Computational Intelligence for Analysis Concerning Financial Modelling and the Adaptive Market Hypothesis

This thesis concerns the field of computational intelligence (CI), an important area of computer science that predominantly endeavours to model complex systems with heuristic algorithms. Heuristic algorithms from CI are generally nature or biologically inspired programs that iteratively learn from experience. More specifically, the research focuses on the overlap between the fields of CI and financial analysis, where the financial markets (in the form of financial time series) provide the complex system of interest. Therefore the objective of the thesis can be summarized as a exercise in improving the abilities of CI algorithms for modelling financial time series. CI has been applied to a whole spectrum of domains where techniques are developed at a more general level and then applied to a particular application area or complex system. The financial markets are somewhat unique. Unlike other complex systems in nature, the financial markets are of our own creation and their evolution is a by product of human nature, where the beliefs and bias of the participants (humans) in the complex system (financial markets) govern how the system behaves. This is in contrast to many complex systems where, for example, the opinions of experts have no effect on the outcome. Thus, the motivation of this work is to quantify meaningful characteristics (behaviour) of the financial markets as a means to improve and understand how heuristic algorithms respond to them. This process of applying more scrutiny to the analysis of the application area, yields an approach to algorithm development that takes into account the unique characteristics of the market. To achieve this goal the thesis is structured into three sections that comprise four contribution chapters. The contribution chapters are labelled: validity, implications and innovations and each is motivated by a separate research question. The validity chapter is based on determining a reasonable characterization of the financial markets. This includes a detailed literature review of the popular competing market theories as well as some new innovative tests. There is not a general consensus as to which theory is correct but from a computational intelligence perspective the adaptive market hypothesis (AMH) is revealed as a reasonable characterization of the financial markets and one that provides quantifiable characteristics to be utilized in following chapters. The implications chapter concerns testing the effect of implications of the AMH, if any, on the robustness of models derived from CI. Specifically three implications are examined, i.e., variable stationarity, variable efficiency and the waxing and waning of investment strategies. The experiments concerned six algorithms from four of the major paradigms in supervised learning. The results from each of the studies demonstrated that the implications of the AMH affect CI derived models. This conclusion reveals that the unique properties of the financial markets should be taken into account when applying CI algorithms for modelling and forecasting. The two chapters concerning innovations explore how CI techniques can be improved based on the results from the validity and implications chapters. The first chapter (chapter 6) concerns the development of a meta learner based on the implication of the waxing and waning of investment strategies, the meta learning algorithm called LATIS (Learning Adaptive Technical Indicator System) is a blend of micro and macro modelling perspectives and allows for online adaptive learning with an interpretable white box framework. The second innovations chapter (chapter 7) concerns the discretization of financial time series data into a finite alphabet. A discretization algorithm is developed, which extends an existing state-of-the-art algorithm to handle the characteristics of financial time series. The proposed algorithm, called alSAX (adaptive local Symbolic Aggregate approXimation), is demonstrated to be superior in terms of its symbolic mappings, in relation to a gold standard, and in the popular time series subsequence analysis task. Additionally, an invalid theoretical assumption of the existing algorithm is revealed. The flaw in the algorithm is discussed and its impact is determined based on the characteristics of the time series and the parameters of the algorithm. From the analysis, the thesis offers viable fixes to compensate for the flaw, where the suitability of the fixes are dependent on the problem domain and objective of the data mining task