  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.
