Applications of artificial neural networks in financial market forecasting

This thesis evaluates the utility of Artificial Neural Networks (ANNs) applied to financial market and macroeconomic forecasting. In application, ANNs are evaluated in comparison to traditional forecasting models to evaluate if their nonlinear and adaptive properties yield superior forecasting performance in terms of robustness and accuracy. Furthermore, as ANNs are data-driven models, an emphasis is placed on the data collection stage by compiling extensive candidate input variable pools, a task frequently underperformed by prior research. In evaluating their performance, ANNs are applied to the domains of: exchange rate forecasting, volatility forecasting, and macroeconomic forecasting. Regarding exchange rate forecasting, ANNs are applied to forecast the daily logarithmic returns of the EUR/USD over a short-term forecast horizon of one period. Initially, the analytic method of Technical Analysis (TA) and its sub-section of technical indicators are utilized to compile an extensive candidate input variable pool featuring standard and advanced technical indicators measuring all technical aspects of the EUR/USD time series. The candidate input variable pool is then subjected to a two-stage Input Variable Selection (IVS) process, producing an informative subset of technical indicators to serve as inputs to the ANNs. A collection of ANNs is then trained and tested on the EUR/USD time series data with their performance evaluated over a 5-year sample period (2012 to 2016), reserving the last two years for out of sample testing. A Moving Average Convergence Divergence (MACD) model serves as a benchmark with the in-sample and out-of-sample empirical results demonstrating the MACD is a superior forecasting model across most forecast evaluation metrics. For volatility forecasting, ANNs are applied to forecast the volatility of the Nikkei 225 Index over a short-term forecast horizon of one period. Initially, an extensive candidate input variable pool is compiled consisting of implied volatility models and historical volatility models. The candidate input variable pool is then subjected to a two-stage IVS process. A collection of ANNs is then trained and tested on the Nikkei 225 Index time series data with their performance evaluated over a 4-year sample period (2014 to 2017), reserving the last year for out-of-sample testing. A GARCH (1,1) model serves as a benchmark with the out-of-sample empirical results finding the GARCH (1,1) model to be the superior volatility forecasting model. The research concludes with ANNs applied to macroeconomic forecasting, where ANNs are applied to forecast the monthly per cent-change in U.S. civilian unemployment and the quarterly per cent-change in U.S. Gross Domestic Product (GDP). For both studies, an extensive candidate input variable pool is compiled using relevant macroeconomic indicator data sourced from the Federal Bank of St Louis. The candidate input variable pools are then subjected to a two-stage IVS process. A collection of ANNs is trained and tested on the U.S. unemployment time series data (UNEMPLOY) and U.S. GDP time series data. The sample periods are (1972 to 2017) and (1960 to 2016) respectively, reserving the last 20% of data for out of sample testing. In both studies, the performance of the ANNs is benchmarked against a Support Vector Regression (SVR) model and a Naive forecast. In both studies, the ANNs outperform the SVR benchmark model. The empirical results demonstrate that ANNs are superior forecasting models in the domain of macroeconomic forecasting, with the Modular Neural Network performing notably well. However, the empirical results question the utility of ANNs in the domains of exchange rate forecasting and volatility forecasting. A MACD model outperforms ANNs in exchange rate forecasting both in-sample and out-of-sample, and a GARCH (1,1) model outperforms ANNs in volatility forecasting.