Prediction of International Stock Market Movements Using a Statistical Time Series Analysis Method

The thesis has used econometric time series models to model and forecast the development in closing prices of main international stock markets. These are New York, London, Tokyo and Shanghai stock market. The time series data set includes the trading days from 1st January, 2008 to 31st December, 2012 i.e. (5 years). After pre-processing the data to substitute the missing values using interpolation method and convert all closing prices to USD currency, the first attempt of this thesis employs the Auto Regressive Moving Average (ARMA) framework, which has been used to model a time series data set. It is found that the model can be used to fit the data in the estimation period. The Root Mean Square Error (RMSE) is used to find an estimating order of the parameter in ARMA model i.e. r, m proper values. The forecasting process is constructed based on the ARMA model to forecast the future value for the data indices in the period (2010-2012) in New York, London, Tokyo, and Shanghai stock market. The idea of forecasting in this work is predicting two-days-ahead closing price based on previous two years closing price for each two days. The forecasting is very important in the analysis of economic and industrial time series, and in sailing and buying movement. The money was invested in these stock markets and the results made it clear that the investment in London stock market is the best investment. Keywords: Time series analysis, ARMA, RMSE, Forecastıng and Investment.