He, Shiye
(2015)
Topological optimisation of artificial neural networks
for financial asset forecasting.
PhD thesis, London School of Economics and Political Science.
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.
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