Thesis Abstract

Abstract
The stock market is a complex and dynamic environment where investors seek to make informed decisions to maximize returns on their investments. Predicting stock prices accurately has been a challenging task due to the multitude of factors influencing market movements. In recent years, the application of machine learning techniques has gained significant attention as a promising approach to predict stock prices with improved accuracy. This thesis investigates the effectiveness of machine learning algorithms in predicting stock prices and aims to provide insights into their practical applications in the financial domain. The study begins with a comprehensive introduction discussing the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The literature review in Chapter Two critically evaluates existing research on machine learning applications in stock price prediction. It covers topics such as the types of machine learning algorithms commonly used, data preprocessing techniques, feature selection methods, and evaluation metrics employed in previous studies. The review highlights the strengths and limitations of different approaches and identifies gaps in the current literature that motivate the present study. Chapter Three outlines the research methodology employed in this study, including data collection, preprocessing, feature engineering, model selection, training, testing, and evaluation. The methodology section discusses the dataset used, the features selected for prediction, and the machine learning algorithms chosen for experimentation. It also details the evaluation metrics used to assess the performance of the models and the experimental setup implemented to ensure the validity and reliability of the results. Chapter Four presents a detailed discussion of the findings obtained from the empirical experiments conducted in this study. The results of applying various machine learning algorithms to predict stock prices are analyzed, compared, and interpreted to assess their performance and predictive accuracy. The discussion delves into the factors influencing the effectiveness of different algorithms and provides insights into the strengths and weaknesses of each approach. Finally, Chapter Five concludes the thesis by summarizing the key findings, implications, and contributions of the study. The conclusion highlights the significance of machine learning in predicting stock prices and its potential to enhance decision-making processes in the financial sector. The thesis concludes with recommendations for future research directions and practical applications of machine learning in stock price prediction. In conclusion, this thesis contributes to the growing body of research on the application of machine learning in predicting stock prices. By evaluating the performance of various machine learning algorithms and discussing their practical implications, this study provides valuable insights for investors, financial analysts, and researchers interested in leveraging advanced computational techniques for stock market forecasting.

Thesis Overview

The project titled "Application of Machine Learning in Predicting Stock Prices" aims to explore the use of machine learning techniques in predicting stock prices. Stock price prediction is a crucial area in finance and investment, as accurate forecasting can help investors make informed decisions and maximize their returns. Traditional methods of stock price prediction typically rely on fundamental analysis, technical analysis, and market sentiment analysis. However, these methods often have limitations in accurately predicting stock prices due to the complex and dynamic nature of financial markets. Machine learning, a subset of artificial intelligence, has gained significant attention in recent years for its ability to analyze large datasets and identify complex patterns. By leveraging machine learning algorithms, such as neural networks, support vector machines, and random forests, this project seeks to develop a more accurate and reliable model for predicting stock prices. These algorithms can analyze historical stock price data, market trends, and other relevant factors to identify patterns and make predictions about future stock prices. The research will begin with a comprehensive literature review to examine existing studies and methodologies related to stock price prediction and machine learning. This review will provide a theoretical foundation for the project and help identify gaps in current research that can be addressed. The project will then outline the research methodology, including data collection, preprocessing, feature selection, model training, and evaluation. Various machine learning algorithms will be implemented and compared to determine the most effective approach for predicting stock prices. The project will utilize historical stock price data from various financial markets to train and test the machine learning models. The performance of the models will be evaluated based on metrics such as accuracy, precision, recall, and F1 score. The results of the experiments will be analyzed to assess the effectiveness of machine learning in predicting stock prices and compare it to traditional prediction methods. The findings of this research are expected to contribute to the existing body of knowledge on stock price prediction and machine learning applications in finance. The project aims to provide insights into the potential benefits of using machine learning techniques for stock price forecasting and offer recommendations for future research and practical applications in the financial industry. Ultimately, the goal of this research is to develop a more robust and accurate model for predicting stock prices, enabling investors to make better-informed decisions and optimize their investment strategies.