Mining the relationship between COVID-19 sentiment and market performance.

In March 2020, the outbreak of COVID-19 precipitated one of the most significant stock market downturns in recent history. This paper explores the relationship between public sentiment related to COVID-19 and stock market fluctuations during the different phases of the pandemic. Utilizing natural language processing and sentiment analysis, we examine Twitter data for pandemic-related keywords to assess whether these sentiments can predict changes in stock market trends. Our analysis extends to additional datasets: one annotated by market experts to integrate professional financial sentiment with market dynamics, and another comprising long-term social media sentiment data to observe changes in public sentiment from the pandemic phase to the endemic phase. Our findings indicate a strong correlation between the sentiments expressed on social media and market volatility, particularly sentiments directly associated with stocks. These insights validate the effectiveness of our Sentiment(S)-LSTM model, which helps to understand the evolving dynamics between public sentiment and stock market trends from 2020 through 2023, as the situation shifts from pandemic to endemic and approaches new normalcy.

How backers' behavior affects financing performance in agri-food reward-based crowdfunding: The moderating mechanism of initiator characteristics and project attributes.

Crowdfunding is a new type of financing favored by entrepreneurs in need of capital. Financing performance is a key concern for crowdfunding project initiators. Although a growing number of studies have investigated the factors that affect the financing performance of crowdfunding projects, there are still some issues that need to be further clarified. How does the investment behavior of backers, as the supply side of finance, affect the financing performance of project in reward-based crowdfunding? What are the moderating mechanisms of this influence by initiator characteristics and project attributes? Based on a panel data set from Zhongchou, a famous agri-food crowdfunding platform in China, this paper finds that the investment speed, the investment intensity, the number of early backers, the backers' comments, and the number of selfless backers all have significant effects on financing performance. The core trust factors of initiator characteristics and project attributes play a moderating role in the relationship between backer investment behavior and financing performance, but there are differences in the moderating mechanisms. Based on the research conclusions, practical enlightenment is proposed for initiators, crowdfunding platforms, and regulators.

Global Financing Facility investments for vulnerable populations: content analysis regarding maternal and newborn health and stillbirths in 11 African countries, 2015 to 2019.

BACKGROUND: The Global Financing Facility (GFF) was launched in 2015 to catalyse increased domestic and external financing for reproductive, maternal, newborn, child, adolescent health, and nutrition. Half of the deaths along this continuum are neonatal deaths, stillbirths or maternal deaths; yet these topics receive the least aid financing across the continuum. OBJECTIVES: To conduct a policy content analysis of maternal and newborn health (MNH), including stillbirths, in GFF country planning documents, and assess the mortality burden related to the investment. METHODS: Content analysis was conducted on 24 GFF policy documents, investment cases and project appraisal documents (PADs), from 11 African countries. We used a systematic data extraction approach and applied a framework for analysis considering mindset, measures, and money for MNH interventions and mentions of mortality outcomes. We compared PAD investments to MNH-related deaths by country. RESULTS: For these 11 countries, USD$1,894 million of new funds were allocated through the PADs, including USD$303 million (16%) from GFF. All documents had strong content on MNH, with particular focus on pregnancy and childbirth interventions. The investment cases commonly included comprehensive results frameworks, and PADs generally had less technical content and fewer indicators. Mortality outcomes were mentioned, especially for maternal. Stillbirths were rarely included as targets. Countries had differing approaches to funding descriptions. PAD allocations are commensurate with the burden. CONCLUSIONS: The GFF country plans present a promising start in addressing MNH. Emphasising links between investments and burden, explicitly including stillbirth, and highlighting high-impact packages, as appropriate, could potentially increase impact.

Main finding: Maternal and newborn health care packages are strongly included in the Global Financing Facility policy documents for 11 African countries, especially regarding pregnancy and childbirth, though less for stillbirth, or postnatal care, or small and sick newborn care.Added knowledge: This study is the first independent content analysis of Global Financing Facility investment cases and related project appraisal documents, revealing mostly consistent content for maternal and newborn health across documents and overall correlation between national mortality burden and investments committed.Global health impact for policy and action: The Global Financing Facility have demonstrated promising initial investments for maternal and newborn health, although there are also missed opportunities for strengthening, especially for some neonatal high-impact packages and counting impact on stillbirths.

The effect of health literacy on health investment intention: a cross-sectional study among petrochemical employees in China.

Backgrounds: In the petrochemical industry, employees are exposed to various health hazards, which pose serious challenges to their health and hinder the sustainable development of the petrochemical industry. Investing in health has proved a potential strategy to enhance general health. However, global health investment is notably insufficient, mainly due to the public's limited intention to invest in their health. While past research has identified various determinants of health investment intentions, the relationship between health literacy and health investment intention remains somewhat controversial and needs more empirical validation. Objectives: This study aims to assess the level of health literacy and health investment intention among employees in one of China's largest petrochemical companies and to explore the effect of health literacy on health investment intention. Methods: A cross-sectional study was conducted in a petrochemical company. The valid sample size for this study was 39,911 respondents. Data were collected using a designed questionnaire, including socio-demographic information, questions about health investment intention, and the "2020 National Health Literacy Monitoring Questionnaire." Several statistical analysis methods were employed, including descriptive analysis, Chi-square test, logistic regression, and multiple linear regression. Results: The study disclosed an average health literacy score of 56.11 (SD = 10.34) among employees, with 52.1% surpassing the qualification threshold. The "Chronic Disease" dimension exhibited the lowest qualification rate at 33.0%. Furthermore, 71.5% of the employees expressed an intention to invest in health, yet a significant portion (34.5%) opted for the minimal investment choice, less than 2,000 RMB. Logistic regression analysis indicated a positive correlation between health literacy and health investment intention (OR = 1.474; p < 0.001). This association's robustness was further indicated by multiple linear regression analyses (ß = 0.086, p<0.001). Conclusion: The employees' health literacy significantly exceeds the national average for Chinese citizens, yet the qualified rate in the "Chronic Disease" dimension remains notably low. A majority of employees have the intention to invest in health, albeit modestly. Furthermore, while health literacy does positively influence health investment intention, this effect is somewhat limited. Accordingly, personalized Health education should be prioritized, with a focus on improving chronic disease knowledge and facilitating the internalization of health knowledge into health beliefs.

Assessing the environmental impacts of clean energy investment in Pakistan using a dynamic autoregressive distributed lag model.

In this study, the authors projected the impacts of clean energy investment on environmental degradation by applying a novel and dynamic Autoregressive Distributed Lag (DARDL) model for Pakistan from 1990 to 2022. Most researchers have used ecological footprint or CO2 emissions indicators to look at how clean energy investment affects environmental degradation, which primarily represents contamination induced by humans' consumption patterns and does not consider the impact of the supply side. Against this background, the study scrutinized the dynamic interaction between clean energy investment and environmental sustainability using the load capacity factor (LCF) as an ecological indicator in Pakistan, including economic growth, population density, trade openness, urbanization, and industrialization in the analysis. The long-run estimates from DARDL indicate that a 1 percent upsurge in clean energy investment mitigates environmental degradation by approximately 0.42 percent on average, controlling for other factors. Further, the study also revealed that a 1 percent increase in clean energy investment diminishes dirty energy consumption by approximately 0.45 percent. The validity of the findings is confirmed using alternate methods, i.e., KRLS. The study recommends that Pakistan prioritize investment in clean energy projects to promote environmental sustainability and enforce environmental regulations to reduce the adverse externalities associated with dirty energy activities.

Environmental judicial reform and corporate investment behavior - Based on a quasi-natural experiment of environmental courts.

Ensuring the effectiveness of environmental legislation and regulations necessitates enhancing the professional caliber of the environmental judiciary. Utilizing a multi-period difference-in-differences model, we explore the impact of environmental judicial reforms, exemplified by the establishment of environmental courts, on corporate investment behavior. We find that firms in regions with established environmental courts significantly increase their environmental investments and productive investments, while financial investments remain unaffected. Mechanism testing reveals that the environmental court affects corporate investment by strengthening local government environmental enforcement and promoting public environmental participation. Furthermore, the marginal effect of environmental courts is more pronounced in regions with fewer environmental regulations and lower economic development levels, as well as in state-owned enterprises. Compared to collegiate benches, environmental resources judicial tribunals exert a greater influence on corporate investment behavior. This study adds to the micro-economic analysis of environmental judiciary by providing empirical evidence on how formal institutional frameworks impact corporate investment behavior.

How are energy transition and energy-related R&D investments effective in enabling decarbonization? Evidence from Nordic Countries by novel WLMC model.

Public interest in climate change-related problems has been developing with the contribution of the recent energy crisis. Accordingly, countries have been increasing their efforts to decarbonize economies. In this context, energy transition and energy-related research and development (R&D) investments can be important strategic tools to be helpful to countries in the decarbonization of economies. Among all, Nordic countries have come to the force because of their well-known position as green economies. Hence, this study examines Nordic countries to investigate the impact of energy transition, renewable energy R&D investments (RRD), energy efficiency R&D investments (EEF) on carbon dioxide (CO2) emissions by performing wavelet local multiple correlation (WLMC) model and using data from 2000/1 to 2021/12. The outcomes reveal that (i) based on bi-variate cases, energy transition and RRD have a mixed impact on CO2 emissions in all countries across all frequencies; EEF has a declining impact on CO2 emissions in Norway (Sweden) at low and medium (very high) frequencies; (ii) according to four-variate cases, all variables have a combined increasing impact on CO2 emissions; (iii) RRD is the most influential dominant factor in all countries excluding Norway, where EEF is the pioneering one. Thus, the reach proves the varying impacts of energy transition, RRD, and EEF investments on CO2 emissions. In line with the outcomes of the novel WLMC model, various policy endeavors, such as focusing on displacement between sub-types of R&D investments, are argued to ensure the decarbonization of the economies.

Estimated Investment Need to Increase England's Capacity to Diagnose Eligibility for an Alzheimer's Treatment to G7 Average Capacity Levels.

BACKGROUND: As disease-modifying Alzheimer's (AD) treatments are becoming available, concerns have been raised that even high-income countries lack the diagnostic capacity to accurately identify eligible patients in a timely manner. OBJECTIVES: We analyze how much NHS England would have to invest in capacity for AD specialists, biomarker testing with PET scans or CSF testing and MRI scans to reach G7 average levels and estimate the effect on wait times in the diagnostic process. DESIGN: Desk research and expert interviews for cost and capacity data. Markov model to estimate wait times. SETTING: NHS England. MEASUREMENTS: AD specialists, and PET and MRI scanners per capita in G7 countries and wait times in England under different investment scenarios. RESULTS: England has the lowest number of PET and MRI scanners and the second-lowest of AD specialists per capita among the G7 countries. An investment of GBP 14 billion over ten years would be needed to reach G7 average levels, of which 31%, 22%, 10%, 37% would be devoted to capacity for memory assessment services, PET scanning, CSF analysis, and MRI scanning, respectively. This investment would reduce estimated average wait times by around 87% between 2023 and 2032. CONCLUSIONS: The NHS England has large gaps in diagnostic capacity for AD. Without substantial investments, AD patients in England would experience substantial wait times and avoidable disease progression.

Systemic risk spillover between the stock market and banking deposits: Evidence from a sustainability perspective in the South Asian countries.

This research explores the link between stock markets and banking deposits in South Asian (Pakistan, India, Sri Lanka, Nepal) countries. This study empirically examines the systemic risk potential of financial institutions in South Asia using current systemic risk statistics. Yearly data on stock prices and banking deposits from January 2000 to December 2020 were analyzed using a two-stage process. In the first phase, we measure VaR (value at risk), and in the second step, we measure the DCC GARCH model for our empirical analysis. The study findings reveal systemic risk spillover between the stock markets of South Asian countries and the relevant country's banking system deposits. The policymakers can use our study findings to create a more sustainable financial sector.

Can co-ordination advancement of two-way FDI improve resource misallocation? Evidence from 285 cities in China.

During the past 40 years of reform and opening-up, the implementation of an unbalanced enhancement strategy has led to severe resource misallocation, making the mitigation and rectification of this misallocation an urgent issue. This paper utilizes urban data from China between 2006 and 2020 to examine the impact of two-way foreign direct investment (FDI) on resource misallocation, as well as the pathways through which coordinated advancement of two-way FDI affects resource misallocation. After undergoing a series of robustness and endogeneity tests, the conclusion remains stable. Mechanism testing reveals that coordinated advancement of two-way FDI improves capital resource misallocation by enhancing the enhancement level of the financial industry, while exacerbating labor misallocation by increasing labor costs. This paper integrates the coordinated advancement of two-way FDI and resource misallocation into the same analytical framework, proposing policy recommendations to alleviate China's resource misallocation issue.

Analysis of human capital effects introducing Bayesian quantile regression in the process of industrial structural upgrading.

In recent years, with the continuous evolution of the global economy and the adjustment of industrial structures, the understanding of the role played by human capital in the process of economic development has become particularly important. However, existing research on the impact of human capital on economic growth often adopts traditional regression methods, failing to comprehensively consider the heterogeneity and nonlinear relationships in the data. Therefore, to more accurately understand the influence of human capital on economic growth at different stages, this study employs Bayesian quantile regression method (BQRM). By incorporating BQRM, a better capture of the dynamic effects of human capital in the process of industrial structure upgrading is achieved, offering policymakers more targeted and effective policy recommendations to drive the economy towards a more sustainable direction. Additionally, the experiment also examines the impact of other key factors such as technological progress, capital investment, and labor market conditions on economic growth. These factors, combined with human capital, collectively promote the upgrading of industrial structure and the sustainable development of the economy. This study, by introducing BQRM, aims to fill the research gap regarding the impact of human capital on economic development during the industrial structural upgrading process. In the backdrop of the ongoing evolution of the global economy and adjustments in industrial structure, understanding the role of human capital in economic development becomes particularly crucial. To better comprehend the direct impact of human capital, the experiment collected macroeconomic data, including GDP, industrial structure, labor skills, and human capital, from different regions over the past 20 years. By establishing a dynamic panel data model, this study delves into the trends in the impact of human capital at various stages of industrial structure upgrading. The research findings indicate that during the high-speed growth phase, the contribution of human capital to GDP growth is 15.2% ± 2.1%, rising to 23.8% ± 3.4% during the period of industrial structure adjustment. Technological progress, capital investment, and labor market conditions also significantly influence economic growth at different stages. In terms of innovation improvement, this study pioneers the use of BQRM to gain a deeper understanding of the role of human capital in economic development, providing more targeted and effective policy recommendations. Ultimately, to promote sustainable economic development, the experiment proposes concrete and targeted policy recommendations, emphasizing government support in training and skill development. This study not only fills a research gap in the relevant field but also provides substantive references for decision-makers, driving the economy towards a more sustainable direction.

Share pledge and accounting conservatism in share-pledging firms: Evidence from a natural experiment in China.

This paper focuses on firms in which insiders pledge their shares as collateral for loans. By investigating a natural experiment-China's enactment of provisions on share reductions that restrict pledge creditors' cashing-out behavior-we find that pledging firms exhibited more conservative financial reporting after the implementation than non-pledging firms. This effect was pronounced in firms with a higher ratio of pledged shares, a longer maturation period of the pledged shares, and more concentrated pledge creditors. Additionally, we show that pledging firms increased their accounting conservatism after the shock, leading to a lower risk of margin calls and stock price crashes. The effect on accounting conservatism was stronger in firms with controlling pledgers or when the pledge creditors were banks. Our results remained consistent after we performed several robustness tests. These behaviors are economically logical because the provisions heighten creditors' liquidity risk and the potential losses of loan default. Pledging shareholders embrace more accounting conservatism to mitigate creditors' concerns about agency costs and avoid triggering margin calls. Our findings provide direct support that creditors have a real demand for accounting conservatism and highlight the impact of shareholder-creditor conflicts on the financial reporting policies of pledging firms.

Analysis of food system drivers of deforestation highlights foreign direct investments and urbanization as threats to tropical forests.

Approximately 90% of global forest cover changes between 2000 and 2018 were attributable to agricultural expansion, making food production the leading direct driver of deforestation. While previous studies have focused on the interaction between human and environmental systems, limited research has explored deforestation from a food system perspective. This study analyzes the drivers of deforestation in 40 tropical and subtropical countries (2004-2021) through the lenses of consumption/demand, production/supply and trade/distribution using Extreme Gradient Boosting (XGBoost) models. Our models explained a substantial portion of deforestation variability globally (R2 = 0.74) and in Asia (R2 = 0.81) and Latin America (R2 = 0.73). The results indicate that trade- and demand-side dynamics, specifically foreign direct investments and urban population growth, play key roles in influencing deforestation trends at these scales, suggesting that food system-based interventions could be effective in mitigating deforestation. Conversely, the model for Africa showed weaker explanatory power (R2 = 0.30), suggesting that factors beyond the food system may play a larger role in this region. Our findings highlight the importance of targeting trade- and demand-side dynamics to reduce deforestation and how interventions within the food system could synergistically contribute to achieving sustainable development goals, such as climate action, life on land and zero hunger.

Examining public private partnership investment in energy towards achieving sustainable development goal 7 for ASEAN region.

The gradual progress in aligning financial flows with the adoption of clean technologies reveals a persistent funding gap, signaling a global misallocation of capital. Addressing this challenge necessitates political leadership and robust policies to counteract the insecurities impeding the redirection of financial flows. This study investigates into the impact of energy-related public-private partnership investments (PPPIE) and macro-environmental variables on the attainment of Sustainable Development Goal 7 (SDG7) across Association of Southeast Asian Nations (ASEAN) member countries from 1999 to 2021. Employing the Dynamac command technique, we conduct autoregressive distribution lag analysis and the Bounds Cointegration Test to evaluate ASEAN's efforts in achieving SDG7. Results indicate that a ten-year exogenous shock to the GDP growth rate initially causes a temporary decline in both GDP and PPPIE, albeit not statistically significant. However, in the long run, the shock becomes statistically significant, correlating with a negative decline in the GDP growth rate. This underscores the negative impact of external factors like the COVID-19 pandemic on the economic growth of ASEAN member countries. Specifically, a percentage increase in PPPIE leads to an 8.3% reduction in the GDP growth rate, revealing a detrimental and unsustainable impact on the economy. This signifies that energy investments in the ASEAN region, are predominantly unsustainable and adversely impact economic growth. Moreover, these energy investments contribute to a significant 52.6% increase in greenhouse gas emissions, indicating a substantial setback in the region's progress towards meeting SDG7's clean energy objectives by 2030. This suggests the present state of PPPIE does not align with sustainable clean energy goals of the region. Therefore, recommendations should include diversifying energy sources and investment strategies to enhance sustainable clean energy. Also, policymakers and researchers should reassess the terms and conditions of PPPIE, refining frameworks for private sector involvement to align with long-term economic sustainability goals.

Investor attention and corporate social responsibility of family businesses in Vietnam: The moderating role of CEO overpower.

This study examines the influence of investor attention and Chief Executive Officers (CEOs) power on Corporate Social Responsibility (CSR) within Vietnamese family businesses. Unlike most of the past literature, this study further investigates the potential moderating effects of CEOs' power on the relationship between investor attention and CSR. Utilizing the dynamic system Generalized Method of Moments (GMM), this study analyzes a dataset comprising 116 Vietnamese family businesses from 2005 to 2020. The findings reveal an inverted U-shape between CEO power and CSR within family businesses; meanwhile, investor attention demonstrates a negative impact on CSR. Moreover, the results report that CEO power is a moderating factor in the relationship between investor attention and CSR. These results are consistent with various theoretical frameworks, including agency theory, overinvestment, career concern, career horizon, and conflict-resolution hypotheses. Finally, our study offers management implications to foster the sustainable development of CSR within family businesses, particularly within emerging markets.

Gender effects in crowdfunded business loan campaigns.

Crowdfunding is a growing source of finance for entrepreneurs. In this paper, we investigate the existence of a gender effect in the time needed to obtain a business loan through crowdfunding. Using data from three Dutch crowdfunding platforms, survival analysis of the time to completion for 934 business loan campaigns shows that female entrepreneurs have a 20% shorter campaign completion time compared to male entrepreneurs, whereas couples do not differ from males. This effect persists across the different platforms. Subsequent analysis shows that female entrepreneurs do not have the disadvantage they face in traditional lending channels when requesting funds through crowdfunding, and that herding behavior by investors benefits female entrepreneurs most.

Global motion filtered nonlinear mutual information analysis: Enhancing dynamic portfolio strategies.

The complex financial networks, with their nonlinear nature, often exhibit considerable noises, inhibiting the analysis of the market dynamics and portfolio optimization. Existing studies mainly focus on the application of the global motion filtering on the linear matrix to reduce the noise interference. To minimize the noise in complex financial networks and enhance timing strategies, we introduce an advanced methodology employing global motion filtering on nonlinear dynamic networks derived from mutual information. Subsequently, we construct investment portfolios, focusing on peripheral stocks in both the Chinese and American markets. We utilize the growth and decline patterns of the eigenvalue associated with the global motion to identify trends in collective market movement, revealing the distinctive portfolio performance during periods of reinforced and weakened collective movements and further enhancing the strategy performance. Notably, this is the first instance of applying global motion filtering to mutual information networks to construct an investment portfolio focused on peripheral stocks. The comparative analysis demonstrates that portfolios comprising peripheral stocks within global-motion-filtered mutual information networks exhibit higher Sharpe and Sortino ratios compared to those derived from global-motion-filtered Pearson correlation networks, as well as from full mutual information and Pearson correlation matrices. Moreover, the performance of our strategies proves robust across bearish markets, bullish markets, and turbulent market conditions. Beyond enhancing the portfolio optimization, our results provide significant potential implications for diverse research fields such as biological, atmospheric, and neural sciences.

Quantifying economic vulnerabilities induced by interdependent networks.

Under economic globalization, countries are linked through trade and investments. This economic interdependence creates vulnerabilities. The indirect vulnerability induced by interdependent networks of trade and investments can put a country's economy at risk, but this risk has yet to be systematically quantified and investigated. In this paper, we developed the novel Potential Indirect Vulnerability Index (PIVI) to capture how interdependencies between networks of trade and foreign direct investment (FDI) may induce economic vulnerabilities. The model consisted of three main components: a target country (the importer of goods), an investing country (the exporter of FDI), and the intermediary countries that export commodities to the target country and receive FDI from the investing country, serving as conduits of the vulnerabilities caused indirectly by the investing country. The PIVI quantifies the indirect vulnerabilities based on the product of two fractions: 1) the dependency of the target country on commodities from each intermediary country; and 2) the dependency of each intermediary country on FDI from the investing country. We demonstrated the utility of PIVI by examining the US economy's vulnerability to China using 2019 trade and FDI data. Several Asian countries and a mix of agricultural products and raw materials were identified as conduits through which China could potentially influence the US economy. Vietnam was a sizeable risk because, while it has been a primary source of many US imports, it also received about 30% of its FDI from China. The US policy makers might opt to increase diversity in trade partners or to promote investment in countries such as Vietnam. We also applied the PIVI analysis to critical minerals, identifying cobalt, tungsten, and copper as the most vulnerability-inducing among them. PIVI is a flexible metric than can be aggregated and modified to provide a more nuanced and focused assessment of an economy's vulnerability.

Uncertainty in banking and debt financing of firms in Vietnam.

This study explores the effects of banking uncertainty on firms' debt financing. Employing data from 2007 to 2022 of Vietnam-a bank-based economy, we document that banking uncertainty negatively impacts corporate debt. The impact firmly holds across various debt maturities and sources, with the most predominant driver witnessed in bank debt. We also investigate the potential underlying mechanism linking banking uncertainty to debt financing, thereby validating the working of three crucial channels, including increased costs of debt, substitution of trade credit, and contractions in firm investment. Furthermore, conducting extended analysis, we find that debt financing exhibits more pronounced reactions to banking uncertainty for firms with closer ties to banks or during macroeconomic shocks, as captured by the financial crisis and the COVID-19 pandemic. Our findings survive after robustness checks by alternative measurement, static and dynamic econometric models, and endogeneity controls.