Forecasting corporate financial performance with deep learning and interpretable ALE method: Evidence from China

Forecasting and analyzing corporate financial performance are of significant value to investors, managers, and regulators. In this paper, we constructed the one-dimensional convolutional neural networks (1D-CNN) and long short-term memory (LSTM) deep learning models to investigate the feasibility of forecasting corporate financial performance with deep learning models, using the corporate financial features and environment, social and governance (ESG) rating index of Chinese A-share listed corporation data from 2015 to 2021. Five evaluation metrics were employed to measure models' forecasting effects, and four competing machine learning models were built to verify the improvement in forecasting accuracy brought by the deep learning models. Furthermore, we also introduced the Accumulated Local Effects method to explore the forecasting processes of the deep learning models. The empirical results show the following: (1) Deep learning models can effectively extract the time-series information in corporate data, thereby solving the task of predicting corporate financial performance with high accuracy. (2) The introduction of ESG information significantly contributes to the forecasting accuracy of corporate financial performance. For both 1D-CNN and LSTM models, the ESG rating index can provide additional useful information for forecasting. (3) The interpretable results reveal the preference and emphasis of the two deep learning models for the different features. This further proves the robustness and reliability of deep learning models in forecasting corporate financial performance.