Forecasting the effect of COVID-19 on the S&P500 Karina Arias-Calluari * and Fernando Alonso-Marroquin † School of Civil Engineering, The University of Sydney, Australia Morteza. N. Najafi Department of Physics, University of Mohaghegh Ardabili, Ardabil, Iran Michael Harr´e Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Australia The outbreak of the novel coronavirus (COVID-19) is considered an exogenous risk, which has caused unprecedented disruptions to financial and economic markets around the globe, leading to one of the fastest U.S. stock market declines in history. However, in the past we have seen the market recover and we can expect the market to recover again, and on this basis we assume the Standard and Poor’s 500 (S&P500) index will reach a minimum before rising again in the not-too- distant future. Here we present four forecast models of the S&P500 based on COVID-19 projections of deaths released on 02/04/2020 by the University of Washington and the 2-months consideration since the first confirmed case occured in USA. The decline and recovery in the index is estimated for the following three months. The forecast is a projection of a prediction with fluctuations described by q-gaussian distributions. Our forecast was made on the premise that: (a) The prediction is based on a deterministic trend that follows the data available since the initial outbreak of COVID-19, and (b) fluctuations derived from the S&P500 over the last 24 years. I. INTRODUCTION Immediately following the outbreak of COVID-19, epi- demiological researchers from around the world have pro- duced an extensive array of analyses in order to model the spread, growth, peak, and ultimately the decline of the disease [1–5]. For countries like China, Japan, Italy, and Iran, their epidemiological curve of COVID-19 pro- gression displays a peak before the second month since the first cases were detected [6–8]. In countries like the UK, Australia and Germany, the governments have taken mitigation measures to slow the impact [1, 2]. As a consequence, these countries display a flattening of the caseload curves with a peak generally estimated to be somewhere between the second to fourth month since the first diagnosed cases. The simultaneous reaction of governments, companies, consumers and media, have created a demand and sup- ply shock, making COVID-19 a qualitatively different economic crisis than previous crises [9]. This economic ‘wedging’ of falling supply and demand is caused by rapidly escalating unemployment decreasing consumer demand where, in the US for example, 17 million Ameri- cans have applied for unemployment insurance in the first three weeks of the crisis [10], and businesses are closing their doors reducing the supply of manufactured goods across the globe [11]. As a consequence financial markets around the world have fallen precipitously and market volatility is at near-all-time highs [12]. For example the S&P500 has registered the worst one-day fall in the last * karina.ariascalluari@uni.sydney.edu.au † fernando.alonso@sydney.edu.au 24 years and the third biggest percentage loss in its his- tory. Events such as COVID-19 pose systemic exogenous risks to markets, similar to the September 11 2001 attacks or the 1995 Kobe earthquake [13] in that the events cannot be endogenised into market prices by ‘rational’ agents ahead of time because they are not foreseeable. Alter- natively endogenous systemic risks are an inherent part of the nonlinear dynamics of a market and may have de- tectable precursor signals that act as warnings similar to those used in other nonlinear systems such as climate and ecology [14]. As an example the 1987 market crash is likely an endogenous event [13] and it has been shown that it had a measurably different effect on market dy- namics from that of the September 11 attacks [15]. Recent work has attempted to capture the exogenous and endogenous systemic risks in their models of market movements [16–18]. Endogenous risks can be modelled in a microeconomic fashion by considering the interactions between agents and how these change over time, causing non-linear disruptions even if system parameters change in a smooth fashion [19, 20]. New “descriptive models” present endogenous systemic risks as a dynamic factor af- fected by the flow of market orders and time scales [17], excessive profits and excessive losses [21] and large posi- tive or negative variation in stock markets index [22] due to market activity. The recent “structural models” which are based on physical model systems such as a combina- tion of oscillation within a basin of free energy [18], spin glasses [23], and kinetic Ising model to model stock mar- ket network [24] proposed a natural non-equilibrium sys- tem. In most of the cases they introduce the exogenous systemic risks as a global risk component which evolves all the time affecting prices of traded assets. The effects of these systemic risks are likely causes of abrupt changes arXiv:2005.03969v3 [q-fin.ST] 26 Jun 2020