This paper examines the intricate relationship between China’s First Five-Year Plan (1953–1957) and the extensive Soviet aid that played a pivotal role in shaping early industrialization and economic policy in New China. Drawing upon both Chinese and Western literature, the study reviews the evolution of planned economy practices and Soviet economic assistance, analyzes the process of plan formulation and the formulation of key industrial targets, and assesses the quantitative and qualitative impact of Soviet aid on the successful implementation of the plan. By employing a multi-perspective literature review, archival research, and statistical data analysis, the paper identifies that Soviet technology, machinery, and planning expertise contributed significantly, estimates suggest that Soviet aid accounted for 30-40% of the capital inputs in key sectors and that this collaboration was instrumental in achieving heavy industry targets before the advent of reform and opening-up policies. Furthermore, the paper explores the subsequent divergence in the economic models of China and the Soviet Union, offering lessons on dependency, technology transfer, and the political dimensions of foreign aid. The findings not only contribute to our understanding of the early stages of China’s economic development but also shed light on the broader dynamics of Cold War-era international economic cooperation.

The global financial system is currently navigating a profound transformation driven by digitalization and the rise of decentralized financial innovations. In response, central banks are increasingly exploring or implementing Central Bank Digital Currencies (CBDCs) as a sovereign digital evolution of fiat money. This study investigates the dual nature of CBDCs, evaluating whether they represent a strategic opportunity to modernize the economy or a systemic threat to existing financial stability. Employing a qualitative methodology, the research analyzes official policy frameworks from the IMF and BIS alongside diverse real-world case studies, including China’s e-CNY, the Bahamas’ Sand Dollar, Nigeria’s eNaira, and the European Central Bank’s Digital Euro. The findings suggest that while CBDCs offer significant benefits—such as enhanced payment efficiency, reduced transaction costs, and improved financial inclusion—they also introduce critical risks. These include the potential disintermediation of commercial banks, heightened cybersecurity vulnerabilities, and concerns regarding individual data privacy and government surveillance. The study concludes that the successful integration of CBDCs is not merely a technical challenge but a social and strategic one. Adoption is heavily dependent on infrastructure, digital literacy, and public trust. Ultimately, the research highlights that there is no "one-size-fits-all" model; the future of money will be shaped by how effectively individual nations balance technological innovation with the preservation of financial architecture.

Partially convertible economies face a market-design problem: trade integration, cross-border investment, and domestic balance-sheet exposure increase the demand for currency hedging before full financial integration is complete. China adopted a distinctive architecture for this problem by fostering a deliverable offshore Renminbi market (CNH) alongside the segmented onshore market (CNY), rather than relying only on non-deliverable forwards. This creates two venues for closely related claims on the same currency. Spot prices are tightly linked, yet CNY and CNH forwards display a persistent and economically large discrepancy. We study that discrepancy in a joint equilibrium model for spot and forward trading with transaction costs and segmented supply. In the benchmark case with common constant supply and deterministic costs, spot parity implies a forward differential with the wrong sign relative to the data. Random offshore stress, modeled as a jump in trading costs, overturns this benchmark while preserving tight spot parity. The model yields a semi-explicit representation in the CNY/CNH application and a calibration of the observed forward discrepancy in terms of the market-implied likelihood and severity of offshore liquidity stress.

The global AI inference market—reaching approximately $90–100 billion annually and growing at 18% CAGR—operates without organized exchange infrastructure. We document three market failures: resource misallocation (80% of China’s newly built compute capacity sits idle), price opacity (100-fold price dispersion across providers of equivalent quality), and unhedged risk exposure (85% of enterprises miss AI cost forecasts by more than 10%). Following the market design tradition of Roth [2002] and Budish et al. [2015], we propose the AI Service Right (ASR) as a transferable property right on AI compute and the AI Service Unit (ASU) as a quality-adjusted, cross-platform unit of account grounded in hedonic price theory [Rosen, 1974]. The ASU is modality-neutral: billing prices across text, image, video, and speech modalities are unified via eq-token conversion factors (κimg ≈ 2,667 eq-tokens per image; κvid ≈ 2,667 per second of video; κspc ≈ 7 per second of audio), and modality-appropriate benchmark sets (MMLU/HumanEval for language; FID/CLIP Score for image; FVD/CLIPSIM for video; MMBench for multimodal) supply the quality in dex via PCA. We design a hybrid secondary market architecture synthesizing mechanisms from four orthogonal market traditions: foreign exchange markets (cross-platform exchange rates and PPP-analog arbitrage via the ASU); equity markets (Central Limit Order Book, market making, clearing); electricity markets (Compute Locational Marginal Pricing for spatial scarcity signals); and decentralized finance (Automated Market Maker for long-tail liquidity). We establish nine formal propositions: bilateral trading is generically inefficient; Compute Locational Marginal Pricing decomposes nodal prices into system marginal cost, capacity congestion, and bandwidth premia; no-arbitrage equi librium holds with capital constraints (extending Shleifer and Vishny 1997); the ASR market Pareto-improves over bilateral trading; market prices are more in formative under ASR; the hybrid CLOB-AMM architecture weakly dominates either mechanism alone; platform adoption admits multiple equilibria with a coordination trap; financialization may improve or reduce price informativeness depending on speculator-hedger composition; and a hedonic micro-foundation justifies the ASU definition. Calibrated agent-based simulation (500 steps, 30 Monte Carlo runs) provides computational validation: the hybrid architecture reduces price dispersion by 90% relative to bilateral trading, and order-of-magnitude welfare estimates suggest enterprise procurement cost savings of 0.2–20% (net of ASR transaction costs; see Table 7) and potential TFP gains from compute reallocation of up to $29.9 billion annually. We propose a phased implementation roadmap from shadow ledger to full financialization, and we engage critically with the concern that financialization may not reduce intermediation costs [Philippon, 2015].

Traditional production function theory treats executive characteristics as exogenous fac-tors, failing to capture how managerial heterogeneity dynamically influences productiv-ity. This study integrates Upper Echelon Theory and Dynamic Capabilities Theory to develop an endogenous production function framework where executive attributes serve as dynamic parameters reshaping capital and labor elasticities. Using fixed effect-s panel-data models, we analyze Chinese listed companies, examining how executives’ education, experience, and compensation interact with production parameters. Re-sults show executive characteristics significantly moderate factor productivity: higher education and board independence enhance capital productivity, while compensation intensity transforms labor elasticity from negative to strongly positive values. Hetero-geneity analysis reveals effects vary across ownership types and technology intensity, with technology-intensive firms showing strongest responsiveness. This research recon-ceptualizes executives as active architects of production processes, provides a rigorous framework for endogenizing management factors, and offers evidence-based guidance for corporate governance and talent strategy optimization.

The increasing institutional participation and deepening integration of physical trading and financial operations in commodity markets have elevated the interconnectedness of energy futures and equity markets to prominence in both scholarly discourse and industry analysis. Employing the Nelson-Siegel framework and Fama-French factor model, this study examines the dynamic relationships between energy futures holding cost variations and equity returns across coal and oil sectors. Our analysis yields three principal findings: First, the Fama-French three-factor model exhibits robust explanatory power in China's energy sector equity market, revealing significant statistical relationships between holding cost curve parameters—level, slope, and curvature—and industry excess returns. Second, holding cost variations manifest substantial heterogeneity in their impact on stock returns across coal and oil sectors. Third, carrying cost components demonstrate dominance over shock transmission effects in explaining industry stock return volatility, indicating complex, asymmetric interaction mechanisms between futures and equity markets. Drawing from these empirical results, we advance targeted policy prescriptions addressing futures market architecture and financial stability.

This study develops a novel deep learning framework for bank distress prediction, designed to overcome the limitations of static network analysis and to enhance model interpretability. We propose a Spatio-Temporal Attention Network that uniquely captures the time-varying nature of systemic risk. Methodologically, it introduces two key innovations: (1) a dynamic interbank network whose connection weights are adjusted by the volatility of the Shanghai Interbank Offered Rate (SHIBOR), reflecting real-time market liquidity changes; and (2) a dual spatio-temporal attention mechanism that identifies critical time steps and pivotal contagion pathways leading to a distress event. Empirical results demonstrate that the model significantly outperforms traditional benchmarks across key metrics including accuracy and F1-score. Most critically, the architecture proves exceptionally effective at reducing Type II errors, substantially minimizing the failure to identify at-risk banks. The model also offers high interpretability, with attention weights visualizing intuitive risk evolution patterns. We conclude that incorporating dynamic, liquidity-adjusted networks is crucial for superior predictive performance in systemic risk modeling.

Traditional decision tree models, which rely exclusively on numerical variables, often encounter difficulties in handling high-dimensional data and fail to effectively incorporate textual information. To address these limitations, we propose the Integrated Multivariate Segmentation Tree (IMST), a comprehensive framework designed to enhance credit evaluation for small and medium-sized enterprises (SMEs) by integrating financial data with textual sources. The methodology comprises three core stages: (1) transforming textual data into numerical matrices through matrix factorization; (2) selecting salient financial features using Lasso regression; and (3) constructing a multivariate segmentation tree based on the Gini index or Entropy, with weakest-link pruning applied to regulate model complexity. Experimental results derived from a dataset of 1,428 Chinese SMEs demonstrate that IMST achieves an accuracy of 88.9%, surpassing baseline decision trees (87.4%) as well as conventional models such as logistic regression and support vector machines (SVM). Furthermore, the proposed model exhibits superior interpretability and computational efficiency, featuring a more streamlined architecture and enhanced risk detection capabilities.

We examine how organizational learning and organizational disruption jointly arise when Chinese audit firms have U.S. cross-listed clients and which effect dominates. Among public companies listed only in China, we define the treatment group as companies audited by Chinese audit firms serving at least one U.S. client, similar companies audited by firms without U.S. clients as the control group. Survey evidence indicates strong incentives and opportunities to learn from U.S. engagements and frequent learning activities in treatment audit firms. The archival evidence however shows that their domestic audit quality declines relative to the control group. The effect is more pronounced when U.S. clients demand more audit resources, when domestic clients are more sensitive to limited audit attention, and when U.S. and domestic clients are more similar. Overall, our findings indicate a negative externality of U.S. cross-listing audit when resource constraints hinder an effective firm-wide learning.

The stock price of a firm is dynamically influenced by its own factors as well as those of its peers. In this study, we introduce a Graph Attention Network (GAT) integrated with WaveNet architecture—termed the GAT-WaveNet model—to capture both time-series and spatial dependencies for forecasting the stock price crash risk of Chinese listed firms from 2012 to 2021. Utilizing node-rolling techniques to prevent overfitting, our results show that the GAT-WaveNet model significantly outperforms traditional machine learning models in prediction accuracy. Moreover, investment portfolios leveraging the GAT-WaveNet model substantially exceed the cumulative returns of those based on other models.