Financial Environment Research Articles

The Mongrel Banking Experience: Emulsifying Physical and Dogital Channels of Banking

The incorporation of modern technology into banking process has transformed the financial environment, providing unrivaled convenience, efficiency and security. In order to accommodate a wide range of consumers taste and demographics, banks have taken strategic approach to adopt technology in combination to traditional banking services

A hierarchical attention-based feature selection and fusion method for credit risk assessment

A stable financial environment is requisite for the continuous growth of the E-business market, emphasizing the importance of credit risk assessment. Generally, credit risk assessment involves evaluating the creditworthiness of the economic entity and predicting the probability of defaulting on their financial obligations. Existing works mainly focus on exploring more features from various sources to help default prediction, but tend to overlook the feature cost and feature fusion problem. In this paper, we proposed a novel credit risk assessment model based on the hierarchical attention method, characterizing the ability to manage feature acquisition cost and adaptively integrate multi-view features. Particularly, to fulfill the utility of multi-source features, three segmenting standards are proposed to categorize multi-source features into different views according to their characteristics and statistical attributes. Then, a feature-level attention mechanism is proposed to estimate feature importance and guide feature selection, while a view-level attention mechanism is proposed to aggregate view representations and produce default predictions. The proposed method is capable of enhancing crucial features in a hierarchical way and undertaking feature selection to meet feature acquisition cost, thereby boosting the model performance with feature cost awareness. Experimental results show that the proposed method can approximate the best performance with 5% of selected features, and can also achieve a 1.1% AUC improvement compared with the best baseline method over the full feature set, demonstrating our effectiveness of enhancing default prediction performance while reducing feature costs.

Unintended effect of reduced patient cost sharing: evidence from China

ABSTRACT In China, hospitals are classified into three tiers based on size and quality to triage patients. Among them, tier-2 and tier-3 hospitals provide similar inpatient services, while tier-3s have higher patient cost sharing due to their ability to provide more sophisticated care. In this study, we exploit a plausibly exogenous change in the financial environment facing tier-2 hospitals: reduced cost sharing in tier-3s. We find that patients are diverted from tier-2s after the reform, and in response to income loss, tier-2s increase the surgical probability and total expense per admission. A 1% decrease in the number of admissions increases the surgical probability by 0.25%, which suggests supplier-induced demand. We also find that tier-2 hospitals induce greater demand from patients with less urgent conditions and more elective procedures. Our analysis suggests that benevolent cost sharing reduction may have unintended consequences beyond the targeted group, and policymakers who neglect these effects would underestimate the costs of such reforms.

An Assessment Methodology for International Hydrogen Competitiveness: Seven Case Studies Compared

Currently, the global energy structure is undergoing a transition from fossil fuels to renewable energy sources, with the hydrogen economy playing a pivotal role. Hydrogen is not only an important energy carrier needed to achieve the global goal of energy conservation and emission reduction, it represents a key object of the future international energy trade. As hydrogen trade expands, nations are increasingly allocating resources to enhance the international competitiveness of their respective hydrogen industries. This paper introduces an index that can be used to evaluate international hydrogen competitiveness and elucidate the most competitive countries in the hydrogen trade. To calculate the competitiveness scores of seven major prospective hydrogen market participants, we employed the entropy weight method. This method considers five essential factors: potential resources, economic and financial base, infrastructure, government support and institutional environment, and technological feasibility. The results indicate that the USA and Australia exhibit the highest composite indices. These findings can serve as a guide for countries in formulating suitable policies and strategies to bolster the development and international competitiveness of their respective hydrogen industries.

Financing of infrastructure projects in territorial communities

The issues of financial support for the restoration of critical infrastructure are of utmost relevance during martial law, charac­terized by limited resources and capabilities and increased risks, and must be addressed compre­hensively at the state and local levels. The structural redistribution of local budget reve­nues and expenditures that occurs during martial law necessitates the search for effective ways to attract off-budget instruments for financial support of infrastructure in commu­nities. The aim of the article is to assess the financial capacities and opportunities for restoring the infrastructure of territorial com­mu­nities and to review promising financial mechanisms. The research is based on the hypothesis that it is necessary to develop combi­ned forms of financial support at the level of territorial communities with an emphasis on intensifying the involvement of international donors. The research methods were used, such as systematic and comparative analysis and synthesis, abstract and logical method, and logical generalization. The research is based on materials from government sources on the state and local budgets and on the basis of scientific works by Ukrainian scholars. The article has presented the opinions of scientists on the financial support essence and systematized its forms, methods and tools for direct finan­cial support of infrastructure projects in communities; revealed the possibilities and prospects for their application under martial law. The expe­diency of choosing combined forms of finan­cial support as those that balance the interests of the participants has been substan­tiated. Based on the state analysis of budgetary financing of infrastructure projects at the state and local levels, the availability of financial capacity in local budgets and the continuing trend of long-term projects by international donors at the national level were established. The main method of financial support for projects at the state level is loan programs from international financial institutions and foreign governments. The author has proposed the development of promising forms of financial support for the restoration of infrastructure projects in the form of loan and grants programs combination from the Nordic Environment Finance Corporation (NEFCO)

Does reform policy of municipal bonds raise firm risk? A quasi-natural experiment from China

Does reform policy of municipal bonds increase firm risk? We address this question by examining the effects of the ‘Local Issuance and Local Repayment’ (LILR) reform policy launched by the Chinese central government on firm stock return volatility between 2009 and 2019. We find that the LILR reform policy increases the volatility of firms' stock returns. Possible explanations for the effect of reform policy are that it exacerbates firms' financial constraints and reduces the effectiveness of fiscal policy. Consistently, we find that the aggravating effect of reform policy is more pronounced in regions characterized by worse financial environment, worse fiscal condition, firms in growth and decline stages and in downstream and service and support industries. We provide empirical evidence from a Chinese quasi-natural experiment to address the long-term economic development issues of local government debts.

Forecasting Corn Futures Prices Using the LSTM Model

The central government's No. 1 document introduced new requirements for “maintaining the stability of agricultural product prices.” However, recent years have seen significant fluctuations in corn prices in China, highlighting the importance of exploring the trends of corn futures prices for stabilizing spot corn prices. Given the increasingly complex financial market environment, traditional linear econometric methods and machine learning approaches face inherent limitations in price forecasting. This study utilizes extensive data and applies the Long-Short Term Memory (LSTM) deep learning model to forecast corn futures prices. During the model training process, historical corn futures data were used as hidden input feature variables, along with additional indicators such as spot corn prices, real-time quotes from corn deep processing enterprises, downstream product prices in the corn industry chain, prices of similar substitutes, and the Baidu Search Index for “corn prices.” The model was trained and fine-tuned with hyperparameters to assess its predictive accuracy under different time windows, learning rates, and iteration counts. Experimental results indicate that incorporating external influencing factors significantly enhances the model's predictive accuracy compared to using only historical price data. The model with a learning rate of 0.01, 50 iterations, a time window of 5, and 4 hidden layers achieved the highest accuracy. The findings demonstrate that integrating external factors and optimizing hyperparameters substantially improve the LSTM model's predictive precision, outperforming models that rely solely on historical data. The study provides policy recommendations on promoting advanced technology, enhancing data collection and information sharing, and improving market transparency and fairness. Additionally, the conclusion discusses the extensive application potential and promising development prospects of the LSTM model in corn futures price forecasting. This model enables market participants to obtain more accurate market trend forecasts, facilitating more informed investment decisions. The research also offers new insights and methodological references for scholars in financial forecasting, contributing to the further advancement of this field.Funding: This study was supported by the 2024 Key Research Project of Higher Education Institutions of the Henan Provincial Department of Education [Project Number 24B790006], the Henan Provincial Department of Education [Project Number 2021GGJS120], and the Henan Provincial Department of Education [Project Number 2021SJGLX478]. Additionally, this study received support from the Xinxiang City Federation of Social Sciences [Project Number SKL-2024-157].

A Review of Central Bank Digital Currency: Current Status and Changing Trends

Since the emergence of the Central Bank Digital Currency (CBDC), which digitally represents the legal currency of the country in which it is created, significant developments have been experienced in the global financial environment as a result of the changes brought about by the innovations. Although CBDCs offer various advantages, such as cost reduction, financial inclusion, and increasing the efficiency of payment systems, they may create some disadvantages, such as the risk of dysfunctionalization of the banking sector or decreasing the level of security. At this point, it is necessary to be aware of the possible risks it may create and to develop an optimum strategy in case such risks arise to establish a successful CBDC system and benefit from its advantages. In this context, the aim of this study is to analyze all aspects (emergence process, positive/negative sides, projects, use cases, and technical infrastructure to be used) of this digital currency and to make a comprehensive evaluation of CBDC applications around the world. The evaluations show that many countries are not indifferent to this new digital currency, which has a very high development potential, and continue their research intensively in order not to fall behind the steps taken in CBDC.

The Development Path of Financial Promotion of Science and Technology Innovation in the Context of New Quality Productivity Development

Science and technology innovation is the kernel of developing new quality productivity, and finance has an important role in promoting science and technology innovation. Based on this, this paper studies the path of financial boosting of science and technology innovation, selects the national panel data from 2013 to 2022, adopts the entropy value method to determine the weight of each index and calculate the comprehensive value, and establishes the VAR model of financial input and science and technology innovation development. It is found that 35% of the changes in the development of science and technology innovation can be explained by financial inputs, and further puts forward suggestions to improve the breadth, depth and precision of financial services for science and technology innovation in four aspects: innovating the form of financial inputs, improving the matching precision between financial services and science and technology innovation needs, innovating the financial service system and optimizing the financial ecological environment, so as to provide reference and reference for the formation and development of the development of new-quality productivity.

Overview of mom's confidence when planning to return low birth weight baby from the hospital

Introduction: Babies with low birth weight are one of the health problems often faced by mothers and families in various countries, including Indonesia. The return of babies with low birth weights from the hospital became a critical stage in their care after a period of intensive hospitalization. Objective: Describe the mother's confidence level when planning to return a baby with a low birth weight to the hospital. Method: quantitative research with a descriptive design involving 29 respondents. This study used non-probability and consecutive sampling techniques to determine subjects who met the inclusion criteria. The measuring instrument uses the Maternal Confidence Questionnaire Instrument, and the analysis results are descriptive data equipped with tables. Results: Most of the self-confidence of mothers planning to return home with high-birth-weight babies from the hospital was high among 16 respondents (55.2%). Factors that influence a mother's confidence include previous experience caring for a child, social support from family and friends, information and education provided by medical personnel, and an adequate financial condition and environment for the care of the baby. Conclusion: The mother's self-confidence plays a crucial role in planning the return of a low-birthweight baby from the hospital. Social support, knowledge of baby care, interaction with health workers, and understanding of the baby's health condition contribute to the mother's confidence. A holistic approach involving adequate social support, information and education, and financial and environmental readiness to increase maternal confidence in caring for a low-birth-weight baby after discharge from the hospital

Ensemble Machine Learning in Algorithmic Trading: Balancing Performance and Risk

This research delves into the potential of ensemble machine learning in algorithmic trading, aiming to strike a balance between performance and risk management. In the increasingly intricate landscape of financial markets, employing ensemble approaches that amalgamate multiple models becomes imperative. The study elucidates how these techniques enhance the accuracy, resilience, and adaptability of algorithmic trading systems. It further emphasizes the significance of model variability, hyperparameter tuning, and the bagging ensemble learning technique for optimizing outcomes. Moreover, the research scrutinizes the benefits of ensemble learning for algorithmic trading portfolios, particularly in terms of tail risk management and risk diversification. By utilizing ensemble approaches, several advantages emerge, including mitigated model bias, heightened generalizability, and an improved Sharpe Ratio. Ensemble methods enable traders to navigate market complexities more effectively, adapting to changing conditions and reducing the impact of outliers. This comprehensive approach not only enhances trading performance but also fortifies risk management strategies, making it indispensable in today's dynamic financial environment. Keywords: Ensemble machine learning, algorithmic trading, risk management, performance, Stock market forecast

Corporate financialization and green technological innovation: Evidence from China

Corporate financialization and changes in the external financial environment have important but unexplored implications for firms' green technological innovation (GTI). Taking a sample of non-financial listed companies in China's A-share market from 2010 to 2020, and using panel and mediation models, this paper investigates the role of corporate financialization on GTI. We reveal that: (1) corporate financialization notably dampens GTI, especially substantive GTI. (2) The “crowding-out” effect proved the main mechanism by which corporate financialization affects GTI and there was no “reservoir” effect during the sample period. (3) Corporate financialization has a larger dark impact on GTI when firms with higher environmental, social, and governance scores and levels of industrial innovation, lower levels of digital financial development and industrial knowledge spillovers, and when firms are younger or non-state owned.

Marketing Strategy of Private Enterprises Based on Bayesian Dynamic Panel Model of Machine Learning Algorithms

Machine learning algorithms have attracted widespread attention in both industry and academia. This article mainly studies the marketing strategy decision-making of private listed enterprises based on Bayesian panel data model. By constructing a Bayesian static panel data model and a Bayesian dynamic panel data model, an empirical analysis was conducted on the debt financing decisions of private enterprises from two aspects: external financial environment and internal governance. The experimental results show that the MC error and standard deviation of parameter estimation for Bayesian static panel data model and Bayesian dynamic panel data model are both very small. This method contains more information, increases observation data and degrees of freedom. This article provides important theoretical guidance for the coordinated development of private listed enterprises and state-owned enterprises. It is conducive to promoting the coordinated development of the entire national economy.

Research on Students' Learning Emotion Regulation and Learning Effectiveness in Intelligent Emotional Assisted Financial Learning Environment

Research on financial behavior demonstrates that a large portion of the bias associated with financial behavior can be explained by personal traits (PT) and emotional intelligence (EI). To prevent financial behavior bias, the present investigation attempts to identify that factors of the PT and EI of students studying finance require additional training. To achieve this, the Big Four Index (BFI) and Trait EI Survey (TEIS) are employed to assess the PT and EI scores of a group of participants and workers in the finance sector. The professional expertise gaps in PT and EI among students were then observed, as then the effects of these gaps on people's financial behavior. Results indicate that students do not possess the EI skills needed for self-worth, self-control, stress reduction, self-motivation, optimism, and empathy. Furthermore, openness, mindfulness, friendliness, and extraversion are connected with PT. Students that have this kind of weakness have a greater probability to be talented, confidence, self-control, fear of losing, and tolerance of risk, among other financial behavior biases. The results indicate that educational institutions should be mindful that providing thorough PT and EI education to finance graduates may assist them prepare for employment.

ANALYSIS OF THE USE OF ARTIFICIAL INTELLIGENCE IN THE DIGITAL FINANCIAL ENVIRONMENT IN UKRAINE

The significance of the research is determined by the fact that the use of AI in the digital financial environment of Ukraine opens numerous opportunities for improving its efficiency, security, accessibility, and innovation. Therefore, this article is devoted to the analysis of the specifics of using artificial intelligence in the digital financial environment in Ukraine. Within the research, it has been proven that artificial intelligence is actively being implemented and adapted to the needs of various areas of the digital financial environment through specific transformations. It has been concluded that among the transformations driven by the use of artificial intelligence in the digital financial environment in Ukraine, we can distinguish the growing popularity of remote payments, the expansion of open banking usage, the development of digital identification, the advancement in financial data analysis, fraud detection, process automation, and the provision of personalized financial advice, the increase in the number of fintech companies working with AI, the heightened focus on cybersecurity, and the active regulation of the digital financial environment.

An Intelligent Financial Investment Decision Model Based on Multi-Agent Reinforcement Learning

Financial investigation decision is a complex affair that highly relies on expert knowledge. In the era of big data, it remains practical to make intelligent financial investigation decisions via computing technology. As a consequence, this work introduces reinforcement learning to search for effective decision schemes that are adaptive to the diverse financial environment. Therefore, an intelligent financial investment decision model based on multi-agent reinforcement learning is proposed in this paper. First, the long short-term memory (LSTM) model is utilized to strengthen financial investment data. Then, the hidden Markov model (HMM) is utilized to make a preliminary judgment on financial investment strategy. Next, a multi-agent deep deterministic policy gradient (MADDPG) is employed to set the goal of agent reinforcement and determine decision indicators based on the financial market environment. Multiple agents (investors) form a cooperative strategy through learning and interaction to achieve the optimal investment decision in the financial market. The experimental results on realistic data indicate that the proposal can play an important role in investment decision-making.

The role of investment knowledge, student perceptions, and technological advances in affecting investment interests in sharia capital markets

This research aims to analyze the influence of investment knowledge, student perceptions, and technological advances on investment interests in Sharia capital markets. Employing a quantitative approach, the study utilizes non-probability sampling methods, specifically the convenience sample system, to select 312 active students from a total population of 5000 at Perbanas Institute. Double linear regression is employed for data analysis. The findings reveal significant implications for the role of technological advancements in shaping student investment interest in Sharia capital markets. Specifically, the study finds that the presence of technological advancements, such as applications and trading platforms, positively impacts student interest in investing in the Sharia market. However, contrary to expectations, investment knowledge and student perceptions do not demonstrate a significant influence on student investment interest in Sharia Capital markets. This suggests that despite possessing knowledge and certain perceptions, students' investment decisions in Sharia capital markets remain unaffected. The practical implications of these findings emphasizes the importance for students to remain abreast of technological advancements to potentially enhance their investment interest in Sharia capital markets. This research contributes to understanding the multifaceted factors influencing investment decisions in Sharia-compliant financial environments and underscores the significance of technological integration in shaping investment behaviors among students.

Navigating the Digital Frontier: Exploring the Landscape of Digital Finance in the United States

Driven by technology innovation and digital change, the US financial environment is going through a significant transition. The exceptional accessibility and simplicity of digital finance—which encompasses fintech, bitcoin, and mobile banking—benefits both consumers and businesses. However, regulatory frameworks, consumer behavior, and cybersecurity risks come along with this paradigm shift. Through the integration of contemporary literature studies and empirical data, this study aims to present a comprehensive picture of the digital financial landscape in the United States. We evaluate the revolutionary impact of digital technologies on financial services, client preferences, emerging risks like decentralized finance (DeFi) and cybersecurity vulnerabilities, and regulatory innovations and limits using a systematic evaluation approach. Our findings underscore the multifaceted nature of digital banking and point to important trends, drivers, and stakeholder implications. We suggest enhancing regulatory coordination, fostering financial education and awareness, putting robust cybersecurity measures in place, supporting regulatory innovation, fortifying consumer protection measures, and keeping an eye on and responding to emerging risks to address the opportunities and challenges that have been identified. By putting these suggestions into practice, regulators, legislators, and business stakeholders will be able to handle the complexities of the digital banking ecosystem and fully utilize its potential to promote innovation, financial inclusion, and economic growth.

Perspective on Artisinal and Small-scale gold Mining: Ecological and Socio-economic Inovation

Global aspirations for stringent mercury regulations have not been translated effectively in Russia, largely due to the emergence of new domestic cinnabar mining ventures. These initiatives have increased locally sourced mercury, reducing its cost and undermining efforts to curtail its use. This study employs Stochastic Multicriteria Acceptability Analysis (SMAA) within an environmental finance framework to rigorously assess the challenges associated with mitigating mercury use in artisanal and small-scale gold mining (ASGM) in Russia. The sampling period for the research spans from June to September 2018 in the Krasnoyarsk Krai area, comprising Yeruda and Chebyzhek villages in the Kurakhinsky district and Abalatskoye village in the Minusinsky district. The paper investigated these complexities using SMAA to identify the multifaceted challenges in implementing the Minamata Agreement, a global treaty committed to regulating and curtailing mercury use. The findings revealed that mercury management is associated with various socioeconomic factors, labor relations, and power structures that have collectively hindered mercury-free practices in gold mining. Therefore, this study highlighted the critical need for a holistic, sustainable economic framework by incorporating new insights into the societal metabolic functions of dynamic mineral finance. Utilizing SMAA within the specific context of native employment conditions, the authors challenged the prevailing norms and offered robust, data-driven strategies aimed at reducing or eliminating mercury usage in ASGM.

Shadow Banking in the United States: Emerging Factors, Operation Modes and the Current Situation of Supervision - Based on the Comparison with Chinese Shadow Banking System and Market

In 2009, Ben Bernanke, chairman of the Federal Reserve, described shadow banking as a financial intermediary that converts savings into investments in addition to regulated deposit-taking institutions. To put it simply, the essence of shadow banking is a financial innovation that breaks through traditional financial institutions in order to maximize profits in the financial market environment. In this paper, we compare the U.S. and Chinese shadow banking system and their market system. From that, we analyze their similarities and differences from the aspects of emerging factors, classification and scale, and operation mode. Based on the above analysis, we then discuss the current situation of supervision of shadow banking in the United States and find that the U.S. shadow banking is negligent in institutional constraints and has practical difficulties in legal supervision. Finally, we put forward some suggestions on the future development and financial structure optimization of shadow banking.