Thesis Abstract

Abstract
Stock price movements are influenced by various factors, making them inherently difficult to predict accurately. This thesis explores the application of machine learning techniques to develop predictive models for stock price movements. The aim is to leverage historical stock data and relevant features to forecast future price trends with improved accuracy and reliability. The research begins with a comprehensive literature review to examine existing studies on stock price prediction, machine learning algorithms, and feature selection techniques. This review highlights the limitations of traditional statistical models and the potential benefits of machine learning in this domain. In the methodology section, the research design, data collection process, feature engineering methods, model selection criteria, and evaluation metrics are detailed. The study utilizes a diverse dataset comprising historical stock prices, volume, market indicators, and macroeconomic variables to train and test the predictive models. The findings from the study demonstrate the effectiveness of machine learning algorithms, such as Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks, in forecasting stock price movements. The models exhibit promising performance metrics, including accuracy, precision, recall, and F1-score, indicating their potential for practical applications in the financial markets. The discussion section analyzes the results in depth, discussing the strengths and weaknesses of each model, the importance of feature selection, and the implications for investment decision-making. The study also explores the interpretability of the models and the challenges associated with real-time implementation in dynamic market conditions. In conclusion, this research contributes to the growing body of literature on stock price prediction by demonstrating the efficacy of machine learning techniques in enhancing forecasting accuracy. The findings suggest that incorporating advanced algorithms and relevant features can lead to more robust and reliable predictions of stock price movements. This study has practical implications for investors, financial analysts, and market participants seeking to make informed decisions based on data-driven insights. Keywords Stock price prediction, Machine learning, Predictive modeling, Feature selection, Financial markets, Algorithm performance.

Thesis Overview