Academic literature on the topic 'Stock exchanges Forecasting Econometric models'
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Journal articles on the topic "Stock exchanges Forecasting Econometric models"
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Chlebus, Marcin, Michał Dyczko, and Michał Woźniak. "Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem." Central European Economic Journal 8, no. 55 (January 1, 2021): 44–62. http://dx.doi.org/10.2478/ceej-2021-0004.
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Abstract Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning models on a selected sample of stocks, indexes etc. without a thorough understanding and consideration of their economic environment. Therefore, the goal of the article is to create an effective forecasting machine learning model of daily stock returns for a preselected company characterised by a wide portfolio of strategic branches influencing its valuation. We use Nvidia Corporation stock covering the period from 07/2012 to 12/2018 and apply various econometric and machine learning models, considering a diverse group of exogenous features, to analyse the research problem. The results suggest that it is possible to develop predictive machine learning models of Nvidia stock returns (based on many independent environmental variables) which outperform both simple naïve and econometric models. Our contribution to literature is twofold. First, we provide an added value to the strand of literature on the choice of model class to the stock returns prediction problem. Second, our study contributes to the thread of selecting exogenous variables and the need for their stationarity in the case of time series models.
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Ni, Zhehan, and Weilun Chen. "A Comparative Analysis of the Application of Machine Learning Algorithms and Econometric Models in Stock Market Prediction." BCP Business & Management 34 (December 14, 2022): 879–90. http://dx.doi.org/10.54691/bcpbm.v34i.3108.
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Forecasting the future price trend of a stock traded on a financial exchange is the aim of stock market prediction. In recent decades, stock market prediction has been a fascinating topic in the domain of Data Science and Finance. In reality, the stock movement is ambiguous and chaotic due to various influencing factors such as government policy, current events, interest rates Etc. At the same time, accurate enough forecasting of stock price movement leads to substantial benefits for investors. This paper provides a comprehensive review of the application and comparison of Machine Learning (ML) algorithms and Econometric Models in stock market prediction. The mentioned models are categorized into (i) ML algorithms, including Linear Regression (LR), K-nearest neighbors (KNN), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM). (ii) Econometric Models, including Autoregressive Integrated Moving Average (ARIMA) Model, Capital Asset Pricing Model (CAPM), and Fama-French (FF) Factor Model.
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Chambi Condori, Pedro Pablo. "Financial contagion: The impact of the volatility of global stock exchanges on the Lima-Peru Stock Exchange." Economía & Negocios 1, no. 1 (June 24, 2020): 13–27. http://dx.doi.org/10.33326/27086062.2019.1.896.
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What happens in the international financial markets in terms of volatility, have an impact on the results of the local stock market financial markets, as a result of the spread and transmission of larger stock market volatility to smaller markets such as the Peruvian, assertion that goes in accordance with the results obtained in the study in reference. The statistical evaluation of econometric models, suggest that the model obtained can be used for forecasting volatility expected in the very short term, very important estimates for agents involved, because these models can contribute to properly align the attitude to be adopted in certain circumstances of high volatility, for example in the input, output, refuge or permanence in the markets and also in the selection of best steps and in the structuring of the portfolio of investment with equity and additionally you can view through the correlation on which markets is can or not act and consequently the best results of profitability in the equity markets. This work comprises four well-defined sections; a brief history of the financial volatility of the last 15 years, a tight summary of the background and a dense summary of the methodology used in the process of the study, exposure of the results obtained and the declaration of the main conclusions which led us mention research, which allows writing, evidence of transmission and spread of the larger stock markets toward the Peruvian stock market volatility, as in the case of the American market to the market Peruvian stock market with the coefficient of dynamic correlation of 0.32, followed by the Spanish market and the market of China. Additionally, the coefficient of interrelation found by means of the model dcc mgarch is a very important indicator in the structure of portfolios of investment with instruments that they quote on the financial global markets.
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Ampomah, Ernest Kwame, Zhiguang Qin, and Gabriel Nyame. "Evaluation of Tree-Based Ensemble Machine Learning Models in Predicting Stock Price Direction of Movement." Information 11, no. 6 (June 20, 2020): 332. http://dx.doi.org/10.3390/info11060332.
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Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market. Investment in the stock market has a big risk associated with it. Minimizing prediction error reduces the investment risk. Machine learning (ML) models typically perform better than statistical and econometric models. Also, ensemble ML models have been shown in the literature to be able to produce superior performance than single ML models. In this work, we compare the effectiveness of tree-based ensemble ML models (Random Forest (RF), XGBoost Classifier (XG), Bagging Classifier (BC), AdaBoost Classifier (Ada), Extra Trees Classifier (ET), and Voting Classifier (VC)) in forecasting the direction of stock price movement. Eight different stock data from three stock exchanges (NYSE, NASDAQ, and NSE) are randomly collected and used for the study. Each data set is split into training and test set. Ten-fold cross validation accuracy is used to evaluate the ML models on the training set. In addition, the ML models are evaluated on the test set using accuracy, precision, recall, F1-score, specificity, and area under receiver operating characteristics curve (AUC-ROC). Kendall W test of concordance is used to rank the performance of the tree-based ML algorithms. For the training set, the AdaBoost model performed better than the rest of the models. For the test set, accuracy, precision, F1-score, and AUC metrics generated results significant to rank the models, and the Extra Trees classifier outperformed the other models in all the rankings.
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Rudzkis, Rimantas, Roma Valkavičienė, and Virmantas Kvedaras. "Prediction of Baltic Sectorial Share Price Indices." Lietuvos statistikos darbai 53, no. 1 (December 20, 2014): 53–59. http://dx.doi.org/10.15388/ljs.2014.13894.
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Extending the research started in [31], the paper uses econometric methods for the short-term forecasting of quarterly values of sector indexes of stock prices from the OMX Baltic stock exchange. The ARMA models and modelling methodology that was used to build the statistical models in the previous paper are now augmented with the algorithms of time series aggregation and identification of special features of the series. Here, the search for informative factors relies on the study of related literature. The specification of models is further tailored using the traditional significance (p-value) analysis of regressors and a cross-validation analysis. The latter is implemented in this paper using the Jack-knife approach. The data period analysed covers the years 2000–2013. The results of the analysis indicate that the inclusion not only of recent autoregressive terms but also of some aggregated characteristics (as certain special features of indexes) improves the precision of forecasting substantially. The calculations were performed using the statistical analysis software SAS.
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Manikandan, Narayanan, and Srinivasan Subha. "Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks." Scientific World Journal 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6709352.
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Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.
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Sheng, Yankai, and Ding Ma. "Stock Index Spot–Futures Arbitrage Prediction Using Machine Learning Models." Entropy 24, no. 10 (October 13, 2022): 1462. http://dx.doi.org/10.3390/e24101462.
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With the development of quantitative finance, machine learning methods used in the financial fields have been given significant attention among researchers, investors, and traders. However, in the field of stock index spot–futures arbitrage, relevant work is still rare. Furthermore, existing work is mostly retrospective, rather than anticipatory of arbitrage opportunities. To close the gap, this study uses machine learning approaches based on historical high-frequency data to forecast spot–futures arbitrage opportunities for the China Security Index (CSI) 300. Firstly, the possibility of spot–futures arbitrage opportunities is identified through econometric models. Then, Exchange-Traded-Fund (ETF)-based portfolios are built to fit the movements of CSI 300 with the least tracking errors. A strategy consisting of non-arbitrage intervals and unwinding timing indicators is derived and proven profitable in a back-test. In forecasting, four machine learning methods are adopted to predict the indicator we acquired, namely Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Back Propagation Neural Network (BPNN), and Long Short-Term Memory neural network (LSTM). The performance of each algorithm is compared from two perspectives. One is an error perspective based on the Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and goodness of fit (R2). Another is a return perspective based on the trade yield and the number of arbitrage opportunities captured. Finally, a performance heterogeneity analysis is conducted based on the separation of bull and bear markets. The results show that LSTM outperforms all other algorithms over the entire time period, with an RMSE of 0.00813, MAPE of 0.70 percent, R2 of 92.09 percent, and an arbitrage return of 58.18 percent. Meanwhile, in certain market conditions, namely both the bull market and bear market separately with a shorter period, LASSO can outperform.
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Moћић, Брaнимир Д. "Крaткoрoчнo прeдвиђaњe принoсa бeрзaнскoг индeксa Рeпубликe Српскe (БИРС) // Short-term return forecasti ng of the Stock Exchange Index of Republic of Srpska (BIRS)." ACTA ECONOMICA 10, no. 17 (June 10, 2012): 155. http://dx.doi.org/10.7251/ace1217155m.
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Резиме: Aктивнo учeствoвaњe рaциoнaлнoг инвeститoрa нa финaнсиjскoм тр-жишту пoдрaзумjeвa њeгoву спoсoбнoст дa приликoм избoрa финaнсиjскихиструмeнaтa зa oдрeђeни пeриoд улaгaњa, бирa oнe инструмeнтe кojипoсjeдуjу нajвeћи oчeкивaни принoс зa дaти нивo ризикa. Имajући у видудa je риjeч o oчeкивaним вриjeднoстимa пaрaмeтaрa, њихoвe вриjeднoсти нису унaприjeд пoзнaтe, стoгa сe oнe мoрajу прeдвидjeти. Oснoви прeдмeтистрaживaњa у oвoм рaду oднoси сe нa упoтрeбу aутoрeгрeсиoних мoдeлa пoкрeтних срeдинa (Aутoрeгрeссивe мoвинг aвeрaгe - AРMA) зa крaткoрoч-нo прeдвиђaњa вриjeднoсти принoсa бeрзaнскoг индeксa Рeпубликe Српскe(БИРС). Oснoвни циљ истрaживaњa jeстe дa сe сaглeдa стeпeн eфикaснoстиу прeдвиђaњу принoсa БИРС-a нa oснoву oвих мoдeлa, тe дa сe крoз стaтистичкo-eкoнoмeтриjску aнaлизу финaнсиjскo тржиштe Рeпубликe Српскeучини инфoрмaциoнo aфирмaтивниjим. Summary: Active participation of rational investors in the fi nanci al markets imply its abilityto select fi nancial instruments that have the highest expected return for a givenlevel of risk for a certain investment period. Bearing in mind that these returns arethe expected values of the parameters, their values are not known in advance, sothey must be forecasted. Main subject of this research refers to the use Autoregressivemodels (Autoregressive moving average - ARMA) in process of short term returnforecasting of the Stock Exchange Index of Republic of Srpska (BIRS). Th e mainobjective of this research is to examine the effi ciency of return forecasting based onautoregressive models, and trough comprehensive statistical-econometric analysis,make fi nancial market of Republic of Srpska more informational affi rmative.
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Markowski, Łukasz, and Jakub Keller. "Fear Anatomy – an Attempt to Assess the Impact of Selected Macroeconomic Variables on the Variability of the VIX S&P 500 Index." Annales Universitatis Mariae Curie-Skłodowska, sectio H – Oeconomia 54, no. 2 (June 29, 2020): 41. http://dx.doi.org/10.17951/h.2020.54.2.41-51.
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<p>This article deals with the subject of volatility of financial markets in relation to the US stock market and its volatility index, i.e. the VIX index. The authors analyzed previous studies on the VIX index and based on them, defined a research gap that relates to the problem of market response to emerging macroeconomic information about the US economy. The vast majority of research on the VIX index relates to its forecasting based on mathematical models not taking into account current market data. The authors attempted to assess the impact of emerging macro data on the variability of the VIX index, thus illustrating the magnitude of the impact of individual variables on the so-called US Stock Exchange fear index. The study analysed 80 macroeconomic variables in the period from January 2009 to June 2019 in order to check which of them cause the greatest market volatility. The study was based on correlation study and econometric modeling. The obtained results allowed to formulate conclusions indicating the most important macroeconomic parameters that affect the perception of the market by investors through the pricing of options valuation on the S&P 500 index. The authors managed to filter the most important variables for predicting the change of VIX level. In the eyes of the authors, the added value of the article is to indicate the relationship between macro variables and market volatility illustrated by the VIX index, which has not been explored in previous studies. The analyzes carried out are part of the research trend on market information efficiency and broaden knowledge in the area of capital investments.</p>
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Razzaq Al Rababa’a, Abdel, Zaid Saidat, and Raed Hendawi. "Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions?" Investment Management and Financial Innovations 18, no. 4 (December 1, 2021): 280–96. http://dx.doi.org/10.21511/imfi.18(4).2021.24.
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Different models have been used in the finance literature to predict the stock market returns. However, it remains an open question whether non-linear models can outperform linear models while providing accurate predictions for future returns. This study examines the prediction of the non-linear artificial neural network (ANN) models against the baseline linear regression models. This study aims specifically to compare the prediction performance of regression models with different specifications and static and dynamic ANN models. Thus, the analysis was conducted on a growing market, namely the Amman Stock Exchange. The results show that the trading volume and interest rates on loans tend to explain the monthly returns the most, compared to other predictors in the regressions. Moreover, incorporating more variables is not found to help in explaining the fluctuations in the stock market returns. More importantly, using the root mean square error (RMSE), as well as the mean absolute error statistical measures, the static ANN becomes the most preferred model for forecasting. The associated forecasting errors from these metrics become equal to 0.0021 and 0.0005, respectively. Lastly, the analysis conducted with the dynamic ANN model produced the highest RMSE value of 0.0067 since November 2018 following the amendment to the Jordanian income tax law. The same observation is also seen since the emerging of the COVID-19 outbreak (RMSE = 0.0042).
Dissertations / Theses on the topic "Stock exchanges Forecasting Econometric models"
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Hakim, Abdul. "Modelling the interactions across international stock, bond and foreign exchange markets." UWA Business School, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0202.
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[Truncated abstract] Given the theoretical and historical evidence that support the benefit of investing internationally. there is Iittle knowledge available of proper international portfolio construction in terms of how much should be invested in foreign countries, which countries should be targeted, and types of assets to be included in the portfolio. The prospects of these benefits depend on the market volatilities, cross-country correlations, and currency risks to change in the future. Another important issue in international portfolio diversification is the growth of newly emerging markets which have different characteristics from the developed ones. Addressing the issues, the thesis intends to investigate the nature of volatility, conditional correlations, and the impact of currency risks in international portfolio, both in developed and emerging markets. Chapter 2 provides literature review on volatility spillovers, conditional correlations, and forecasting both VaR and conditional correlations using GARCH-type models. Attention is made on the estimated models, type of assets, regions of markets, and tests of forecasts. Chapter 3 investigates the nature of volatility spillovers across intemational assets, which is important in determining the nature of portfolio's volatility when most assets are seems to be connected. ... The impacts of incorporating volatility spillovers and asymmetric effect on the forecast performance of conditional correlation will also be examined in this thesis. The VARMA-AGARCH of McAleer, Hoti and Chan (2008) and the VARMA-GARCH model of Ling and McAleer (2003) will be estimated to accommodate volatility spillovers and asymmetric effect. The CCC model of Bollerslev (1990) will also be estimated as benchmark as the model does not incorporate both volatility spillovers and asymmetric effects. Given the information about the nature of conditional correlations resulted from the forecasts using a rolling window technique, Section 2 of Chapter 4 investigates the nature of conditional correlations by estimating two multivariate GARCH models allowing for time-varying conditional correlations, namely the DCC model of Engle (2002) and the GARCC model of McAleer et al. (2008). Chapter 5 conducts VaR forecast considering the important role of VaR as a standard tool for risk management. Especially, the chapter investigates whether volatility spillovers and time-varying conditional correlations discussed in the previous two chapters are of helps in providing better VaR forecasts. The BEKK model of Engle and Kroner (1995) and the DCC model of Engle (2002) will be estimated to incorporate volatility spillovers and conditional correlations, respectively. The DVEC model of Bollerslev et al. (1998) and the CCC model of Bollerslev (1990) will be estimated to serve benchmarks, as both models do not incorporate both volatility spillovers and timevarying conditional correlations. Chapter 6 concludes the thesis and lists somc possible future research.
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Li, Heng. "New econometrics models with applications." HKBU Institutional Repository, 2010. http://repository.hkbu.edu.hk/etd_ra/1165.
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Lin, Gang. "Nesting regime-switching GARCH models and stock market volatility, returns and the business cycle /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1998. http://wwwlib.umi.com/cr/ucsd/fullcit?p9906497.
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Clayton, Maya. "Econometric forecasting of financial assets using non-linear smooth transition autoregressive models." Thesis, University of St Andrews, 2011. http://hdl.handle.net/10023/1898.
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Following the debate by empirical finance research on the presence of non-linear predictability in stock market returns, this study examines forecasting abilities of nonlinear STAR-type models. A non-linear model methodology is applied to daily returns of FTSE, S&P, DAX and Nikkei indices. The research is then extended to long-horizon forecastability of the four series including monthly returns and a buy-and-sell strategy for a three, six and twelve month holding period using non-linear error-correction framework. The recursive out-of-sample forecast is performed using the present value model equilibrium methodology, whereby stock returns are forecasted using macroeconomic variables, in particular the dividend yield and price-earnings ratio. The forecasting exercise revealed the presence of non-linear predictability for all data periods considered, and confirmed an improvement of predictability for long-horizon data. Finally, the present value model approach is applied to the housing market, whereby the house price returns are forecasted using a price-earnings ratio as a measure of fundamental levels of prices. Findings revealed that the UK housing market appears to be characterised with asymmetric non-linear dynamics, and a clear preference for the asymmetric ESTAR model in terms of forecasting accuracy.
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Humpe, Andreas. "Macroeconomic variables and the stock market : an empirical comparison of the US and Japan." Thesis, St Andrews, 2008. http://hdl.handle.net/10023/464.
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Milunovich, George Economics Australian School of Business UNSW. "Modelling and valuing multivariate interdependencies in financial time series." Awarded by:University of New South Wales. School of Economics, 2006. http://handle.unsw.edu.au/1959.4/25162.
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This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this
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Yoldas, Emre. "Essays on multivariate modeling in financial econometrics." Diss., [Riverside, Calif.] : University of California, Riverside, 2008. http://proquest.umi.com/pqdweb?index=0&did=1663051691&SrchMode=2&sid=2&Fmt=6&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1265225972&clientId=48051.
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Thesis (Ph. D.)--University of California, Riverside, 2008. Thesis (Ph. D.)--University of California, Riverside, 2009.Includes abstract. Title from first page of PDF file (viewed February 3, 2009). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 135-137). Includes bibliographical references (leaves ). Also issued in print.
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Fodor, Bryan D. "The effect of macroeconomic variables on the pricing of common stock under trending market conditions." Thesis, Department of Business Administration, University of New Brunswick, 2003. http://hdl.handle.net/1882/49.
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Thesis (MBA) -- University of New Brunswick, Faculty of Administration, 2003.Typescript. Bibliography: leaves 83-84. Also available online through University of New Brunswick, UNB Electronic Theses & Dissertations.
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Yang, Wenling. "M-GARCH Hedge Ratios And Hedging Effectiveness In Australian Futures Markets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2000. https://ro.ecu.edu.au/theses/1530.
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This study deals with the estimation of the optimal hedge ratios using various econometric models. Most of the recent papers have demonstrated that the conventional ordinary least squares (OLS) method of estimating constant hedge ratios is inappropriate, other more complicated models however seem to produce no more efficient hedge ratios. Using daily AOIs and SPI futures on the Australian market, optimal hedge ratios are calculated from four different models: the OLS regression model, the bivariate vector autoaggressive model (BVAR), the error-correction model (ECM) and the multivariate diagonal Vcc GARCH Model. The performance of each hedge ratio is then compared. The hedging effectiveness is measured in terms of ex-post and ex-ante risk-return traHe-off at various forcasting horizons. It is generally found that the GARCH time varying hedge ratios provide the greatest portfolio risk reduction, particularly for longer hedging horizons, but hey so not generate the highest portfolio return.
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O'Grady, Thomas A. "The profitability of technical analysis and stock returns from a traditional and bootstrap perspective : evidence from Australia, Hong Kong, Malaysia and Thailand." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2012. https://ro.ecu.edu.au/theses/506.
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This research questions whether technical trading rules can help predict stock price movements for a sample of stocks selected from four equity markets from the Asia-Pacific region: Australia, Malaysia, Hong Kong and Thailand for the period 1989-2008. The research is split into two stages. Stage-1 of the research tests the predictability of technical trading rules against a buyand- hold strategy. The variable moving average (VMA), fixed moving average (FMA) and the trading range break (TRB) trading rules are applied to this research. Economic predictability of these rules is examined by comparing returns conditional on a trading rule buy (sell) signal against an unconditional buy-and-hold return. Any existence of excess returns can thus be established. This follows with a statistical analysis of returns using a traditional t-test methodology. Traditional statistical tests assume normally distributed returns with independent observations and a non-changing distribution across time. In Stage-2 of this research a bootstrap checks whether features such as non-normality, time-varying moments and serial correlation bias test statistics. The bootstrap involves assumptions regarding the underlying returns generating process (RGP) and allows returns conditional on a trading rule buy (sell) signal from the original stock price series to be compared with conditional returns simulated from four common null models: RW, AR (1), GARCH-M and E-GARCH models. Simulated p-values are calculated in conjunction with simulated distributions and are applied in lieu of the theoretical normal distribution. Given this process it is possible to infer as to whether non-linear dependencies in returns can be captured by any of the three trading rules. Given the null model output standard t-test outcomes of predictability of technical trading rules may be diminished and/or eliminated. Conclusions are drawn as to the predictability and profitability of the VMA, FMA and TRB trading rules when applied to the chosen stock samples. Findings of this research indicate returns conditional on technical trading rules exceed unconditional buy-and-hold returns for all stocks. Thai sample output indicates strong support in favour of the predictability of standard test results supporting the use of technical trading rules. Output for Australia, Hong Kong and Malaysia indicates that previous standard t-test outcomes of predictability may be diminished and/or eliminated. This implies that the underlying RGP may be characterised by underlying features of some/all of the stochastic models.
Books on the topic "Stock exchanges Forecasting Econometric models"
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Zhongguo zheng quan shi chang liu dong xing yi jia ji qi wen ding xing he xiao ying ji liang yan jiu. Beijing: Zhongguo she hui ke xue chu ban she, 2011.
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Mercereau, Benoît. Stock markets and the real exchange rate: An intertemporal approach. [Washington, D.C.]: International Monetary Fund, African Department, 2003.
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Hong, Harrison G. A unified theory of underreaction, momentum trading and overreaction in asset markets. Cambridge, MA: National Bureau of Economic Research, 1997.
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Cochrane, John H. Where is the market going?: Uncertain facts and novel theories. Cambridge, MA: National Bureau of Economic Research, 1997.
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Mercereau, Benoît. The role of stock markets in current account dynamics: A time-series approach. [Washington, D.C.]: International Monetary Fund, Asia and Pacific Dept., 2004.
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Jung, Jeeman. One simple test of Samuelson's dictum for the stock market. Cambridge, Mass: National Bureau of Economic Research, 2002.
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Mercereau, Benoît. The role of stock markets in current account dynamics: Evidence from the United States. [Washington, D.C.]: International Monetary Fund, African Department, 2003.
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Noh, Jaesun. A test of efficiency for the S&P 500 index option market using variance forecasts. Cambridge, Mass: National Bureau of Economic Research, 1993.
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Gompers, Paul A. Institutional investors and equity prices. Cambridge, MA: National Bureau of Economic Research, 1998.
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Flood, Robert P. Testable implications of indeterminacies in models with rational expectations. Cambridge, MA: National Bureau of Economic Research, 1989.
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