Journal of Trends in Financial and Economics
Journal of Trends in Financial and Economics (JTFE) is an international peer-reviewed, open access journal.
THE DEVELOPMENT MODELS OF CHINA’S NEW ENERGY VEHICLE INNOVATION CONSORTIUMS: CASE ANALYSES OF CHANGAN
Against the strategic backdrop of the "dual carbon" goals, the new energy vehicle (NEV) industry has become a core sector driving energy transition and low-carbon development in the transportation sector. Innovation consortia, as collaborative innovation organizations integrating enterprises, universities, research institutes, and government resources, are of great significance for breaking through key generic technologies and enhancing industrial innovation capabilities. This study selects Changan Automobile as a case study, comprehensively employing case analysis and evolutionary game models to systematically investigate the internal and external challenges it faces in the process of building an NEV innovation consortium, the cooperation mechanisms it adopts, and its game strategies. By constructing a two-party game model between the enterprise and research institutions, the analysis focuses on the impact of key parameters such as total cooperation benefits and profit distribution ratios on the stability of bilateral cooperation. The findings reveal that: the increase in cooperation benefits and the fairness of the distribution mechanism are core elements for the stable operation of innovation consortia; the technology integration and resource coordination capabilities of leading enterprises play a dominant role in the consortium's effectiveness; and the design of systematic cooperation mechanisms (including benefit distribution, risk sharing, breach penalties, and communication and coordination mechanisms) constitutes an important foundation for ensuring the long-term development and sustained innovation capability of the consortium. Accordingly, this paper proposes managerial recommendations such as establishing a reasonable benefit distribution mechanism, increasing government investment in R&D, and accelerating the formation of innovation consortia, aiming to provide theoretical support and practical references for the collaborative innovation and high-quality development of China's NEV industry.
Industry-university-research collaboration
Copyright (c) 2026 TianChen Yang https://creativecommons.org/licenses/by/4.0
FORECASTING STOCK PRICES WITH DEEP LEARNING MODELS: A COMPARISON OF LONG-SHORT TERM MEMORY (LSTM), GATED RECURRENT UNIT (GRU), ATTENTION MECHANISM, AND TRANSFORMER MODE
With the expansion of the stock market, more and more people have started to use deep learning models to predict the stock market and facilitate their trading decisions. This paper compares four mainstream deep learning models for stock price prediction: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Attention Mechanism, and Transformer Model. Using MSE and RMSE as the evaluation metrics, we found LSTM performs the best in stock price prediction of the four companies of selection: Boeing Co, General Electric Co, Coca-Cola Co, and Johnson & Johnson. With a deeper analysis of the result, we found several limitations of LSTM, such as inconsistency of accuracy when forecasting the stock price of different firms. Hence, we suggested corresponding ways of improvement: adding more training data, introducing external factors, and integrating LSTM with other models.
Copyright (c) 2025 ZeTong Li; JiuRu Lyu; ZiHan Wang; Liu Yang https://creativecommons.org/licenses/by/4.0
DEEP LEARNING APPROACHES TO STOCHASTIC VOLATILITY MODEL CALIBRATION: A COMPARATIVE ANALYSIS OF NEURAL SDES AND TRADITIONAL METHODS
The calibration of stochastic volatility models remains a computationally demanding challenge in quantitative finance, where traditional optimization algorithms often encounter difficulties with numerical stability, convergence speed, and local minima entrapment. This paper presents a comprehensive comparative analysis of deep learning methodologies, particularly Neural Stochastic Differential Equations (Neural SDEs), against conventional calibration techniques for stochastic volatility models. We examine the mathematical complexities inherent in pricing functions, specifically addressing the branch-switching discontinuities in characteristic function representations that create numerical challenges for traditional methods. Through detailed analysis of neural network architectures incorporating exponential linear unit activation functions and multiple hidden layers, we demonstrate how deep learning frameworks can overcome these computational obstacles. Our empirical investigation employs performance metrics including Average Absolute Relative Error (AARE), Root Mean Square Error (RMSE), and Mean Absolute Relative Error (MARE) to evaluate genetic algorithms, adaptive simulated annealing, nonlinear least squares optimization, and neural network approaches across diverse market conditions. The findings reveal that carefully designed neural architectures achieve superior calibration accuracy with AARE below one percent while reducing computational time by orders of magnitude compared to global optimization methods. Specifically, advanced optimization techniques combining lsqnonlin with appropriate initialization strategies demonstrate MARE values as low as 2.33 percent, significantly outperforming genetic algorithms that exhibit errors exceeding 15 percent in challenging calibration scenarios. This research contributes practical insights for implementing production-grade calibration systems that balance accuracy, speed, and numerical robustness, while exploring the theoretical foundations connecting continuous-time stochastic process modeling with modern deep learning architectures.
Copyright (c) 2025 YiFan Zhang; Wei Chen; Oliver Schmidt https://creativecommons.org/licenses/by/4.0
FAIRNESS-AWARE GRAPH CONTRASTIVE LEARNING FOR FRAUD DETECTION IN FINANCIAL NETWORKS
Financial fraud detection has become increasingly critical as digital transactions proliferate across global financial networks. Traditional machine learning approaches often exhibit bias against certain demographic groups and fail to capture complex relational patterns inherent in financial transaction networks. This paper proposes a novel fairness-aware graph contrastive learning framework that simultaneously addresses algorithmic bias and improves fraud detection accuracy in financial networks. Our approach leverages graph neural networks (GNNs) enhanced with contrastive learning mechanisms while incorporating fairness constraints to ensure equitable treatment across different user groups. The framework introduces a dual-objective optimization strategy that balances fraud detection performance with fairness metrics, utilizing counterfactual graph augmentation techniques to mitigate discriminatory patterns. Experimental results on real-world financial datasets demonstrate that our method achieves superior fraud detection accuracy while significantly reducing bias compared to existing approaches. The proposed framework represents a significant advancement in developing trustworthy artificial intelligence systems for financial fraud detection that maintain both effectiveness and ethical considerations.
Copyright (c) 2025 Jorge Martinez; Caroline Davis https://creativecommons.org/licenses/by/4.0
CODE VERSUS PRECEDENT: BLOCKCHAIN-DRIVEN GOVERNANCE AS A RESPONSE TO THE CRISIS OF TRUSTS IN MODERN CANADIAN FINANCE
Canada's trust law is caught in a structural crisis, torn between the rigid formalism of common law and the need for adaptive governance in a digitalized global economy. Drawing on Frederick Schauer's theory of "rules as exclusionary reasons" and Douglass North's concept of "path dependence," this paper argues that Canada's regulatory framework—exemplified by Section 122 of the Income Tax Act and Section 56 of the Ontario Securities Act - prioritizes procedural compliance over substantive resilience, leading to systemic failures such as the collapse of Penn West Petroleum Trust and the judicial rejection of cryptocurrency trusts in QuadrigaCX. Through case studies and comparative analysis, we demonstrate how Canada's adherence to outdated doctrines undermines both domestic stability and international alignment with OECD standards. We propose a dual-track solution: legislative modernization through a Uniform Digital Trust Act and the integration of blockchain-based smart contracts (e.g., ERC-1400) to encode fiduciary duties into programmable legal structures. This approach not only resolves the Schauer-North paradox but also positions Canada as a leader in responsive, technology-driven trust governance.
Copyright (c) 2025 ShuYu Zhou; ZhaoXia Deng https://creativecommons.org/licenses/by/4.0
INTEROPERABILITY TECHNOLOGIES AND CREDIT RECOGNITION MECHANISMS AMONG TRADE FINANCE BLOCKCHAIN NETWORKS
Trade finance is the backbone of global commerce but remains technologically fragmented, leading to inefficiency, risk, and high costs. This study analyzes the technical and institutional barriers to interoperability among blockchain trade finance platforms, evaluates credit recognition mechanisms, and proposes a path toward seamless, secure financial integration. Comprehensive case studies from Asia and global consortia demonstrate that interoperability and automated credit recognition can reduce costs by 73–89%, settlement time from days to seconds, and significantly improve access for SMEs. Drawing on empirical data from eTradeConnect, mBridge, Marco Polo, and PBCTFP, we present a three-layer architectural framework integrating technical, institutional, and regulatory dimensions. The paper presents a phased implementation roadmap through 2028 and identifies policy recommendations for central banks, financial regulators, and industry consortia seeking to develop robust interoperability infrastructure.
Copyright (c) 2025 Yang Wu https://creativecommons.org/licenses/by/4.0
A CROSS-NATIONAL EMPIRICAL STUDY ON CLIMATE DISASTER RISK AND ECONOMIC DEVELOPMENT LEVEL
Against the backdrop of intensifying global warming, climate disasters pose severe challenges to socio-economic systems worldwide, and the issue of "Climate Inequality" is becoming increasingly prominent. To deeply investigate the correlation between economic development levels and the capacity to withstand climate disaster risks, this study selects a sample of 20 representative countries, covering different income levels and geographical regions. The Global Climate Risk Index (CRI, 2019-2023 average) published by Germanwatch is used as the proxy variable for disaster risk, and the GDP per capita (PPP) provided by the World Bank for the same period is used as the indicator of economic development. Visual analysis was conducted using scatter plots, linear trend lines, and grouped box plots, and Pearson correlation coefficients were calculated for statistical testing. The research results indicate a significant positive correlation between GDP per capita and the Climate Risk Index (r = 0.91, P < 0.001). That is, the higher the level of economic development, the larger the Climate Risk Index value, indicating lower vulnerability to climate disasters and stronger coping capabilities. Group analysis further reveals that the median Climate Risk Index of the low-income country group is significantly lower than that of the high-income country group. These empirical results confirm the objective existence of "Climate Inequality" and emphasize that the level of economic development is a key factor affecting a country's climate resilience. This study provides a quantitative basis for the international community to formulate more targeted climate aid and adaptation policies.
Copyright (c) 2026 SiYuan Pan https://creativecommons.org/licenses/by/4.0
CRITICAL ANALYSIS OF THE IMPACT OF THE IMF DEAL ON PAKISTAN’S ECONOMY
This study is concerned with examining Pakistan’s deals with the International Monetary Fund (IMF). As an international economic institution, the IMF has a significant impact on the borrower’s market by implementing its economic agenda. The economic landscape of Pakistan in recent years has been significantly impacted by the implementation of neoliberal policies, which the IMF has imposed. The country has a long history of relying on IMF loans. Pakistan has entered into numerous IMF programs, often as a last resort to address the balance of payment crises. However, these programs have been criticized for imposing unrealistic conditions on the country, which has further exacerbated the economic instability and social inequality. Methodologically, this study is based on a mixed method of analysis by analyzing the secondary data. Academically, it is significant while drawing attention to the conditions that have pushed the country into the deals.
Copyright (c) 2025 Sajjad Ali; Shakir Ullah; Feng Liang; Mukht Yar Ali https://creativecommons.org/licenses/by/4.0
PREDICTIVE ANALYTICS FOR TRANSFER PRICING AND ITS REGULATORY IMPLICATIONS
Transfer pricing (TP) has become increasingly complex in the era of globalization, requiring multinational enterprises (MNEs) to establish arm's length prices for intercompany transactions across jurisdictions. Traditional transfer pricing methodologies, while established through decades of regulatory practice, face significant challenges in addressing the complexity and volume of modern cross-border transactions. The emergence of predictive analytics (PA) and machine learning (ML) techniques offers transformative potential for enhancing transfer pricing determination, documentation, and compliance. This review examines the application of predictive analytics in transfer pricing contexts, exploring how artificial intelligence (AI), big data analytics (BDA), and advanced statistical methods are reshaping both corporate tax planning strategies and regulatory enforcement mechanisms. The regulatory implications of these technological advances are profound, raising questions about data transparency, algorithmic accountability, and the evolution of arm's length principle (ALP) interpretation. This paper synthesizes current research on predictive modeling approaches including neural networks (NN), random forests (RF), gradient boosting machines (GBM), and support vector machines (SVM) applied to comparable company selection, profit allocation, and risk assessment. We examine how tax authorities worldwide are deploying similar technologies for audit selection and compliance monitoring, creating both opportunities and challenges for MNEs navigating increasingly data-driven regulatory environments. The review addresses critical implementation considerations including data quality requirements, model interpretability standards, and the alignment of predictive systems with existing legal frameworks under Organisation for Economic Co-operation and Development (OECD) guidelines and local regulations. Findings indicate that while predictive analytics significantly improves accuracy and efficiency in transfer pricing processes, successful implementation requires careful attention to regulatory acceptability, documentation standards, and cross-functional integration between tax, finance, and data science teams.
Copyright (c) 2025 JiaYing Chen; Pan Li; Huijie Fan https://creativecommons.org/licenses/by/4.0
THE STYLE PREFERENCE SWITCHING OF CHINESE INSTITUTIONAL INVESTORS AND STOCK RETURNS
This paper investigates the relationship between investment style preference switching among Chinese institutional investors and stock returns. Against the backdrop of China’s emerging and imperfect stock market, institutional investors—often assumed to be rational—exhibit significant behavioral biases and irrational tendencies, such as short-term speculation and style-driven trading. By categorizing stocks into extreme style pairs (e.g., large-cap vs. small-cap, value vs. growth, winner vs. loser portfolios), the study analyzes institutional holdings and style-switching behaviors using quantitative models. The results indicate that institutional investors frequently engage in style preference switching driven by past returns and macroeconomic factors, which in turn significantly affects stock price volatility and market stability. The findings suggest that such behavior often amplifies market fluctuations and contradicts the expected role of institutional investors as market stabilizers. Accordingly, the paper proposes policy recommendations aimed at improving internal governance, enhancing transparency, and strengthening regulatory guidance to promote long-term value investing and mitigate irrational market impacts.
Copyright (c) 2025 NiJia Gu; JiaNing Lu; Cong Chen https://creativecommons.org/licenses/by/4.0
HIGH-QUALITY DEVELOPMENT FOR RURAL CULTURAL INDUSTRIES IN THE GUANGDONG-HONG KONG-MACAO GREATER BAY AREA FROM THE PERSPECTIVE OF NEW QUALITY PRODUCTIVITY FORCES
This study examines the high-quality development of rural cultural industries in the Guangdong-Hong Kong-Macao Greater Bay Area from the perspective of new-type productive forces. Findings reveal a gradient distribution of cultural resources across the region, challenges to industrial advancement include limited application of new-type productive forces, shortages of talent and capital, inadequate infrastructure, weak technological capabilities, and insufficient cross-border policy coordination. To address these challenges, this paper constructs an analytical framework centered on “new factors-integration models-policy coordination” and empirically proposes three development pathways. These pathways aim to provide theoretical support and practical solutions for achieving urban-rural integration and rural revitalization: First, digital empowerment, involving infrastructure development and the creation of digital cultural and creative products; Second, creative activation, focusing on cultivating design talent and building intellectual property systems; Third, cross-border coordination, aimed at breaking down policy barriers and facilitating project implementation.
Guangdong-Hong Kong-Macao Greater Bay Area
Copyright (c) 2025 TingTing Zheng; ZiChen Wang https://creativecommons.org/licenses/by/4.0
BEYOND AUTOMATION: ETHICAL RISK AND GOVERNANCE OF AI IN BUSINESS PRACTICE
The rapid proliferation of artificial intelligence (AI) across various business functions is fundamentally changing how organizations make decisions. While AI-driven systems promise to improve efficiency and analytical accuracy, their widespread application also introduces new ethical risks related to accountability, fairness, and transparency. This paper explores the ethical impact of AI applications in the business environment by analyzing corporate AI investment trends, the rise of internal ethics projects, and the distribution of ethical risks across different application areas. Using public reporting and governance frameworks, the study finds that ethical risk exposure increases dramatically as AI systems shift from operational support to high-impact decision-making. The findings suggest that responsible AI governance should be integrated into strategic management processes, rather than being viewed as an isolated compliance issue.
Copyright (c) 2026 YiCheng Wu https://creativecommons.org/licenses/by/4.0
GREEN TECHNOLOGY INNOVATION AND CORPORATE RESILIENCE: EVIDENCE FROM CHINA UNDER THE DUAL-CARBON GOAL
Based on the panel data of Chinese A-share listed companies from 2008 to 2022, this study systematically examines the impact of green technology innovation on corporate resilience and its mechanism. The results show that green technology innovation significantly strengthens corporate resilience, and the relationship remains robust after multiple tests. Mechanism analysis suggests that the effect operates mainly through improved environmental performance and reduced stock price volatility. Moreover, the impact is more pronounced in technology-intensive industries and among firms in eastern regions. This study extends the literature by linking green innovation to organizational resilience in an emerging economy context and highlights the strategic importance of green technology for firms navigating the transition toward a low-carbon economy.
Copyright (c) 2025 Nian Li; XueYing Liu; YanJun Li; MengMei Li https://creativecommons.org/licenses/by/4.0
WHAT DRIVES THE EFFICIENCY OF GREEN TECHNOLOGY INNOVATION IN INDUSTRIAL SECTORS? AN ANALYSIS BASED ON THE TOE FRAMEWORK USING NCA AND FSQCA
As the nexus of the "innovation-driven" and "green development" national strategies, green technology innovation resonates with China's dual carbon targets and represents an essential pathway toward achieving high-quality development. Existing literature has seldom employed a holistic framework to investigate the complex causal mechanisms through which technological, organizational, and environmental conditions influence green technology innovation efficiency, thereby largely overlooking the configurational effects among these antecedent conditions. To further advance green technology innovation and enhance its efficiency, this study examines China's industrial sectors. Drawing on the Technology-Organization-Environment (TOE) framework, we utilize both Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) on a sample of 38 industrial sectors above a designated size. The analysis explores how six antecedent conditions across the technological, organizational, and environmental dimensions combine to impact green technology innovation. The findings are threefold. First, no single antecedent condition is necessary for achieving high green technology innovation efficiency, although technological factors exert a relatively strong constraint. Second, three distinct configurational pathways lead to high efficiency: a "technology-led, government-supported" path, a "technology-led, independent-innovation" path, and an "environment-technology-organization synergy" path. Third, in an otherwise favorable market environment, ill-suited environmental regulations can suppress innovation efficiency, a context where even strong market demand fails to be effective, suggesting that the impact of organizational conditions is subject to a threshold.
Fuzzy-set qualitative comparative analysis (fsQCA)
Copyright (c) 2025 XiangLe Shangguan; YuSha Wei https://creativecommons.org/licenses/by/4.0
MACHINE LEARNING-ENHANCED TEXT ANALYTICS FOR EFFICIENT AUDIT DOCUMENTATION REVIEW
Audit documentation review represents a critical yet time-intensive component of financial auditing processes, requiring extensive manual analysis of textual evidence, supporting documents, and work papers. Traditional audit documentation review methods rely heavily on manual examination and keyword-based searches, leading to inconsistent coverage, potential oversight of critical issues, and significant resource allocation challenges. This study proposes a machine learning-enhanced text analytics framework designed to automate and improve the efficiency of audit documentation review processes. The framework integrates Natural Language Processing (NLP) techniques with supervised learning algorithms to automatically classify, prioritize, and extract relevant information from audit documentation. Advanced text mining capabilities enable the identification of risk indicators, compliance issues, and anomalous patterns within large volumes of textual audit evidence. Experimental validation using real-world audit documentation datasets demonstrates that the proposed framework achieves 91.4% accuracy in document classification and reduces manual review time by 68%. The system successfully identifies high-risk documentation requiring detailed examination while automating the processing of routine audit materials. Implementation results show significant improvements in audit efficiency, consistency, and coverage, supporting enhanced audit quality and regulatory compliance.
Copyright (c) 2025 Matthew Reid; Julia Stone; Paul Whittaker https://creativecommons.org/licenses/by/4.0
EMPOWERING THE ENTIRE AGRICULTURAL VALUE CHAIN WITH DIGITAL TECHNOLOGIES: A MECHANISM STUDY ON DRIVING LEAPS IN NEW QUALITY AGRICULTURAL PRODUCTIVITY
In the context of the information age, digital technology is profoundly empowering the entire agricultural value chain, driving a qualitative leap in agricultural productivity across production, processing, circulation, and sales. This paper first constructs a theoretical framework for digital agriculture, systematically outlining the application pathways of key technologies such as the Internet of Things, big data, artificial intelligence, and blockchain in monitoring the agricultural production environment, precision irrigation and fertilization, pest and disease prediction and control, intelligent processing and quality traceability, cold chain logistics optimization, and precision marketing and interaction. Secondly, through typical cases, it verifies the significant effects of digital technology on indicators such as water saving, pesticide reduction, loss reduction, lower defect rates, reduced spoilage rates, and lower transportation costs. Finally, based on four mechanisms—technological innovation, data-driven decision-making, industrial chain synergy, and market responsiveness—it proposes policy recommendations for building rural networks and big data platforms, strengthening talent cultivation and recruitment, increasing special funds and financial support, improving standards and data security, and promoting industrial synergy. This research provides theoretical support and practical pathways for the digital transformation and high-quality development of agriculture.
Copyright (c) 2025 JiaQi Zhang https://creativecommons.org/licenses/by/4.0
EFFICIENCY EVALUATION OF MAJOR BANKS BASED ON DEA-MALMQUIST INDEXES
In this paper, we conducted an in-depth evaluation study on the efficiency of major banks in China using the DEA-Malmquist index method. By collecting, organizing and analyzing the data of major banks, we constructed the DEA model and used the Malmquist index to monitor the efficiency changes of each bank dynamically. The results of the study show that there are significant differences in the efficiency of different banks, and at the same time, with the passage of time, the efficiency of each bank shows different trends of change. The findings of this paper are of great guiding significance for understanding the efficiency situation of China's banking industry, optimizing the allocation of bank resources, and improving the competitiveness of banks.
Copyright (c) 2025 Hui Liu; Yu Tian; XiaoXiao Wu; JiaYi Luo; SiSi Tong; MinShi He https://creativecommons.org/licenses/by/4.0
TIME SERIES FORECASTING IN BUSINESS INTELLIGENCE: A COMPARATIVE STUDY OF CLASSICAL AND MACHINE LEARNING APPROACHES FOR SALES TREND PREDICTION
Sales trend forecasting is a critical function within modern business intelligence (BI) systems, enabling organizations to optimize inventory management, allocate resources effectively, and make strategic decisions in an increasingly volatile market. Traditionally, classical time series models such as ARIMA and Exponential Smoothing have been widely used due to their interpretability and robust theoretical foundations. However, the emergence of machine learning (ML) methods such as Random Forests, Gradient Boosting Machines, and Long Short-Term Memory (LSTM) networks has introduced new opportunities for capturing complex, non-linear patterns in sales data. This review provides a comprehensive comparison between classical and machine learning approaches for sales trend prediction, examining their strengths, limitations, and practical applications. Classical models offer simplicity, computational efficiency, and strong performance with stationary and linear data, making them suitable for well-structured datasets. Conversely, machine learning models excel in handling large, noisy, and multi-dimensional datasets, offering superior accuracy at the cost of higher computational demands and reduced interpretability. Real-world applications across retail, finance, supply chain management, and healthcare are explored, highlighting the transformative impact of time series forecasting in business operations. Key challenges including data quality, model maintenance, and the need for explainable AI are discussed alongside future directions such as real-time forecasting, transfer learning, and federated learning. By synthesizing insights from both classical and contemporary forecasting paradigms, this review aims to guide researchers, data scientists, and business leaders in selecting appropriate methodologies for enhancing predictive capabilities within business intelligence ecosystems.
Copyright (c) 2025 Ramchorn Gharami; Delwar Karim; Amit Kumar; Rashid Khan https://creativecommons.org/licenses/by/4.0
THE PRACTICAL LOGIC OF ACHIEVING COMMON PROSPERITY IN RURAL AREAS: A VALUE CO-CREATION PERSPECTIVE
Achieving common prosperity is both the objective and the mission of China, with rural areas serving as the key focal point in this endeavor. Existing studies have predominantly focused on the logic of single-subject intervention, while paying insufficient attention to the operational logic of achieving common prosperity. Guided by value co-creation theory, this study constructs a logical analytical framework of "common prosperity value orientation-multi-stakeholder value co-creation-endogenous transition in rural areas" to examine the operational logic of achieving common prosperity in rural areas. Drawing on the representative case of Gantian Village, the study identifies an integrated mechanism in which governments restructure factor flow mechanisms through top-level institutional design, village collectives collaboratively establish market-oriented operational entities to mobilize land resources, social organizations extend industrial chains via technological capital, and farmers engage in value distribution through multi-dimensional role transitions. This institutional architecture establishes a self-reinforcing cycle of resource revitalization, value creation, and equitable benefit-sharing. By driving the transformation of traditional agriculture into standardized production systems and branded operations, it simultaneously enables coordinated progress in economic productivity, governance innovation, and cultural revitalization.
Copyright (c) 2025 ZiYuan Zhou; Jing Wen; GuoRun Liu https://creativecommons.org/licenses/by/4.0
CHARACTERISTICS, ISSUES, AND COUNTERMEASURES OF CHINA’S DIGITAL ECONOMY DEVELOPMENT
Under the wave of the new technological revolution, the digital economy is reshaping the allocation of production factors, making it a pivotal arena in global competition. While China's digital economy is advancing rapidly, it faces challenges such as platform monopolies, data security risks, and the digital divide. Digital productivity not only enhances wealth creation efficiency but also profoundly transforms production relations, necessitating the establishment of a new governance framework. Accordingly, this study proposes targeted governance strategies from the perspective of government intelligence to support high-quality economic development.
Copyright (c) 2025 ZhenJie Guo https://creativecommons.org/licenses/by/4.0
STOCK PRICE RESEARCH BASED ON ARIMA-GARCH-LSTM HYBRID MODEL
As financial markets become increasingly complex, the demand for stock price forecasting is growing. To capture both linear trends and volatility in sequences as well as nonlinear dependencies, this paper proposes an ARIMA-GARCH-LSTM hybrid model. First, ARIMA is used to extract linear factors, followed by GARCH to express residual volatility conditions, and finally LSTM to capture deep nonlinear features. Based on the closing prices of the Shanghai Composite Index over 1,027 trading days from 2021 to 2025, RMSE, MAE, and MAPE were used for moving forecasts and multi-indicator estimates. The experiments show that the hybrid model outperforms individual ARIMA, GARCH, or LSTM models in all metrics, confirming its accuracy and robustness. Additionally, the hybrid model demonstrates strong adaptability during periods of high volatility.
Copyright (c) 2025 ChaoYan Wei; LanLan Li; PangLeYi Chen; MeiHui Huang; HuiLin Wei; KunYao Yao; XuYang Wang; Xin Ya; ChaoHai Wei https://creativecommons.org/licenses/by/4.0
"TARO MEETS NEW FOOD GENERATION" —— MARKET INVESTIGATION OF DEEP-PROCESSED TARO PRODUCTS IN LIPU
The old proverb "A steamed taro makes all the neighbors fragrant" proves the unique charm of Lipu taro. In the cloud mountain area of Lipu County, Guangxi, the century-old planting technology is realizing the modernization transformation of traditional technology through technological innovation. Nowadays, the deep processing of Lipu taro has formed a complete industrial chain. In recent years, the public awareness of the deep-processed products of Lipu taro has been very high, but the homogeneity of the products is serious, and the innovation is insufficient. There are still some people who have never bought or rarely bought them, and there is still great development potential in the consumer market. Therefore, this paper analyzes the factors that affect consumers to buy such products, and provides suggestions and future development marketing strategies for Lipu taro deep-processing products merchants. In this study, by using SPSS statistical analysis, this paper investigates and analyzes the basic information of consumers of Lipu taro deep-processed products, as well as the factors influencing consumers' satisfaction and recognition of Lipu taro, users' emotion and stickiness. The questionnaire survey was carried out with the help of Tencent's questionnaire platform, and the quality of the questionnaire was controlled by reliability and validity analysis in the pre-survey stage, and the Cronbach coefficient was 0.969, which showed that the questionnaire data had good reliability and validity. Finally, 550 questionnaires were collected, 93 invalid questionnaires were eliminated, and 457 valid questionnaires were obtained, with an effective rate of about 83.09%. Structural equation model was used to explore the influencing factors of consumption willingness of taro in Lipu. By using the grey relational analysis, it is concluded that there is a good correlation between the selected variables. Exploratory factor analysis is used to put forward path hypothesis for three different perceptions. According to the fitting results of the structural equation model, the fitness index has reached the ideal standard, indicating that the overall model has a good path fitting degree, in which two path assumptions, product quality and purchase perception, are established, and new strategies are formulated to influence customers' purchase intention from these two aspects. The K-means cluster analysis is used to classify the consumer groups, and the consumer groups are divided into developmental consumers, key consumers and potential consumers, which shows that different groups have different consumption levels and attitudes towards the deep-processed products of Lipu taro. According to their consumption habits and psychological characteristics, personalized marketing strategies are formulated respectively. From the market point of view, this paper gives the reference strategy of product marketing model from three aspects: product sales promotion, target consumer groups and product elements promotion, and proposes to deepen market research to accurately locate demand, strengthen product innovation to create differential advantages, pay attention to brand building to enhance influence, strengthen marketing promotion to expand share, strengthen industrial chain cooperation and coordinated development, and inherit and carry forward the social responsibility and sustainable development of Lipu taro deep-processed products.
Copyright (c) 2025 RongJin Li https://creativecommons.org/licenses/by/4.0
THE APPLICATION OF DEEP REINFORCEMENT LEARNING IN ASSET ALLOCATION: A THEORETICAL FRAMEWORK AND EMPIRICAL ANALYSIS
Asset allocation is a fundamental challenge in investment management, traditionally addressed through models such as mean-variance optimization. However, dynamic market environments and multi-period investment horizons limit the effectiveness of static methods. In recent years, the emergence of deep reinforcement learning (DRL) has provided a powerful tool for addressing complex sequential decision-making problems in finance. This paper conducts a comprehensive academic analysis of the application of DRL in asset allocation. First, we introduce the asset allocation problem and its challenges, then review the basic concepts of DRL and its relevance to financial decision-making. Next, we propose a theoretical framework for transforming the asset allocation problem into a Markov decision process and describe in detail how DRL agents learn optimal investment strategies under various assumptions and structures within this framework. Subsequently, through a review of foreign academic literature, this paper examines existing findings on the application of DRL in asset allocation from a qualitative perspective, including the superior performance of DRL strategies relative to traditional methods in certain scenarios and cautionary results where DRL remains competitive even under simple benchmarks. We discuss the current limitations of DRL methods, high transaction costs, and potential directions for improvement and future research priorities. The study concludes that while DRL holds great potential for enhancing asset allocation theory and practice, several key practical challenges must be addressed before its full potential can be realized.
Copyright (c) 2025 ZiLin Zhou https://creativecommons.org/licenses/by/4.0
MACHINE LEARNING APPROACHES FOR ACCURATE DEMAND FORECASTING IN SUPPLY CHAIN MANAGEMENT
Accurate demand forecasting is a cornerstone of effective supply chain management, enabling companies to align production, inventory, and distribution with market needs. Traditional statistical models often fail to capture the nonlinear and complex patterns in consumer demand, particularly in the presence of seasonal shifts, promotional events, and external shocks. In recent years, machine learning (ML) has emerged as a powerful tool for enhancing demand forecasting accuracy by leveraging large-scale historical and real-time data. This paper reviews the core machine learning techniques applied to demand forecasting, including supervised learning, time series forecasting models, and ensemble methods. We develop and evaluate a hybrid forecasting framework that integrates Long Short-Term Memory (LSTM) neural networks with gradient boosting to capture both sequential patterns and feature-based dependencies. The proposed approach is validated using a retail demand dataset, and its performance is benchmarked against traditional models. The results demonstrate that ML-based methods significantly outperform classical forecasting techniques, offering improvements in forecast precision, robustness to noise, and responsiveness to dynamic market signals.
Copyright (c) 2025 Liu Zhen; Yang Lin https://creativecommons.org/licenses/by/4.0
ENHANCING TAX PREPARATION THROUGH LARGE LANGUAGE MODELS: A USER-CENTRIC FRAMEWORK
The complexity and opacity of modern tax systems present significant challenges for individuals and small businesses during tax preparation. In recent years, large language models (LLMs) have demonstrated the potential to understand, interpret, and generate human-like language at scale. This paper proposes a user-centric framework that leverages LLMs to enhance tax preparation through personalized assistance, error detection, regulatory compliance guidance, and intelligent document analysis. We analyze the capabilities and limitations of current LLMs, present a system architecture for integrating these models into tax platforms, and evaluate their performance using simulated taxpayer scenarios. The framework emphasizes explainability, privacy protection, and real-time adaptability to user input. Results indicate that LLMs significantly reduce user burden, improve accuracy, and foster greater financial literacy. The findings highlight the transformative potential of language-based AI to democratize access to complex tax knowledge and reduce dependency on traditional, costly tax advisory services.
Copyright (c) 2025 Thomas Eriksson; Karin Olsson https://creativecommons.org/licenses/by/4.0
LITERATURE REVIEW OF SELF-IMPROVEMENT PRODUCTS
The rise of self-improvement products is closely related to lifelong learning, skill iteration, and mental health needs in modern society. This paper integrates the theories of evolutionary psychology and sociology to reveal that they drive individual development through the dual path of compensatory psychological compensation and developmental multidimensional optimization. Products cover physical, cognitive, economic and spiritual categories, and consumption behavior is influenced by emotional perception (e.g., guilt triggers immediate consumption, and awe reinforces long-term commitment), cognitive motivation (goal setting and social comparison), and social environment (resource competition and group norms). Although technological empowerment has eased class anxiety, it has led to digital exclusion and ethical risks. In the future, it is necessary to strengthen interdisciplinary integration and long-term evaluation, and to balance technological rationality and humanistic care at the practical level in order to promote sustainable development.
Copyright (c) 2025 DongPing Zheng; Pei Feng; JinFu Huang https://creativecommons.org/licenses/by/4.0
THE DISBURSEMENT OF COMMON FUND IN THE KWAHU WEST MUNICIPALITY ASSEMBLY EASTERN REGION GHANA
The disbursement of the Common Fund in kwahu West Municipal Assembly in the Eastern Region of Ghana is guided by the District Common Fund (DACF). The DACF is a fund allocated by the government to support the development of metropolitan, municipal, and district Assemblies. The Kwahu West Municipal Assembly receives a portion of the DACF, which is used to finance development projects in the Municipality. The Assembly medium - term development plan outlines the priorities for the use of the Common Fund, including infrastructure development, education, and health. The disbursement of the Common Fund is typically done on a quarterly basis, and the Assembly is required to submit reports on the use of the funds. The reports are reviewed by the Ministry of Local Government and Rural Development to ensure that the funds are being used for the intended purposes.
Copyright (c) 2025 Godfred Asante; Benzier Isaac Adu Okoore; Eric Amankwaa; Doris Fiassergbor; Richard Nimako; Alex Andrew James https://creativecommons.org/licenses/by/4.0
ENHANCING FULL-ENGLISH TEACHING QUALITY IN SINO-FOREIGN COOPERATIVE PROGRAMS IN HIGHER VOCATIONAL COLLEGES
The globalization of education has led to the expansion of Sino-foreign cooperative programs in higher vocational colleges, offering students the opportunity to receive internationalized training. However, the effectiveness of full-English teaching in these programs remains a critical challenge. This paper explores the primary obstacles facing English-medium instruction (EMI) in Sino-foreign cooperative education, including the mismatch between students’ English proficiency and the requirements of all-English teaching, the limited time available for language development, and the divergence in educational philosophies between Chinese and foreign teachers. It proposes several optimization strategies in the areas of teacher development, curriculum reform, teaching methods, and assessment mechanisms. The aim is to provide practical insights and recommendations to enhance the quality of teaching and learning in these programs.
Copyright (c) 2025 XinYi Huang https://creativecommons.org/licenses/by/4.0
CORRUPTION, ECONOMIC DEVELOPMENT AND SOCIAL WELFARE IN NIGERIA
This study assessed the impacts of corruption on economic development and social welfare in Nigeria. Mixed-methods research design, combining both quantitative and qualitative methods to collect and analyse data. The study collected secondary data from reputable sources such as the World Bank, International Monetary Fund (IMF) and Nigerian Bureau of Statistics (NBS). National bureau of Statistics estimates the entire population at 230,700,000 as of 2024, through the use of Taro Yamane formula, the population size was reduced to 400 sample size. A survey questionnaire of 400 was administered to the respondents in Nigeria to gather data on their perceptions of corruption and its impact on economic development and social welfare of which 291 returned. Findings of the study shows that corruption reduces economic growth by discouraging investment, reduced productivity, increased business cost, led to inefficient allocation of resources, diversion of government funds for private used, exacerbates poverty and inequality, reduced access to healthcare, education, and infrastructure, deters foreign investment, increased mortality rates, and finally increased social unrest by creating inequality. The study concluded and recommended that government should strengthen anti-corruption institutions, increase transparency and accountability, improve access to basic services and promote public participation.
Copyright (c) 2025 Juliet Nkonyeasua Ossai https://creativecommons.org/licenses/by/4.0
BOARD AND GENERAL MEETINGS OF A COMPANY IN NIGERIA: A LEGAL DISCOURSE
This paper seeks to discuss board and general meetings of a company in Nigeria. Hence, as one of the most important post-incorporation compliance requirements for a company in Nigeria is the direction for its directors, management and members to have specific types of meetings for the purpose of running the course of the company’s progression as well as for the purpose of passing resolutions authorizing certain actions or transactions of the company. Such types of meetings are known as board and general meetings as provided under the Companies And Allied Matters Act, 2020. Furthermore, this research work will be addressing the following subtopics: What board and General meetings entails, procedures, types, quorum, notices and resolutions of board and general meetings; reasons for board and general meetings, comparative discourse of board and general meetings, as well as raising observations and recommendations.
Copyright (c) 2025 Gambo Jonathan https://creativecommons.org/licenses/by/4.0