Abstract
The motivation for this chapter is to introduce novel short-term adaptive models to trade the FTSE100 index. There are five major contributions to this chapter, which include the introduction of an input selection criteria when utilizing 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 optimize 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 optimize the weights in a traditional RBF neural network (NN) and the AR (autoregressive) and MA (moving average) terms in an ARMA model. Other benchmarks include a traditional MLPNN (multi-layer perceptron neural network) and a GAMLPNN (genetic assisted multi-layer perceptron neural network) that uses a genetic algorithm for input selection and to determine optimal weights.
The empirical results indicate that the adaptive PSO RBF (particle swarm optimization radial basis function) model outperforms all other examined models in terms of trading accuracy and profitability.
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Notes
- 1.
As at 31 August 2007—source: FTSE Group (2007).
- 2.
As at 29 March 2013—source: FTSE Group (2013).
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Middleton, P.W., Theofilatos, K., Karathanasopoulos, A. (2016). Trading the FTSE100 Index: ‘Adaptive’ Modelling and Optimization Techniques. In: Dunis, C., Middleton, P., Karathanasopolous, A., Theofilatos, K. (eds) Artificial Intelligence in Financial Markets. New Developments in Quantitative Trading and Investment. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-48880-0_2
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