Modelling and trading the English stock market with new forecasting techniques

The motivation of this paper is to introduce a short term adaptive model (Partial Swarm Optimizer combined with linear and nonlinear models when applied to the task of forecasting and trading the daily closing returns of the FTSE100 exchange traded funds (ETFs). This is done by benchmarking its results with a higher order neural network (HONN), a recurrent neural network (RNN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD), plus a buy and hold strategy. More specifically, the trading performance of all models is investigated in forecast and trading simulations on the FTSE 100 ETF time series over the period January 2000 to June 2016 using the last two years for out-of-sample testing. As it turns out, the proposed adaptive models do remarkably well and outperform its benchmarks in terms of correct directional change and trading performance.