Summary of Mana-net: Mitigating Aggregated Sentiment Homogenization with News Weighting For Enhanced Market Prediction, by Mengyu Wang et al.
MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction
by Mengyu Wang, Tiejun Ma
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computational Finance (q-fin.CP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel method for extracting market sentiments from news data to improve market predictions. The existing methods are simplistic and rely on equal-weighted and static aggregation of sentiments, which leads to “Aggregated Sentiment Homogenization” and loss of predictive value. To address this issue, the Market Attention-weighted News Aggregation Network (MANA-Net) is introduced, which uses 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. The paper evaluates MANA-Net using the S&P 500 and NASDAQ 100 indices, along with financial news spanning from 2003 to 2018, demonstrating that it outperforms recent market prediction methods, enhancing Profit & Loss by 1.1% and daily Sharpe ratio by 0.252. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a big problem in finance: using news to predict the stock market is hard because most methods are too simple. They take many news articles and just add them up to get an overall sentiment, but this makes all the important details get lost. The new method, MANA-Net, is better because it looks at each news article and says how much it matters for predicting the market. This helps the predictions be more accurate. |
Keywords
» Artificial intelligence » Attention
