Study On The Efficiency Of Technical Analysis In China's Stock Market

One of the greatest gulfs between modem capital theory and financial practice isthe separation that exists between Efficient Market Hypothesis (EMH) and TechnicalAnalysis (TA).Based on a survey of the early and recent empirical literatures on testingthe efficiency of TA,the author finds that although most early studies denied theusefulness of TA in stock markets,a number of recent studies since 1990s indicated thatTA can generate profits.This reversal of conclusion can be explained in terms of twomain differences in the testing procedures.First,early studies tend to consider only thelinear correlation between price changes,while recent studies place greater emphasis onthe actual dynamic process of returns.Second,early studies usually test several simpletechnical trading rules,while recent studies investigate TA through a morecomprehensive scrutiny of various technical trading rules and strategies.However,most of the existing researches on testing the efficiency of TA in China'sstock market are still focused on the early studies and their methods.As a consequence,their empirical results do not provide an accurate assessment of the efficiency of TA andthe market itself.Accordingly,this thesis investigates the predictability and profitabilityof TA in China's stock market by utilizing and extending some advanced statistic tools,econometric methods and modeling theories,and by dealing with some limitations thatexisted in recent studies.Firstly,we study how the actual return dynamics affect the assessment of theefficiency of TA by integrating the Bootstrap method and Artificial Neural Network(ANN).Assuming that the actual return process follows various popular linear nullmodels and a nonlinear model constructed by ANN,we test the predictability oftechnical rules through Bootstrapping.The empirical results indicate that,although noneof the linear processes can replicate the stochastic properties of returns obtained fromtechnical trading rules in the actual return dynamic process,the nonlinear process canexplain the predictions and profits of technical rules better.Thus,linear correlation isnot sufficient to evaluate the efficiency of TA.Besides,evidences based onHeterogeneous Market Hypothesis indicate that trading behaviors of different types of investors at different time horizons and their interreactions may cause return process topresent nonlinear correlation.Secondly,we construct a nonlinear model based on the past information containedin moving average rules and volumes,then compare its predictive power with somepopular linear models.Specifically,we apply the thick modeling method to try toresolve the inevitable problem of model uncertainty for ANN modeling.We find thatthick models based on individual ANN models with different parameters can not onlydecrease model uncertainty bias and improve the statistic prediction accuracy,but alsooutperform linear models according to the out-of-sample forecasting criteria.Moreover,despite the negative performance of moving average rules themselves comparing withthe buy-and-hold strategy,the technical trading strategies directed by the predictions ofthick models based on these rules can generate significant excess profits.Thirdly,in contrast with existing studies on China's stock market which merelyfocus on testing some simple technical trading rules,we investigate some popularcomplex technical patterns among technical practitioners,which are quantitativelydefined and automatically recognized using nonparametric kernel regression method.By comparing the unconditional empirical distribution of daily returns to the conditionaldistribution,we find that most of the complex patterns can provide incrementalinformation that may be used to forecast further prices changes.Furthermore,theinformation contained in some patterns can generate significant excess trading profitseven after risk adjustment.