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This study examines the relationship between Environmental, Social and Governance (ESG) practices and financial distress in times of uncertainty.

Thomson Reuters ESG database, Compustat and Center for Research in Security Prices (CRSP) were used to derive a final sample size of 1,572 firms and 11,618 firm-year observations from 2003 to 2022. Fixed-effects regression was used to analyze the data.

It was found that increasing ESG involvement leads to an increase in Z score (i.e. lower financial distress), and this impact was more profound during the COVID-19 period and also when firms' innovativeness increased. However, during the COVID-19 period, increases in capital expenditures weaken the positive effect of ESG on financial distress.

This study contributes to the growing body of literature on the impact of ESG performance on financial distress and the nature of this relationship during times of uncertainty such as COVID-19.

This study offers insights to managers and practitioners when developing their corporate financial strategies, particularly financial distress management, showing the potential benefits of innovativeness and capital intensity during turbulent times similar to COVID-19.

Little knowledge exists on how ESG engagement helps weather financial distress during periods of uncertainty due to external shocks (e.g. COVID-19). This paper looks at the effect of ESG engagement on financial distress and how capital intensity and innovativeness could influence this relationship while giving fresh insights into the impact of COVID-19.

This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.

The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.

This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.

This study produced a reliable, accurate forecasting model considering risk and competitor behavior.

This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.

This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.