The Community for Technology Leaders
Integrating AI and Data Mining, International Workshop on (2006)
Hobart, Tasmania
Dec. 4, 2006 to Dec. 5, 2006
ISBN: 0-7695-2730-2
pp: 42-49
C.D. Tilakaratne , University of Ballarat, Australia
M.A. Mammadov , University of Ballarat, Australia
C.P. Hurst , University of Ballarat, Australia
ABSTRACT
This study investigates how intermarket influences can be used to help the prediction of the direction (up or down) of the next day's close price of the Australian All Ordinary Index (AORD). First, intermarket influences from the potential influential markets on the AORD are quantified by assigning weights for all influential markets. The weights were defined as a solution to an optimization problem which aims to maximise rank correlation between the current day's relative return of the AORD and the weighted sum of lagged relative returns of the potential influential markets. Then, the next day's relative return of the AORD is predicted by applying the neural networks as a classifier. Two different scenarios were compared: 1) using the current day's relative returns of different sets of influential markets as separate inputs; and, 2) using only the weighted sum of these relative returns as a "combined market". The results revealed that the second approach provides better predictions in all cases. This shows the effectiveness of the proposed approach for quantifying intermarket influences and the potential of using the "weighted combined markets" for the prediction
INDEX TERMS
correlation methods, neural nets, optimisation, prediction theory, stock markets
CITATION

C. Tilakaratne, M. Mammadov and C. Hurst, "Quantification of Intermarket Influence Based on the Global Optimization and Its Application for Stock Market Prediction," 2006 First International Workshop on Integrating AI and Data Mining(AIDM), Hobart, Tas., 2007, pp. 42-49.
doi:10.1109/AIDM.2006.14
88 ms
(Ver 3.3 (11022016))