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We find striking correlations between the presidential election outcome probability and major financial indicators, including USD currency pairs, bond prices, stock index futures, and a market volatility measure. The correlations are consistent with 'risk-on' behavior in markets, a term which describes investors moving toward riskier asset classes, as the election results became clearer. Further, we decompose the market reaction into a 'reduction in uncertainty' component and a 'probability of a Democratic party presidency' component. This decomposition reveals how markets reacted to the increasing certainty of the outcome as election results came in. Finally, we analyze the differing market reactions to the presidential election and the Senate election, including data from the unique Georgia runoffs, and demonstrate that bond prices were particularly sensitive to the probability of a combined Democratic Senate and Presidency.

We use machine learning techniques on textual data to identify financial crises. The onset of a crisis and its duration have implications for real economic activity, and as such can be valuable inputs into macroprudential, monetary, and fiscal policy. The academic literature and the policy realm rely mostly on expert judgment to determine crises, often with a lag. Consequently, crisis durations and the buildup phases of vulnerabilities are usually determined only with the benefit of hindsight. Although we can identify and forecast a portion of crises worldwide to various degrees with traditional econometric techniques and using readily available market data, we find that textual data helps in reducing false positives and false negatives in out-of-sample testing of such models, especially when the crises are considered more severe. Building a framework that is consistent across countries and in real time can benefit policymakers around the world, especially when international coordination is required across different government policies. [ABSTRACT FROM AUTHOR]