Thesis Abstract

**Abstract
** This thesis presents an in-depth exploration into the development of a machine learning algorithm for predicting stock prices. With the rapid advancement of technology and the increasing complexity of financial markets, the need for accurate and efficient stock price prediction tools has become more pronounced. Machine learning algorithms have shown great promise in this domain due to their ability to analyze large volumes of data and identify patterns that traditional methods may overlook. The research begins with a comprehensive review of existing literature on stock price prediction methods, highlighting the strengths and limitations of various approaches. This review sets the foundation for the development of a novel machine learning algorithm tailored specifically for predicting stock prices. The methodology section outlines the data collection process, feature selection techniques, model training, and evaluation methods employed in the algorithm development. Various machine learning models such as support vector machines, random forests, and recurrent neural networks are explored and compared to identify the most effective approach for stock price prediction. The findings of the study are discussed in detail, including the performance metrics of the developed algorithm compared to traditional forecasting methods. The results demonstrate the effectiveness of the machine learning algorithm in accurately predicting stock prices, thereby providing valuable insights for investors and financial analysts. In conclusion, this thesis contributes to the field of stock price prediction by introducing a novel machine learning algorithm that offers enhanced accuracy and efficiency. The significance of this research lies in its potential to assist investors in making informed decisions and optimizing their investment strategies. Future research directions include further refinement of the algorithm and its application to other financial forecasting tasks.

Thesis Overview