Technical Analysis for Financial Market - oscillators - page 3

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The study develops an innovative and flexible methodology for re-defining the traditional convergence–divergence indicators in the light of multi frequency trading behaviour of the heterogeneous agents. The developed indicator is labelled as multi-resolution convergence divergence indicator (MRCD). In contrast to the traditional moving average convergence divergence (MACD), the MRCD is “flexible” as it reacts to fluctuations arising at any frequency interval and is thereby capable of adapting to a wide variety of future possibilities. The “innovative dimension” underpinning this methodology is the replacement of the traditional trend extractor (moving-average) with a more novel methodology—the multi-resolution analysis. The forecasting ability of this newly engineered indicator is examined by structuring a neural network based MRCD–NARX model. The performance of this model is bench-marked against that of a similar model developed using the traditional MACD indicator. Out-of-the sample mean square error and the Diebold–Mariano test are used to examine the statistical accuracy of the forecasts. The profitability of the indicator is ascertained using the correlation measure and the hit ratio. A “long-short trading rule” is developed and back-tested on the testing data-sample to validate the practical applicability and “reproducibility” of the methodology.
 
This article compares the properties of the European Commission's Consumer Confidence Indicator (CCI) for the euro area with three alternative indices which differ from the former in that they (i) consider a richer set of survey questions and (ii) are the result of data-driven statistical techniques, rather than the simple arithmetic mean of the input series. The alternative indicators are shown to perform only slightly better than the CCI in tracking real private consumption growth and to fail to produce significantly better forecasts of expansions and contractions in private consumption, once information from relevant, timely available hard data is controlled for. The conclusions change, however, if the analysis is re-conducted on well-defined subsets of survey questions. Concretely, the application of the alternative construction techniques to a data set which is limited to questions about consumers' personal finances produces an indicator which, combined with relevant macro-economic time series, yields significant improvements in forecasting expansions and contractions in private consumption.
 

Chart patterns and indicators are popular technical tools for making investment decisions. This article presents a trading strategy combining price movement patterns, candlestick chart Divergence, and Stochastic Oscillator, with the aim to increase the return on investment. A neural network ensemble is employed to determine buy and sell signals on the next trading day. Experimental results, using stocks from five different industries in Stock Exchange of Thailand, show that the proposed strategy yields higher returns than do traditional technical trading methods



 

This second part examines the results that can be obtained with the combination of indicators, as well as the utility of the Indicators that determine the periods of tendency and trading (Filters). First, combinations of an oscillator and a follower of tendency were generated, operating according to the period determined by one of the Filters. Then, depending again on the periods indicated by the Filters, the indicators were tested individually, operating only in the periods for which theoretically they have better return. A problem of combinatorial optimization was also constructed, when trying to examine all the possible combinations that can be generated, for which the development of a Tabu Search algorithm was necessary. This work made possible to conclude, that although the combinations of Indicators improve the individual performances of many of the indicators, eliminating the volatility of its signals, it is possible to obtain greater returns with the use of individual Indicators of high performance.


 
This paper assesses the state of informational efficiency in stock markets of 75 countries around the world by empirically evaluating the economically relevance of a very popular technical analysis indicator, namely the Moving Average Convergence Divergence. There are many published papers that evaluate market efficiency around the world, but none looks at as many countries as this one does. In total, 1336 companies are selected in the sample, with temporal data starting January 1st 2001 and ending December 31, 2012. The methodology used here is based on trading simulation using an optimized trading rule that is applied on out of sample quotes. To be in accordance with the latest guidelines in the field, several statistical tests, including a bootstrap based one, are performed to validate the estimators, thus ensuring bias-free results and more relevant conclusions. Several important statements can be made based on the obtained results, the most important being that traders using the MACD as an technical analysis investment method on the stock market could some times and for certain companies obtain abnormal cost and risk adjusted returns, this pointing out that the world's stock markets present important inefficiencies.
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