NDLI: Financial time series prediction using exogenous series and combined neural networks

Content Provider	IEEE Xplore Digital Library
Author	Amorim Neto, M.C. Calvalcanti, G. Ren, T.I.
Copyright Year	2009
Description	Author affiliation: Center of Informatics (CIn), Federal University of Pernambuco (UFPE), Av. Prof Luis Freire s/n, Cidade Universitária, Cep: 50.740-540 - Recife PE Brazil (Amorim Neto, M.C.; Calvalcanti, G.; Ren, T.I.)
Abstract	Time series forecasting have been a subject of interest in several different areas of research such as: meteorology, demography, health, computer and finance. Since it can be applied to various practical problems in real world, techniques to predict time series have been a topic of increasing research activities, especially in the financial sector that has a great interest in the forecast of the stock market. In this article, we are interested in the forecast of the time series related to the Brazilian oil company, Petrobras (PETR4). A methodology based on information obtained from exogenous series was used in combination with a neural network to predict the PETR4 stock series. Exogenous series were selected by analyzing the correlation between the series with the Petrobras stocks series. In this way, the prediction was obtained by not just using the previous values of the series but also by using information external to the PETR4 series. The values of the selected series were used as features for a prediction stage based on combined neural networks. To evaluate the performance of the system classical measurements were used, however we also introduce a new performance index called Sum of the Losses and Gains (SLG).
Starting Page	149
Ending Page	156
File Size	5867449
Page Count	8
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178949
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Weather forecasting Economic forecasting Meteorology Demography Finance Stock markets Petroleum Performance loss Gain measurement
Content Type	Text
Resource Type	Article

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