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.

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.

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.

Causality-driven multivariate stock movement forecasting.

Our study aims to investigate the interdependence between international stock markets and sentiments from financial news in stock forecasting. We adopt the Temporal Fusion Transformers (TFT) to incorporate intra and inter-market correlations and the interaction between the information flow, i.e. causality, of financial news sentiment and the dynamics of the stock market. The current study distinguishes itself from existing research by adopting Dynamic Transfer Entropy (DTE) to establish an accurate information flow propagation between stock and sentiments. DTE has the advantage of providing time series that mine information flow propagation paths between certain parts of the time series, highlighting marginal events such as spikes or sudden jumps, which are crucial in financial time series. The proposed methodological approach involves the following elements: a FinBERT-based textual analysis of financial news articles to extract sentiment time series, the use of the Transfer Entropy and corresponding heat maps to analyze the net information flows, the calculation of the DTE time series, which are considered as co-occurring covariates of stock Price, and TFT-based stock forecasting. The Dow Jones Industrial Average index of 13 countries, along with daily financial news data obtained through the New York Times API, are used to demonstrate the validity and superiority of the proposed DTE-based causality method along with TFT for accurate stock Price and Return forecasting compared to state-of-the-art time series forecasting methods.

WTI, Brent or implied volatility index: Perspective of volatility spillover from oil market to Chinese stock market.

This study investigates the impact of oil market uncertainty on the volatility of Chinese sector indexes. We utilize commonly used realized volatility of WTI and Brent oil price along with the CBOE crude oil volatility index (OVX) to embody the oil market uncertainty. Based on the sample span from Mar 16, 2011 to Dec 31, 2019, this study utilizes vector autoregression (VAR) model to derive the impacts of the three different uncertainty indicators on Chinese stock volatilities. The empirical results show, for all sectors, the impact of OVX on sectors volatilities are more economically and statistically significant than that of realized volatility of both WTI and Brent oil prices, especially after the Chinese refined oil pricing reform of March 27, 2013. That implies OVX is more informative than traditional WTI and Brent oil prices with respect to volatility spillover from oil market to Chinese stock market. This study could provide some important implications for the participants in Chinese stock market.

The influence of cultural friction on foreign divestment of multinational enterprises--the moderating role of formal institutional distance and political connections.

Multinational enterprises frequently divest their foreign assets in the current economic environment. Existing research, based on friction theory, has mainly focused on the impacts of political and economic disparities on foreign divestment while neglecting the nuanced influence of cultural factors. To address this gap, this paper draws on the cultural friction perspective to capture the diverse cultural resistance faced by each enterprise and explore the relationship between cultural friction and foreign divestment. Data from Chinese publicly listed enterprises engaged in foreign investment are leveraged, and a dual-level analysis is conducted using Logit panel regression and Cox survival analysis to examine the relationship between cultural friction and foreign divestment from both the viewpoints of the parent company and the overseas subsidiary. Additionally, the paper examines the marginal factors that affect the relationship between them from an institutional perspective. The findings reveal that cultural friction has a positive influence on the propensity of multinational enterprises to divest from foreign markets. Interestingly, a "formal institutional distance paradox" is demonstrated in our study, and politically connected enterprises are found to be more vulnerable to foreign divestment due to the "curse of political affiliations".

Foreign shareholder, overseas sale and corporate profit margin.

China is actively promoting the development of a robust trading nation. In this context, utilizing data from China's A-share listed companies spanning from 2003 to 2021, this study investigates the impact of foreign shareholders on enterprises in a scenario where overseas sales reduce the profit margin of Chinese firms. The findings reveal that overseas sales do indeed decrease the profit margin of Chinese enterprises; however, foreign shareholders mitigate this negative effect and various robustness tests support this conclusion. Mechanism analysis confirms that foreign shareholders primarily enhance enterprise productivity through improved production technology spillover effects, thereby alleviating the adverse impact of overseas sales on Chinese firms' profit margins. Heterogeneity analysis demonstrates that both longer holding periods for foreign shareholders and multiple foreign shareholders significantly alleviate the negative influence of overseas sales on Chinese firms' profit margins. Moreover, there is significant heterogeneity in how foreign shareholders alleviate these detrimental consequences based on property rights nature, institutional environment, overseas related party transactions and subsidiaries, as well as industry attributes. These findings have important reference value for China's efforts towards becoming a strong trading nation and can contribute to enhancing trade capacity in other countries.

"Investor attention fluctuation and stock market volatility: Evidence from China".

This paper examines the linkage between Chinese stock market volatility and investor attention fluctuation. In Heterogeneous autoregressive (HAR) model, first, we analyzed the linkage between both decomposed and undecomposed stock market realized volatility and investor attention fluctuations across full-sample and two-year moving window sub-samples. Second, we compare the predictive power of four models in short-, medium-, and long-term volatility forecasting. Empirical results show large positive attention fluctuation amplified Chinese stock market volatility after the outbreak of COVID-19, and negative small attention fluctuation significantly stabilized stock market volatility before COVID-19, and the impact dwindled in after COVID-19. The model incorporating decomposed realized volatility and decomposed attention fluctuation performs better in volatility Forecasting. This research underscores a shift in the dynamics between stock market volatility and investor attention fluctuations, and investor attention fluctuation improves the volatility forecasting accuracy of the Chinese stock market.

Common institutional investors and the quality of management earnings forecasts-Empirical and machine learning evidences.

Based on the data of the Chinese A-share listed firms in China Shanghai and Shenzhen Stock Exchange from 2014 to 2021, this article explores the relationship between common institutional investors and the quality of management earnings forecasts. The study used the multiple linear regression model and empirically found that common institutional investors positively impact the precision of earnings forecasts. This article also uses graph neural networks to predict the precision of earnings forecasts. Our findings have shown that common institutional investors form external supervision over restricting management to release a wide width of earnings forecasts, which helps to improve the risk warning function of earnings forecasts and promote the sustainable development of information disclosure from management in the Chinese capital market. One of the marginal contributions of this paper is that it enriches the literature related to the economic consequences of common institutional shareholding. Then, the neural network method used to predict the quality of management forecasts enhances the research method of institutional investors and the behavior of management earnings forecasts. Thirdly, this paper calls for strengthening information sharing and circulation among institutional investors to reduce information asymmetry between investors and management.

Research on U-shaped relationship between short-term debt for long-term use and supply chain enterprise default risk: Evidence from Chinese listed firms.

This paper empirically investigates the impact mechanism of short-term debt for long-term use and the default risk of supply chain firms with the data of Chinese A-share listed firms from 2007 to 2021. The study shows that there is a significant U-curve relationship between short-term debt for long-term use and supply chain firms' default risk, and too high or too low a level of short-term loans and long-term investments will worsen firms' default risk. In addition, firm performance plays an mediating effect in the process of short-term debt for long-term investment affecting the default risk of supply chain firms. Finally, customer effect and firm heterogeneity play a moderating role in the impact of short-term loans and long-term investments on the default risk of supply chain firms, and the U-shaped relationship will be strengthened under the high-intensity customer effect. This study has important theoretical and practical significance for analyzing the impact of default risk contagion in supply chain enterprises.

The impact of green financial development on stock price crash risk from the perspective of information asymmetry in Chinese listed companies.

Solving the crash risk problem of corporate stock price caused by information asymmetry can mitigate the negative externality of its carbon emission to become green, low-carbon, and high-quality development. Green finance generally profoundly impacts micro-corporate economics and macro-financial systems but remains a giant puzzle of whether they can effectively resolve the crash risk. This paper examined the impact of green financial development on the stock price crash risk using the sample data of non-financial listed companies in Shanghai and Shenzhen A stock market in China from 2009 to 2020. We found that green financial development significantly inhibits the stock price crash risk; this is more obvious in listed companies with a high level of asymmetric information. And companies in high-level regions of green financial development attracted more attention from institutional investors and analysts. As a result, they disclosed more information about their operational status, thus reducing the crash risk of corporate stock price from the torrential public pressure of lousy environmental details. Therefore, this study will help continuously discuss the costs, benefits, and value promotion of green finance for synergy between corporate performance and environmental performance to improve ESG capabilities.

Fintech and investment risk of digital finance: mediating role of clean energy and green bonds through the dynamics of spill over.

This study examines how financial technology (FinTech) and green bonds have affected the ability of firms to finance energy efficiency measures by using data obtained from a subset of Chinese companies listed on the A-share market between 2011 and 2021. We apply the quantile-on-quantile method, which allows us to examine the interdependence of time series in each economy separately and yields data on the global and national levels indicating the relationship between the variables. The results show that an increase in both direct and indirect financing for businesses, as well as inter-bank competition, can greatly mitigate the financial limitations that firms suffer as a result of FinTech expansion. Our estimates show that the energy efficiency of the countries we chose improves when they are financed with green bonds across all quantiles of the data. Organizations not owned by the state, SMBs, and the more rapidly developing eastern half of China promise to benefit the most from the moderating effect of FinTech because of the faster pace of development there. The immediate ameliorating effect that financial technology has on reduced lending criteria mostly benefits businesses with either a strong innovation rate or a poor social responsibility performance rate. This is because businesses sharing either of these features are more likely to experiment and develop new products. Both theoretical and practical repercussions of this finding are explored.

Digital finance and regional green innovation: the perspective of environmental regulation.

The relationship between digital finance and regional green innovation has been partially confirmed, yet the role of environmental regulation in it remains unexplored. Therefore, this paper examines the impact of digital finance on regional green innovation and tests the moderating role of environmental regulation using Chinese city-level data from 2011 to 2019 as a research sample. The results show that digital finance can significantly promote regional green innovation by alleviating regional financing constraints and increasing regional R&D investment. Besides, digital finance has apparent regional difference effects (the contribution of digital finance to regional green innovation is greater in eastern China than in western China, and the development of digital finance in neighbouring regions has a negative transmission effect on local green innovation). Finally, environmental regulation positively moderates the relationship between digital finance and regional green innovation. This paper explores the relationship between digital finance and regional green innovation from the perspective of environmental regulation, providing empirical evidence to promote regional green innovation.

Absence of ultimate controller and investment efficiency: Evidence from China.

In modern enterprises with a separation of powers, the ultimate controller can effectively influence the implementation of corporate strategy and operational management efficiency, as well as improve corporate governance by monitoring and limiting the management entrenchment effect within enterprises. Based on the information pertaining to ultimate controllers disclosed by enterprises in their annual reports, this study empirically tested whether the absence of the ultimate controller impacts investment efficiency using the data of Chinese A-share listed companies from 2007 to 2020. It was found that the investment efficiency of enterprises without ultimate controllers is relatively lower than those with ultimate controllers. This is reflected in the insufficient investment of enterprises without an ultimate controller. Moreover, the effect is more significant when the financial environment, internal governance environment, and external governance environment of firms are worse. The mechanism analysis demonstrated that the absence of an ultimate controller causes a more severe insider agency problem and a significantly higher degree of financing constraints, which leads to underinvestment and reduces investment efficiency of firms. The economic consequence test also found that the inefficient investment caused by the absence of ultimate controllers would damage the future value of enterprises, but would increase managers' compensation. Overall, this study suggests that ultimate controllers are an important part of a firm's internal governance, especially for monitoring management behavior and resolving agency conflicts.

Commentary: Reconsidering Pharmaceutical Research and Development Investments.

Following Lee and colleagues' (2023) article explaining how Canadians are being shortchanged by drug companies when it comes to investments in research and development (R&D), this rejoinder adds context and appends two other very problematic elements in the debate between wishful narratives over the industry's contribution in R&D and actual numbers. First, even the current stricter definition of R&D investment might simply be too large considering that elements such as seeding trials - a well-known marketing device - can be accounted for as R&D expenditures. Second, this rejoinder identifies how Statistics Canada acted in concert with Innovative Medicines Canada to reinforce the industry's preferred narratives around R&D expenditures. This situation puts into question the trustworthiness of Canada's statistical agency.

The Investment development path theory: Evidence from developing countries' agricultural sector.

Background: We examined the investment development path (IDP) through the perspective of developing countries' agricultural sector. Our analytical approach indirectly accounts for interactions among countries regarding cross-border resource transfers. Aside from providing knowledge on testing the IDP by inferential statistics, the information would be relevant for policymaking. Identifying the stage(s) in the IDP not only highlights the global appeal of agriculture but also guides firms seeking to expand beyond borders. This information is essential for developing an effective economic strategy. Methods: We employed data from 1991 to 2021 for 55 countries from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and applied a fixed effects estimator corrected for serial correlation and non-constant variances. Results and conclusions: We found that agriculture in developing countries is currently in stages I and II of the IDP. Broadly, agricultural production requires policies that would increase outward foreign direct investment and inward foreign direct investment. Domestic agricultural businesses in developing countries must develop capacity by learning from foreign multinationals. This would enable agricultural businesses to invest abroad. Such a move would lead to an increase in outward FDI. As this would have resulted from increased GDP per capita, it will lead to movement from the existing stage to higher ones.

Impact of Financing Constraints on Firm Performance: Moderating Effect Based on Firm Size.

This paper selects the relevant data of Shanghai and Shenzhen NG listed companies from 2016 to 2020 as the research object. By drawing on relevant research results at home and abroad, the variable KZ is used to measure the degree of corporate financing constraints, and the variable Tobin Q is used to measure corporate performance. The test draws the following conclusions: financing constraints are conducive to improving corporate performance. The reason is that the higher the degree of corporate financing constraints is, the more cautious the managers are in the use of funds, and the company managers can formulate more scientific management strategies, so as to improve corporate performance. Through further research, it is found that, with the expansion of the scale of the enterprise, the degree of financing constraints has less negative impact on the performance of the enterprise. Performance has a positive impact. According to the above conclusions, the government should improve the financial market system, build a multilevel capital market, and fundamentally ease the financing constraints of enterprises, to develop and improve self-management.

MSPM: A modularized and scalable multi-agent reinforcement learning-based system for financial portfolio management.

Financial portfolio management (PM) is one of the most applicable problems in reinforcement learning (RL) owing to its sequential decision-making nature. However, existing RL-based approaches rarely focus on scalability or reusability to adapt to the ever-changing markets. These approaches are rigid and unscalable to accommodate the varying number of assets of portfolios and increasing need for heterogeneous data input. Also, RL agents in the existing systems are ad-hoc trained and hardly reusable for different portfolios. To confront the above problems, a modular design is desired for the systems to be compatible with reusable asset-dedicated agents. In this paper, we propose a multi-agent RL-based system for PM (MSPM). MSPM involves two types of asynchronously-updated modules: Evolving Agent Module (EAM) and Strategic Agent Module (SAM). An EAM is an information-generating module with a Deep Q-network (DQN) agent, and it receives heterogeneous data and generates signal-comprised information for a particular asset. An SAM is a decision-making module with a Proximal Policy Optimization (PPO) agent for portfolio optimization, and it connects to multiple EAMs to reallocate the corresponding assets in a financial portfolio. Once been trained, EAMs can be connected to any SAM at will, like assembling LEGO blocks. With its modularized architecture, the multi-step condensation of volatile market information, and the reusable design of EAM, MSPM simultaneously addresses the two challenges in RL-based PM: scalability and reusability. Experiments on 8-year U.S. stock market data prove the effectiveness of MSPM in profit accumulation by its outperformance over five different baselines in terms of accumulated rate of return (ARR), daily rate of return (DRR), and Sortino ratio (SR). MSPM improves ARR by at least 186.5% compared to constant rebalanced portfolio (CRP), a widely-used PM strategy. To validate the indispensability of EAM, we back-test and compare MSPMs on four different portfolios. EAM-enabled MSPMs improve ARR by at least 1341.8% compared to EAM-disabled MSPMs.

The source of financial contagion and spillovers: An evaluation of the covid-19 pandemic and the global financial crisis.

This study investigates the reaction of stock markets to the Covid-19 pandemic and the Global Financial Crisis of 2008 (GFC) and compares their influence in terms of risk exposures. The empirical investigation is conducted using the modified ICSS test, DCC-GARCH, and Diebold-Yilmaz connectedness analysis to examine financial contagion and volatility spillovers. To further reveal the impact of these two crises, the statistical features of tranquil and crisis periods under different time intervals are also compared. The test results show that although the outbreak's origin was in China, the US stock market is the source of financial contagion and volatility spillovers during the pandemic, just as it was during the GFC. The propagation of shocks is considerably higher between developed economies compared to emerging markets. Additionally, the results show that the COVID-19 pandemic induced a more severe contagious effect and risk transmission than the GFC. The study provides an extensive examination of the COVID-19 pandemic and the GFC in terms of financial contagion and volatility spillovers. The results suggest the presence of strong co-movements of world stock markets with the US equity market, especially in periods of financial turmoil.

Investor sentiment-aware prediction model for P2P lending indicators based on LSTM.

In recent years, online lending has created many risks while providing lending convenience to Chinese individuals and small and medium-sized enterprises. The timely assessment and prediction of the status of industry indicators is an important prerequisite for effectively preventing the spread of risks in China's new financial formats. The role of investor sentiment should not be underestimated. We first use the BERT model to divide investor sentiment in the review information of China's online lending third-party information website into three categories and analyze the relationship between investor sentiment and quantitative indicators of online lending product transactions. The results show that the percentage of positive comments has a positive relationship to the borrowing interest rate of P2P platforms that investors are willing to participate in for bidding projects. The percentage of negative comments has an inverse relationship to the borrowing period. Second, after introducing investor sentiment into the long short-term memory (LSTM) model, the average RMSE of the three forecast periods for borrowing interest rates is 0.373, and that of the borrowing period is 0.262, which are better than the values of other control models. Corresponding suggestions for the risk prevention of China's new financial formats are made.