论文标题
深度学习模型和新闻头条的表现是否优于外汇数据的常规预测技术？
Do Deep Learning Models and News Headlines Outperform Conventional Prediction Techniques on Forex Data?
论文作者
论文摘要
外汇（外汇）是交换货币的分散全球市场。外汇市场是巨大的，每天24小时运行。除特定于国家 /地区的因素外，外汇交易还受到越野联系和各种全球事件的影响。最近的大流行情景（例如Covid19和地方选举）也会对市场定价产生重大影响。我们测试并将各种预测与本工作中的新闻项目（例如新闻项目）进行了比较。此外，我们将经典的机器学习方法与深度学习算法进行了比较。我们还使用基于NLP的Word Embeddings添加了新闻头条中的情感功能，并比较了性能。我们的结果表明，诸如线性，SGD和Bagged的简单回归模型的表现要比深度学习模型（例如LSTM和RNN）更好，例如单步预测，例如接下来的两个小时，第二天和7天。令人惊讶的是，新闻文章未能改善指示基于域和相关信息的预测，只会增加价值。在文本矢量化技术中，word2vec和senterbert的表现更好。
Foreign Exchange (FOREX) is a decentralised global market for exchanging currencies. The Forex market is enormous, and it operates 24 hours a day. Along with country-specific factors, Forex trading is influenced by cross-country ties and a variety of global events. Recent pandemic scenarios such as COVID19 and local elections can also have a significant impact on market pricing. We tested and compared various predictions with external elements such as news items in this work. Additionally, we compared classical machine learning methods to deep learning algorithms. We also added sentiment features from news headlines using NLP-based word embeddings and compared the performance. Our results indicate that simple regression model like linear, SGD, and Bagged performed better than deep learning models such as LSTM and RNN for single-step forecasting like the next two hours, the next day, and seven days. Surprisingly, news articles failed to improve the predictions indicating domain-based and relevant information only adds value. Among the text vectorization techniques, Word2Vec and SentenceBERT perform better.
