The Investigation of Stock Price Prediction Based on Machine Learning

Abstract

As the global economy experiences unprecedented growth, the stock market is undergoing a period of expansion and prosperity. Driven by the allure of high risks and high returns, a significant number of individuals have committed themselves to stock trading. In the age of the Internet of Everything, the trajectory of the stock market is intertwined not only with the fortunes of individuals and businesses but also deeply influences the fate of nations and societies. However, stock market forecasting is quite a hard task owing to its volatile nature, where even seemingly unrelated factors can lead to dramatic changes. Therefore, the importance of constructing reliable stock prediction methods is self-evident. In recent years, scholars have mainly focused on machine learning and deep learning methods to complete this task. After literature review, this article summarizes and discussed several commonly used and effective methods, such as Linear Regression, Random Forest, Long Short-Term Memory (LSTM), etc. The advantages, disadvantages and applications of different methods are discussed, like the shortcoming of Linear Regression to handle non-linear models well and the relatively excellent ability of LSTM to predict long sequences. This article would be helpful for emerging scholars to have a grasp of the characteristics, advantages and scope of application of these methods, and facilitate the development of future research.