Forecasting stock closing prices with an application to airline company data

Forecasting stock market movements is a challenging task from the practitioners' point of view. We explore how model selection via the least absolute shrinkage and selection operator (LASSO) approach can be better used to forecast stock closing prices using real-world datasets of daily stock closing prices of three major international airlines. Combining the LASSO method with multiple external data sources in our model leads to a robust and efficient method to predict stock behavior. We also compare our approach with ridge, tree, and support vector machine regressions, as well as neural network approaches to model the data. We include lags of each external variable and response variable in the model, resulting in a total of 870 predictor variables. The empirical results indicate that the LASSO-fitted model is the most effective when compared to other approaches we consider. The results show that the closing price of an airline stock is affected by its closing price for the previous days and those of other types of airlines and is significantly correlated with the Shanghai Composite Index for the previous day and 3 days prior. Other influencing factors include the positive impact of the Shanghai Composite Index daily share volume, the negative impact of loan interest rates, the amount of highway passenger and railway freight turnover, etc.