Xinjie Shen
Papers
1
Total Citations
10
H-Index
1
About
Xinjie Shen is a rising researcher at the intersection of natural language processing and quantitative finance, with a focus on explainable AI for financial forecasting. Their most notable contribution is the development of **FinReport**, a novel news factor analyzing model for explainable stock earnings forecasting. This work, published in 2024 and already garnering 10 citations, addresses a critical gap: while financial institutions can leverage complex models, ordinary investors struggle to mine actionable factors from news. Shen’s approach integrates large language models to not only predict earnings but also provide transparent, human-readable explanations of the underlying news-driven factors. This bridges the gap between sophisticated AI and practical usability, democratizing access to financial insights. By focusing on explainability, Shen’s research enhances trust and interpretability in high-stakes financial decisions, offering a pathway for retail investors to make informed choices. With a growing citation impact and a clear focus on real-world applicability, Xinjie Shen is establishing themselves as a key voice in making AI-driven financial analysis both powerful and accessible.
Research Focus
Key Achievements
Top Papers
- 1