Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/1113
Title: Isolating Stock Prices Variation with Neural Networks
Authors: Draganova, Chrisina
Christodoulou, Chris
Lanitis, Andreas 
Keywords: Stock Price Prediction;Neural networks;Multivariate Statistics;One-to-Many Mapping
Category: Arts
Field: Humanities
Issue Date: 2009
Publisher: Springer Berlin Heidelberg
Source: 11th International Conference on Engineering Applications of Neural Networks, Communications in Computer and Information Science, Springer, Vol 43, pp 401-408, 2009
Abstract: In this study we aim to define a mapping function that relates the general index value among a set of shares to the prices of individual shares. In more general terms this is problem of defining the relationship between multivariate data distributions and a specific source of variation within these distributions where the source of variation in question represents a quantity of interest related to a particular problem domain. In this respect we aim to learn a complex mapping function that can be used for mapping different values of the quantity of interest to typical novel samples of the distribution. In our investigation we compare the performance of standard neural network based methods like Multilayer Perceptrons (MLPs) and Radial Basis Functions (RBFs) as well as Mixture Density Networks (MDNs) and a latent variable method, the General Topographic Mapping (GTM). According to the results, MLPs and RBFs outperform MDNs and the GTM for this one-to-many mapping problem.
URI: http://ktisis.cut.ac.cy/handle/10488/1113
ISBN: 9783642039683 (Print)
9783642039690 (Online)
ISSN: 1865-0929 (Print)
1865-0937 (Online)
DOI: 10.1007/978-3-642-03969-0_37
Rights: © Springer
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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