Nonlinear Dynamic Analysis of the U.S. Defense Stock Markets under the Russia–Ukraine Conflict

Abstract

In this paper, we adopt multifractal detrended fluctuation analysis (MF-DFA) to explore relationships between the Russia–Ukraine conflict and defense stock markets. Specifically, we analyze the behaviors of 20 U.S. defense stock markets confronting with the Russia–Ukraine conflict. By using the stock price charts, combined with multifractal spectra and singularity exponents calculated by MF-DFA, we explore how the conflict affects the defense stock markets in perspectives of closing price, market efficiency and stability. In addition, the obtained results reveal high level of consistency while each type shows distinct features. According to singularity exponents, we observe that all 20 stocks can be divided into three types which we note as [Formula: see text] A, B and C. We infer from the singularity spectra that the [Formula: see text] A stock price will experience plummet after the conflict, instead, stock price of [Formula: see text] A increases based on stock price charts, while stock price of [Formula: see text] B and C rises as predicted. For [Formula: see text] A stocks, their market efficiency and stability show increments where we draw completely opposite conclusion for [Formula: see text] B stocks. Furthermore, we also note that [Formula: see text] C stocks include two defense stocks having a special phenomenon, and their multifractal spectra indicate the increase in stock price which behave like [Formula: see text] B stocks. However, their singularity exponents reduce during the conflict, meaning the slump in their market efficiency, which share the same characteristic as [Formula: see text] A stocks. Hence, we treat [Formula: see text] C stocks as a unique type. To mitigate the influence of stochastic elements in the experimental process, three comparative analyses are undertaken. We humbly believe that the induced implications are aroused by the Russia–Ukraine conflict.