The second chapter presents a holistic review of the fundamental concepts and literature that are prerequisites for the main research. It starts with a brief description of the structure of the stock market and its operations, followed by a comprehensive analysis on financial statements and fundamental analysis. The chapter then moves to an analysis of machine learning in finance, including the application of time series classification and highlights the potential of algorithms such as Support Vector Machines, Logistic Regression, Random Forests, K Nearest Neighbours, and Ensemble Methods in optimising financial market predictions. The section on related work reviews the literature on the use of ensemble of machine learning methods in finance: it highlights the potential for improved prediction and investment performance. This review establishes the theory for the dissertation and positions the study within the context of existing research.