  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.
