Methods for forecasting the effect of exogenous risks on stock markets

Karina Arias-Calluari, Fernando Alonso-Marroquin, Morteza N. Najafi and Michael Harré

Physica A: Statistical Mechanics and its Applications, 2021, vol. 568, issue C

Abstract: Markets are subjected to both endogenous and exogenous risks that have caused disruptions to financial and economic markets around the globe, leading eventually to fast stock market declines. In the past, markets have recovered after any economic disruption. On this basis, we focus on the outbreak of COVID-19 as a case study of an exogenous risk and analyze its impact on the Standard and Poor’s 500 (S&P500) index. We assumed that the S&P500 index reaches a minimum before rising again in the not-too-distant future. Here we present a forecast model of the S&P500 index based on the breaking news and publicly available information. We assumed that the biggest fall of the S&P500 during the COVID-19 outbreak will occur when the largest daily number of deaths was confirmed. We inferred that the peak number of deaths occurs 2-months since the first confirmed case was reported in the USA based on previous COVID-19 situation reports from other countries. We also compare the S&P500 and the DAX market dynamics around the COVID-19 crisis as well as other previous crises, demonstrating that the impact of market news is highly consistent across these multiple market crises. The forecast is a projection of a prediction with stochastic fluctuations described by q-gaussian diffusion process with three spatio-temporal regimes. Our forecast was made on the premise that any market response can be decomposed into an overall deterministic trend and a stochastic term. The prediction was based on the deterministic part and for this case study is approximated by the extrapolation of the S&P500 data trend in the initial stages of the outbreak. The stochastic fluctuations have the same structure as the one derived from the past 24 years. A reasonable forecast was achieved with 85% of accuracy.

Keywords: Probability theory; Anomalous diffusion; Data analysis; Diffusion; Stochastic processes (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:568:y:2021:i:c:s0378437120308852

DOI: 10.1016/j.physa.2020.125587

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