Impact of global financial crunch on financially innovative microfinance institutions in South Asia

The wave of global financial crises(2008-2009)caused a surge in the capital flows of developed countries particularly,between developed and developing countries.The crunch hit all financial sectors with unanticipated severity.The study evaluates the role of a country’s political practices in moderating the impact of global financial crunch on microfinance performance.Using the fixed effect panel regression method on the dataset comprising of 95 MFIs operating in South Asia from 2003 to 2012,we determine that microfinance operational capability shares a positive relationship with the institutional attributes of a country and our output reveals that impact of country’s political practices is pervasive on the financial output of MFIs,liable to different levels of implementation.The findings further reveals that MFIs situated in countries having vigorous political practices are less severely affected by the economic crunch.

Corporate Governance and the Financial Value of Financially Distressed Listed Companies

The article combines the background of Chinese system, theoretically derivates the relationship between corporate governance and their financial value, selects a sample of loss listed companies from 2003 to 2009, and studies how the level of corporate governance affects the value of listed company losses. Research results show that, among corporate governance factors, the largest shareholder and the market for corporate control have obvious positive effects on the financial value of loss listed companies; the proportion of state-owned shares, the type of audit opinion, and corporate govemance factors have obvious negative effects on the financial value of loss listed companies; and managerial ownership, the proportion of independent directors, and the size of the board have no obvious driving effect on the financial value of loss listed companies.

An Empirical Analysis on the Prediction of Chinese Financially Distressed Listed Companies

This paper presents an in-depth analysis of financially distressed listed companies in China between 1998 and 2002. We compare the predictive power of multiple discriminant analysis （MDA）, logistic regression, and neural network models. We design and implement 126 different forecasting models using different predictive methods, different sample proportions, and different initial independent variables. The aim is to determine which model（s） and variables are best applicable for the short-term prediction of financial distress in China. We find that logistic regression models are superior to multiple discriminant analysis models in terms of prediction accuracy rate, restriction of sample distribution or prediction cost, but the neural network models show promise in their low Type I and Type II errors. The paper also inherently tests the applicability of variables traditionally used for bankruptcy prediction to the purpose of financial distress prediction in China.

Audit Reports of Financially Distressed Companies： Emphasis of Matter （EOM） Versus Disclaimers

This paper examines audit reports issued to 39 Malaysian listed companies in financial distress categorized as Practice Note 17 （PNI7） companies by Bursa Malaysia. The study finds that for companies which experienced financial distress, the audit reports are not similar, despite all companies are similarly troubled financially. Companies receive either a disclaimer or an emphasis of matter （EOM） report. The study finds that which of the two reports is given is associated with three variables： current-year operating loss, shareholders＇ deficit, and default status, implying that audit reports do convey information that financial distress is not of the same level and severity among PN 17 companies.

Distinguishing Financially Healthy From Unhealthy SMEs in China

This paper presents empirical evidence on the financial characteristics of Chinese entrepreneurial SMEs, both state-owned and private enterprises, listed in the Chinese stock markets during 2006-2009. Building on extant literature and using a parametric approach on 359 sample SMEs for 2006-2009 period, the study examines the financial characteristics that are embedded in the financially healthy and unhealthy Chinese SMEs. The findings of the study suggest that financially healthy Chinese SMEs have strength in terms of liquidity, profitability, and leverage ratios as compared to financially unhealthy SMEs with the exception of a few cases in leverage and profitability. These findings are worth noting to understand the uniqueness of financial characteristics of the Chinese SMEs and useful for policy makers to deal with the issues related to financially distressed SMEs in China.

On fractional discrete financial system:Bifurcation,chaos,and control

The dynamic analysis of financial systems is a developing field that combines mathematics and economics to understand and explain fluctuations in financial markets.This paper introduces a new three-dimensional(3D)fractional financial map and we dissect its nonlinear dynamics system under commensurate and incommensurate orders.As such,we evaluate when the equilibrium points are stable or unstable at various fractional orders.We use many numerical methods,phase plots in 2D and 3D projections,bifurcation diagrams and the maximum Lyapunov exponent.These techniques reveal that financial maps exhibit chaotic attractor behavior.This study is grounded on the Caputo-like discrete operator,which is specifically influenced by the variance of the commensurate and incommensurate orders.Furthermore,we confirm the presence and measure the complexity of chaos in financial maps by the 0-1 test and the approximate entropy algorithm.Additionally,we offer nonlinear-type controllers to stabilize the fractional financial map.The numerical results of this study are obtained using MATLAB.

2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.

Moderating effect of corporate financialization on the impact of climate policy on corporate green innovation:Evidence from China

With intensifying global climate change,humanity is confronted with unparalleled environmental challenges and risks.This study employs the staggered difference-in-difference model to examine the relationship between climate policy and green innovation in the corporate financialization context.Using Chinese-listed company data from 2008 to 2020,our analysis reveals a favorable correlation between China’s carbon emission trading policy(CCTP)and advancements in green innovation.Furthermore,we find that the level of corporate financialization moderates this correlation,diminishing the driving effect of CCTP on green innovation.Additionally,results of heterogeneity analysis show that this moderating consequence is more evident in non-state owned and low-digitization enterprises compared with state-owned and high-digitization ones.Our findings contribute to the existing literature by clarifying the interaction between CCTP,green innovation,and corporate financialization.Our research provides valuable insights for policymakers and stakeholders seeking to strengthen climate policies and encourages green innovation in different types of businesses.

Does community-based health insurance affect lifestyle and timing of treatment seeking behavior?Evidence from Ethiopia

Objectives This paper aims to investigate the effects of enrollment in the Ethiopian community-based health insurance(CBHI)scheme on household preventive care activities and the timing of treatment-seeking behavior for illness symptoms.There is growing concern about the financial sustainability of CBHI schemes in developing countries.However,few empirical studies have identified potential contributors,including ex-ante and ex-post moral hazards.Methods We implement a household fixed-effect panel data regression model,drawing on three rounds of household survey data collected face to face in districts where CBHI scheme is operational and in districts where it is not operational in Ethiopia.Results The findings show that enrolment in CBHI does not significantly influence household behaviour regarding preventive care activities such as water treatment before drinking and handwashing before meals.However,CBHI significantly increases delay in treatment-seeking behaviour for diseases symptoms.Particularly,on average,we estimate about 4-6 h delay for malaria symptoms,a little above 4 h for tetanus,and 10-11 h for tuberculosis among the insured households.Conclusions While there is evidence that CBHI improve the utilization of outpatient or primary care services,our study suggests that insured members may wait longer before visiting health facilities.This delay could be partly due to moral hazard problems,as insured households,particularly those from rural areas,may consider the opportunity costs associated with visiting health facilities for minor symptoms.Overall,it is essential to identify the primary causes of delays in seeking medical services and implement appropriate interventions to encourage insured individuals to seek early medical attention.

A Brief Introduction to Sichuan Academy of Social Sciences

Sichuan Academy of Social Sciences is the think tank for the People’s Government of Sichuan Province and the Sichuan Provincial Committee of the CPC,with full financial backing of the Party and the government.The Academy consists of 16 institutes,1 graduate school,10 departments of scientific research management services.

Robotics in total knee replacement:Current use and future implications

Robotic total knee replacement(TKR)surgery has evolved over the years with the aim of improving the overall 80% satisfaction rate associated with TKR surgery.Proponents claim higher precision in executing the pre-operative plan which results in improved alignment and possibly better clinical outcomes.Opponents suggest longer operative times with potentially higher complications and no superiority in clinical outcomes alongside increased costs.This editorial will summarize where we currently stand and the future implications of using robotics in knee replacement surgery.

The Impact of the Digital Economy on Financial Regulatory Costs: An Empirical Analysis Based on the Solow Growth Model

As a novel economic form,the digital economy is reshaping the financial regulatory landscape and significantly impacting regulatory costs.This paper incorporates the digital economy and financial regulatory costs into the classic Solow growth model,uncovering an inverted U-shaped relationship between them.A subsequent mechanism analysis explains the rationale behind this relationship.To empirically examine this relationship in China,the paper utilizes inter-provincial panel data from 2013 to 2021 and employs methodologies such as the two-way fixed effects and moderating effects models.These analyses have important implications for the sound and sustainable development of China’s financial industry.The findings indicate:(a)As China’s digital economy develops,its impact on financial regulatory costs follows an inverted U-shaped pattern,initially increasing and then declining.This conclusion remains valid after robustness tests.(b)The influence of the digital economy on regulatory costs depends on favorable external conditions.Specifically,the impact is more pronounced in regions and periods with better digital infrastructure and more abundant human capital.(c)Additionally,redundant resources moderate this impact,which can weaken the inverted U-shaped relationship.Our findings not only provide a theoretical foundation for understanding the impact of the digital economy on financial regulatory costs but also offer valuable policy insights for optimizing financial regulation in China.

Impact of US Financial Sanctions on the Dollar's International Status

In 2022,the United States stepped up its sanctions on Russia.Most notably,it restricted the flow of the Russian Central Bank's foreign exchange(forex)assets,using financial administrative power as a source of strategic leverage.This move should have reduced the appeal of US dollar assets but in reality has not accelerated as expected the decline of the greenback as a store of value.The US dollar's share of global forex reserves increased instead of decreased during 2022 and 2023.Despite the rise of economic costs caused by tightened US financial sanctions,countries that recognize the geopolitical role of the United States have further accepted the dollar's international status;their continued willingness to live with the dollar's“security premium”has given a fillip to the US dollar in the short term,boosting its appeal as a reserve currency.Meanwhile,de-dollarization of forex reserves has yet to reach a sufficient scale,thus falling short of significantly challenging the dollar's reign.From a longer-term perspective,as economic and security conditions shift,countries that accept the dollar's international role or seek de-dollarization may change their choices.As a result,four possible scenarios may arise:(i)the preeminence of the US dollar remains unthreatened;(ii)the international monetary system splits into blocs;(iii)the international monetary system fragments;and(iv)the dollar loses its throne.The author believes that the last scenario is the most likely outcome.

China Intensifies Monetary Policy Adjustments and Boosts Market Confidence

On September 27,the People's Bank of China (hereinafter referred to as the"central bank") announced that,from September 27,2024,the RRR (required reserve ratio)of financial institutions would be lowered by 0.5 percentage points (excluding those that are subject to an RRR of 5%).The weighted average required reserve ratio for financial institutions is about 6.6% after this cut.At the same time,the central bank also announced that from September27,the interest rate for 7-day reverse repo operations in the open market will be adjusted from1.70%to 1.50%.

Assessment of Barriers and Challenges to the Screening and Linkages of Non-Communicable Diseases by Community Health Volunteers in Nyeri County, Kenya

Non-communicable diseases (NCDs) are a significant global health challenge, contributing to 50% of worldwide morbidity and 63% of mortality. The burden is particularly substantial in low—and middle-income countries (LMICs), where 80% of NCD-related deaths occur. A quasi-experimental study addressed this challenge from May 2022 to March 2023. This study utilized a non-equivalent pre-and post-test design, with 300 participants in the quantitative and 70 in the qualitative. The study employed multistage cluster and random sampling to select ten community units, resulting in 150 community health volunteers (CHVs) in the control unit and 150 in the intervention group. Data collection was facilitated through the KOBO app. Qualitative data analysis involved six homogeneous focus group discussions (FGDs) and ten key informant interviews (KIIs), audio-recorded, transcribed, and analyzed using N-Vivo 12. Despite efforts to implement screening programs and improve linkages to care, significant barriers persist. This article reviews these barriers, drawing on current literature and empirical evidence. Key obstacles identified include limited awareness, inadequate healthcare infrastructure, cultural beliefs, financial constraints, fragmented healthcare systems, and challenges linking individuals to appropriate care services. The article explores strategies to overcome these barriers, emphasizing the importance of collaborative approaches involving stakeholders at various levels. Addressing these challenges aims to strengthen NCD screening and linkages to care, ultimately improving health outcomes for populations globally. Several recommendations emerge from the study’s findings and literature review. Raising awareness about NCDs and preventive measures is crucial and can be achieved through targeted health education campaigns and community outreach programs. Addressing healthcare infrastructure deficiencies, such as inadequate facilities and workforce shortages, is essential to ensure access to quality care. Cultural beliefs and practices also play a significant role in shaping health-seeking behavior. Engaging with local communities and incorporating cultural sensitivity into healthcare delivery can help bridge the gap between traditional beliefs and modern healthcare practices. Financial constraints pose a significant barrier to healthcare services, particularly in LMICs. Innovative financing mechanisms, such as health insurance schemes or subsidies, can help alleviate this burden and improve access to care. Furthermore, the fragmented nature of healthcare systems can hinder effective NCD management. Enhancing coordination and integration between primary care providers, specialists, and community health workers is essential to ensure seamless care delivery and patient follow-up. Finally, strengthening linkages between screening programs and care services is critical for the timely diagnosis and management of NCDs. This requires establishing robust referral systems and ensuring continuity of care for patients throughout their healthcare journey. In conclusion, addressing the multifaceted barriers to NCD screening and care linkage is essential for improving health outcomes globally. By implementing targeted interventions and fostering collaboration among stakeholders, progress can be made towards reducing the burden of NCDs and promoting population health.

Financing the Future

Two major finance forums in Beijing provide directions for developing sustainable climate funding.WHEN Mark Carney was the governor of the Bank of England,he found that the number of extreme weather events had tripled and the cost of these disasters increased fivefold in the past 25 years.The financial sector must do something to make a change,he thought.

China Continues to Be World’s Second Largest Insurance Market

Allianz Group recently released the Allianz Global Insurance Report,predicting that China will consolidate its position as the world’s second largest insurance market in the next decade.The report analyzes the business performance of the global insurance market in 2023 and forecasts the development direction and trends of the global insurance industry in the next decade.2023:A year of significant growth According to the report,in 2023,the global insurance industry grew by an impressive 7.5%,which is the fastest rate since the pre-Global Financial Crisis(GFC)era.

A“Going Concern”and the Need to Fully Integrate Economics in the Business School Curriculum

In response to the recommendation by the American Assembly of Collegiate Schools of Business(AACSB,2002),which urged business schools to embark on interdisciplinary programs to facilitate boundary-spanning teaching and learning,many colleges have conducted one form of curriculum integration or the other.Many of these team-taught course integrations,however,concentrate on core business courses without reaching out to related courses in other disciplines.Moreover,due to some factors,the informational contents of management disclosures in annual reports and audit unqualified opinions may not align with the future viability of an enterprise.Using a“going concern concept”,this paper demonstrates how the addition of economics in business school curriculum integration could produce well-rounded business graduates.Economics concepts could unambiguously support the tests that cast doubts on firms’ability to continue operations.