Forecasting Corporate Financial Time Series using Multi-phase Attention Recurrent Neural Networks

Abstract

Recently, attention-based RNNs have been studied to represent multivariate temporal or spatio-temporal structure underlying multivariate time series. One recent study has achieved improved performance by employing attention structure that simultaneously capture the spatial relationships among multivariate time series and the temporal structure of those time series. That method assumes a single time-series sample of multivariate explanatory variables, and thus, no prediction method was designed for multiple time-series samples of multivariate explanatory variables. Moreover, such previous studies have not explored on financial time series incorporating macroeconomic time series, such as Gross Domestic Product (GDP) and stock market indexes, to our knowledge. Also, no neural network structure has been designed for focusing a specific industry. We aim in this paper to achieve effective forecasting of corporate financial time series from multiple time-series samples of multivariate explanatory variables. We propose a new industry specific model that appropriately captures corporate financial time series, incorporating the industry trends and macroeconomic time series as side information. We demonstrate the performance of our model through experiments with Japanese corporate financial time series in the task of predicting the return on assets (ROA) for each company.