# MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction

arXiv: https://arxiv.org/abs/2409.05698 · HTML: https://mlsanity.com/paper/2409.05698/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 68 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [One Fits All:Power General Time Series Analysis by Pretrained LM](https://mlsanity.com/paper/2302.11939/) (2023)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Online Knowledge Distillation with Diverse Peers](https://mlsanity.com/paper/1912.00350/) (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)
- [Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction](https://mlsanity.com/paper/2305.08740/) (2023)
- [Decision support from financial disclosures with deep neural networks and transfer learning](https://mlsanity.com/paper/1710.03954/) (2017)
- [A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News](https://mlsanity.com/paper/2007.12620/) (2020)
- [Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting](https://mlsanity.com/paper/2009.05135/) (2020)
- [Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction](https://mlsanity.com/paper/2309.00073/) (2023)
- [Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing](https://mlsanity.com/paper/1909.04497/) (2019)
- [Transformation of Dense and Sparse Text Representations](https://mlsanity.com/paper/1911.02914/) (2019)
- [A Sentiment Analysis Approach to the Prediction of Market Volatility](https://mlsanity.com/paper/2012.05906/) (2020)
- [Crowds on Wall Street: Extracting Value from Social Investing Platforms](https://mlsanity.com/paper/1406.1137/) (2014)

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

- [Modeling News Interactions and Influence for Financial Market Prediction](https://mlsanity.com/paper/2410.10614/) (2024)
- [A Survey of Financial AI: Architectures, Advances and Open Challenges](https://mlsanity.com/paper/2411.12747/) (2024)
- [FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval](https://mlsanity.com/paper/2509.12042/) (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)
- [Tree-of-Experience: Hierarchical Experience Management for Self-Evolving Agents](https://mlsanity.com/paper/2608.09044/) (2026)

## Abstract (arXiv)

It is widely acknowledged that extracting market sentiments from news data benefits market predictions. However, existing methods of using financial sentiments remain simplistic, relying on equal-weight and static aggregation to manage sentiments from multiple news items. This leads to a critical issue termed ``Aggregated Sentiment Homogenization'', which has been explored through our analysis of a large financial news dataset from industry practice. This phenomenon occurs when aggregating numerous sentiments, causing representations to converge towards the mean values of sentiment distributions and thereby smoothing out unique and important information. Consequently, the aggregated sentiment representations lose much predictive value of news data. To address this problem, we introduce the Market Attention-weighted News Aggregation Network (MANA-Net), a novel method that leverages a dynamic market-news attention mechanism to aggregate news sentiments for market prediction. MANA-Net learns the relevance of news sentiments to price changes and assigns varying weights to individual news items. By integrating the news aggregation step into the networks for market prediction, MANA-Net allows for trainable sentiment representations that are optimized directly for prediction. We evaluate MANA-Net using the S&P 500 and NASDAQ 100 indices, along with financial news spanning from 2003 to 2018. Experimental results demonstrate that MANA-Net outperforms various recent market prediction methods, enhancing Profit & Loss by 1.1% and the daily Sharpe ratio by 0.252.