We investigate whether China's 2021 mining ban transformed Bitcoin from a speculative vehicle into a macro-sensitive asset. Using daily data from 2017-2025, we document a decisive structural break. Pre-2021, volatility was endogenous, driven by raw trading volume rather than fundamentals. Post-ban, however, internal microstructure noise loses predictive power. Instead, volatility is now driven by macroeconomic anxiety: Wikipedia searches for "Inflation" improve forecast accuracy by over 7%. We further uncover a "dual narrative" where inflation attention predicts crash risk, while "Recession" queries (pivot speculation) drive rallies. These findings suggest the regulatory shock successfully curtailed noise trading, allowing macro-fundamentals to dominate price discovery.

This paper examines how potential disruptions from generative artificial intelligence (AI) to firms' core businesses affect their cross-industry acquisition decisions. Using data on Chinese A-share listed firms, we employ a large language model to assess the potential positive and negative effects of generative AI on core businesses and construct a measure of net AI risk exposure. We measure cross-industry M&A using the semantic distance between acquirers' and targets' business descriptions. A difference-in-differences analysis exploiting the commercial diffusion of generative AI shows that more exposed firms acquire more distant targets, with an effect equivalent to 8.2% of the sample mean and robust to alternative specifications. The effect is stronger among firms with narrower innovation boundaries and more concentrated business portfolios, consistent with innovation capability reconfiguration and strategic risk diversification. It is also stronger among firms with smaller market shares, in more competitive industries, and in more marketized regions. Further analyses show that high-exposure firms acquire targets with higher AI patent shares and receive more favorable announcement-day market reactions to distant acquisitions. Our findings suggest that potential AI-related disruptions shape acquisition target selection and firms' business boundaries.

This paper studies the unintended spillover effects of China's 2020 "Three Red Lines" policy – a macroprudential intervention aimed at deleveraging real estate developers – on employment in the upstream manufacturing firms. Exploiting variation in industries' exposure to the real estate sector via input-output linkages and employing a generalized difference-in-differences (DID) design on panel data of listed manufacturing firms (2017-2022), we find a significant decline in employment among upstream suppliers, while there is no substantial effect on downstream firms. We identify two transmission channels: a demand propagation channel, evidenced by falling sales and inventory overhang, and a balance sheet contagion channel, where liquidity strain is passed upstream via extended payables and a shift to riskier short-term debt. Furthermore, we show that downward nominal wage rigidity shaped the adjustment, as firms resorted to layoffs rather than wage cuts for ordinary workers. From a regional labor market perspective, the employment losses represent a net reduction at the aggregate level, as displaced workers are not absorbed by other local firms in the same city or province. Our study reveals a significant real economic cost of sectoral macroprudential regulation and clarifies how such shocks propagate through production networks and interact with labor market frictions.

We investigate how investors assess transition risks arising from the discretionary regulatory mandates for nature conservation. We focus on the UN Biodiversity Conference in Kunming, China, a policy shock signaling heightened political commitment to protection. During the conference, firms exhibited negative abnormal stock returns on average. Using a machine learning approach, we identify historical nature-related subsidies to capture firm-level variations in expectations of mandated conservation engagement, beyond measuring corporate environmental (ecological) impacts. This metric negatively correlates with returns during the conference, particularly for firms with high operational volatility, and those in regions with limited fiscal flexibility and stringent conservation pressures. Investor concerns primarily stem from policy uncertainty, without positive earning expectations from conservation. Our findings underscore the role of authoritative discretion in shaping nature transition risks.

We study machine-learning forecasts and portfolio allocation for Chinese government bonds. Penalised linear regressions and gradient-boosted trees predict next-month total returns for eight maturity buckets. Their forecasts feed mean–variance, Black–Litterman, and risk-budgeting implementations with weight, duration, and trading-cost controls; convexity, tail-risk, and attribution constraints are analysed separately as theoretical extensions. From January 2019 through December 2025, the three optimised cash portfolios earn net excess-return Sharpe ratios of 1.94–2.26 and maximum drawdowns of 0.18–0.25%. Three long-only bond-selection variants attain Sharpe ratios of 1.39–1.70. A futures-hedged arbitrage variant attains 1.08 under an 80% hedge-P&L scaling and basis-cost assumption. A stylised 10-year futures overlay reduces rather than improves cash-portfolio Sharpe ratios.

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.

Despite strong GDP growth and a favourable demographic profile, India faces an impending jobs crisis. A large share of the workforce remains employed in lowproductivity agriculture, while many new labour market entrants are absorbed into the persistently large informal sector. By contrast, China's rapid ascent was driven by manufacturing-led, export-oriented industrialisation, underpinned by large inflows of foreign direct investment and sustained technology transfer. India's manufacturing base remains modest in contrast. The bulk of well-paid, formal employment continues to be concentrated in the high-skill services sector. This paper contrasts the development trajectories of these two economies and identifies several underutilised jobs-growth levers in India: manufacturing, goods exports, manufacturing-oriented foreign direct investment and innovation. All of these remain underdeveloped, yet together they offer a pathway to more labour-absorbing, durable growth. Leveraging them effectively would be central to achieving India's 'Viksit Bharat 2047' ambition of attaining high-income status. The scale of India's challenge to employ eight to ten million labour-market entrants per year implies that job creation must become an explicit policy priority. This calls for greater trade openness, particularly with Asia and Europe, to integrate India into Asia-centric global supply chains as an alternative to China. Labour market reform is equally critical, making the effective implementation of the new labour codes essential. Strengthening innovation ecosystems and realigning education and skills policies to support industrialisation are also key. Without these structural shifts, India's current pattern of jobless growth risks transforming its demographic dividend into a long-term liability.

We study whether commodity risk is efficiently priced in the cross-section of US equities. We first provide novel evidence on firm-level risk exposure to up to 42 individual commodities. We then construct a mispricing measure as the realized return less the commodity-risk-implied return, and find that it strongly predicts next-month return reversal beyond the traditional short-term reversal. This commodity-risk-mispricing reversal is substantially stronger among small, less analyst- or media-covered stocks, is weakly associated with investor sentiment, and is less prevalent among firms that engage in commodity hedging. In particular, underpricing among these hedgers often remains persistent and even intensifies after the 2004 financialization of commodity markets. This implies that despite the overall decline in mispricing, financialization has likely introduced more costs than benefits for corporate risk management.

Firm characteristics help estimate conditional factor loadings. Larger libraries contain new and overlapping information. We use the cross-sectional distribution of firm characteristics as a ruler to evaluate IPCA loading spaces. With factor counts fixed, expanding from 36 to 153 characteristics can improve in-sample fit while representing momentum and size positions less fully. Reallocating shared exposures changes attribution without changing joint fitted returns. With coefficients fixed, exposure references alter holdings, trading, and subsequent risk at the same characteristic target. Statistical factor models should be evaluated not only by return fit, but also by the exposures they represent and the investment decisions they support.

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.