Thesis Abstract

Abstract
Stock price prediction is a crucial area in finance and investment that has garnered significant attention from researchers and practitioners. With the emergence of machine learning techniques, there has been a growing interest in utilizing these methods to forecast stock prices accurately. This thesis investigates the application of machine learning algorithms in predicting stock prices, aiming to enhance the performance and reliability of stock market predictions. The study begins with an exploration of the theoretical background of stock price prediction and the role of machine learning in this context. It delves into the various challenges and limitations associated with traditional stock price forecasting methods, highlighting the need for innovative approaches to address these issues effectively. The research objectives are outlined to provide a clear direction for the study. These objectives include developing machine learning models for stock price prediction, evaluating the performance of different algorithms, and comparing the results with traditional forecasting techniques. The study also defines the scope and limitations of the research, setting boundaries for the investigation. A comprehensive literature review is conducted to analyze existing studies on stock price prediction using machine learning. The review covers various machine learning algorithms, data sources, feature selection techniques, and evaluation metrics employed in stock market forecasting. By synthesizing the findings of previous research, the study aims to identify gaps and opportunities for further exploration in the field. The research methodology section outlines the data collection process, feature selection techniques, model development, and evaluation methodologies employed in the study. Various machine learning algorithms, including regression models, support vector machines, neural networks, and ensemble methods, are implemented and compared to assess their effectiveness in predicting stock prices accurately. The findings of the study are presented and discussed in detail, highlighting the performance of different machine learning models in stock price prediction. The results demonstrate the potential of machine learning algorithms to outperform traditional forecasting methods in terms of accuracy and reliability. The implications of these findings for investors, financial analysts, and policymakers are discussed, emphasizing the importance of adopting innovative approaches in stock market prediction. In conclusion, the study summarizes the key findings, implications, and contributions to the field of stock price prediction using machine learning. The study highlights the significance of leveraging advanced computational techniques to enhance the accuracy and efficiency of stock market forecasts. Recommendations for future research and practical applications of machine learning in stock price prediction are provided to guide further exploration in this area. Ultimately, this research contributes to the growing body of knowledge on the application of machine learning in predicting stock prices, offering valuable insights and opportunities for future advancements in the field.

Thesis Overview

The project titled "Application of Machine Learning in Predicting Stock Prices" aims to explore the use of machine learning algorithms in predicting stock prices. Stock price prediction is a challenging task due to the complex and dynamic nature of financial markets. Traditional methods of stock price prediction often rely on technical analysis, fundamental analysis, and market trends, which may not always be accurate or reliable. Machine learning, a branch of artificial intelligence, offers a promising approach to predicting stock prices by analyzing historical data, identifying patterns, and making predictions based on those patterns. This research project will focus on applying various machine learning algorithms, such as regression models, decision trees, support vector machines, and neural networks, to predict stock prices. The project will involve collecting and preprocessing historical stock price data, selecting relevant features, and training the machine learning models using the data. The performance of the models will be evaluated using metrics such as mean squared error, accuracy, and precision-recall curves to assess their predictive capabilities. The significance of this project lies in its potential to provide investors, financial analysts, and traders with valuable insights into future stock price movements. By leveraging machine learning techniques, this project aims to develop more accurate and reliable stock price prediction models that can help stakeholders make informed investment decisions and mitigate risks in the financial markets. Overall, this research project seeks to contribute to the growing field of financial technology by demonstrating the effectiveness of machine learning in predicting stock prices. By combining advanced data analysis techniques with financial market data, the project aims to enhance the accuracy and efficiency of stock price prediction, ultimately benefiting investors and stakeholders in the financial industry.