Transactions on Economics, Business and Management Research
Transactions on Economics, Business and Management Research (TEBMR) aims to publish peer-reviewed proceedings from conferences on the theories and methods in all fields of economics, business and management.
How Fintech is Reshaping Investment Decision-Making Processes at Asset Management Companies
The global asset management industry's digital transformation trend is driving the deep application of fintech in investment decision-making processes. This paper explores the technological pathways through which fintech is reshaping investment decisions, analyzing the innovative application of core technologies such as big data and artificial intelligence in the decision-making phase. Research indicates that technological restructuring significantly enhances decision accuracy and operational efficiency, yet also faces challenges including model interpretability and data compliance. Future industry development will advance toward human-machine collaboration and ESG intelligence, propelling the digital transformation of asset management.
Fintech, Investment Decision-Making, Artificial Intelligence, Quantitative Investment, Robo-Advisory.
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Digital Twin-Driven Optimization of Production Projects in the Plastics Manufacturing Industry: Efficiency Enhancement and Economic Benefit Assessment
Traditional production models in the plastics manufacturing industry face challenges such as lagging process control and frequent quality fluctuations. This paper proposes an optimization solution based on digital twin technology, establishing a technical framework encompassing IoT data collection, high-fidelity modeling, process simulation, and big data analytics to achieve precise control over production processes. By reconstructing and optimizing workflows, a closed-loop management mechanism has been established that bridges physical mapping with accumulated expertise, effectively addressing efficiency and quality issues inherent in conventional manufacturing. The research findings provide a systematic solution for the digital transformation of the plastics manufacturing industry, offering significant guidance for advancing high-quality development within the sector.
Digital Twin, Process Simulation, Smart Manufacturing, Injection Molding, Predictive Maintenance
Explore the per capita GDP gap between China and developed countries
With the status of China in the world and the continuous growth of China's GDP, we can see a problem: in the developed countries, the per capita GDP is high, the price level is high; In developing countries, the per capita GDP is not high, the price level is low; In view of the gap between China and developed countries, the annual wage gap between Chinese taxi drivers and American taxi drivers is further explored. By exploring why there is such a big gap between China and developed countries, the paper recognizes the efforts that China can make in improving its per capita GDP. It is found that countries with high per capita GDP also need to have strong high-tech industries. In general, it is also necessary to recognize that the per capita GDP gap between China and developed countries exists but is narrowing, and China is moving toward the level of developed and rich countries.
per capita GDP, China and developed countries, gap.
The Multifactor Time-Varying Effects of Risk Shocks on Foreign Exchange Reserves
This paper employs the TVP-SV-VAR model to analyze the time-varying characteristics of China's foreign exchange reserve changes from the perspectives of interest rates, exchange rates, and inflation, with a focus on four critical periods. The findings reveal that the positive impact of the federal funds rate on foreign exchange reserves is more pronounced in the medium to long term, while the negative impact is more significant in the short term. During the 2008 financial crisis, the impulse effects of various variables on foreign exchange reserves were more pronounced compared to other crisis periods.
foreign exchange reserves, time-varying characteristics, TVP-SV-VAR, risk shocks
Digital Trade and the Enhancement of National Export Competitiveness— Evidence from Cross-Country Panel Data
The rise of digital trade has triggered profound changes in the pattern of international trade interest distribution and exerted multiple complex impacts on the enhancement of a country's competitiveness. Based on cross-country panel data of 37 countries from 2000 to 2020, this paper empirically studies the impact of digital trade on the improvement of national export competitiveness. The research conclusions show that digital trade significantly enhances a country's export competitiveness, and this finding not only enriches the research results in related fields but also provides useful reference for understanding the new changes in contemporary international trade.
Digital trade, Export competitiveness, Cross-Country Panel Data, Two-Way Fixed Effects Model.
ESG Performance and Systemic Risk: Evidence from Listed Commercial Banks
Against the backdrop of the goals to achieve carbon peak by 2030 and carbon neutrality by 2060, the Environmental, Social, and Governance (ESG) concept aligns with the requirements of China‘s sustainable development. Favorable ESG performance by banks is of great significance for the advancement of green finance in China. Concurrently, risk prevention is a perpetual theme in China’s financial industry. Therefore, utilizing quarterly panel data of 36 listed banks in China from 2009 to 2023, this paper employs the DCC-GARCH model to calculate two metrics for systemic risk: CoVaR and MES. It empirically analyzes the impact of ESG ratings performance on the systemic risk of China’s listed banks. Furthermore, heterogeneity analysis is conducted based on three dimensions: ownership structure, asset size, and analyst coverage. The results of robustness tests support the main conclusions of this study. The findings reveal that favorable ESG ratings performance by banks significantly exacerbates their systemic risk. This exacerbating effect is more pronounced among non-state-owned banks, large-scale banks, and banks with high analyst coverage. In view of this, it is imperative to fully refine the ESG rating system and implement differentiated supervision based on the varying effects of ESG ratings across different types of banks, thereby enhancing the risk prevention and control capabilities of listed banks.
ESG Performance, Systemic Risk, Listed Commercial Banks.
The Impact of Institutional Investor Holdings on Short-Term Debt Financing for Long-Term Use in Listed Companies
Reasonable investment and financing arrangements are a crucial prerequisite for the high-quality development of listed companies. However, Chinese listed companies exhibit the phenomenon of short-term debt financing for long-term use. Institutional investors can mitigate this practice by participating in corporate governance. This study examines the impact of institutional investor holdings on short-term debt financing for long-term use among Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2007 to 2024. Results indicate that institutional investor holdings effectively reduce this practice, with findings remaining robust after stability tests. Mediation analysis reveals that institutional holdings mitigate short-term debt for long-term use by easing the financing constraints faced by listed companies. Heterogeneity analysis further indicates that stable institutional investors and independent institutional investors exert a stronger moderating effect on this practice.
Institutional investors, short-term debt for long-term use, financing constraints.
Out-of-Sample Forecasting of the CSI 300 Index: AR versus Machine Learning Models
This study examines the out-of-sample predictability of daily returns of the CSI 300 Index by comparing a linear autoregressive (AR) benchmark with several machine learning models within a unified rolling forecasting framework. Using one-step-ahead forecasts, we evaluate predictive performance in terms of loss-based measures, directional accuracy, and statistical significance. The results show that machine learning models generally outperform the AR benchmark in terms of mean squared prediction error, with LSTM delivering the strongest gains and XGBoost achieving superior directional accuracy. However, predictive improvements are not uniformly distributed over time. Relative cumulative error analysis and volatility-based subsample tests indicate that machine learning advantages are primarily concentrated in high-volatility periods, while the linear benchmark remains competitive in tranquil markets. These findings provide evidence that return predictability and model performance may vary across market conditions.
Return predictability, Machine learning, Out-of-sample forecasting, CSI 300 Index.
A Review of the Impact of Performance-based Compensation on Organizational Citizenship Behavior
This study focuses on the influence of performance-based compensation on organizational citizenship behavior. By reviewing relevant literature, the concepts of performance-based compensation and organizational citizenship behavior are clarified, and the roles of the organic integration theory, social exchange theory, and equity theory in explaining the relationship between the two are expounded. Empirical research has found that performance-based compensation has positive or negative impacts on organizational citizenship behavior in different situations, affected by mediating variables such as perception of fairness and job satisfaction, as well as moderating variables such as leadership style and team cohesion. The existing research has deficiencies in aspects such as theoretical integration, research methods, variable exploration and cross-cultural research. Future research can be carried out in the directions of constructing a unified theoretical framework, optimizing research methods, expanding variable research and strengthening cross-cultural research, etc., to provide a more scientific basis for enterprise salary management.
Performance-based compensation, organizational citizenship behavior, Impact research.
Digital Skinner Box: Research on the Interaction Mechanism between Gambler's Fallacy and Availability Heuristic in Labubu Blind Box Consumption in the Algorithmic Era
Labubu's blind box products have continued to move away from their original categorical product properties as toys. Instead, they have become highly volatile financial assets. Conventional consumer psychology perspectives are unable to explain this phenomenon. This study uses neuroeconomics, probability theory, and algorithmic communication studies to assist in integrating various areas of study. Data penetrating the surface shows that the "variable ratio reinforcement" design of blind boxes is by no means a simple random reward, but a precise manufacturing of the dopaminergic system's "prediction error" to maintain addiction. Consumers are deeply mired in "negative expected value" (negative EV) games yet are completely unaware, which is rooted in the cognitive distortion regarding independent events (gambler's fallacy) and social media algorithms using survivorship bias to drastically amplify the "availability heuristic" effect. The two interlock into a closed-loop system of "induction—reinforcement—lock-in," leading to the systemic solidification of irrational decision-making. The article does not stop at theoretical disassembly, but based on bounded rationality theory, evaluates the limitations of the current regulatory framework, and proposes a new path for regulatory governance targeting algorithm auditing and probability visualization design.
Labubu, gambler's fallacy, availability heuristic, prospect theory, variable ratio reinforcement.
Time-Varying Spillovers among Carbon, Oil, and Stock Market Volatility: Evidence from a TVP-VAR-DY Framework
Against the backdrop of global climate governance and increasing financial market integration, understanding the dynamic risk transmission among carbon, energy, and financial markets has become increasingly important. This paper investigates the time-varying volatility spillovers among the international carbon market, the crude oil market, and the Chinese and U.S. stock markets by distinguishing between good and bad volatility. Using monthly data from January 2008 to November 2024 and employing the TVP-VAR-DY connectedness framework, we examine the magnitude, direction, and evolution of volatility spillovers across markets. The empirical results show that the crude oil market consistently acts as a net volatility transmitter under both good and bad volatility systems, while the Chinese and U.S. stock markets mainly serve as net receivers. Moreover, spillovers associated with bad volatility are significantly stronger and more persistent than those associated with good volatility, highlighting the asymmetric nature of cross-market risk transmission. Pairwise connectedness analysis further indicates that the oil market exerts dominant spillover effects on both the carbon market and stock markets, whereas the carbon market transmits risk to the Chinese stock market during certain periods. These findings provide important implications for cross-market risk management, portfolio diversification, and policy coordination in the context of the global low-carbon transition.
Carbon market, Crude oil, Stock market volatility, Good and bad volatility, TVP-VAR-DY, Time-varying spillovers.
Opportunities and Challenges Faced by Financial Accounting in the Era of Big Data
Advanced technologies such as big data are driving the transformation and upgrading of various industries and fields. Their application in the financial accounting sector can improve data processing efficiency and financial analysis capabilities, but also faces challenges such as data security, a shortage of talent, and an imperfect policy system. At the same time, big data technology is also a key focus for improving the financial accounting system. This requires adhering to the principle of analyzing specific problems with specific solutions and taking effective measures to change the current situation. This paper mainly discusses the opportunities and challenges faced by financial accounting in the era of big data and proposes corresponding countermeasures, aiming to provide reference for achieving steady progress in the financial accounting field.
Big data, finance, financial accounting.
Cost-Benefit Optimization of Packaging in the Green Transformation of E-commerce Logistics: A Case Study of JD.com
The green transition of e-commerce logistics is a pivotal pathway to sustainable development. In practice, however, enterprises face the dual challenges of high initial investment and low short-term returns. Using JD Logistics as a case study, this research focuses on sustainable packaging to explore strategies for balancing environmental goals with cost–benefit considerations. The findings suggest that although green packaging requires substantial initial expenditure, enterprises can achieve both operational cost reduction and environmental performance enhancement in the medium to long term through standardization, scalability, and technological innovation. JD’s “Green Stream Initiative”, by promoting reusable packaging, optimizing packaging algorithms, and fostering industry-wide collaboration, has significantly improved resource utilization efficiency and reduced unit costs, thereby demonstrating the synergistic interaction between commercial viability and environmental responsibility. Building on this case, the study develops a cost–benefit analysis (CBA) framework for sustainable packaging and incorporates enterprise-level operational insights, offering a micro-level perspective and actionable pathways for the green transformation of the e-commerce logistics industry. Furthermore, it contributes to filling the empirical research gap in this field.
E-commerce Logistics, Sustainable Packaging, JD Logistics, Green Stream Initiative, Cost–Benefit Analysis (CBA).
An Analysis of the Impact of Artificial Intelligence Technology on Supply Chains
Significant developments in artificial intelligence (AI) research in recent years have made people increasingly aware of its transformative impact on society. With the rapid development of AI, its applications are expanding across various fields. AI technology is now being increasingly used in supply chains, which can significantly improve their efficiency and performance. AI can not only accurately match the resource supply and demand in various links of the supply chain, but also dynamically avoid risks and optimize processes, thereby significantly improving the overall efficiency and comprehensive operational performance of the supply chain. To understand how AI technology specifically influences supply chains, this paper reviews existing investigation into how supply chains are affected by AI, summarizing the current research focus from different perspectives. It reveals that AI technology has been applied to multiple core links in the supply chain, benefiting various aspects of supply chain management. Furthermore, emerging technologies combining AI with other technologies are reshaping supply chain management in innovative ways. This paper also explores what should be focused on in this field.
Artificial Intelligence Technology, Supply Chain Management, Chain Management.
Trust Mechanisms in Amazon’s Supply Chain
Drawing on social capital theory, this study explores the trust mechanisms within Amazon's supply chain, focusing on the significant impact of information sharing and relationship governance on collaborative efficiency. In a globalized economy, supply chain networks are becoming increasingly complex, involving multinational suppliers, logistics networks, and multi-tiered distribution systems. Trust has become a core driver of stable and efficient collaboration. Using Amazon as a case study, this study examines how trust can improve demand forecast accuracy by optimizing data quality, reducing inventory overhang, stock-out risks, and operational costs, while also enhancing supply chain resilience and agility in dynamic markets. The study demonstrates that Amazon, through its transparent data-sharing platform, vendor-managed inventory (VMI) system, and long-term partnerships, not only effectively mitigates the amplification effect of demand fluctuations but also significantly improves market responsiveness and collaborative stability. It recommends that companies further adopt blockchain technology to enhance data transparency and trustworthiness, and deepen long-term partnerships through shared performance incentives. By integrating theory and practice, this study provides important theoretical and practical guidance for companies to build efficient and stable supply chains in a complex and volatile global market, laying the foundation for subsequent research.
Trust Mechanism, Amazon Supply Chain, Social Capital, Information Sharing, Collaborative Efficiency.
Analysis of Research Progress on Inventory Optimization in Green Supply Chain
Against the backdrop of the "dual carbon" strategy and global sustainable development goals, optimizing green supply chain inventory has gradually become an important research topic in both academia and industry. This article provides a systematic review and overview of relevant research in the past five years. Research has found that traditional inventory optimization models have gradually expanded from a single cost minimization objective to simultaneously consider multidimensional objectives such as carbon emissions and service levels. In terms of methodology, mathematical programming, intelligent optimization algorithms, and simulation have been widely applied, and the introduction of uncertainty handling, policies, and financial factors has made the model more practical. In terms of industry applications, the manufacturing industry focuses on closed-loop inventory and recycling strategies, while the e-commerce and fast-moving consumer goods industries rely on the Internet of Things, big data, and dynamic replenishment technology to achieve high turnover and low-carbon operations. Despite some progress, existing research still faces issues such as inconsistent carbon accounting standards, lack of consideration for social performance and institutional implementation costs, and incomplete incentive mechanisms upstream and downstream. Based on this, this article proposes that future research should strengthen interdisciplinary integration and real data verification, explore supply chain collaboration mechanisms, interpretability of deep optimization, and effective integration of policies and practices, in order to promote the further development of green supply chain management theory and practice.
Green Supply Chain, Inventory Optimization, Carbon Emission.
Comparative Analysis of Standardized Warehouses and Traditional Manual Warehouses
In the current systems of supply lines where the smooth execution of warehousing and sorting processes is the focus of operation as the decisive means to carry out a logistic system efficiently. As the development and penetration of e-commerce "Double 11" shopping festival, the management of other logistics is more challenging due to the unprecedented surge of orders. Traditional manual warehouse will need a great amount of labour to undertake picking and sorting labour, and will rely on increased numbers of shifts, increased numbers of people in peak periods and therefore, require temporary workers too. In contrast, the standard warehouse with connected information and automation devices and information systems has an efficient yet stable capacity, and the capacity is appropriately maintained during the peak time, and the quality is well maintained as well. The research is examining the differences between automated ware housing compared to the manual ware housing with the help of production efficiency and labor cost analysis as JD Logistics and SF Express Holding automated warehouses that have advantages in longer durations and may be costly depending on construction time; and are popular for their cost-efficiency. Manuel warehouse was developed with retained value of flexibility coupled with low barrier to entry. Overall, automated warehouse will be the mainstream solution; whereas manual warehouse will be used as a supporting solution in some cases.
Standardized Warehouse, Traditional Manual Warehouse, Supply Chain Management.
The Mutual Feedback Mechanism and Collaborative Management of Green Cost Allocation and Bullwhip Effect
Against the backdrop of the "dual carbon" goals in China and the rising threshold for green trade, how to reasonably allocate the incremental costs generated by the greening of the supply chain among the upstream and downstream has become a key factor affecting operational stability and collaborative performance. This study takes green cost allocation and the bullwhip effect as its theme, sorts out the causes of the bullwhip effect and its amplification mechanism in the green transformation, and reveals how the allocation uncertainty forms a negative cycle of "rising green costs - intensified demand fluctuations" through information delay and order batch adjustment. It also proposes a positive path with carbon data transparency, dynamic contracts and inventory/scheduling coordination as the key points, and builds a closed loop of "fair allocation - accurate information - fluctuation suppression - performance improvement". The research conclusion indicates that in manufacturing, fast-moving consumer goods and cross-border supply chains, a green information sharing platform should be established based on the specific conditions of each chain, order fluctuation thresholds should be set, and a green processing window should be included to achieve dual goals of the environment and operation.
Green Cost Allocation, The Bullwhip Effect, Information Sharing, Dynamic Contract, Game Model.
Supply Chain and Logistics Optimization Strategy of Fresh Agricultural Products in the Internet Era
This study investigates logistics and retail optimization strategies for fresh agricultural products in the internet era, establishing an efficient and sustainable digital supply chain system. Based on the systematic summary of previous research and cases, this paper points out the problems in the current supply chain operation system of fresh agricultural products and proposes solutions. Through literature review, case studies, quantitative analysis, and system simulation, it examines existing challenges, including data silos, high loss rates, and cold chain disruptions, while exploring digital technology applications, a collaborative optimization framework encompassing production, warehousing, transportation, and sales, providing theoretical foundations and practical guidance for industry transformation. This research demonstrates that the application of technologies such as IoT and blockchain can significantly reduce product loss rates in supply chain transportation, enhance operational efficiency, and improve consumer shopping experiences. Future research will focus on advancing smart cold chain systems and supply chain finance to drive the sustainable development of fresh agricultural products toward greater efficiency, transparency, and universal applicability.
Agricultural Products, Supply Chain, Optimization Strategy, Logistics.
BIM-Driven Optimization of Collaborative Cost Budgeting in Prefabricated Building Supply Chains
Amidst the digital transformation of the construction industry, prefabricated buildings are gaining prominence as a pivotal strategy for achieving low-carbon goals and enhancing construction efficiency. However, the industry faces significant challenges, primarily low supply chain collaboration efficiency and inaccurate cost budgeting, which impede its broader adoption and effectiveness. In traditional models, data is fragmented across the design, production, and logistics phases, which not only leads to resource waste but also drives up carbon emissions from prefabricated component transportation. Additionally, BIM technology faces challenges such as high upfront investment and low model reusability, further intensifying cost pressures. This study addresses these issues by developing a cloud-based collaboration model to enable real-time data sharing, establishing a quantitative formula for delay costs, and exploring a standardized design to reduce marginal costs. Employing methods including literature analysis, case comparison, and model construction, the research uses game theory to analyze collaboration efficiency and cloud models to assess supply chain resilience. Results show that modular design can reduce component costs by 48.64%. Conversely, while the lack of collaborative mechanisms causes significant waste in transportation and warehousing. The findings demonstrate that cloud-based collaboration and standardized design can provide practical guidance for enterprises to improve capital efficiency and reduce carbon emissions. The study also contributes to the theoretical framework for integrating BIM technology with supply chain management.
Building Information Modeling (BIM), Prefabricated Construction Supply Chain, Cost Optimization, Supply Chain Collaboration, Carbon Emission Reduction.
Against the backdrop of the continuous improvement of the green financial system and the growing demand for supply chain stability, this study takes Chinese A-share listed companies from 2014 to 2022 as the research sample. It systematically explores the impact of green bond issuance on firms’ supply chain stability and its underlying mechanisms using multiple empirical methods, including the staggered difference-in-differences (Staggered DID), instrumental variable two-stage least squares (IV-2SLS), and propensity score matching-difference-in-differences (PSM-DID) methods. The findings reveal that green bond issuance significantly reduces firms’ supply chain concentration and enhances supply chain stability. This conclusion remains robust after a series of robustness tests, such as placebo tests, replacement of the dependent variable, and handling of extreme values. Mechanism tests indicate that green bonds exert their effects through two core channels: first, improving firms’ ESG performance to alleviate information asymmetry between upstream and downstream supply chain partners and strengthen cooperative trust; second, mitigating financing constraints to provide financial support and risk resistance capabilities for the diversified layout of supply chains. Heterogeneity analysis shows that this effect is more pronounced for firms located in eastern China and those in non-heavy-polluting industries. This study fills the research gap in the cross-field of green finance and supply chain management, and provides empirical evidence and practical implications for enterprises to optimize their supply chain structures through green bonds and for policymakers to improve the green financial support system.
Green Bonds; Supply Chain stability; ESG Performance; Financing Constraints; Staggered DID.
Theoretical Reconstruction Research on Data Element Pricing Mechanism in Digital Platform Economy
The in-depth development of the digital economy has made the importance of data, a key production factor, increasingly prominent, and quantification of data value and reasonable pricing are core matters in the development of the platform economy. Based on the particularity of the digital platform economy, this paper systematically explores the theoretical basis and practical difficulties of the data element pricing mechanism. The research shows that traditional economic pricing theories have limitations when dealing with data elements, because the characteristics of data such as non-competitiveness, externalities, and network effects make traditional marginal cost pricing difficult to apply. The article also constructs a multi-dimensional framework for assessing the value of data elements, including the intrinsic attribute value of data (accuracy, completeness, timeliness), use value (application potential, innovation space), and social value (privacy protection, data security). Based on these, the study proposes a pricing mechanism theory reconstruction scheme that integrates marginal utility theory, cooperative game theory, and platform bilateral market characteristics. This scheme emphasizes the realization of the differentiated value of data in different scenarios and introduces a data contribution allocation mechanism to handle the value allocation problem involving multiple parties. The case analysis verified the feasibility of the reconstruction theory in practical application and proposed policy recommendations for improving the data property rights system, establishing a data value assessment standard system, constructing a data trading market mechanism, and strengthening supervision and compliance for the development of China's digital platform economy. This study fills the academic gap in the theory of data element pricing and provides practical guidance for the realization of data value in the digital platform economy.
Digital platform economy; Data elements; Pricing mechanism; Value assessment; Theoretical reconstruction.
The Impact of Cultural Differences among Multinational Enterprises on Management Communication Efficiency from the Perspective of Hofstede's Cultural Dimension
Based on Hofstede's cultural dimension theory, this paper delves into the impact of cultural differences in multinational corporations on the efficiency of internal management communication within organizations. Due to the accelerated process of globalization, the obstacles encountered by multinational corporations in terms of culture are becoming increasingly obvious. The differences in the six cultural dimensions of power distance, uncertainty avoidance, individualism versus collectivism, masculinity versus femininity, long-term orientation versus short-term orientation, and indulgence versus restraint have a profound impact on corporate communication. A case study of 15 multinational companies in China and a questionnaire survey of 350 managers showed significant differences among employees of different cultural backgrounds in terms of information transmission methods, participation in decision-making processes, conflict resolution mechanisms, and understanding of non-verbal communication symbols. And the study shows that in high power-distance cultures, employees tend to accept top-down one-way communication, while in low power-distance cultures, employees value two-way interaction more, in cultures with a high uncertainty avoidance index, employees need clear communication instructions, collectivist cultures focus more on group harmony and implicit communication, while individualist cultures focus more on direct expression. Based on the research findings, this paper proposes management strategies such as building a multicultural awareness training system, establishing flexible communication mechanisms, optimizing cross-cultural team structures, and leveraging technology to facilitate cross-cultural communication, with the aim of enhancing the communication efficiency of multinational enterprises in cross-cultural environments and thereby promoting overall organizational performance.
Hofstede Cultural Dimension; Multinational enterprises; Cultural differences; Management communication; Communication efficiency.
Research on Defi Lending Forecasting based on GRA and Rolling GM(1,N) Model
This paper investigates the short-term dynamics of decentralised lending conditions on Aave, a leading DeFi pooled lending protocol whose rapid TVL growth has made its variable interest-rate mechanism systemically relevant for on-chain investors and risk managers. In contrast to traditional fixed-income markets, DeFi interest rates are endogenously determined by pool utilisation and are observed over relatively short and highly volatile time series, which poses challenges for conventional econometric models. To address this, we develop a grey-system forecasting framework tailored to the USDC pool on Aave v3. Weekly data from January to November 2025 are collected on the utilisation rate (U), total value locked (TVL) and the Crypto Fear & Greed Index (FG). Grey relational analysis (GRA) is first employed to screen explanatory factors; both TVL and FG exhibit relational grades above 0.7 with respect to U (0.7138 and 0.7673, respectively), confirming that on-chain liquidity and market sentiment jointly drive utilisation. On this basis, a multivariate rolling GM(1,3) model is specified, estimated over an 8-week moving window and used to generate one-step-ahead forecasts of weekly utilisation. The resulting pseudo out-of-sample forecasts for 46 weeks achieve a mean absolute percentage error of 5.97%, with a posterior error ratio C=0.6475 and small error probability P=0.7632, indicating acceptable forecasting accuracy under standard grey-model criteria. The model successfully reproduces the medium-term high-utilisation regime (around 0.8–0.88) that places the USDC pool near the kink of Aave’s dual-slope interest-rate curve, implying frequent transitions into a high-rate environment for borrowers while offering attractive yields to liquidity suppliers. By linking grey-based utilisation forecasts to the protocol’s endogenous rate formula, the paper provides an interpretable and data-efficient tool for anticipating Aave’s interest-rate conditions and supporting lending, borrowing and arbitrage decisions in DeFi.
Does Algorithmic Recommendation Weaken Consumers' Original Preferences? -- An Economic Analysis Based on Information Constraints
The paper explores the topic of whether algorithmic recommendation systems radically change the preferences of consumers by constraining information. We construct a theoretical model of search costs, dynamic learning and endogenous preference formation, in which algorithms act as information intermediaries that selectively filter information about available options in a systematic manner. On the basis of extensive e-commerce purchases records of 35,874 purchases made by 1,000 participants over a period of 12 months, we apply difference-in-differences techniques to determine causal impacts. Our findings indicate that algorithmic exposure enhances concentration of consumption by 52% (measured using Herfindahl Index) and decreases the diversity of preferences by 91 percent (measured using Shannon entropy) and both are statistically significant at p<0.001. The analysis of mechanisms shows that feedback loops have 45% of the total effect, the information filtering has 35%, and cognitive dependence characterizes 20%. The effect of treatment has a heterogeneous search cost sensitivity and accrual over time due to the accumulation of user history over time among algorithms. We do not make any evidence of other readings like refinement of tastes or supply limitation. The results of this research contradict the traditional perspective that algorithms only support the expression of preference, pointing rather to its active involvement in the formation of preferences with unclear welfare implications. Some policy recommendations suggested are platform design that promotes diversity, compulsory impact evaluation of algorithms, and consumer empowerment interventions.
Algorithmic recommendations, preference formation, filter bubbles, consumer behavior, information constraints.
Preface: 6th International Conference on World Economy and Project Management (WEPM 2026)
The 2026 6th International Conference on World Economy and Project Management (WEPM 2026) was successfully held during February 07-08, 2026 in Singapore. This conference is the premier forum for the presentation of new advances and research results in the fields of world economy, economic policy, financial investment, project management, innovation management, e-business management, and social science. The events brought together researchers, scientists, experts, and scholars from various areas to discuss problems and solutions in these fields, to identify new issues, and to shape future directions for research. WEPM 2026 was composed of 2 sessions with 4 keynote speeches and 16 oral reports presented in this conference. Each keynote speech and oral report took 20-30 minutes and 5-10 minutes, respectively. All papers included in the conference proceedings were peer reviewed according to international standards. We would like to deeply acknowledge all the parties involved in making WEPM 2026 successfully held. Besides, we wish to thank all authors and participants for providing their valuable contributions for this proceeding as well as the reviewers for their constructive recommendations and criticism aiding to improve the presented papers. With best regards, WEPM Conference Organizing Committees Singapore