Applying News and Media Sentiment Analysis for Generating Forex Trading Signals

Oluwafemi F Olaiyapo
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Abstract

The objective of this research is to examine how sentiment analysis can be employed to generate trading signals for the Foreign Exchange (Forex) market. The author assessed sentiment in social media posts and news articles pertaining to the United States Dollar (USD) using a combination of methods: lexicon-based analysis and the Naive Bayes machine learning algorithm. The findings indicate that sentiment analysis proves valuable in forecasting market movements and devising trading signals. Notably, its effectiveness is consistent across different market conditions. The author concludes that by analyzing sentiment expressed in news and social media, traders can glean insights into prevailing market sentiments towards the USD and other pertinent countries, thereby aiding trading decision-making. This study underscores the importance of weaving sentiment analysis into trading strategies as a pivotal tool for predicting market dynamics.
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应用新闻和媒体情绪分析生成外汇交易信号
本研究的目的是探讨如何利用情感分析来生成外汇市场的交易信号。作者采用基于词典的分析和奈维贝叶斯(Naive Bayes)机器学习算法相结合的方法,评估了与美元(USD)有关的社交媒体帖子和新闻文章中的情感。研究结果表明,情感分析在预测市场动向和设计交易信号方面很有价值。值得注意的是,其有效性在不同的市场条件下是一致的。作者总结道,通过分析新闻和社交媒体中表达的情绪,交易者可以洞察市场对美元和其他相关国家的普遍情绪,从而帮助做出交易决策。这项研究强调了将情绪分析作为预测市场动态的重要工具纳入交易策略的重要性。
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