Development disparities within regions cause carbon inequality (CI), which exacerbates the Chinese unbalanced development problem. However, there is still a lack of research that measures CI from the aspect of intra-regional differences and examines its impact factors. Using sub-provincial data covering 420 cities, counties, and districts, this study measures CI in 30 Chinese provinces and investigates the influence of green finance (GF) on CI and the role of climate risk (CR) in the GF-CI linkage. Several findings have been found. First, using the population-weighted Williamson index to capture intra-provincial disparities, our analysis reveals pronounced regional variation in CI, with the East and Middle regions following a U-shaped pattern between 2008 and 2017, while the West experiences a consistent decline. Second, the dynamic panel analysis reveals that GF can reduce CI, particularly in midwestern regions and provinces characterised by lower carbon intensity. Third, the moderating effect demonstrates that climate risk (CR) weakens the inhibitory role of GF on CI. Under low CR, GF mitigates CI, whereas when CR exceeds a critical threshold, the beneficial effect of GF is reversed. This study contributes empirical insights for policymakers seeking to mitigate CI through financial instruments.
Although information technology has revolutionized trading and delivered substantial gains in efficiency and responsiveness, it has also produced an adverse side effect: episodes of intensified informed trading, of which flash crashes are the most visible manifestation. The structural conditions that distinguish markets undergoing such episodes from calmer ones remain incompletely understood. This paper contributes to closing that gap by establishing a set of network metrics that capture how the topology of the trading network reorganizes in response to information asymmetries. These market configurations refer to the evolving structure of trading activity, shaped by the distribution of participants, their interactions and the timing of information flow. To this purpose, we use an agent-based model to analyze crucial aspects of market dynamics that traditional analytical models struggle to capture, namely the sudden changes in the topology of the market network that reflect shifts in trading behavior or connectivity. We find that the proposed network metrics correlate strongly with standard indicators of order-flow toxicity, confirming that shifts in market topology reliably reflect the underlying levels of adverse selection and informed trading.
Using data from Chinese A-share listed firms and their top five customers over the period 2010-2023, this study finds that supplier firms allocate more financial assets when their customers face higher environmental regulatory pressure and that this effect is primarily driven by a precautionary savings motive. This finding remains robust across a series of endogeneity and robustness tests. Mechanism analyses reveal that Customer Environmental Regulatory Pressure (CERP) affects suppliers' financial asset allocation through two channels: financing constraints and green transition pressure. In addition, the moderating effects indicate that stronger ESG performance and higher government subsidies weaken the positive impact of CERP on suppliers' financial asset allocation. Heterogeneity analyses reveal that the effect is more pronounced among suppliers with weaker external financing capacity, higher technological innovation intensity, greater customer concentration and those operating in heavily polluting industries or in regions with more developed financial markets. Finally, the results suggest that CERP does not lead to excessive or over-investment in financial assets, but rather reflects a precautionary adjustment in response to increased uncertainty. Overall, the study provides theoretical insights for market participants to better understand the drivers of financial asset allocation in real-sector firms and offers empirical evidence and practical guidance for policymakers in formulating relevant policies.
We investigate whether industry portfolios contain information that forecasts aggregate stock market returns. Earlier research reports evidence of their usefulness as predictors, yet later studies find weaker and unstable effects. Using updated U.S. data, we confirm that industry portfolios offer little forecasting power when market excess returns are modelled directly. We then evaluate two component-based designs: a return-component decomposition and a sum-of-the-parts framework. Both generally improve out-of-sample performance, with the sum-of-the-parts forecasts producing the strongest results. This pattern is consistent with the gradual diffusion of information across industries and the aggregate market, while showing that the findings are not confined to one decomposition method. We extend the analysis with forecast combination methods and trading strategy evaluations, showing that component-based industry forecasts can improve statistical performance and generate economic gains.
In this study we identify low-risk anomalies affiliated with industry in frontier markets, offering valuable insights for fund managers and investors managing an internationally diversified portfolio. Utilising a two-beta model-incorporating both market and industry betas-we reveal a distinct low-beta anomaly with a monthly magnitude of 2.13% tied to risk exposure in affiliated industries, which remains robust after controlling for size and value factors. We extend our analysis further to explore intra-industry anomalies. We identify a low-beta anomaly within the energy, healthcare, and industrial sectors, and idiosyncratic volatility (IVOL) anomalies in the energy, healthcare, and real estate sectors, showing consistency when controlling for relevant factors. We also incorporate transaction costs into our analysis and provide recommendations to enhance the profitability of these anomalies by reducing portfolio rebalancing frequency and accounting for tax implications.
