Browsing by Author "Dassanayake, W."

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Open Access
Determinants of stock market index movements: Evidence from New Zealand stock market
(Faculty of Graduate Studies and Research, 2017-01-27) Dassanayake, W.; Jayawardena, C.
his study examines the impact of a selected macroeconomic variables on the New Zealand stock market(S&P/NZX 50) index. We use exchange rate, interest rate, inflation rate and foreign stock market index (S&P 500 index) to evaluate their influence on the New Zealand stock market (S&P/NZX 50) index. Daily data from January 2014 to September 2016 are evaluated. Unit root tests, cointegration tests, vector error correction model (VECM) and Granger causality test are employed to examine both long run and short run dynamic relationship between these variables. The study finds that there is no statistically significant long run causality from inflation rate, exchange rate, interest rate and S&P 500 index on the New Zealand stock market index. However, S&P 500 index has a strong significant short run Granger causality to the New Zealand stock market index.
Embargo
Effectiveness of Stock Index Forecasting using ARIMA model: Evidence from New Zealand
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Dassanayake, W.; Ardekani, I.; Gamage, N.; Jayawardena, C.; Sharifzadeh, H.
Time series of stock market indices are dynamic, interdependent, and considered sensitive to many factors. Thus, the prediction of such indexes is always challenging. A comprehensive review carried out by the authors finds that no attempts have yet been carried out to test ARIMA models’ predictive efficacy applied to the New Zealand financial markets. Thus, technical analysis based ARIMA prediction models are developed and empirically tested on the New Zealand stock market (NZX50) index. Daily NZX50 index data are used, and the forecasting precision of the models is assessed based on Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE]. Our study finds that ARIMA (1, 1, 0) plus intercept is the best order forecasting model out of the models we examined. Once a substantiate algorithm training is implemented, formulated ARIMA models could be successfully employed to forecast the time series of other stock market indexes or the same index for varied periods. Future researchers could compare the forecasting efficiencies of ARIMA with a deep-learning model such as long short-term memory (LSTM). The presence of limited published research of ARIMA applied to the financial markets of New Zealand validates the need and the contribution of this paper.