Artificial intelligence based hybrid systems for financial forecasting.
Current research carried out on financial forecasting has highlighted some limitations of
classical econometric methods based on the assumption that the investigated time series
can be described as stationary stochastic processes with Gaussian probability density
functions. Chaotic behaviour, fractal characteristics and non-linear dynamics have been
emerging in different aspects of the financial forecasting problem.
The objective of this thesis is to take a system level perspective of the financial forecasting
problem and to explore a number of approaches to enhance more 'traditional' decision
making flows for stock market forecasting, with particular emphasis on stock selection
To achieve this purpose, a number of stock selection and timing computational 'modules'
are investigated. From a computational point of view, the investigation performed in this
work encompass techniques such as artificial neural networks, genetic algorithms, chaos
theory and fractal geometry, as well as more traditional methods such as clustering,
screening, ranking, and statistics based models. From a financial data point of view, this
research takes advantage of both fundamental and technical information to enhance the
stock selection and timing processes and to cover several investment horizons.
Three computational modules are proposed. First, a multivariate stock ranking module
which uses fundamental information and is optimised through genetic algorithms. Second,
a multivariate forecasting module which uses technical information and is based on
artificial neural networks. Third, a univariate price time series forecasting module based
on artificial neural networks. In addition, an integrated flow that takes advantage of some
synergies and complementary properties of the devised modules is proposed.
The effectiveness of the developed modules and the viability of the proposed integrated
flow are evaluated over a number of investment horizons using (out-of-sample) historical