Nonlinear Prediction of The Standard & Poor's 500 and The Hang Seng Index under A Dynamic Increasing Sample
AbstractThis study attempts to forecast the next dayâ€™s returns of two time series in the Hang Seng Index (HSI) and Standard & Poorâ€™s (S&P) 500 indices using Artificial Neural Networks (ANN) with past returns as input variables. Results from ANN are compared with those from the autoregressive integrated moving average (ARIMA) model. This study uses a longer time period than ARIMA (i.e., daily data of 80 and 35 years for the S&P 500 and HSI, respectively) to develop and test the models. The two competing models are rigorously evaluated in terms of widely-used penalty-based criteria, such as directional accuracy, as well as in terms of trading performance criteria like annualised return, the Sharpe ratio and annualised volatility via a simple trading strategy. Moreover, the robustness of the two models is tested for 36 test periods. Empirical results show that ANN works better than ARIMA and delivers consistent results across the periods tested. These results support ANNâ€™s robustness and its use in formulating a strategy for trading in the S&P 500 and HSI time series.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Penerbit Universiti Sains Malaysia in its journal Asian Academy of Management Journal of Accounting and Finance.
Volume (Year): 5 (2009)
Issue (Month): 2 ()
ARIMA; artificial neural network; forecasting; stock market; time series analysis;
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Journal Division).
If references are entirely missing, you can add them using this form.