This paper examines whether a socially embedded soft trait, systematically omitted from AI-automated credit decisions, can predict financial outcomes in fully automated lending environments, and through which economic channel. We develop theoretically and examine empirically a labor-market competitiveness mechanism in which facial trustworthiness can serve as a soft signal under information asymmetry, shaping income growth and repayment capacity. Leveraging a unique proprietary dataset from a nationwide FinTech platform in China with AI-based underwriting that explicitly excludes facial information, we link loan performance to employment histories and construct trustworthiness scores using computer-vision algorithms. We find that borrowers with more trustworthy facial appearances experience greater upward job mobility and significantly lower default risk, conditional on rich hard information. A one-standard-deviation increase in facial trustworthiness is associated with a 4.6 percent reduction in default relative to the sample mean. The effect is strongest among borrowers with limited formal credentials, consistent with facial trustworthiness substituting for hard-to-observe labor-market signals. Our findings identify an economically grounded channel through which biometric appearance predicts financial resilience and highlight limits of algorithmic models that omit socially embedded information.

Using 4724 A-share listed firms in the Chinese stock market from 2009 to 2024, we investigate how CPU, an index constructed from textual analysis of major Chinese newspapers, affects investors' behavior, measured by turnover ratios and abnormal returns. The baseline regression shows that higher CPU significantly reduces stock turnover ratio and abnormal returns, indicating heightened investor caution and negative sentiment. Extended regression with provincial-level CPU further supports these findings. Heterogeneity analysis reveal that low-carbon firms, non-state-owned enterprises (non-SOEs), and firms in Central and Western China are more negatively affected by CPU, while high-carbon firms and SOEs are relatively resilient when facing higher CPU, mainly due to expected policy support and institutional investor dominance. Further mechanism analysis uncovers that ESG performance moderates CPU's effects. Firms with higher ESG ratings experience smaller valuation declines but lower trading volumes. In addition, consumer sentiment mediates CPU's effects. CPU suppresses public confidence, which in turn reduces investor participation and firm valuations. These findings highlight the importance of improving policy transparency, promoting standardized ESG disclosure, and mitigating retail investor overreaction. The study contributes to behavioral and climate finance literature by linking policy-driven uncertainty with micro-level investor responses in a key emerging market.

This paper investigates how firm-wide organizational competence shapes corporate tax outcomes. Exploiting China's Tax Compliance Rating (TCR) system, which evaluates the administrative execution of daily transaction flows across procurement, human resources, and supply chain operations, we examine whether operational rigor translates into superior tax performance. We document that top-tier (Grade A) firms achieve significantly lower effective tax rates. This association persists after controlling for general internal control quality, managerial ability, statutory tax incentives, auditor expertise, and state ownership. Importantly, this tax efficiency does not stem from aggressive or risky tax positions, as Grade A firms exhibit significantly lower tax volatility and no elevation in accounting or tax restatements. Dynamic tests further show that these tax advantages compound steadily with cumulative compliance experience. Overall, our findings demonstrate that corporate tax efficiency represents a tangible economic return on day-to-day administrative execution and cross-departmental coordination, establishing compliance ratings as a credible market signal of organizational competence.

This study utilizes the broadcast of the popular television series Game of Hunting in mainland China as a quasi-natural experiment to examine how investor attention impacts stock price volatility. The empirical findings reveal that the broadcast of Game of Hunting increased stock price volatility for headhunting companies. Notably, headhunting companies that attracted lower levels of investor attention before the broadcast exhibited greater stock price volatility. Conversely, headhunting companies with more complex business operations, which posed greater challenges for investors in processing information, experienced smaller fluctuations in stock prices. The broadcast of Game of Hunting drew retail investors’ attention to headhunting stocks, prompting them to purchase these stocks. This attention-driven buying then triggered arbitrage behavior by other retail investors, who sold the stocks, ultimately causing fluctuations in the share prices of headhunting companies. In contrast, institutional investors’ trading of these stocks was neither affected by the popularity of Game of Hunting nor influenced by the trading behavior of retail investors.

This paper examines the relationship between childhood left-behind experience and household risky financial asset investment. Based on the 2010 China Family Panel Studies (CFPS) data, we find that the childhood left-behind experience raises both the probability and the share of household risky financial asset investment by enhancing personal material aspiration. Moreover, the left-behind experience depresses household investment returns by depleting human capital accumulation. The heterogeneity analyses tell us that the rural, low-age and low-income households seem to be affected more by left-behind experience. Our evidence further suggests that enhancing information accessibility and financial resource availability constitutes a critical pathway to mitigate the adverse impact of left-behind experience on household investment outcomes. Our key results are robust to a series of sensitivity checks. Our study sheds light on the far-reaching influence of personal experience on households' well-being from the perspective of portfolio choice.

Salience theory posits that investors’ attention is disproportionately drawn to returns that stand out relative to a reference point. Building on this insight, we empirically examine how a stock’s salience theory value (ST) affects its future price crash risk. Using a comprehensive sample of Chinese A-share listed firms from 2005 to 2024, we find that stocks with higher ST are significantly more prone to subsequent price crashes. Mechanism analysis suggests that this relation operates through transient retail speculation and impaired pricing efficiency. High-ST stocks attract retail speculative demand and experience temporary overvaluation, which delays the incorporation of negative information into prices and amplifies the likelihood of abrupt price corrections. We further show that the effect is more pronounced during high-sentiment market conditions and is largely driven by stocks exhibiting salient upside return realizations. Overall, our findings highlight stock salience as a novel behavioral driver of crash risk and offer cautionary insights for investors and regulators.

China’s green-finance expansion shifts attention from how much firms disclose to whether transition claims remain credible when verification tightens. Using a 2007–2022 panel of Chinese A-share listed firms (crash-risk measures available through 2021), we measure transition credibility by decomposing firms’ transition signals into verifiable actions, narrative disclosure, and realized environmental outcomes, and we propose a transparent narrative–action gap measure (WordsMinusActions = WordsIndex − ActionIndex) to capture ‘words without deeds.’ Exploiting the staggered rollout of China’s Central Environmental Protection Inspections (CEPI) as a plausibly exogenous increase in verification and enforcement intensity, we find that firms with high pre-CEPI narrative–action gaps experience a sharp increase in stock price crash risk around inspections: NCSKEW rises by about 0.09 in the inspection year and 0.20 in the following year (≈0.24 SD), and DUVOL rises by about 0.04 and 0.10 (≈0.27 SD) relative to low-gap firms. Consistent with enforcement discipline, these high-gap firms subsequently reduce disclosure intensity and increase verifiable green actions, narrowing the narrative–action gap after CEPI. The evidence supports governance that rewards verifiable action and consistency over disclosure volume in markets.

The development of industrialization runs through the history of the People’s Republic of China. In the early years of the founding of the People’s Republic of China, the Soviet Union provided important help for China’s industrialization construction, capital lending, talent training and technology transfer. By analyzing the proportion of China’s industrial output value in the total output value and the output of major industrial products during the period of the Soviet Union, it shows that the assistance of the Soviet Union had a very positive role in promoting the realization of ChinaIs industrialization, and helped China to lay the foundation of industrialization.In addition, some problems in accepting the Soviet Union’s aid to China also deserve our attention when promoting the new industrialization. The research explores both the internal and external factors that led to China’s acceptance of Soviet assistance.The study examines the political, economic, and historical contexts that created a favorable environment for such aid, analyzes the various modes and scales of the assistance implemented, and presents statistical data that underscore the transformative social and economic achievements derived from this partnership.Furthermore, the paper highlights the contributions of quantitative analyses in understanding the aid’s impact, discusses the experiences and lessons learned from the Soviet aid period, and assesses the evolving dynamics of Sino—Soviet relations,particularly the gradual divergence in economic development models. The findings shed light on how Soviet aid laid the groundwork for China’s industrialization while also foreshadowing future ideological and developmental separations.

This study investigates the role of contextual cues in memory-based decision-making within high-stakes trading environments. Using trade records from a large Chinese brokerage firm, I show that an everyday contextual cue—geographic location—triggers the recall of previously traded stocks: when an investor returns to a city she has visited before, her probability of retrading stocks associated with that city rises by 2.1 percentage points under the most saturated specification (up to 12.2 percentage points under less saturated ones). The effect displays the signatures of similarity-based recall: it is diluted by intervening trading experiences, decays with the age of the memory trace, survives when the city is not the investor's home, is not explained by mechanical recency, and is present for orders submitted via both smartphones and PCs. I then examine an extreme-event cue, COVID-19 quarantine, and find a smaller and less robust effect; the interference and decay signatures are absent precisely for these strongly encoded traces, as the memory account predicts. Memory-cued transactions underperform: stocks bought upon the cue earn about 50 basis points less per week than stocks bought for other reasons on the same day. The study provides causal field evidence for memory-based theories, particularly similarity-based recall, in real-world trading.

This paper asks whether a personally experienced bubble crash leaves a lasting imprint on investment style. We study this in China’s stock market, where two technology-growth booms —the 2014–2015 Internet boom and the 2025–2026 AI hardware boom—share a similar narrative but are separated by a decade of generational turnover. Following managers across the mutual funds they run from 2013Q1 to 2026Q1, we compare those who lived through the 2015 crash with otherwise similar managers who entered afterwards. We find that the caution of veteran managers reflects a scar rather than a symptom of ageing. Conditional on age and career length, managers who lived through the 2015 crash hold more value exposure and chase technology stocks less when prices have been rising, and this reluctance deepens with how much the crash hurt them—crash-window tenure, performance shortfalls, redemption pressure—rather than with age. These demand differences reach asset prices as well: stocks held disproportionately by managers without bubble memories carry significantly higher subsequent left-tail risk, whereas crash-experienced ownership stabilises the stock, particularly when ownership is broad. Heterogeneous institutional beliefs rooted in career experience therefore shape not only how managers invest but also the crash risk of the assets they hold.