用动态加权避免新闻情感趋同，提升股市预测精度。
MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction
- 根据新闻与价格变化的相关性动态分配权重，避免情感平均化
- 在标普500和纳指100上使收益提升1.1%，夏普比率提高0.252
- 适合金融量化研究者，尤其关注新闻情感建模的场景
从新闻数据中提取市场情绪有助于股市预测，但现有方法多采用等权静态聚合，导致‘情感趋同’问题：大量新闻情感聚合后趋向均值，丢失关键信息。我们分析了2003–2018年行业级金融新闻数据，发现此现象削弱了新闻的预测价值。为此提出市场注意力加权新闻聚合网络（MANA-Net），通过动态市场-新闻注意力机制，学习每条新闻对价格变动的相关性并自适应赋权。将新闻聚合嵌入预测网络，实现可训练的情感表示，直接优化预测目标。在标普500和纳斯达克100指数上验证，显著优于多种近期方法，利润提升1.1%，日度夏普比率提高0.252。
原文摘要 · Abstract (English)
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.
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