Thesis Abstract

Abstract
This thesis explores the application of machine learning algorithms in predicting stock market trends, aiming to enhance decision-making processes for investors and traders. The study investigates how historical stock market data can be leveraged to build predictive models that forecast future market movements. The research methodology involves data collection from various financial markets, feature selection, model training, and evaluation. Ten machine learning algorithms are implemented and compared for their effectiveness in predicting stock market trends. Chapter One provides an introduction to the research topic, presents the background of the study, articulates the problem statement, outlines the objectives of the study, discusses the limitations and scope of the research, highlights the significance of the study, and provides an overview of the thesis structure. Chapter Two comprises a detailed literature review that explores existing research on predictive modeling in finance, machine learning algorithms, and their applications in stock market prediction. Chapter Three focuses on the research methodology and includes sections on data collection, data preprocessing, feature selection, model selection, model training, model evaluation, and performance metrics. The chapter also discusses the experimental setup and describes the dataset used for training and testing the predictive models. Chapter Four presents a comprehensive discussion of the findings obtained from implementing the machine learning algorithms for stock market trend prediction. The chapter analyzes the performance of each algorithm, compares their predictive accuracy, identifies key factors influencing model performance, and discusses the implications of the results for investors and traders. Finally, Chapter Five offers a conclusion and summary of the thesis, highlighting the key findings, discussing the implications of the research, and suggesting future research directions. The study contributes to the field of finance by demonstrating the efficacy of machine learning algorithms in predicting stock market trends and providing valuable insights for decision-making in the financial markets.

Thesis Overview