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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front Comput Sci Chin    2009, Vol. 3 Issue (2) : 177-191    https://doi.org/10.1007/s11704-009-0025-3
RESEARCH ARTICLE
Daily prediction of short-term trends or crude oil prices using neural networks exploiting multimarket dynamics
Heping PAN1,2,3(), Imad HAIDAR4(), Siddhivinayak KULKARNI4()
1. Prediction Research Centre (PRC), University of Electronic Science and Technology of China, Chengdu 610054, China.; 2. Finance Research Centre of China, Southwest University of Finance and Economics, Chengdu 610074, China; 3. Swingtum Institute of Intelligent Finance, Swingtum Prediction Pty Ltd 17 Southern Court, Delacombe, VIC 3356, Australia; 4. School of ITMS, University of Ballarat, VIC 3350, Australia
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Abstract

This paper documents a systematic investigation on the predictability of short-term trends of crude oil prices on a daily basis. In stark contrast with longer-term predictions of crude oil prices, short-term prediction with time horizons of 1-3 days posits an important problem that is quite different from what has been studied in the literature. The problem of such short-term predicability is tackled through two aspects. The first is to examine the existence of linear or nonlinear dynamic processes in crude oil prices. This sub-problem is addressed with statistical analysis involving the Brock-Dechert-Scheinkman test for nonlinearity. The second aspect is to test the capability of artificial neural networks (ANN) for modeling the implicit nonlinearity for prediction. Four experimental models are designed and tested with historical data: (1) using only the lagged returns of filtered crude oil prices as input to predict the returns of the next days; this is used as the benchmark, (2) using only the information set of filtered crude oil futures price as input, (3) combining the inputs from the benchmark and second models, and (4) combing the inputs from the benchmark model and the intermarket information. In order to filter out the noise in the original price data, the moving averages of prices are used for all the experiments. The results provided sufficient evidence to the predictability of crude oil prices using ANN with an out-of-sample hit rate of 80%, 70%, and 61% for each of the next three days’ trends.

Keywords crude oil prediction      short-term trend      crude oil futures      heating oil      neural networks      intermarket analysis     
Corresponding Author(s): PAN Heping,Email:panhp@swingtum.com; HAIDAR Imad,Email:ihaidar@students.ballarat.edu.au; KULKARNI Siddhivinayak,Email:s.kulkarni@ballarat.edu.au   
Issue Date: 05 June 2009
 Cite this article:   
Heping PAN,Imad HAIDAR,Siddhivinayak KULKARNI. Daily prediction of short-term trends or crude oil prices using neural networks exploiting multimarket dynamics[J]. Front Comput Sci Chin, 2009, 3(2): 177-191.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-009-0025-3
https://academic.hep.com.cn/fcs/EN/Y2009/V3/I2/177
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