Trading the FTSE100 Index – ‘Adaptive’ Modeling and Optimisation Techniques

Dr. Andreas Karathanasopoulos
Greece, University of East London, Department of Finance
The motivation for this paper is to introduce novel short-term adaptive models to trade the FTSE100 index. There are five major contributions
of this paper which include the introduction of an input selection criteria when utilising an expansive universe of inputs, adaptive sliding window modelling, a hybrid combination of PSO and RBF algorithms, the application of a PSO algorithm to a traditional ARMA model, and finally the introduction of a multi-objective algorithm to optimise statistical and trading performance when trading an equity index.
Both machine learning-based methodologies and more conventional models are adapted and optimized to model the index. A PSO algorithm is used to optimise the weights in a traditional RBF neural network (NN) and the AR and MA terms in an ARMA model. Other benchmarks include a traditional MLP NN, and a GA MLP NN which uses a genetic algorithm for input selection and to determine optimal weights.
The empirical results indicate that the adaptive PSO RBF model out- performs all other examined models in terms of trading accuracy and profitability.