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.
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References
Rumelhart, D.E., Hinton, D.E., Williams, R.J.: Learning representations by back-propagation errors. Nature 323, 533–536 (1986)
Powell, M.J.D.: Radial basis functions for multivariable interpolation: A review. In: IMA Conference on Algorithms for the approximation of Functions and Data, pp. 143–167. RMCS, Shrivenham (1985)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1995)
Bishop, C.M.: Mixture Density Networks. Technical Report NCRG/94/004, Neural Computing Research Group, Aston University (1994), http://research.microsoft.com/~cmbishop/downloads/Bishop-NCRG-94-004.ps
Bishop, C.M., Svensén, M., Williams, C.K.I.: GTM: The Generative Topographic Mapping. Neural Computation 10(1), 215–234 (1998)
Richmond, K.: Mixture Density Networks, Human articulatory data and acoustic-to-articulatory inversion of continuous speech (2001), http://www.cstr.ed.ac.uk/downloads/publications/2001/Richmond_2001_a.ps
Bartholomew, D.J.: Latent Variable Models and Factor Analysis. Charles Griffin & Company Ltd., London (1987)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B 39(1), 1–38 (1977)
Carreira-Perpinan, M.A.: One-to-many mappings, continuity constraints and latent variable models. In: Proc. IEE Colloquium on Applied Statistical Pattern Recognition, Birmingham, pp. 14/1–14/6 (1999)
MacKay, D.J.C., Gibbs, M.N.: Density networks. In: Proceedings of Society for General Microbiology, Edinburgh (1997)
Zeng, X., Yeung, D.S.: A Quantified Sensitivity Measure for Multilayer Perceptron to Input Perturbation. Neural Computation 15, 183–212 (2003)
FTSE Guide to UK Calculation Methods, http://www.ftse.com/Indices/UK_Indices/Downloads/uk_calculation.pdf
Møler, M.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6(4), 525–533 (1993)
Carreira-Perpiñán, M.Á.: Continuous latent variable models for dimensionality reduction and sequential data reconstruction. PhD thesis, Dept. of Computer Science, University of Sheffield, UK (2001), http://faculty.ucmerced.edu/mcarreira-perpinan/papers/phd-thesis.html
Brouwer, R.K.: Feed-forward neural network for one-to-many mappings using fuzzy sets. Neurocomputing 57, 345–360 (2004)
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Draganova, C., Lanitis, A., Christodoulou, C. (2009). Isolating Stock Prices Variation with Neural Networks. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_37
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DOI: https://doi.org/10.1007/978-3-642-03969-0_37
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