Thesis Abstract

Abstract
The financial market is a dynamic and complex system that is influenced by numerous factors, making it challenging to accurately predict stock prices. Traditional methods of stock price prediction have limitations in capturing the intricate patterns and relationships within financial data. In recent years, machine learning algorithms have shown promising results in enhancing the accuracy of stock price prediction. This research project aims to develop a predictive modeling framework for stock prices using machine learning algorithms. The study begins with a comprehensive review of the existing literature on stock price prediction, machine learning algorithms, and their applications in the financial domain. The literature review highlights the strengths and limitations of current approaches and provides insights into the potential of machine learning in improving stock price forecasting. The research methodology section outlines the process of data collection, preprocessing, feature engineering, model selection, and evaluation. Various machine learning algorithms, including support vector machines, random forests, and neural networks, will be implemented and compared to identify the most effective model for stock price prediction. The findings of the study will be presented in the discussion chapter, analyzing the performance of different machine learning algorithms in predicting stock prices. The results will be evaluated based on metrics such as accuracy, precision, recall, and F1-score to assess the effectiveness of the predictive models. In conclusion, this research project contributes to the field of stock price prediction by demonstrating the utility of machine learning algorithms in improving forecasting accuracy. The study findings provide valuable insights for investors, financial analysts, and policymakers in making informed decisions based on more reliable stock price predictions. Overall, this research project enhances our understanding of the potential of machine learning algorithms in predicting stock prices and opens up avenues for further research in this domain.

Thesis Overview