DocumentCode :
2497443
Title :
Improving financial time series prediction using exogenous series and neural networks committees
Author :
Neto, Manoel C Amorim ; Tavares, Gustavo ; Alves, Victor M O ; Cavalcanti, George D C ; Ren, Tsang Ing
Author_Institution :
Facilit Technol. Co., Brazil
fYear :
2010
fDate :
18-23 July 2010
Firstpage :
1
Lastpage :
8
Abstract :
Time series forecasting is useful in many researches areas. The use of models that provide a reliable prediction in financial time series may bring valuable profits for the investors. This paper proposes a methodology based on information obtained from exogenous series used in combination with neural networks to predict stock series. The best trained neural networks were used in combination to improve the prediction capacity of a single networks. To evaluate the proposed prediction models, some known metrics were applied. Moreover, we also proposed one new metric called Prediction in Direction and Accuracy (PDA), which benefits models with great performance in prediction accuracy and trend. Addictionally, there was used an evolutionary algorithm to choose the best trained models that maximize PDA. Experiments with two of the most important Brazilian companies stock quotes have shown the usefulness of the proposed prediction system to generate profits in investments.
Keywords :
evolutionary computation; financial management; forecasting theory; investment; neural nets; profitability; stock markets; time series; evolutionary algorithm; exogenous series; financial time series prediction; investment; neural networks committees; prediction capacity; prediction in direction and accuracy; profit; stock series prediction; time series forecasting; Artificial neural networks; Biological system modeling; Databases; Mathematical model; Measurement; Predictive models; Time series analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2010 International Joint Conference on
Conference_Location :
Barcelona
ISSN :
1098-7576
Print_ISBN :
978-1-4244-6916-1
Type :
conf
DOI :
10.1109/IJCNN.2010.5596911
Filename :
5596911
Link To Document :
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