This paper constructed a robust methodology to investigate the impact of news regarding macroeconomic policies on exchange rate fluctuations, and to examined the applicability of qualitative information alongside historical data to predict exchange rates. To do so, hybrid machine learning algorithms comprised of natural language processing, fuzzy logic, and support vector regression have been constructed. This study emphasizes the significance of qualitative information on investors’ subjective consideration, the decision-making process, and causality on exchange rate volatility. To perceive the causality of expected and unexpected macroeconomic news on exchange rate fluctuations, news regarding the inflation rate, interest rate, unemployment rate, balance of trade, and credit ratings has been extracted from the web. Learning automata has been adopted to construct a unique lexicon for textual analysis. Subjective considerations of decision makers based on news have been evaluated by processing using the prospect theory and composing fuzzy antecedents for the fuzzy logic phase. The fuzzy logic method attained the correlation value between the macroeconomic news and the exchange rate. Finally, support vector regression predicted the exchange rate on a daily basis. The statistical test results indicated a strong correlation between recently published macroeconomic news on daily exchange rate fluctuations and their usability for predicting exchange rates in the short term, while emphasizing the significance of sustainable macroeconomic policies on exchange rate stability.