Macroeconomic indicators alone can predict the monthly closing price of major U.S. indices: Insights from artificial intelligence, time-series analysis and hybrid models

• Published in: Applied soft computing 2018-10, Vol.71, p.685-697
• Main Authors: Weng, Bin, Martinez, Waldyn, Tsai, Yao-Te, Li, Chen, Lu, Lin, Barth, James R., Megahed, Fadel M.
• Format: Article
• Language: English
• Subjects: ARIMA; Deep learning; Ensembles; GARCH; Long short-term memory (LSTM) networks
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2018.07.024

[Display omitted] •A 2-stage method is proposed to predict the 1-month ahead price for 13 U.S. indices.•Ensembles of macroeconomic factors alone are more predictive than time-series models.•Errors in time-series models are explained by the ensembles of macroeconomic factors.•A decision support system for predicting the monthly stock price is presented.•The code is freely available for investors and researchers. This paper proposes a two-stage approach that can be used to investigate whether the information hidden in macroeconomic variables (alone) can be used to accurately predict the one-month ahead price for major U.S stock and sector indices. Stage 1 is constructed to evaluate the hypothesis that the price for different indices is driven by different economic indicators. It consists of three phases. In phase I, the data is automatically acquired using freely available APIs (application programming interfaces) and prepared for analysis. Phase II reduces the set of potential predictors without the loss of information through several variable selection methods. The third phase employs four ensemble models and three time-series models for prediction. The prediction performance of the seven models are compared using the Mean Absolute Percent Error (and two additional metrics). If the hypothesis were to be true, one expects that the performance of the ensemble models to outperform the time-series models since the information in the economy is more important than the information in previous prices. In Stage 2, a hybrid approach of the recurring neural network used for time-series prediction (i.e., the LSTM) and the ensemble models is constructed to examine the secondary hypothesis that the residuals from the time-series models are not random and can be explained by the macroeconomic indicators. To test the two hypotheses, the monthly closing prices for 13 U.S. stock and sector indices and the corresponding values for 23 macroeconomic indicators were collected from 01/1992–10/2016. Based on the case study, the four ensembles prediction performance were superior to that of the three time-series models. The MAPE of the best model for a given index was < 1.87%. The Stage 2 results also show that the three evaluation metrics (RMSE, MAPE and MAE) can be typically improved by 25–50% by incorporating the information hidden in the macroeconomic indicators (through the ensemble approach). Thus, this paper shows that, for the analysis period and the indices studied, the