Thesis Abstract

Abstract
This thesis investigates the application of machine learning techniques in predicting stock prices, aiming to enhance the accuracy and efficiency of stock market forecasting. The utilization of machine learning algorithms has gained significant attention in the financial industry due to their ability to analyze vast amounts of data and identify complex patterns that may influence stock prices. The study begins by providing an overview of the background of stock market prediction and the challenges faced in accurately forecasting stock prices. The research problem is identified as the need for more accurate and reliable stock price predictions to assist investors in making informed decisions. The objectives of the study are to explore the effectiveness of machine learning algorithms in predicting stock prices, analyze the impact of different features on prediction accuracy, and compare the performance of various machine learning models in the context of stock market forecasting. The study also aims to identify the limitations and scope of applying machine learning in stock price prediction and highlight the significance of the research findings in improving investment strategies. The methodology chapter outlines the research design, data collection methods, feature selection techniques, and model evaluation criteria used in the study. Various machine learning algorithms, including decision trees, random forests, support vector machines, and neural networks, are implemented and compared based on their predictive performance. The study evaluates the impact of different types of features, such as historical stock prices, trading volumes, technical indicators, and news sentiment, on the accuracy of stock price predictions. The findings chapter presents a detailed analysis of the experimental results, comparing the performance of the different machine learning models in predicting stock prices. The study investigates the influence of feature selection and model hyperparameters on prediction accuracy and identifies the most effective combination of features and algorithms for stock market forecasting. The discussion highlights the strengths and limitations of each machine learning approach and provides insights into the factors that can impact the accuracy of stock price predictions. In conclusion, the study emphasizes the potential of machine learning techniques in improving the accuracy of stock price forecasting and guiding investment decisions. The research findings contribute to the existing literature on stock market prediction and provide practical insights for investors and financial analysts. The study recommends further research to explore advanced machine learning models and incorporate additional data sources for more robust stock price predictions. Keywords Machine Learning, Stock Price Prediction, Financial Forecasting, Feature Selection, Algorithm Comparison, Investment Strategies.

Thesis Overview