Perceived climate risk and stock prices: An empirical analysis of pricing effects.

Increasing awareness of climate change and its potential consequences on financial markets has led to interest in the impact of climate risk on stock returns and portfolio composition, but few studies have focused on perceived climate risk pricing. This study is the first to introduce perceived climate risk as an additional factor in asset pricing models. The perceived climate risk is measured based on the climate change sentiment of the Twitter dataset with 16 million unique tweets in the years 2010-2019.

The role of Contact-Tracing Mobile Apps in pandemic prevention: A multidisciplinary perspective on health beliefs, social, and technological factors.

During the recent COVID-19 pandemic, governments implemented mobile applications for contact tracing as a rapid and effective solution to mitigate the spread of the virus. However, these seemingly straightforward solutions did not achieve their intended objectives. In line with previous research, this paper aims to investigate the factors that influence the acceptance and usage of contact-tracing mobile apps (CTMAs) in the context of disease control.

The walking dead: Are Zombie firms environmentally and socially responsible? A global perspective.

This study examines the association between zombie firms and their environmental and social performance. Using a global dataset of listed firms from 49 countries between 2002 and 2019, we find that zombie firms perform poorly on environmental and social responsibility fronts. This finding supports the argument that zombie firms are characterized by consistent losses and that their existence is risky without external support.

Financing sources, green investment, and environmental performance: Cross-country evidence.

This article investigates the influence of financing sources and financial constraints on green investment, based on a study conducted with a sample of Eastern European SMEs from 2018 to 2020. We constructed a green investment proxy using principal component analysis, revealing two principal pillars: pure green investment and mixed green investment. Employing two-stage least squares regression analysis (2SLS) and instrumental probit (IV Probit), our results demonstrate that internal finance positively impacts green investment.

Renewable energy deployment and geopolitical conflicts.

This paper investigates whether geopolitical conflicts play a critical role in stimulating countries to shift toward clean energy solutions. We use the panel regime-switching models, which allow us to capture the nonlinear dynamics of the energy transition. Our results for a panel of developed and emerging countries reveal that the geopolitical context does not impact the renewable-income nexus; however, we find that adverse geopolitical events would impact the diffusion of alternative energy sources depending on the level of economic development.

Climate change and government policy: Fresh insights from complexity theory.

Governments worldwide are increasingly concerned about ensuring a balance between economic and environmental well being. Global economies, particularly developing ones, emphasize the importance of achieving escofriendly growth to maintain the levels of the ecological footprint while achieving higher economic growth. The ecological footprint is a comprehensive indicator of environmental degradation.

Time-varying effects of fuel prices on stock market returns during COVID-19 outbreak.

This article explores the impact of fuel price movements on the stock market return of 2020 during the COVID-19 disruptions. In doing so, a monthly data of seven selected stock market indices representing developed and emerging economies globally was used for analysis. The study used a time-varying parameter VAR model to examine a time-varying causal association between oil prices and stock market returns and a novel quantile-causality approach to capture the fluctuations of these markets under COVID-19's varying market conditions.

Forecasting commodity prices: empirical evidence using deep learning tools.

Since the last two decades, financial markets have exhibited several transformations owing to recurring crises episodes that has led to the development of alternative assets. Particularly, the commodity market has attracted attention from investors and hedgers. However, the operational research stream has also developed substantially based on the growth of the artificial intelligence field, which includes machine learning and deep learning.

What can we learn about the market reaction to macroeconomic surprise? Evidence from the COVID-19 crisis.

We investigate the impact of macroeconomic surprise and uncertainty on G7 financial markets around COVID-19 pandemic using two real-time, real-activity indexes recently constructed by Scotti (2016). We applies the wavelet analysis to detect the response of the stock markets to the macroeconomic surprise and an uncertainty indexes and then we use NARDL model to examine the asymmetric effect of the news surprise and uncertainty on the equity markets. We conduct our empirical analysis with the daily data from January, 2014 to September, 2020.

Systematic and Unsystematic Determinants of Sectoral Risk Default Interconnectedness.

Assessing the financial stability of the banking industry, particularly in credit risk management, has become extremely crucial in times of uncertainty. Given that, this paper aims to investigate the determinants of the interconnectedness of sectoral credit risk default for developing countries. To that purpose, we employ a dynamic credit risk model that considers a variety of macroeconomic indicators, bank-specific variables, and household characteristics.

Venture capital activities under uncertainty: US and UK investors behavior.

We investigate how in the context of Corporate Venture Capital (CVC), the investment decisions affect the likelihood of their subsequent exit strategies. We use OLS and probit regression as well as Weibull distribution of residual values, given its reliability and validity for studying lifetime analysis. Based on a sample of 8722 VC-backed ventures with the first investment dates between 1999 and 2018 in United States (US) and United Kingdom (UK), the results show that the presence of CVCs positively affects the funding amounts and the duration of the investment.

Global pandemic crisis and risk contagion in GCC stock markets.

This study investigates how the COVID-19 outbreak has shaped the volatility spillover between oil and Gulf Cooperation Council (GCC) stock markets. Contagion analysis is conducted by implementing a vector error correction (VECM) asymmetric BEKK model, wherein both cointegration and asymmetric features are considered. Financial market uncertainty caused by the recent health crisis is captured using Baker et al.

Covid-19 vaccination, fear and anxiety: Evidence from Google search trends.

Covid-19 vaccination was associated with a general feeling of hesitancy, and its arrival increased fear and economic anxiety. This paper investigates the impacts of Covid-19 vaccination on fear and economic anxiety using a worldwide sample of 194 countries observed from December 1st, 2020 to March 4th, 2021. The difference-in-differences investigation approach shows that with the vaccine's arrival, the Google search trends measuring fear and anxiety are increasing.

Oil price, US stock market and the US business conditions in the era of COVID-19 pandemic outbreak.

This paper contributes to Covid-19 outbreak impacts literature. We investigate the connectedness between stock market and oil prices under bullish and bearish economic conditions and uncertainty level at different investment horizons. We applied the wavelet framework on daily dataset cover the pre-COVID-19 and COVID-19 period.

Machine Learning-Based Modeling of the Environmental Degradation, Institutional Quality, and Economic Growth.

This study was aimed at investigating the determinants of environmental sustainability in 86 countries from 2007 to 2018. The natural gradient boosting (NGBoost) algorithm was implemented along with five machine learning models to forecast the trends of CO emissions. In addition, the SHapley Additive exPlanation (SHAP) technique was used to interpret the findings and analyze the contribution of the individual factors.

Is gold a hedge or safe haven against oil and currency market movements? A revisit using multifractal approach.

We investigate gold's role as a hedge or safe haven against oil price and currency movements across calm and extreme market conditions. For the empirical analysis, we extend the intraday multifractal correlation measure developed by Madani et al. (Bankers, Markets & Investors, 163:2-13, 2020) to consider the dependence for calm and extreme movement periods across different time scales.

Does it pay to go green? The environmental innovation effect on corporate financial performance.

The current global economy demands synergies between ecological responsiveness and business models. To analyse this dynamic, this study investigates the relationship between green innovation and corporate financial performance for German HDAX companies from 2008 to 2019 by constructing an green innovation measure. A two-step GMM system and penalised-spline estimation are used to test the linear relationship between green innovation and financial proxies (return on assets, return on invested capital, and the market-to-book ratio).

Business case complexity and environmental sustainability: Nonlinearity and optimality from an efficiency perspective.

The question of whether it is possible to "do well by going green" has been debated at length in the literature on environmental sustainability, but no consensus has been reached to date. Building on stakeholder theory in that a firm's environmental sustainability can improve its competitive advantage, this study investigates the impacts of sustainable environmental practices on the competitiveness of 28 international airlines over 2010-2018. First, we use dynamic network data envelopment analysis to estimate airline operational efficiency as a measure of competitiveness.

Spatial contagion between financial markets: new evidence of asymmetric measures.

The objective of this paper is to identify the presence, direction and time at which the pure contagion effect occurred between financial markets. In so doing, the aim is to prove the existence of both spatial and temporal asymmetries of pure contagion effects. Firstly, a new empirical framework is proposed in order to define a spatial contagion index using the conditional cumulative distribution function as a parameter to estimate a conditional copula.

The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning.

This study aims to predict oil prices during the 2019 novel coronavirus (COVID-19) pandemic by looking into green energy resources, global environmental indexes (ESG), and stock markets. The study employs advanced machine learning, such as the LightGBM, CatBoost, XGBoost, Random Forest (RF), and neural network models. An accurate forecasting framework can effectively capture the trend of the changes in oil prices and reduce the impact of the COVID-19 pandemic on such prices.

Revisiting the relationship between spot and futures markets: evidence from commodity markets and NARDL framework.

This study aims to investigate the relationship between the spot and futures commodity markets. Considering the complexity of the relationship, we use a nonlinear autoregressive distributed lag (NARDL) framework that considers the asymmetry and nonlinearity in both the long and short run. Based on the daily returns of six commodity indices reaggregated on three commodity types, our study reaches some interesting findings.

Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?

This study aims to examine the issue of cryptocurrency volatility modelling and forecasting based on high-frequency data. More specifically, this study assesses whether crisis periods, particularly the coronavirus disease pandemic, influence the dynamic of cryptocurrency volatility. We investigate the four main cryptocurrency markets (Bitcoin, Ethereum Classic, Ethereum, and Ripple) from April 2018 to June 2020.

Does non-fundamental news related to COVID-19 matter for stock returns? Evidence from Shanghai stock market.

The COVID-19 outbreak generates various types of news that affect economic and financial systems. No studies have assessed the effects of such news on financial markets. This study sheds light on the impact of non-fundamental news related to the COVID-19 pandemic on the liquidity and returns volatility.

Effects of the COVID-19 pandemic on the US stock market and uncertainty: A comparative assessment between the first and second waves.

This paper makes the first comparative assessment of the impacts of the first and second waves of the ongoing COVID-19 pandemic for the US stock market and its uncertainty. To this end, we investigate the dynamic conditional correlation and the asymmetric impacts of shocks on the correlation between the US and Chinese stock markets before and during the COVID-19 crisis. Furthermore, we analyze and compare the relationship between the COVID-19 pandemic and US returns and uncertainty during the first and second waves of the pandemic.

Measuring the global economic impact of the coronavirus outbreak: Evidence from the main cluster countries.

This study measures the global economic impact of the coronavirus outbreak. This pandemic is characterized by demand and supply shocks, leading to restrictions on trade, product and service transactions, and capital flow mobility. We investigate its impact on currency markets, stock market performance, and investor fear sentiment.