same time, further research should be conducted to govern whether predictability is neither compulsory nor a sufficient need for profitability (Thaler & De Bondt, 1992). A technical tool for trading in the foreign markets should also be made necessary to help in balancing. Efficiency in the market normally helps to assert that financial markets are informational efficient. A market is said to be very efficient when several investors try to make a profitable productivity, as well as struggling to outdo the market itself. Hence, the aim is to generate a good return that the capital was invested on. Another possible future research would be to adopt other alternative means of assessing predictability (Asness, Moskowitz & Pedersen, 2013). It should also be noted that other economic forces are strong, and it is of interest for these interesting parts to be closely examined. References Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. The Journal of Finance, 68(3), 929-985. Cooper, M. J., Dimitrov, O., & Rau, P. R. (2005). A` Rose.com by Any Other Name, Journal of Finance, LVI (6), December, 2371-88. International Library of Critical Writings in Economics. 2, 500-51 Escanciano J.C., & Lobato I.N. (2009). An automatic Portmanteau test for serial correlation. Journal of Econometrics. 151, 140-149. Fama, E. F. (1998). Market efficiency, long-term returns, and behavioral finance. Journal of financial economics, 49(3), 283-306. Frankel, J. A., & Froot, K. A. (1990). Chartists, fundamentalists, and trading in the foreign exchange market. The American Economic Review (Evanston). 80, 181-185. Johnson, T. C. (2002). Rational momentum effects. The Journal of Finance, 57(2), 585-608. Kim, J. H., Shamsuddin, A., & Lim, K. P. (2011). Stock return predictability and the adaptive markets hypothesis: Evidence from century-long US data. Journal of Empirical Finance, 18(5), 868-879. Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of economic perspectives, 59-82. Parker, J. A., & Julliard, C. (2005). Consumption Risk and the Cross Section of Expected Returns. The Journal of Political Economy. 113, 185. Phillips, P. C. B., & Yu, J. (2011). Dating the timeline of financial bubbles during the subprime crisis. Quantitative Economics. 2, 455-491. Thaler, R. H., & De Bondt, W. F. M. (1992). A mean reverting walk down Wall Street. In The winners curse: Paradoxes and anomalies of economic life. Free Press New York. Timmermann, A., & Granger, C. W. J. (2002). Efficient market hypothesis and forecasting. London, Centre for Economic Policy Research Read More