Indiana's Public Health Investment Holds Insights For Other States.

Indiana has a business-friendly environment, but historical underinvestment in public health has yielded poor health outcomes. In 2023, when trust in governmental public health was strained nationwide, Indiana increased public health spending by 1,500 percent. In this article, we explain how Indiana achieved this unprecedented legislative victory for public health, describing the context, approach, and lessons learned. Specifically, an Indiana University report linking economic vitality and overall health sparked the creation of a governor's commission charged with exploring ways to address Indiana's shortcomings. Working with the Indiana Department of Health, the commission developed multisectoral coalitions and business and government partnerships, and it maintained consistent and coordinated communication with policy makers. Lessons learned included the value of uncoupling public health from partisan narratives, appointing diverse commission membership with strategically selected cochairs, involving local leaders, and ensuring local decision-making control. We believe that Indiana's approach holds insights for other states interested in strengthening public health funding in the current era.

Wealthy individual investors and stock markets' tail risk.

This paper employs a unique data set to analyze the trading behavior of wealthy individual investors across Mainland China and their impact on Chinese stock markets' tail risk. Results show that the wealthy individual investors' trading behavior can explain Chinese stock markets' tail risk, and the daily investment portfolios based on the network density of wealthy individual investors have significant excess returns. This paper also investigates the determinants of wealthy individual investors' trading behavior with the social network method and the spatial econometric model, and reveals that wealthy individuals benefit from the spillover effect of their trading behavior through the investor networks. The results of this paper not only reveal micro evidence for the formation mechanism of asset prices, but also provide insight into the behavior of wealthy individual investors.

Green Assets and Global Portfolio Tail Risk? A Stress-Testing exercise under multiple asset classes under distinct market phases.

Despite a burgeoning literature in the sphere of cryptocurrencies and green assets, yet, as of date, the literature fares poorly in terms of a holistic assessment of all asset classes, let alone stress testing such global portfolio risk under various market conditions. Our paper fulfills such a gap in the literature. Findings reveal that, irrespective of bearish or bullish market phases, green assets should be incorporated to mute down portfolio tail risk. Robustness tests performed either via different distribution type or CVaR analysis do not materially alter the main findings of our paper. The confluence of two forces substantially boosts tail risk, namely, passive investment strategy coupled with a crypto-augmented base model. Overall, our paper advocates the inclusion of green assets not as a choice but as an obligation to portfolio managers in view of curtailing both VaR and CVaR risk levels, all geared towards hitting two birds with one stone-simultaneously buttressing a greener world while effectively mitigating global portfolio tail risk.

Does strategic coupling of TNCs affect their exit from host regions: Empirical analysis of the exit of Taiwanese enterprises from mainland China.

This study aims to investigate the temporal and spatial attributes of the exit of Taiwanese enterprises from mainland China between 2001 and 2021, by applying enterprise database data. Furthermore, the influence of strategic coupling on Taiwanese enterprises' exit from mainland China was also investigated. The following are the key findings: The spatial distribution pattern of the exit rate of Taiwanese enterprises in mainland China varied at different phases. In contrast, the inland regions of the country's central and western zones, which are characterized by comparatively less developed economies, maintained consistently high exit rates, whereas the eastern coastal region retained a low exit rate. Particularly, the relationship between Taiwanese enterprises and the invested areas changed from Captive coupling to Cooperative coupling and subsequently to Absorptive Coupling. Similarly, the coupling modes significantly influenced the exit of Taiwanese enterprises from mainland China. Moreover, the COVID-19 pandemic has contributed to the backward connection of Taiwanese corporations, which have become more reliant on the mainland China market and local suppliers than earlier. Taiwan-favoring policies and the regional innovation environment have consequently emerged as the primary locational advantages for retaining Taiwanese enterprises in the aftermath of the global financial crisis. Therefore, the aforementioned factors may help to reduce the Taiwanese enterprises' exit from mainland China and possess valuable policy implications for Taiwan investment zones in mainland China.

Heteroscedasticity effects as component to future stock market predictions using RNN-based models.

Heteroscedasticity effects are useful for forecasting future stock return volatility. Stock volatility forecasting provides business insight into the stock market, making it valuable information for investors and traders. Predicting stock volatility is a crucial task and challenging. This study proposes a hybrid model that predicts future stock volatility values by considering the heteroscedasticity element of the stock price. The proposed model is a combination of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and a well-known Recurrent Neural Network (RNN) algorithm Long Short-Term Memory (LSTM). This proposed model is referred to as GARCH-LSTM model. The proposed model is expected to improve prediction accuracy by considering heteroscedasticity elements. First, the GARCH model is employed to estimate the model parameters. After that, the ARCH effect test is used to test the residuals obtained from the model. Any untrained heteroscedasticity element must be found using this step. The hypothesis of the ARCH test yielded a p-value less than 0.05 indicating there is valuable information remaining in the residual, known as heteroscedasticity element. Next, the dataset with heteroscedasticity is then modelled using an LSTM-based RNN algorithm. Experimental results revealed that hybrid GARCH-LSTM had the lowest MAE (7.961), RMSE (10.466), MAPE (0.516) and HMAE (0.005) values compared with a single LSTM. The accuracy of forecasting was also significantly improved by 15% and 13% with hybrid GARCH-LSTM in comparison to single LSTMs. Furthermore, the results reveal that hybrid GARCH-LSTM fully exploits the heteroscedasticity element, which is not captured by the GARCH model estimation, outperforming GARCH models on their own. This finding from this study confirmed that hybrid GARCH-LSTM models are effective forecasting tools for predicting stock price movements. In addition, the proposed model can assist investors in making informed decisions regarding stock prices since it is capable of closely predicting and imitating the observed pattern and trend of KLSE stock prices.

How air pollution affects corporate total factor productivity?

To explore the relationship between air pollution and total factor productivity and new pathways, This paper examines the impact of air pollution on total factor productivity of A-share listed companies in Shanghai and Shenzhen between 2015 and 2019. It investigates this relationship by considering two pathways: investor sentiment and government attention. The findings indicate that air pollution suppresses total factor productivity of firms. However, air pollution stimulates investor sentiment, which in turn increases R&D investment and total factor productivity, reducing to some extent the dampening effect of air pollution on total factor productivity. There exists a notable positive correlation between air quality and government attention, acting as a mediating variable. This implies that air pollution has the potential to capture the attention of governmental entities, leading to the implementation of appropriate measures aimed at managing and mitigating the occurrence of air pollution caused by industrial enterprises.And the relevant governments should formulate a series of policies to meet the different needs of different enterprises. These two approaches have varying impacts depending on the type of enterprises, thus governments should develop laws to cater to the various demands of different types of enterprises.

The impact of Chinese foreign direct investment and environmental tax revenues on air degradation in Europe: a spatial regression approach, 2000-2020.

This study analyzes air pollution through the effects of China's FDI in 27 European countries over a 20-year period, with a focus on the impact of environmental tax revenues (ETRs) and the environmental context in China. The relationship is estimated through spatial regressions that account for the presence of air pollutants in neighboring countries. The findings suggest that China's FDI in Europe does not contribute to air pollution but rather has a positive impact. The presence of environmental charges filters out non-polluting investments, which has a non-linear relationship with PM2.5 pollution rates. The study also concludes that air pollution is closely linked to the global environmental context, highlighting the positive effects of international agreements in the fight against climate change. Specifically, the study finds a link between China's efforts to address its polluting activities and their impact on European air quality.

Driving force of value reversal in Chinese overleveraged firms: The mechanism and path of private placement.

To stimulate economic growth, China has launched multiple economic stimulus plans in recent years, intensifying corporate debt financing and subsequently elevating the leverage levels. Addressing and effectively reducing the leverage levels of our country's enterprises has emerged as a pressing issue in the trajectory of our economic development. This paper primarily investigates the drivers, pathways, and mechanisms for reversing the over-leveraged values of enterprises. Key findings include: (1) Excessive indebtedness exerts a negative impact on corporate value, with the suppressing effect intensifying as the degree of over-leverage increases; (2) Over-leveraged enterprises can effectively decrease their debt levels and enhance their value through private placement. Further research suggests that this mechanism operates by amplifying the operational leverage of over-leveraged enterprises post private placement and alleviating financing constraints, thereby elevating corporate value. (3) Compared to non-state-owned enterprises, state-owned enterprises exhibit higher levels of indebtedness. Among over-leveraged firms, enhancements in corporate governance and increased investment efficiency can positively transform corporate value. This study offers valuable insights for the ongoing supply-side structural reforms and governance guidance from the regulatory bodies.

[STATE-OF-THE-ART OF GLOBAL START-UP INVESTMENT FOR ALLERGY AND IMMUNOLOGY 2022].

BACKGROUND: In 2022, the "New Capitalism Grand Design and Implementation Plan" was adopted in Japan, emphasizing the promotion and environmental development of startups. Given this context, an investigation into the startup and investment landscape in the allergy sector, both domestically and internationally, becomes imperative. METHODS: We analyzed 156 allergy-related startups from Japan, the US, and Europe from 2010 to 2021. Data on corporate information and investment trends were extracted from databases and VC websites. RESULTS: The total investment reached approximately 7.2 billion USD, with a ratio of 20:6:1 for the US, Europe, and Japan, respectively. The US showed a decline post its peak from 2016-2018, while Europe and Japan experienced growth. Notably, the US primarily invested in biopharmaceuticals for atopic dermatitis and food allergies, Europe in asthma-related apps, and Japan in healthcare apps and cross-border startups. DISCUSSION AND CONCLUSION: While Japan's investment environment in the allergy sector remains in its nascent stages and has room for development, the US and Europe are evidently ahead. Considering the rise of startups and funding limitations in Japan, external funding from regions like the US becomes a potential avenue. These findings are anticipated to contribute to the strategic activation of startups in allergy research and development.

Exploring the nexus between FDI inflows and economic growth: A sectoral level analysis.

This study investigates the relationship between Foreign Direct Investment (FDI) inflows and economic growth at sectoral levels in Bangladesh, employing a panel study framework. Utilizing sectoral-level panel data spanning six sectors from 2007-08 to 2018-19, the analysis is conducted using Panel Vector Error Correction Model (Panel VECM). Results from panel unit root tests confirm that all variables are integrated of order one I (1), indicating stationarity. The Pedroni panel co-integration test further supports the presence of co-integration among the variables. Notably, the Panel VECM reveals evidence of a unidirectional causal relationship from Real Gross Domestic Product (RGDP) to Real Foreign Direct Investment (RFDI) across all six sectors of Bangladesh. The findings underscore the significance of formulating pragmatic policies and implementing them effectively to attract FDI across sectors, thereby contributing to the overall economic growth of Bangladesh.

Government infrastructure investment stimulation through booming natural resources: Evidence from a lower-middle-income country.

The dynamics of central government funding to regions depend on local investments. In regional autonomy, local governments are encouraged to be more self-reliant from the central government. For regions with high natural resource yields, they will not encounter difficulties in meeting their fiscal needs. Community welfare can be realized through fulfilling basic needs, one of which is infrastructure development. High-quality infrastructure will be able to contribute to further progress in trade, thus enhancing production efficiency. The objective of this research is to analyze the extent of the influence of central government transfer funds, especially the Natural Resource Revenue Sharing Funds (DBH SDA), on local government investments in infrastructure across 508 districts/cities in Indonesia. The method used is dynamic panel regression using the Generalized Method of Moment (GMM) Arellano-Bond approach. This study finds that the role of DBH SDA is still low in infrastructure spending. The role of the central government remains significant in determining infrastructure spending at the district/city level in Indonesia. This indicates that local governments rely more on other sectors in infrastructure investment. By enhancing the role of DBH SDA through technological advancements, it is hoped that the market value of natural resources can be higher through resource downstreaming. This strategy will have broader impacts, as labor needs can be absorbed not only in raw material production activities but also in the processing technology sector. Furthermore, the utilization of natural resources with modern technology can increase extraction efficiency, support sustainable development, and minimize environmental impacts.

Does corporate governance and balance sheet feature influence the financial policy of cooperatives? PLS-SEM approach.

Effective financial policy minimizes business risk, increases the net present value of the Company's investment programs and increases value for shareholders. However, the impact hasn't yet been examined in the research area. The purpose of this study is to empirically investigate, how corporate governance and balance sheet aspects affect the financial policy of cooperatives in south-western Ethiopia using the PLS-SEM model. Information covering three years from 2020 to 2022 was gathered from 145 cooperatives. The study used corporate governance and balance sheet features as the latent factors that affect the dependent variable cooperative financial policy measured by both short-term debt and long-term debt. Managerial characteristics were used as the control variables. The study discovered that corporate governance has negative and significant effect on the financial policy of cooperatives in southwest Ethiopia. The study also revealed that balance sheet features have significant and positive effect the financial policy of cooperatives in southwest Ethiopia. Additionally, managerial characteristics' have a significant impact on the financial policy and balance sheet features but have no impact on the corporate governance of cooperatives. The study concludes that the financial policy of cooperatives in southwest Ethiopia is significantly influenced by all aspects of corporate governance, balance sheet features, and management characteristics'. The study advises cooperatives to consider managerial characteristics', corporate governance, and balance sheet characteristics while establishing their financial policy.

Qualitative analysis as a tool for reducing investment risks in post-mining areas located in urban structures.

Urban development is not a process of even and planned progression on residential-industrial sites. Enclaves of high-standard space separate degraded and abandoned areas after industrial use has ended. The idea of the compact city is challenged by the need to search for niches for possible development and even to respond to crisis situations. Changing the approach to postmining sites located inside urban spaces generates an alternative to urban sprawl and the squandering of the stock of fertile suburban agricultural land. The aim of this article is to draw attention to the urban presence of postmining sites, to take a systemic view of ways to identify and describe their specific elements and to determine their impact, from the perspective of different user groups, on the quality of space. This research combined expert knowledge and the practical experience of users to create a model for a multilevel audit of postmining spaces. Knowledge about the postmining environment was transferred to landscape and urban design, creating a universal tool for developing strategies to increase the standard utilitarian functions of revitalized postmining areas. This tool will be useful at an early stage of urban development, management and planning.

Direct interaction in digital interactive media and stock performance: Evidence from Panorama.

Media information plays an essential role in the stock market. Recent financial research has verified that media information could shock stock price by influencing investors' expectation. Now, a new type of interactive media, called Digital Interactive Media (DIM), is popular in Chinese stock market and becomes the main channel for investors to understand listed companies. Unlike general news media or investor forums, DIM enables direct interaction between listed companies and investors. In the modern society where digital economy is booming, media information would largely affect investors' decisions. Therefore, it is urgent to use natural language processing (NLP) technology to deconstruct the massive questions and answers (Q&A) interactive information in DIM and extract valuable factors that affect stock prices and stock performances to explore the influence mechanism of digital interactive information on stock performances. This paper firstly uses web crawling technology to obtain approximately 110000 Q&A text information from the digital interactive platform ('Panoramic Network') from 2015 to 2021. Then we use big data text analysis technology and emotional quantification technology to extract valuable influencing factors from the massive text. A Multiple Linear Regression (MLR) model was created to explore specific influence mechanism of digital interactive information on stock price performance. The empirical results show that the emotions implicit in investors' questions do not significantly impact stock performance. However, the emotions and attitudes of the answers by listed companies can significantly affect corresponding stock prices, which indirectly confirms the Proximate Cause Effect of behavioral finance. This effect is particularly evident in the stock prices on the current trading day and the next trading day. In the Robustness Test, this paper replaces dependent variable and adds relevant control variables, and the conclusion remains valid. In the Endogeneity Test, this paper selects sample data before the launch of Panorama Network in 2014 as a comparison, and uses a Difference-in-Difference (DID) model to prove the significant impact of the launch of Panorama Network on Chinese stock market. In the Heterogeneity Test, the paper classifies the market value, region, and industry of listed companies and regressed the sub samples, once again confirming the reliability of the empirical conclusions. The results of Robustness Test, Endogeneity Test, and Heterogeneity Test conducted in this paper all support empirical conclusions.

Change in hierarchy of the financial networks: A study on firms of an emerging market in Bangladesh.

We investigate the hierarchical structure of Dhaka stocks' financial networks, known as an emerging market, from 2008 to 2020. To do so, we determine correlations from the returns of the firms over a one-year time window. Then, we construct a minimum spanning tree (MST) from correlations and calculate the hierarchy of the tree using the hierarchical path. We find that during the unprecedented crisis in 2010-11, the hierarchy of this emerging market did not sharply increase like in developed markets, implying the absence of a compact cluster in the center of the tree. Noticeably, the hierarchy fell before the big crashes in the Bangladeshi local market, and the lowest value was found in 2010, just before the 2011 Bangladesh market scam. We also observe a lower hierarchical MST during COVID-19, which implies that the network is fragile and vulnerable to financial crises not seen in developed markets. Moreover, the volatility in the topological indicators of the MST indicates that the network is adequately responding to crises and that the firms that play an important role in the market during our analysis periods are financial, particularly the insurance companies. We notice that the largest degrees are minimal compared to the total number of nodes in the tree, implying that the network nodes are somewhat locally compact rather than globally centrally coupled. For this random structure of the emerging market, the network properties do not properly reflect the hierarchy, especially during crises. Identifying hierarchies, topological indicators, and significant firms will be useful for understanding the movement of an emerging market like Dhaka Stock exchange (DSE), which will be useful for policymakers to develop the market.

Piercing the corporate veil system and creditors protection: Based on the investigation of debt paying ability--Empirical evidence from the 2005 Company Law amendment.

This paper uses the difference-in-differences model to research how the "piercing the corporate veil" system marked by the 2005 Company Law amendment affects the level of corporate creditor protection. The research results show that private enterprises and local state-owned enterprises are sensitive and significant to this legal amendment. In contrast, local state-owned enterprises are more sensitive and have a stronger motivation to protect the interests of creditors. The motivation of companies with weaker profitability for creditor protection lasts not only for the year of law revision but also extends to the year of implementation. With the law's implementation, the growth effect of creditor protection for local state-owned enterprises has become more significant. Further analysis shows that the main findings of this article are more significant in companies with larger debt scales, companies with a higher year-on-year growth rate of operating income, companies with controlling shareholders, and companies with higher stock market capitalization. From an empirical research view, this paper explains the economic effect and mechanism of the whole corporate personality under the complete system and adds economic evidence for how the law acts on the capital market.

Safe Food Supply Chain as Health Network: An Evolutionary Game Analysis of Behavior Strategy for Quality Investment.

There is a natural relation between human health and the quality of their food and drinks, and elevating the quality input level of food production for all enterprises within the food supply chain system forms the foundation for preventing various potential food safety risks that may be encountered. Unlike the previous research on quality investment of food production by enterprises, this paper probes into the evolutionary routes of the behavior strategy selection of subjects in the food supply chain and the preconditions for the equilibrium points of the social co-governance system. It takes the approach of establishing a tripartite evolutionary game model of food suppliers, food manufacturers and consumers on the basis of the social co-governance framework, in view of the above, this paper focuses on the influence of the reputation mechanism and the market contracts among supply chain subjects on the selection of a behavior strategy for quality investment by enterprises under the condition of lawful regulation by government. The results show that every subject selects their own behavior strategy on the basis of the balance of their respective interests. The net disbursement incurred by enterprises for quality investment and the costs of participation in governance by consumers constitute the dominant factors that influence both enterprises' selection of a behavior strategy and the level of social co-governance. Compared with the increase in economic punishment imposed on suppliers for production of risky food raw materials, it is more efficient to control food safety risks by lowering the costs of quality investment by suppliers. Accordingly, this paper proposes advice on policy in an attempt to provide inspiration for preventing and controlling food safety risks.

The role of ICT investment, digital financial inclusion, and environmental tax in promoting sustainable energy development in the MENA region: Evidences with Dynamic Common Correlated Effects (DCE) and instrumental variable-adjusted DCE.

His research investigates the interplay among investment in Information and Communication Technology [ICT], digital financial inclusion, environmental tax policies, and their impact on the progression of sustainable energy development within the Middle East and North Africa [MENA] region. Recognizing the distinctive hurdles impeding sustainable energy advancement, effective policy formulation and implementation in MENA necessitate a comprehensive understanding of these variables. Employing a Dynamic Common Correlated Effects [DCE] model alongside an instrumental variable-adjusted DCE approach, this study explores the relationship between ICT investment, digital financial inclusion, environmental tax, and sustainable energy development. The DCE model facilitates the analysis of dynamic effects and potential correlations, while the instrumental variable-adjusted DCE model addresses issues pertaining to endogeneity. The results indicate that both ICT investment and the promotion of digital financial inclusion significantly and positively impact sustainable energy development in the MENA region. Additionally, the study underscores the importance of environmental tax implementation in fostering sustainable energy advancement, highlighting the critical role of environmental policy interventions. Based on these findings, governmental prioritization of ICT investment and initiatives for digital financial service integration is recommended to bolster sustainable energy growth in MENA. Furthermore, the adoption of efficient environmental tax measures is essential to incentivize sustainable energy practices and mitigate environmental degradation. These policy recommendations aim to create a conducive environment for sustainable energy progression in the MENA region, contributing to both economic prosperity and environmental conservation.

Impact of R&D innovation and political background on corporate growth: A study based on private listed companies in China.

Based on a review of related concepts and theories this study investigates the different impacts of research and development (R&D) innovation and political background on corporate growth in a particular context. Unlike other studies, we integrate these two factors. We empirically analyze 6079 sets of data from 1292 A-share private manufacturing enterprises in Shanghai and Shenzhen from 2012 to 2019. The results show that these factors directly impact corporate growth and have heterogeneous effects at different enterprise growth levels. We find the effect of R&D innovation on corporate growth is more pronounced for young firms. These findings highlight the need for firms to adjust their investments in R&D innovation and political backgrounds at different stages of development to adapt to different markets and political environments.

High-frequency enhanced VaR: A robust univariate realized volatility model for diverse portfolios and market conditions.

In the field of financial risk management, the accuracy of portfolio Value-at-Risk (VaR) forecasts is of critical importance to both practitioners and academics. This study pioneers a comprehensive evaluation of a univariate model that leverages high-frequency intraday data to improve portfolio VaR forecasts, providing a novel contrast to both univariate and multivariate models based on daily data. Existing research has used such high-frequency-based univariate models for index portfolios, it has not adequately studied their robustness for portfolios with diverse risk profiles, particularly under changing market conditions, such as during crises. Our research fills this gap by proposing a refined univariate long-memory realized volatility model that incorporates realized variance and covariance metrics, eliminating the necessity for a parametric covariance matrix. This model captures the long-run dependencies inherent in the volatility process and provides a flexible alternative that can be paired with appropriate return innovation distributions for VaR estimation. Empirical analyses show that our methodology significantly outperforms traditional univariate and multivariate Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models in terms of forecasting accuracy while maintaining computational simplicity and ease of implementation. In particular, the inclusion of high-frequency data in univariate volatility models not only improves forecasting accuracy but also streamlines the complexity of portfolio risk assessment. This research extends the discourse between academic research and financial practice, highlighting the transformative impact of high-frequency data on risk management strategies within the financial sector.