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Over the last decade, investments in green energy companies have witnessed noticeable growth rates. However, the glacial pace of the world economic restoration due to COVID-19 pandemic placed a high degree of uncertainty over this market. Therefore, this study investigates the short- and long-term relationships between COVID-19 new cases and WilderHill New Energy Global Innovation Index (NEX) using daily data over the period from January 23, 2020 to February 1, 2023.

The authors utilize an autoregressive distributed lag bounds testing estimation technique.

The results show a significant positive impact of COVID-19 new cases on the returns of NEX index in the short run, whereas it has a significant negative impact in the long run. It is also found that the S&P Global Clean Energy Index has a significant positive impact on the returns of NEX index. Although oil has an influential effect on stock returns, the results show insignificant impact.

Governments have the chance to flip this trend by including investment in green energy in their economic growth stimulation policies. Governments should highlight the fundamental advantages of investing in this type of energy such as creating job vacancies while reducing emissions and promoting innovation.

First, as far as the authors are aware, the authors are the first to examine the effect of oil prices on clean energy stocks during COVID-19. Second, the authors contribute to studies on the relationship between oil prices and renewable energy. Third, the authors add to the emerging strand of literature on the impact of COVID-19 on various sectors of the economy. Fourth, the findings of the paper can add to the growing literature on sustainable development goals, in specific the papers related to energy sustainability.

This study aims to investigate the response of green investments of emerging countries to own-market uncertainty, oil-market uncertainty and COVID-19 effect/geo-political risks (GPRs), using the tail risks of corresponding markets as measures of uncertainty.

This study employs Westerlund and Narayan (2015) (WN)-type distributed lag model that simultaneously accounts for persistence, endogeneity and conditional heteroscedasticity, within a single model framework. The tail risks are obtained using conditional standard deviation of the residuals from an asymmetric autoregressive moving average – ARMA(1,1) – generalized autoregressive conditional heteroscedasticity – GARCH(1,1) model framework with Gaussian innovation. For out-of-sample forecast evaluation, the study employs root mean square error (RMSE), and Clark and West (2007) (CW) test for pairwise comparison of nested models, under three forecast horizons; providing statistical justification for incorporating oil tail risks and COVID-19 effects or GPRs in the predictive model.

Green returns responds significantly to own-market uncertainty (mostly positively), oil-market uncertainty (mostly positively) as well as the COVID-19 effect (mostly negatively), with some evidence of hedging potential against uncertainties that are external to the green investments market. Also, incorporating external uncertainties improves the in-sample predictability and out-of-sample forecasts, and yields some economic gains.

This study contributes originally to the green market-uncertainty literature in four ways. First, it generates daily tail risks (a more realistic measure of uncertainty) for emerging countries’ green returns and global oil prices. Second, it employs WN-type distributed lag model that is well suited to account for conditional heteroscedasticity, endogeneity and persistence effects; which characterizes financial series. Third, it presents both in-sample predictability and out-of-sample forecast performances. Fourth, it provides the economic gains of incorporating own-market, oil-market and COVID-19 uncertainty.

Since 2008’s financial crisis, attention toward supply chain finance (SCF) has increased. However, most research investigates SCF considering single supply chain (SC) stages or buyer–supplier dyads and focuses on a single SCF solution. It is important to see how different solutions are adopted at different SC stages, by actors with different financing needs. This study aims to analyze SCF at different SC stages, to understand why different solutions are implemented at different SC stages and the contingency factors (regulation, SC stage, product category and size) influencing their adoption.

The paper is based on multiple exploratory case studies in the Italian agri-food industry, considering firms distributed at different SC stages and adopting multiple SCF solutions. The paper exploits a contingent approach (Sousa and Voss, 2008) to analyze how contingent factors influence SCF adoption at different SC stages.

Findings explain how and why different SC stages (producer, cooperative, processor and retailer) implement different SCF solutions (reverse factoring, dynamic discounting, inventory finance and Minibond), describing contingency variables’ impact on their adoption.

To the best of the authors’ knowledge, the research is original in its description of SCF at different SC stages, considering different SC actors’ drivers and barriers, and questioning the importance of a coordinated approach in SCF adoption along an entire SC. Moreover, the paper adopts a contingent approach, contributing to SCF research, seldomly based on theoretical lenses.

Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data.

This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price.

The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price.

In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.

The purpose of this paper is to show that the environmental income drives economic growth of a large open country.

The authors detect that the relative environmental income has double effect of “conspicuous consumption” on the international renewable resource stock changes when a new social norm shapes to environmental-friendly behaviors by using normal macroeconomic approaches.

Every unit of extra demand for renewable resource consumption increases the net premium of domestic capital asset. Even if the technology spillovers are inefficient to the substitution of capital to labor force in a real business cycle, the relative income with scale effect increases drives savings to investment. In this case, the renewable resource consumption promotes both the reproduction to a higher level and saving the potential cost of environmental improvement. Even if without scale effects, the loss of technology inefficient can be compensated by net positive consumption externality for economic growth in a sustainable manner.

It implies how to earn the environment income determines the future pathway of China’s rural conversion to the era of eco-urbanization.

We test the tax incidence to demonstrate an experimental taxation for environmental improvement ultimately burdens on international consumption side.

In Korea and abroad, this paper investigates the use of blockchains in the financial sector. This study aims to examine how blockchains are applied to the financial sector and how to respond to the Korean conditions.

This paper investigates the movements of the financial sector and related services using the blockchain in the current market.

First, as a result of examining domestic and foreign cases, it can be seen that the areas where blockchains are most actively applied in the financial sector are expanding into settlement, remittance, securities and smart contracts. Also, in Korea, many of the authentication procedures based on the equipment possessed by the consumers are used so that introduction of the blockchain in the authentication part is prominent. Second, the move to introduce a closed (private) distributed ledger that does not go through the central bank is accelerating in payments between banks. Third, domestic financial institutions also need joint action by financial institutions through a blockchain consortium to apply blockchain technology to the financial sector. Fourth, consumer needs and technological developments are changing. At the same time, as the opportunity to infringe on the information held by individuals has expanded, the need for blockchain technology is strongly emerging because of the efforts of the organizations to defend it.

This paper contributes to understanding the changes in the financial sector using the blockchain.

A positive outlook on the impact of Industry 4.0 (I4.0) on sustainability prevails in the literature. However, some studies have highlighted potential areas of concern that have not yet been systematically addressed. The goal of this study is to challenge the assumption of a sustainable Fourth Industrial Revolution by (1) identifying the possible unintended negative impacts of I4.0 technologies on sustainability; (2) highlighting the underlying motivations and potential actions to mitigate such impacts; and (3) developing and evaluating alternative assumptions on the impacts of I4.0 technologies on sustainability.

Building on a problematization approach, a systematic literature review was conducted to develop potential alternative assumptions about the negative impacts of I4.0 on sustainability. Then, a Delphi study was carried out with 43 experts from academia and practice to evaluate the alternative assumptions. Two rounds of data collection were performed until reaching the convergence or stability of the responses.

The results highlight various unintended negative effects on environmental and social aspects that challenge the literature. The reasons behind the high/low probability of occurrence, the severity of each impact in the next five years and corrective actions are also identified. Unintended negative environmental effects are less controversial than social effects and are therefore more likely to generate widely accepted theoretical propositions. Finally, the alternative hypothesis ground is partially accepted by the panel, indicating that the problematization process has effectively opened up new perspectives for analysis.

This study is one of the few to systematically problematize the assumptions of the I4.0 and sustainability literature, generating research propositions that reveal several avenues for future research.

There are a number of differences in the current Sharīʿah screening guidelines formulated by Sharīʿah scholars associated with world-renowned index providers and financial institutions. The purpose of this study is to highlight the consequences of such differences on the portfolio level outcomes for Sharīʿah-compliant investors. This study also investigates the cost of adopting an alternative stock selection methodology.

Seven Sharīʿah-compliant equity portfolios (SCEPs) are created from the active constituents of the S&P 500. Size, sector allocation and financial performance of the resulting seven portfolios are evaluated for the period 1984–2019. Style analysis is performed to attribute the difference in financial performance caused by the choice of selection criteria to different risk factors. The cost of switching the selection criteria is evaluated with turnover analysis and break-even transaction cost.

The choice of stock selection criteria has a significant effect on the size, sector bets and financial performance of the portfolios. Those portfolios which are constructed with market capitalization-based screens outperform portfolios constructed with total assets-based screens. The turnover analysis revealed that SCEPs are relatively costly in practice.

This study investigates the performance of Sharīʿah-compliant portfolios in the context of seven different screening guidelines. The effects of transaction cost and performance attribution to different risk factors represent the key contributions of this study.

The purpose of this paper is to investigate whether sentiment and mood, which are distinct theoretical concepts, can also be distinguished empirically.

Using a sample of German small-cap stocks and linear techniques, the effect of sentiment and mood on short-term abnormal stock return following earnings announcements is tested separately.

Mood tends to be a positive factor in predicting short-term abnormal stock return, as its biologically based impact uniformly affects the risk aversion of all market participants. Notably, negative mood influences stock return significantly negatively. Sentiment is no factor, however, as its cognitively based impact affects only unsophisticated investors, namely, their cash-flow expectations.

As the sample is restricted to small-cap stocks from a single stock market and only two proxies of sentiment and mood, respectively, are used, the findings should be generalized with caution. Future research might investigate other markets and employ different proxies of sentiment and mood.

Market participants should be aware of the different effect of sentiment and mood on stock return and adjust investment strategies accordingly.

As sophisticated investors are likely to profit from the irrational behavior of unsophisticated investors, who are prone to sentiment, the financial literacy of retail investors should be enhanced.

This paper is unique in distinguishing between sentiment and mood, both theoretically and empirically. Such distinction was largely ignored by related past research.

The socialist political economy with Chinese characteristics reflects the characteristics of ushering into a new era, and the research object thereof shifts to productive forces. Emancipating and developing productive forces and achieving common prosperity become the main theme. Wealth supersedes value as the fundamental category of economic analysis.

The theoretical system of socialist political economy with Chinese characteristics cannot proceed from transcendental theories but is problem-oriented. Leading problems involve development stages and research-level problems.

The economic operation analysis is subject to the goal of optimal allocation of resources with micro-level analysis focused on efficiency and macro-level analysis focused on economic growth and macroeconomic stability also known as economic security. The economic development analysis explores the laws of development and related development concepts in compliance with laws of productive forces. The new development concepts i.e. the innovative coordinated green open and shared development drive the innovation of development theory in political economy.

Accordingly, the political economy cannot study the system only, but also needs to study the problems of economic operation and economic development. Therefore, the theoretical system of the political economy tends to encompass three major parts, namely economic system, economic operation and economic development (including foreign economy). The basic economic system analysis needs to understand the relationship between public ownership and non-public ownership, between distribution according to work and factor payments, and between socialism and market economy from the perspective of coexistence theory, thus transforming institutional advantage into governance advantage.