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首页> 外文期刊>The North American journal of economics and finance >News will tell: Forecasting foreign exchange rates based on news story events in the economy calendar
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News will tell: Forecasting foreign exchange rates based on news story events in the economy calendar

机译：新闻将告诉：根据经济日历的新闻故事事件预测外汇汇率

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摘要

The paper proposes a novel approach to predict intraday directional-movements of currency-pairs in the foreign exchange market based on news story events in the economy calendar. Prior work on using textual data for forecasting foreign exchange market developments does not consider economy calendar events. We consider a rich set of text analytics methods to extract information from news story events and propose a novel sentiment dictionary for the foreign exchange market. The paper shows how news events and corresponding news stories provide valuable information to increase forecast accuracy and inform trading decisions. More specifically, using textual data together with technical indicators as inputs to different machine learning models reveals that the accuracy of market predictions shortly after the release of news is substantially higher than in other periods, which suggests the feasibility of news-based trading. Furthermore, empirical results identify a combination of a gradient boosting algorithm, our new sentiment dictionary, and text-features based-on term frequency weighting to offer the most accurate forecasts. These findings are valuable for traders, risk managers and other consumers of foreign exchange market forecasts and offer guidance how to design accurate prediction systems.
机译：本文提出了一种基于经济日历的新闻故事事件预测外汇市场货币对的盘中定向运动的新方法。在使用文本数据预测外汇市场发展的情况下，不考虑经济日历活动。我们考虑丰富的文本分析方法，以从新闻故事事件中提取信息，并提出外汇市场的新颖情报局。本文显示了新闻事件和相应的新闻故事如何提供有价值的信息，以提高预测准确性并告知交易决策。更具体地说，使用文本数据与技术指标一起作为不同机器学习模型的输入，表明，在释放新闻后不久的市场预测的准确性显着高于其他时期，这表明新闻交易的可行性。此外，经验结果识别基于术语频率权重的梯度升压算法，我们的新情绪字典和文本特征的组合，以提供最准确的预测。这些调查结果对交易商，风险管理人员和外汇市场的其他消费者有价值，并提供如何设计准确的预测系统的指导。

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