Application of Machine Learning in Predicting Stock Market Trends

Thesis Abstract

Abstract
The stock market is a complex and dynamic system that is influenced by a multitude of factors, making it challenging for investors to predict trends accurately. In recent years, machine learning algorithms have emerged as powerful tools for analyzing and predicting stock market trends. This thesis explores the application of machine learning techniques in predicting stock market trends and their effectiveness compared to traditional methods. The study begins with an introduction to the stock market and the challenges associated with predicting its trends. The background of the study provides an overview of the historical context and evolution of machine learning in finance. The problem statement highlights the limitations of traditional forecasting methods and the need for more accurate and reliable predictive models. The objectives of the study include evaluating the performance of machine learning algorithms in predicting stock market trends, identifying the key factors that influence stock prices, and comparing the results with traditional forecasting methods. The limitations of the study are also discussed, including data availability, model complexity, and market volatility. The scope of the study covers the application of machine learning techniques such as regression analysis, neural networks, and support vector machines to predict stock market trends. The significance of the study lies in its potential to improve investment decision-making, reduce risks, and enhance overall portfolio performance. The structure of the thesis is outlined, including the chapters on introduction, literature review, research methodology, discussion of findings, and conclusion. Definitions of key terms such as machine learning, stock market trends, and prediction models are provided to clarify the terminology used throughout the thesis. The literature review in Chapter Two examines previous studies on machine learning in finance, forecasting models, and stock market prediction techniques. The research methodology in Chapter Three details the data collection process, model development, and evaluation criteria used in the study. Chapter Four presents a detailed discussion of the findings, including the performance of machine learning algorithms in predicting stock market trends, the key factors influencing stock prices, and the comparison with traditional forecasting methods. The results are analyzed and interpreted to draw meaningful conclusions. In Chapter Five, the thesis concludes with a summary of the key findings, implications for investors, and suggestions for future research. The study contributes to the growing body of knowledge on the application of machine learning in predicting stock market trends and offers valuable insights for academics, practitioners, and policymakers in the finance industry.