Portfolio decision analysis for pandemic sentiment assessment based on finance and web queries

Abstract

COVID-19 has spread worldwide, affecting people’s health and the socio-economic environment. Such a pandemic is responsible for people’s deteriorated mood, pessimism, and lack of trust in the future. This paper presents a portfolio decision analysis framework for policymakers aiming at recovering the population from psychological distress. Specifically, we explore the relative relevance of a country to the overall “mood of the world” in light of pursuing predefined targets through optimization criteria. Toward this aim, we design a statistical indicator for measuring the mood by considering the financial markets’ outcomes and the people’s online searches about COVID-19. Then, we adapt existing portfolio selection models to evaluate the role of an extensive collection of countries and stock markets based on different criteria. More precisely, such criteria are established assuming “rational” goals of a policymaker, namely to aspire to a general and stable optimism and avoid waves of opposite moods or excess pessimism. Empirical experiments validate the theoretical proposal. The employed dataset contains 39 countries selected on the basis of data reliability and relevance in the context of COVID-19. Data on daily Google Trends searches of the term “coronavirus” (and its translations) and closing prices of relevant domestic stock indexes are considered for 2020 to develop the statistical mood indicator. Results offer different insights based on the selected optimization criteria. The practical implications of the proposed models have been illustrated through arguments based on a National Recovery and Resilience Plan-type normative framework.