Field-aware attentive neural factorization with fuzzy mutual information for company investment valuation

The proliferation of a digital transformation area is inspiring researchers and practitioners in finance to embrace emerging innovative fintech development (i.e., finance + technology). In this study, we propose a field-aware attentive neural factorization machine (FAFM) model for large-scale data-driven company investment valuation. The proposed FAFM model utilizes the advantage of factorization machine (FM) to efficiently capture nonlinear feature interactions in a sparse dataset. We additionally consider field heterogeneity among features with fuzzy mutual information and develop an attention neural network to learn predictive strengths of pair-wise feature interactions. FAFM contributes to the literature by overcoming the limitation of FM that ignores field heterogeneity by factorizing pair-wise feature interactions with same weight. Further more, FAFM learns the prediction strengths in a stratified manner by using the attention deep learning mechanism, which demonstrates more structured control ability and allows for more leverage in tweaking the interactions in the feature-wise level. Experiments are conducted on a unique real dataset set consisting of 3,500 listed companies in the Chinese market with features from eight fields: demographics, annual reports, stock financial disclosure, land use, intellectual property, tax, bond financing, and certification. Results showed the superiority of FAFM on prediction accuracy and model interpretability over existing baselines. Our study provides a useful tool for company investment valuation that can not only generate accurate investment valuations but also provide interpretations of both individual features and their pair-wise interactions effects, thereby allowing investors better investment decisions.