论文标题
从股票预测到财务相关性：重新利用注意力权重以评估新闻相关性而无需手动注释
From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations
论文作者
论文摘要
我们提出了一种使用股票价格变动和新闻头条作为投入的方法，可以自动确定与财务相关的新闻。该方法重新利用了最初训练的神经网络的注意力权重，以预测股票价格，以将相关得分分配给每个标题，从而消除了对手动标记的培训数据的需求。我们对四个最相关的美国股票指数和150万新闻头条的实验表明，该方法对相关新闻的排名很高，与初始股票价格预测任务的准确性正相关。
We present a method to automatically identify financially relevant news using stock price movements and news headlines as input. The method repurposes the attention weights of a neural network initially trained to predict stock prices to assign a relevance score to each headline, eliminating the need for manually labeled training data. Our experiments on the four most relevant US stock indices and 1.5M news headlines show that the method ranks relevant news highly, positively correlated with the accuracy of the initial stock price prediction task.
