Personalised Modelling for Multiple Time-Series Data Prediction: A Preliminary Investigation in Asia Pacific Stock Market Indexes Movement
نویسندگان
چکیده
The behaviour of multiple stock markets can be described within the framework of complex dynamic systems (CDS). Using a global model with the Kalman Filter we are able to extract the dynamic interaction network (DIN) of these markets. The model was shown to successfully capture interactions between stock markets in the long term. In this study we investigate the effectiveness of two different personalised modelling approaches to multiple stock market prediction. Preliminary results from this study show that the personalised modelling approach when applied to the rate of change of the stock market index is better able to capture recurring trends that tend to occur with stock market data.
منابع مشابه
An Improved Hybrid Model with Automated Lag Selection to Forecast Stock Market
Objective: In general, financial time series such as stock indexes have nonlinear, mutable and noisy behavior. Structural and statistical models and machine learning-based models are often unable to accurately predict series with such a behavior. Accordingly, the aim of the present study is to present a new hybrid model using the advantages of the GMDH method and Non-dominated Sorting Genetic A...
متن کاملCombining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes
With the economic successes of several Asian economies and their increasingly important roles in the global financial market, the prediction of Asian stock markets has becoming a hot research area. As Asian stock markets are highly dynamic and exhibit wide variation, it may more realistic and practical that assumed the stock indexes of Asian stock markets are nonlinear mixture data. In this res...
متن کاملDynamic interaction networks in modelling and predicting the behaviour of multiple interactive stock markets
The behaviour of multiple stock markets can be described within the framework of complex dynamic systems (CDS). A representative technique of the framework is the dynamic interaction network (DIN), recently developed in the bioinformatics domain (Chan et al., 2006). DINs are capable of modelling dynamic interactions between genes and predicting their future expressions. In this paper, we adopt ...
متن کاملStock Market Modeling Using Artificial Neural Network and Comparison with Classical Linear Models
Stock market plays an important role in the world economy. Stock market customers are interested in predicting the stock market general index price, since their income depends on this financial factor; Therefore, a reliable forecast in stock market can be extremely profitable for stockholders. Stock market prediction for financial markets has been one of the main challenges in forecasting finan...
متن کاملA Non-linear Time Series Aproach to Modelling Asymmetry in Stock Market Indexes
In this paper we propose an approach to modelling non-linear conditionally heteroscedastic time series characterised by asymmetries in both the conditional mean and variance. This is achieved by combining a TAR model for the conditional mean with a Changing Parameters Volatility (CPV) model for the conditional variance. Empirical results are given for the daily returns of the S&P 500, NASDAQ co...
متن کامل