%0 Journal Article %1 Jin_GP_Wavelet %A Li, Jin %A Shi, Zhu %A Li, Xiaoli %D 2006 %J Transactions of the Institute of Measurement and Control %K algorithms, analysis, financial forecasting genetic programming, wavelet %N 3 %P 285--297 %T Genetic Programming with Wavelet-Based Indicators for Financial Forecasting %U http://tim.sagepub.com/content/vol28/issue3/ %V 28 %X Wavelet analysis, as a promising technique, has been used to approach numerous problems in science and engineering. Recent years have witnessed its novel application in economic and finance. This paper is to investigate whether features (or indicators) extracted using the wavelet analysis technique could improve financial forecasting by means of Financial Genetic Programming (FGP), a genetic programming based forecasting tool (i.e., Li, 2001). More specifically, to predict whether Down Jones Industrial Average (DJIA) Index will rise by 2.2per cent or more within the next 21 trading days, we first extract some indicators based on wavelet coefficients of the DJIA time series using a discrete wavelet transform; we then feed FGP with those wavelet-based indicators to generate decision trees and make predictions. By comparison with the prediction performance of our previous study (i.e., Li and Tsang, 2000), it is suggested that wavelet analysis be capable of bringing in promising indicators, and improving the forecasting performance of FGP.

@article{Jin_GP_Wavelet, abstract = {Wavelet analysis, as a promising technique, has been used to approach numerous problems in science and engineering. Recent years have witnessed its novel application in economic and finance. This paper is to investigate whether features (or indicators) extracted using the wavelet analysis technique could improve financial forecasting by means of Financial Genetic Programming (FGP), a genetic programming based forecasting tool (i.e., Li, 2001). More specifically, to predict whether Down Jones Industrial Average (DJIA) Index will rise by 2.2per cent or more within the next 21 trading days, we first extract some indicators based on wavelet coefficients of the DJIA time series using a discrete wavelet transform; we then feed FGP with those wavelet-based indicators to generate decision trees and make predictions. By comparison with the prediction performance of our previous study (i.e., Li and Tsang, 2000), it is suggested that wavelet analysis be capable of bringing in promising indicators, and improving the forecasting performance of FGP.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Li, Jin and Shi, Zhu and Li, Xiaoli}, biburl = {https://www.bibsonomy.org/bibtex/21479875c7618d52df36fa285c9166ca6/brazovayeye}, interhash = {33b5f93bbd3d46e9f8378b118623ec06}, intrahash = {1479875c7618d52df36fa285c9166ca6}, journal = {Transactions of the Institute of Measurement and Control}, keywords = {algorithms, analysis, financial forecasting genetic programming, wavelet}, month = {August}, number = 3, pages = {285--297}, timestamp = {2008-06-19T17:45:31.000+0200}, title = {Genetic Programming with Wavelet-Based Indicators for Financial Forecasting}, url = {http://tim.sagepub.com/content/vol28/issue3/}, volume = 28, year = 2006 }