使用深度学习方法在金融论坛中寻找专家作者

Sahar Sohangir, Dingding Wang
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引用次数: 13

摘要

现代股票市场是增加财富和创造收入的热门场所,但何时买卖股票或购买哪些股票的根本问题尚未解决。在投资者中,拥有专业的财务顾问是很常见的,但支持这些人做出决策的最佳资源是什么?高盛(Goldman Sachs)、雷曼兄弟(Lehman Brothers)和所罗门兄弟(Salomon Brothers)等投资银行几十年来一直主导着金融咨询领域。然而,由于互联网和金融社交网络的普及,如StockTwits和Seeking Alpha,世界各地的投资者有了一个新的机会来聚集和分享他们的经验。这就提出了新的问题:这些用户提供的信息可信吗?我们怎样才能找到专家呢?在本文中,我们试图确定神经网络模型是否可以帮助我们在一组StockTwits推文中找到专家。我们应用了两种神经网络模型——doc2vec和卷积神经网络——根据他们的消息在StockTwits中找到顶级作者。我们的研究结果表明,卷积神经网络是该数据集中预测顶级作者的最佳模型。
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Finding Expert Authors in Financial Forum Using Deep Learning Methods
The modern stock market is a popular place to increase wealth and generate income, but the fundamental problem of when to buy or sell shares, or which stocks to buy has not been solved. It is very common among investors to have professional financial advisers, but what is the best resource to support the decisions these people make? Investment banks, such as Goldman Sachs, Lehman Brothers, and Salomon Brothers have dominated the world of financial advice for decades. However, due to the popularity of the Internet and financial social networks, such as StockTwits and Seeking Alpha, investors around the world have a new opportunity to gather and share their experiences. This raises new questions: is the information these users provide trustworthy? How can we find the experts? In this paper, we seek to determine if neural network models can help us find the experts in a set of StockTwits tweets. We applied two neural network models - doc2vec and convolutional neural networks - to find top authors in StockTwits based on their messages. Our results showed that a convolutional neural network is the best model to predict such top authors in this data set.
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