Minute-by-Minute: Financial Markets' Reaction to the 2020 U.S. Election
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Trends and Reversion in Financial Markets on Time Scales from Minutes to Decades
We empirically analyze the reversion of financial market trends with time horizons ranging from minutes to decades. The analysis covers equities, interest rates, currencies and commodities and combines 14 years of future…
Visibility graph analysis of crude oil futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict
Drawing inspiration from the significant impact of the ongoing Russia-Ukraine conflict and the recent COVID-19 pandemic on global financial markets, this study conducts a thorough analysis of three key crude oil futures …
ClusteringTrade in Minutes! Rationality-Driven Agentic System for Quantitative Financial Trading
Recent advancements in large language models (LLMs) and agentic systems have shown exceptional decision-making capabilities, revealing significant potential for autonomic finance. Current financial trading agents predomi…
Mathematical ReasoningCryptocurrency Portfolio Management with Deep Reinforcement Learning
Portfolio management is the decision-making process of allocating an amount of fund into different financial investment products. Cryptocurrencies are electronic and decentralized alternatives to government-issued money,…
Decision MakingDeep Reinforcement LearningManagementreinforcement-learning+2Combining supervised and unsupervised learning methods to predict financial market movements
The decisions traders make to buy or sell an asset depend on various analyses, with expertise required to identify patterns that can be exploited for profit. In this paper we identify novel features extracted from emerge…