Stock price growth prediction model of transport enterprises in Vietnam

Abstract

This study aims to build a model to forecast stock price appreciation of transportation enterprises in Vietnam in the period of 2020-2021 using artificial intelligence-artificial neural network (ANN) method. The study uses a quarterly database collected from Vietstock of 93 transport enterprises. The results from ANN show that the important predictors of the stock price appreciation model include revenue (DT), previous stock price (PT-1), earnings per share, and earnings per share. share (EPS), inflation (INF), number of Covid-19 infections (CNM) and price-to-earnings (P/E) ratio. This study can also rank these predictors by impact level including revenue, previous stock price, EPS, INF, Covid-19 cases, and P/E. In addition, the study also compares the prediction ratio between the ANN model and the Logistics regression model. The results show that for the small input data set, the Logistics regression model has higher predictive accuracy than the ANN model.