Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.677629
Title: Topological optimisation of artificial neural networks for financial asset forecasting
Author: He, Shiye
ISNI:       0000 0004 5369 2186
Awarding Body: London School of Economics and Political Science (LSE)
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 2015
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Abstract:
The classical Artificial Neural Network (ANN) has a complete feed-forward topology, which is useful in some contexts but is not suited to applications where both the inputs and targets have very low signal-to-noise ratios, e.g. financial forecasting problems. This is because this topology implies a very large number of parameters (i.e. the model contains too many degrees of freedom) that leads to over fitting of both signals and noise. This results in the ANN having very good in-sample performance on the data used for its training but poor performance outof-sample for forecasting. The main contribution of my research is to develop a new heuristic method called “ANN reduction” for optimising the topological structure of a feed-forward ANN in order to improve its out-of-sample performance (using an RMS measure). The research concentrated on the topological optimization of the graph representing an ANN, which reduces the effective degrees of freedom of the ANN whilst still maintaining its feed-forward (but incomplete) topology. Such reductions in the number of parameters have been attempted before in the literature, but our procedure is of a different (graph theoretic) nature and (in extremis) optimal for small-size ANNs. Two applications of the ANN reduction are also implemented and programmed for empirical simulations. For this purpose, two datasets generated from deterministic functions and three datasets derived from foreign exchange market prices are used for evaluating the ANN reduction applications. These applications generate new ANN topologies with some clear performance advantages over those obtained by the best complete ANNs, improving the generalization (out-of-sample) performance by up to 27.6% compared to the complete ANN on the function generated datasets and up to 14.1% on the financial forecasting problem for the FX data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.677629  DOI: Not available
Keywords: HG Finance
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