# Modeling News Interactions and Influence for Financial Market Prediction

arXiv: https://arxiv.org/abs/2410.10614 · HTML: https://mlsanity.com/paper/2410.10614/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 53 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States](https://mlsanity.com/paper/2002.05780/) (2020)
- [Applications of deep learning in stock market prediction: recent progress](https://mlsanity.com/paper/2003.01859/) (2020)
- [REST: Relational Event-driven Stock Trend Forecasting](https://mlsanity.com/paper/2102.07372/) (2021)
- [A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News](https://mlsanity.com/paper/2007.12620/) (2020)
- [MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction](https://mlsanity.com/paper/2409.05698/) (2024)
- [Transformation of Dense and Sparse Text Representations](https://mlsanity.com/paper/1911.02914/) (2019)

## Built on by (most cited first, 6 of 6)

- [From Deep Learning to LLMs: A survey of AI in Quantitative Investment](https://mlsanity.com/paper/2503.21422/) (2025)
- [FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval](https://mlsanity.com/paper/2509.12042/) (2025)
- [FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios](https://mlsanity.com/paper/2605.00706/) (2026)
- [Exploring the Synergy of Quantitative Factors and Newsflow Representations from Large Language Models for Stock Return Prediction](https://mlsanity.com/paper/2510.15691/) (2025)
- [Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models](https://mlsanity.com/paper/2605.24564/) (2026)
- [Self Knowledge Re-expression: A Fully Local Method for Adapting LLMs to Tasks Using Intrinsic Knowledge](https://mlsanity.com/paper/2604.22939/) (2026)

## Abstract (arXiv)

The diffusion of financial news into market prices is a complex process, making it challenging to evaluate the connections between news events and market movements. This paper introduces FININ (Financial Interconnected News Influence Network), a novel market prediction model that captures not only the links between news and prices but also the interactions among news items themselves. FININ effectively integrates multi-modal information from both market data and news articles. We conduct extensive experiments on two datasets, encompassing the S&P 500 and NASDAQ 100 indices over a 15-year period and over 2.7 million news articles. The results demonstrate FININ's effectiveness, outperforming advanced market prediction models with an improvement of 0.429 and 0.341 in the daily Sharpe ratio for the two markets respectively. Moreover, our results reveal insights into the financial news, including the delayed market pricing of news, the long memory effect of news, and the limitations of financial sentiment analysis in fully extracting predictive power from news data.