This paper studies the role of the soft budget constraint in shaping firm dynamics and long-run economic growth. We develop a Schumpeterian model with two sectors: consumption and investment, and two types of firms: state-owned enterprises (SOEs) and privately owned enterprises (POEs). Within this framework, multi-product firms require fixed inputs of investment goods to operate — a resource that is also crucial for firm expansion, and the soft budget constraint allows subsidized firms to continue operating on loss-making product lines. This distortion raises the price of investment goods, suppresses firm dynamics, and dampens aggregate growth. To quantify the mechanism, the model is calibrated using Chinese firm-level data. Our quantitative exercise shows that eliminating the SOE soft budget constraint friction reduces the price of investment goods by 9.82 percent, increases the overall TFP growth rate by 0.12 percentage points, and generates a welfare gain of 4.94 percent.

This paper investigates the outstanding economic and scientific-technological achievements of the Soviet Union during the 1945–1953 period and analyzes how these successes influenced New China’s decision to adopt the Soviet development model. Drawing upon a review of the key Chinese studies and the influential Western works, this study synthesizes diverse research methods,including historical institutional analysis, archival research, and econometric modeling, to examine the driving forces behind Soviet economic development, its quantitative achievements, and its evolving international status relative to the United States. Moreover, the paper reviews the development of Sino-Soviet relations during both the Kuomintang era and the early years of the People’s Republic of China, and it provides a comprehensive factor analysis of why New China chose to introduce the “Soviet model.” The paper also discusses recent quantitative research that employs statistical models to assess the impact of Soviet achievements on China’s economic planning. The findings underscore that the rapid reconstruction and modernization of the Soviet economy not only set new benchmarks in industrial output and scientific progress but also provided a persuasive example that shaped China’s early development strategy.

Traditional portfolio optimization typically relies on a single fitted return distribution, thus ignores non-normality and uncertainty across distributional states. Mean–variance–ambiguity (MVA) models, motivated by the smooth-ambiguity preferences of Klibanoff, Marinacci, and Mukerji (2005) and the KMM-to-MVA Arrow–Pratt reduction of Maccheroni, Marinacci, and Ruffino (2013), extend conventional risk penalties to account for distributional uncertainty, but they missed how state-conditional return distributions affect portfolio optimization stability. We develop Deep Mean–Variance–Ambiguity (Deep MVA) to resolve this gap by learning state-conditional return distributions and embedding the resulting conditional information loss into the ambiguity penalty. The resulting local certainty equivalent combines reduced-law mean–variance risk with a quadratic penalty for second-order disagreement. Conditional-mixture state resolution yields a Deep ambiguity covariance equal to the Traditional ambiguity covariance plus a positive-semidefinite state-feature increment. This increment is the conditional L2 loss from averaging state-specific distributions before optimization. On compact convex feasible sets, we show that the state-resolving program has stronger ambiguity-relevant curvature, lower mean-input sensitivity, and a unique global solution under restricted positive curvature or a common ridge; its projected iterations converge linearly. An LSTM-MDN learns state-conditional distribution densities from 60-minute bars, and Probability and VoV scores enter weekly MVA, with GARCH_Resid as a comparator. Tests on the SSE 50, CSI 300, and CSI 500 universes from 2017–2025 find statistically significant Sharpe out-of-sample improvements over Traditional MVA and MVO across all three universes and ambiguity specifications.

This study examines the role of artificial intelligence (AI) adoption in the market for corporate control using a sample of Chinese listed firms from 2011 to 2021. We construct a novel firm-level AI Index through textual analysis of annual reports and find that AI adoption significantly enhances both the likelihood of becoming an acquisition target and the valuation premiums commanded in M&A transactions. Specifically, a one-standard-deviation increase in the AI Index is associated with a significant increase in the probability of being acquired and higher deal premiums measured by price-to-earnings multiples. We identify two channels through which AI adoption creates value recognized by the M&A market: an efficiency channel, whereby AI reduces agency costs and improves profitability, and an innovation channel, evidenced by increased high-quality patent output. The persistence of these effects over time further suggests that AI adoption generates substantive improvements in firm fundamentals rather than serving as a transitory informational signal. Importantly, we document significant heterogeneity across ownership structures: the positive effects of AI adoption are substantially weaker for State-Owned Enterprises (SOEs) than for non-SOEs. Our findings contribute to the literature on digital transformation and corporate finance by demonstrating that AI adoption serves as a value-relevant firm attribute that shapes outcomes in the market for corporate control.

This paper constructs a call pressure index (CP) from China's SSE 50 ETF option market and finds a robust U-shaped nonlinear predictability for directional option returns as measured by log returns. The effect reflects that extreme call pressures—whether unusually low (reversal) or high (momentum)—contain information, while moderate levels are dominated by noise trading. Robustness checks using delta-hedged returns confirm that predictability stems primarily from directional exposure rather than volatility dynamics. The predictability is stronger in high-volatility and down-market states and survives controlling for implied skewness, variance risk premium, and other common predictors. A simple timing strategy based on rolling-window forecasts achieves a Sharpe ratio of 0.97, which further increases to 2.43 after applying a prediction threshold. A parsimonious volume-based indicator captures unique predictive information beyond complex proxies, offering a feasible path for emerging markets lacking proprietary order flow data.

Technology firms are often financed on narratives about products, contracts, customers, and technological progress well before these developments appear in accounting statements. We ask when such narratives become economically informative. Our central idea is that narratives should matter more once they can be linked to later verifiable outcomes rather than treated as stand-alone text.Using listed Chinese technology firms, we develop a validated corporate narrative framework for bank-affiliated investment, a setting in which investors must screen with soft information ex ante and then monitor hard realization and downside risk ex post. We use GPT-5.1 to extract business claims from management discussion, investor-relations records, exchange Q&A, and earnings-roadshow materials, and to label later claim–evidence pairs as support, partial support, conflict, duplicate, or irrelevant. We then connect these labels to official announcements, procurement awards, permits, project updates, and negative-event disclosures to construct a validated firm-month signal. The broad merged panel contains 592 firms and 30,169 firm-month observations; the main return tests use 576 firms and 18,230 firm-month observations over 2022–2024. A simple production rule that combines a low-narrative-premium component with hard-narrative and hard-event anchors, together with a separate downside-risk gate, delivers an implementable annualized long-short return of 8.93% in bank-invested firms after trading costs. The signal is much weaker in non-bank firms, predicts future gross-margin improvement more strongly than future ROE, and improves downside screening.

This study challenges democratic citizenship theory by examining altruistic behaviour in authoritarian China. We find that financial resources influence prosocial engagement primarily through psychological mediators such as long-term planning and self-discovery, rather than directly providing material means for participation. Communist Party membership shapes specific domestic ideological orientations, but not attitudes toward market regulation or income equality, while cultural tradition emerges as a stronger predictor of certain political attitudes than party affiliation. Cluster analysis identifies three distinct citizen profiles: (1) future-oriented altruists; (2) status-driven contributors; and, (3) short-termpragmatists, revealing motivational heterogeneity within authoritarian publics. These findings suggest that authoritarian citizenship operates through psychological mediation and plural motivational pathways fundamentally different from democratic societies, with potential applicability to other single-party regimes where states actively structure prosocial behaviour. The study contributes to comparative authoritarianism literature by theorizing how regime type moderates the relationship between socio-economic resources, political identity, and civic engagement.

Bankruptcy abuse prevention has been criticized for increasing foreclosure rates, imposing negative impacts on housing markets, and aggravating the financial crisis. By contrast, this paper documents that bankruptcy abuse prevention reduces household debt overhang, a phenomenon harmful to home values and housing markets. Using a difference-in-differences analysis, we find that households in recourse states increased their home improvement and maintenance expenditures after the Bankruptcy Abuse Prevention and Consumer Protection Act, a period during which the households paid considerable attention to the downside risk of the housing market, and that the effects vary by home equity level. The results remain unchanged with alternative specifications and cannot be explained by credit changes, judicial and nonjudicial foreclosures, homestead exemption, house sales, or heterogeneous expectations. Last but not least, we use entropy balancing to eliminate the differences between the treatment and control groups and get similar results. 预防破产滥用的政策被认为推高止赎率、对住房市场造成负面影响以及加剧金融危机而饱受批评。与之相反，本文证明破产滥用预防能够缓解家庭债务积压（debt overhang）—— 而债务积压恰恰是一种损害房屋价值与住房市场的现象。采用双重差分（DID）分析，我们发现：在《破产滥用预防与消费者保护法案》（BAPCPA）实施后，**追索权州（recourse states）**的家庭增加了住房改善与维护支出；该时期家庭对住房市场的下行风险高度关注，且上述效应因房屋净值水平的不同而存在异质性。在多种替代设定下结果依然稳健，且不能被信贷变化、司法与非司法止赎、宅基地豁免、房屋销售或异质性预期所解释。最后，我们使用熵平衡（entropy balancing）方法消除处理组与控制组之间的差异，同样得到了一致的结果。

Firm survival, as a key indicator of regional economic resilience, has gained increasing attention in the context of global economic uncertainty and the deep adjustments in industrial structures. In the Pearl River Delta (PRD), a core region of China’s Guangdong-Hong Kong-Macau Greater Bay Area, the characteristics of firm life cycles are crucial for understanding spatial development inequalities and institutional effects in emerging economies. This study focuses on firms in the PRD, using full life-cycle data from registration, operation, to deregistration. An XGBoost regression model is employed, incorporating the SHAP explanation algorithm, to systematically analyze the main factors influencing firm survival. The results show that: (1) Firm establishment time is the primary factor influencing survival, with significant “survival threshold” and “growth leap” effects—mature firms exhibit a distinct survival advantage; (2) Among spatial structure variables, moderate industry specialization and diversity enhance firm survival rates, while excessive concentration and diversification show diminishing or negative returns, reflecting an “ecological threshold” effect; (3) External shocks have a significant suppressive impact on startups, while policy support and capital size show limited explanatory power; (4) The ownership structure, especially state-holding, has a positive moderating effect on firm survival in specific contexts, indicating that private enterprises’ flexibility and adaptability can compensate for institutional gaps. This study offers insights into the spatial heterogeneity of firm survival mechanisms, providing quantitative evidence for regional economic policy adjustments and firm resilience-building. Recommendations include promoting differentiated policies, optimizing industrial ecological spatial layouts, and piloting systems to enhance survival resilience, especially for mixed-ownership firms.

The endurance of empire in China over much of the past two millennia contrasts starkly with the political fragmentation in Europe since the fall of Rome. To explain the persistence of China’s empire, we analyze the role of keju, the imperial civil service examination system. We argue that the systematic implementation of keju from the Song dynasty onward tied local elites to the imperial state, sustaining political integration and reducing secessionist conflicts. Exploiting panel data covering 4,243 grid cells and 22 centuries, we find robust evidence that areas producing more jinshi – the top exam passers – experienced significantly fewer secessionist wars. Our study shows how the examination system created the political incentives for imperial durability.