Latent Dirichlet Allocation with Residual Convolutional Neural Network Applied in Evaluating Credibility of Chinese Listed Companies
This provides a more efficient and accurate method for banks and investors to assess risk by reducing human bias in evaluating corporate credibility, though it is incremental as it applies existing NLP techniques to a specific domain.
The authors tackled the problem of evaluating corporate credibility for Chinese listed companies by combining Latent Dirichlet Allocation and a residual convolutional neural network to analyze news reports, resulting in a ranking of 3065 companies based on transparency.
This project demonstrated a methodology to estimating cooperate credibility with a Natural Language Processing approach. As cooperate transparency impacts both the credibility and possible future earnings of the firm, it is an important factor to be considered by banks and investors on risk assessments of listed firms. This approach of estimating cooperate credibility can bypass human bias and inconsistency in the risk assessment, the use of large quantitative data and neural network models provides more accurate estimation in a more efficient manner compare to manual assessment. At the beginning, the model will employs Latent Dirichlet Allocation and THU Open Chinese Lexicon from Tsinghua University to classify topics in articles which are potentially related to corporate credibility. Then with the keywords related to each topics, we trained a residual convolutional neural network with data labeled according to surveys of fund manager and accountant's opinion on corporate credibility. After the training, we run the model with preprocessed news reports regarding to all of the 3065 listed companies, the model is supposed to give back companies ranking based on the level of their transparency.