FinReport: Explainable Stock Earnings Forecasting via News Factor
  Analyzing Model
Xiangyu Li Xinjie Shen Yawen Zeng Xiaofen Xing Jin Xu
The task of stock earnings forecasting has received considerable attention
due to the demand investors in real-world scenarios. However, compared with
financial institutions, it is not easy for ordinary investors to mine factors
and analyze news. On the other hand, although large language models in the
financial field can serve users in the form of dialogue robots, it still
requires users to have financial knowledge to ask reasonable questions. To
serve the user experience, we aim to build an automatic system, FinReport, for
ordinary investors to collect information, analyze it, and generate reports
after summarizing.
  Specifically, our FinReport is based on financial news announcements and a
multi-factor model to ensure the professionalism of the report. The FinReport
consists of three modules: news factorization module, return forecasting
module, risk assessment module. The news factorization module involves
understanding news information and combining it with stock factors, the return
forecasting module aim to analysis the impact of news on market sentiment, and
the risk assessment module is adopted to control investment risk. Extensive
experiments on real-world datasets have well verified the effectiveness and
explainability of our proposed FinReport. Our codes and datasets are available
at https://github.com/frinkleko/FinReport.