Propose an explainable stock earning framework via news factor analyzing model. Formalizing news into semantic graph and learn embedding of it as a more expressible factor. Stock earning foresting and explainable earning module are built via aggregating and utilizing news factor and numeric stock factor, achieving SOTA performance on A-stock dataset. A detailed report will be generated with the power of LLM.
