Applying News and Media Sentiment Analysis for Generating Forex Trading Signals
This study addresses the need for improved trading decision-making tools in the Forex market, but it is incremental as it applies existing sentiment analysis methods without major innovations.
This research tackled the problem of forecasting Forex market movements by applying sentiment analysis to news and social media, finding that it is valuable for generating trading signals with consistent effectiveness across market conditions.
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.