Regression Analysis of Corporate Financial Data using LSTM Networks

Abstract

In the economic and financial fields, there is a growing interest in obtaining new knowledge from large quantities of data, such as corporate financial data and international exchange transactions. On the other hand, as a technical trend of recent data analysis, deep learning-based models have been successfully applied to various data, such as images, text, and audio. Especially, Recurrent Neural Network (RNN) and its extension of Long Short-Term Memory Network (LSTM) have been developed as deep learning for sequential data or time series. However, regardless of its importance, LSTM has not applied to corporate financial time series, such as in the Financial Statements Statistics of Corporations, to the best of my knowledge. In this research, considering the above-mentioned trends, we conduct regression analysis using LSTM for corporate financial time series. For experiments, we obtain the capital investment rate and other financial indicators, such as the cash flow ratio, for each target company from the Financial Statements Statistics of Corporations, and then use them as the objective and explanatory variables, respectively. By changing the number and types of explanatory variables used in the experiments, we evaluate the contribution of each explanatory variable to regression power to the objective variable at several time steps ahead. Furthermore, as baseline methods for the regression tasks, we evaluate the regression power of classical methods: Autoregressive Integrated Moving Averaging (ARIMA), and discuss the comparative evaluation with the LSTM approach.