Application of Machine Learning in Predicting Stock Prices

Thesis Abstract

Abstract
The rapid advancement of technology has revolutionized the financial industry, enabling the development of sophisticated tools and techniques for predicting stock prices. One such tool that has gained significant attention in recent years is machine learning. This thesis explores the application of machine learning algorithms in predicting stock prices, with a focus on enhancing prediction accuracy and reliability. The study begins with an introduction to the research topic, providing a background of the study and highlighting the significance of employing machine learning in stock price prediction. The problem statement delves into the challenges faced by traditional methods and sets the stage for the objectives of the study, which aim to improve prediction accuracy, reduce risk, and enhance decision-making in stock trading. The literature review in this thesis encompasses an in-depth analysis of existing studies and methodologies related to stock price prediction using machine learning techniques. It examines various algorithms such as linear regression, decision trees, random forests, and neural networks, highlighting their strengths and limitations in predicting stock prices accurately. Moreover, the review discusses the importance of feature selection, data preprocessing, and model evaluation in enhancing the performance of machine learning models. The research methodology section outlines the process of data collection, feature selection, model training, and evaluation. The study employs historical stock data from diverse sources, preprocesses the data to remove noise and outliers, and selects relevant features for model training. Various machine learning algorithms are implemented and optimized to predict stock prices accurately, with an emphasis on model selection and evaluation metrics. The discussion of findings chapter presents the results of the empirical analysis, comparing the performance of different machine learning algorithms in predicting stock prices. The chapter evaluates the accuracy, precision, recall, and F1-score of the models, highlighting the strengths and weaknesses of each algorithm in capturing stock market trends and patterns. Furthermore, the chapter discusses the impact of different features, hyperparameters, and training techniques on prediction performance. In conclusion, this thesis summarizes the key findings, implications, and contributions to the field of stock price prediction using machine learning. The study demonstrates the potential of machine learning algorithms in enhancing prediction accuracy and reliability, enabling investors and traders to make informed decisions in the stock market. The thesis also outlines future research directions, including the incorporation of alternative data sources, ensemble learning techniques, and deep learning models for more robust stock price prediction. Keywords Machine Learning, Stock Price Prediction, Financial Markets, Algorithm, Data Analysis, Prediction Accuracy, Decision-making.