Data analytics enhanced component volatility model

Volatility modelling and forecasting have attracted many attentions in both finance and computation areas. Recent advances in machine learning allow us to construct complex models on volatility forecasting. However, the machine learning algorithms have been used merely as additional tools to the existing econometrics models. The hybrid models that specifically capture the characteristics of the volatility data have not been developed yet. We propose a new hybrid model, which is constructed by a low-pass filter, the autoregressive neural network and an autoregressive model. The volatility data is decomposed by the low-pass filter into long and short term components, which are then modelled by the autoregressive neural network and an autoregressive model respectively. The total forecasting result is aggregated by the outputs of two models. The experimental evaluations using one-hour and one-day realized volatility across four major foreign exchanges showed that the proposed model significantly outperforms the component GARCH, EGARCH and neural network only models in all forecasting horizons

Fundamental, Sentiment and Technical Analysis for Algorithmic Trading Using Novel Genetic Programming Algorithms

This thesis explores genetic programming (GP) applications in algorithmic trading, addressing significant advancements in the field. Investors typically rely on fundamental analysis (FA) or technical analysis (TA) indicators, with sentiment analysis (SA) gaining recent attention. Consequently, algorithms have become the primary method for developing pre-programmed trading strategies, leading to substantial financial benefits. While each analysis type has been studied individually, their combined exploration remains limited. Our motivation is to assess if integrating FA, SA, and TA indicators can improve financial profitability. Therefore, we propose the use of novel GP algorithms for the combination of the three analysis types, along with the use of a novel fitness function, and a novel GP operator that encourages active trading by injecting trees into the GP population that perform a high number of trades while achieving high profitability at low risk. To evaluate our GP variants’ performance, we conduct experiments on stocks of 42 international companies, comparing the novel algorithm with the GP variants introduced in the same chapter. Moreover, we compare the proposed GP algorithm against four machine learning benchmarks and a financial trading strategy. The evaluation employs three financial metrics: Sharpe ratio, rate of return, and risk. Results consistently show that the proposed GP algorithms in each chapter enhance the financial performance of trading strategies, surpassing the benchmarks

Novel neural network models for financial prediction

Financial markets are an important feature of modern economies, where trading decisions can be critical because of their significant impact on social and economic life. Various models and techniques have been applied to describe and predict financial time series in order to develop effective tools in financial prediction. In particular, neural networks have recently gained significant research interest in financial markets as well as in other domains. As financial time series data show a high degree of non-linearity, neural networks represent an attractive approach in this area. This thesis introduces a novel neural network model, the FL-SMIA model, as well as several variations and extensions, namely the FL-SMIA*, D-FL-SMIA, MD-FLSMIA, MD-FL-SMIA-2, M FL-SMIA, and FL-SMIA-RBM. The FL-SMIA model is a model that uses the principles of the Functional Link Neural Network (FLNN) and the Self-organising Multilayer Neural Network using the Immune Algorithm (SMIA). The FL-SMIA model combines the higher-order inputs , i.e. the products of raw input features, with the self-organising hidden layer (SMIA) that dynamically grows and adapts to the input vectors. Based on the promising results of the FL-SMIA network in initial experiments, variations and extensions have been developed using deeper architectures (D FLSMIA), mixed input representations (M-FL-SMIA), a combination of deep and mixed architectures (MD-FL-SMIA), and of the FL-SMIA with the Restricted Boltzmann Machine in the FL-SMIA-RBM. The proposed models have also been compared with other neural network architectures: FLNN, the Multilayer perceptron (MLP), and SMIA. All networks have been evaluated for one day and five days ahead prediction using financial and statistical metrics, focusing on the Relative Profit (RP) and Annualised Volatility (AV). Data-sets of three different types have been used: exchange rates (USD/UKP, USD/EUR, JPY/USD), stock price indices (NASDAQ, DJIA), and commodity prices (OIL and GOLD). In terms of average RP results for the one day ahead prediction, the FL-SMIA was slightly worse than the best model (FLNN) but FL-SMIA model reduced the investment risk by producing the lowest average AV value. We have also observed notable differences between data types. For the five days ahead prediction, the M-FL-SMIA model has the highest average RP and the lowest average AV results. Correlation analysis on the residuals has shown differences in behaviour between FLNN model and FL-SMIA model, encouraging further extensions and variations. Overall, the FL-SMIA model and its extensions will be useful for time series prediction because of their competitive performance and different behaviour to standard neural networks

A comparison of forecasting approaches for capital markets

In recent years, machine learning algorithms have become increasingly popular in financial forecasting. Their flexible, data-driven nature makes them ideal candidates for dealing with complex financial data. This paper investigates the effectiveness of a number of machine learning algorithms, and combinations of these algorithms, at generating one-step ahead forecasts of a number of financial time series. We find that hybrid models consisting of a linear statistical model and a nonlinear machine learning algorithm are effective at forecasting future values of the series, particularly in terms of the future direction of the series