Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques in predicting stock prices. The study aims to investigate the effectiveness and accuracy of using machine learning algorithms to forecast stock prices, with a focus on enhancing investment decision-making processes. The research involves the development and implementation of various machine learning models, utilizing historical stock data and relevant features to predict future stock prices. The study also evaluates the impact of different factors on stock price prediction accuracy, such as data preprocessing techniques, feature selection methods, and model evaluation metrics. The introductory chapter provides an overview of the research topic, highlighting the significance of utilizing machine learning in predicting stock prices. The background of the study delves into the existing literature on stock price prediction and the role of machine learning in financial forecasting. The problem statement identifies the challenges faced by traditional stock price prediction methods and the potential benefits of incorporating machine learning techniques. The objectives of the study outline the specific goals and research questions that guide the investigation. The methodology chapter details the research design, data collection process, and the implementation of machine learning models. Various machine learning algorithms, such as linear regression, decision trees, support vector machines, and neural networks, are applied to predict stock prices based on historical data. Feature engineering techniques, model training, and evaluation methods are also discussed to ensure the accuracy and reliability of the predictions. The findings chapter presents the results of the experiments conducted using machine learning models to predict stock prices. The analysis includes the evaluation of prediction accuracy, model performance comparison, and the impact of different features on the forecasting results. The discussion highlights the strengths and limitations of the machine learning models in predicting stock prices and provides insights into potential areas for future research and improvement. In conclusion, the study summarizes the key findings and contributions of using machine learning in predicting stock prices. The research demonstrates the potential of machine learning algorithms to enhance stock price forecasting accuracy and assist investors in making informed decisions. The implications of the study extend to the financial industry, offering new perspectives on leveraging technology for improved investment strategies. Overall, the thesis contributes to the growing body of knowledge on the application of machine learning in financial forecasting and provides valuable insights for future research and practical applications in predicting stock prices.

Thesis Overview

The project titled "Application of Machine Learning in Predicting Stock Prices" aims to explore and demonstrate the effectiveness of machine learning algorithms in predicting stock prices. This research focuses on utilizing machine learning techniques to analyze historical stock price data and develop predictive models that can assist investors in making informed decisions in the stock market. The stock market is known for its complexity and volatility, making it challenging for investors to accurately predict stock prices. Traditional methods of stock price prediction often rely on fundamental analysis, technical analysis, and market sentiment. However, these methods may not always provide accurate and timely predictions due to the dynamic nature of the market. Machine learning, a branch of artificial intelligence, offers a promising approach to stock price prediction by leveraging algorithms that can analyze large datasets, identify patterns, and make predictions based on historical data. By training machine learning models on historical stock price data, this research aims to develop predictive models that can forecast future stock prices with improved accuracy and reliability. The research will involve collecting historical stock price data from various sources, preprocessing the data to ensure its quality and consistency, and selecting appropriate machine learning algorithms for model development. Different machine learning techniques such as linear regression, decision trees, random forests, and neural networks will be explored and compared to identify the most suitable approach for predicting stock prices. Furthermore, the research will evaluate the performance of the developed models using metrics such as mean squared error, root mean squared error, and accuracy to assess their predictive capabilities. The findings of this research will provide valuable insights into the application of machine learning in stock price prediction and its potential benefits in enhancing investment decisions in the stock market. Overall, the project "Application of Machine Learning in Predicting Stock Prices" aims to contribute to the existing body of knowledge on stock price prediction by demonstrating the efficacy of machine learning techniques in improving the accuracy and reliability of stock price forecasts. It is anticipated that the results of this research will be beneficial for investors, financial analysts, and researchers interested in leveraging machine learning for stock market analysis and prediction.