Effectiveness of Stochastic Neural Network for Prediction of Fall or Rise of TOPIX
AbstractStochastic neural network is a hierarchical network of stochastic neurons which emit 0 or 1 with the probability determined by the values of inputs. We have developed an efficient training algorithm so as to maximize the likelihood of such a neural network. This algorithm enables us to apply the stochastic neural network to a practical problem like prediction of fall or rise of Tokyo Stock Price Index (TOPIX). We trained it with the data from 1994 to 1996 and predicted the fall or rise of 1 day ahead of TOPIX for the period from 1997 to 2000. The result is quite promising. The accuracy of the prediction of the stochastic network is the 60.28%, although those of non-stochastic neural network, autoregressive model and GARCH model are 50.02, 51.38 and 57.21%, respectively. However, the stochastic neural network is not so advantageous over other networks or models for prediction of the TOPIX used for training. This means that the stochastic neural network is less over fitting to the training data than others, and results in the best prediction. We will demonstrate how the stochastic neural network learns well non-linear structure behind of the data in comparison to other models or networks, including Generalized Linear model (GLM). Copyright Springer Science + Business Media, Inc. 2003
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Bibliographic InfoArticle provided by Springer in its journal Asia-Pacific Financial Markets.
Volume (Year): 10 (2003)
Issue (Month): 2 (September)
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Web page: http://springerlink.metapress.com/link.asp?id=102851
binary prediction; generalized linear model; stochastic modeling; stochastic neural network; TOPIX;
Find related papers by JEL classification:
- D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
- L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Fernandez-Rodriguez, Fernando & Gonzalez-Martel, Christian & Sosvilla-Rivero, Simon, 2000. "On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market," Economics Letters, Elsevier, vol. 69(1), pages 89-94, October.
- Kaizoji, Taisei, 2000.
"Speculative bubbles and crashes in stock markets: an interacting-agent model of speculative activity,"
Physica A: Statistical Mechanics and its Applications,
Elsevier, vol. 287(3), pages 493-506.
- Taisei Kaizoji, 2000. "Speculative bubbles and crashes in stock market: an interacting-agent model of speculative activity," Papers cond-mat/0010263, arXiv.org.
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