Improving stock price prediction using machine learning: a comparative study of LSTM, CNN and traditional methods.

Abstract

The stock market is known for its extreme complexity and volatility, and people are always looking for an accurate and effective way to guide stock trading. The accurate prediction of stock prices is of paramount importance in the financial market where returns and risks fluctuate wildly, and both financial institutions and regulatory authorities pay close attention to it. Stocks have always been favoured by investors as a method of asset allocation due to their higher returns. In recent years, researchers have been studying various methods to effectively predict stock market price and machine learning algorithms have emerged as one of the most promising techniques. This paper proposes different methods for predicting stock market prices using machine learning architectures, with a focus on identifying latent dynamics in the data. Traditional methods, such as artificial neural networks, are also explored. The objective of the project is to improve the quality of the output of stock market predictions by using stock value as a predictor. The paper presents a comparative study of machine learning architectures, including Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), and traditional methods like Arima, Random forest, logistic regression and K- nearest neighbors. The study analyzes historical stock data and compares the performance of each method based on various evaluation metrics. The results of the study demonstrate that LSTM and CNN outperform traditional methods in terms of accuracy, providing valuable insights for traders and investors. Overall, this paper contributes to the existing literature on stock price prediction by providing a comprehensive analysis of machine learning methods for accurate stock price prediction. By identifying the best-performing machine learning architecture, this study can help traders and investors make more informed decisions and minimize financial risks in the volatile stock market.