Thesis Abstract

Abstract
The stock market is a complex and dynamic environment that is influenced by numerous factors, making it challenging for investors to accurately predict trends and make informed decisions. In recent years, the field of machine learning has emerged as a powerful tool for analyzing large datasets and identifying patterns that can be used to predict future outcomes. This study explores the application of machine learning algorithms in predicting stock market trends, with the aim of providing investors with a reliable and accurate tool for making investment decisions. The research begins with a comprehensive review of existing literature on stock market prediction and machine learning techniques. The literature review covers topics such as technical analysis, fundamental analysis, sentiment analysis, and the application of various machine learning algorithms in predicting stock prices. By synthesizing findings from previous studies, this research aims to build on existing knowledge and contribute to the development of more accurate and reliable prediction models. The research methodology section outlines the data sources, variables, and methods used to train and evaluate the machine learning models. Historical stock market data is collected and preprocessed to create a dataset that is suitable for training and testing the models. Various machine learning algorithms, including decision trees, random forests, support vector machines, and neural networks, are implemented and evaluated based on their predictive performance. The findings of the study reveal that machine learning algorithms can effectively predict stock market trends with a high degree of accuracy. The models demonstrate the ability to identify patterns and trends in historical data that can be used to make informed predictions about future stock prices. By comparing the performance of different algorithms, this research identifies the most effective techniques for predicting stock market trends and provides insights into the factors that influence prediction accuracy. The discussion section delves into the implications of the findings and their relevance to investors and financial professionals. The study highlights the potential benefits of using machine learning algorithms for stock market prediction, including improved accuracy, efficiency, and automation of the decision-making process. The limitations of the study, such as data quality and model complexity, are also discussed, along with recommendations for future research and practical applications. In conclusion, this thesis contributes to the growing body of knowledge on the application of machine learning algorithms in predicting stock market trends. By demonstrating the effectiveness of these techniques in analyzing large datasets and identifying patterns, this research provides investors with a valuable tool for making informed investment decisions. The findings of this study have implications for the financial industry and underscore the potential for machine learning to revolutionize stock market analysis and prediction.

Thesis Overview