Yield-chasing and risk-taking behaviors in the government bond market have become increasingly pronounced amidst monetary policy adjustments and rising economic uncertainty. We construct a theoretical model of fund manager portfolio allocation to analyze the duration channel underlying these behaviors and empirically test this mechanism using data on Chinese bond funds. The results show that: (1) When liquidity tightens following open market operations (OMO), bond fund returns decline, accompanied by a distinct shift in risk contribution. (2) During liquidity easing, funds engage in yield chasing by extending portfolio duration to capture returns through increased interest rate risk exposure. (3) High economic uncertainty amplifies yield-chasing behavior while attenuating the risk conversion phenomenon. (4) Portfolio analysis indicates that long-short strategies based on duration management generate significant risk-adjusted returns but fail to deliver excess returns. These findings offer theoretical insights and practical implications for managing and mitigating interest rate risk in the government bond market.

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.

Against the backdrop of recurrent global crises, economic uncertainty, and mounting environmental and social pressures, corporate resilience—defined as a firm’s capability to withstand external systemic shocks—has emerged as a critical determinant of long-term sustainability. This study empirically exames the effect of ESG (Environmental, Social, and Governance) performance on corporate resilience in China’s A-share market, using the COVID-19 pandemic as a natural experiment to identify causal effects. The sample comprises 651 A-share listed firms, excluding financial institutions, real estate firms, and ST/*ST companies, over the period from January 20, 2020, when the pandemic was officially announced in China, to June 30, 2024. ESG performance is measured as the average of 2018–2019 ratings issued by three major domestic agencies, thereby capturing firms’ pre-shock conditions and mitigating concerns of reverse causality. Corporate resilience is evaluated along two dimensions: resistance, measured by the severity of losses in net income, revenue, and stock price, and recovery, measured by the time required for ROA, EBIT, stock price, and Tobin’s Q to return to pre-shock levels. To ensure the robustness of the findings, this study employs linear regression models with industry-clustered robust standard errors, an instrumental-variable approach using R&D intensity and analyst coverage as instruments, and a Cox accelerated failure time model to estimate recovery duration. The empirical results indicate that stronger pre-shock ESG performance significantly enhances corporate resistance and shortens recovery time. Mechanism analyses further reveal that ESG strengthens corporate resilience by improving total factor productivity, alleviating financing constraints, and enhancing corporate reputation. These findings remain robust to multicollinearity diagnostics and a range of additional robustness tests. Overall, this study provides empirical evidence of the value of ESG in strengthening corporate resilience and offers important implications for firms, policymakers, and investors.

This paper proposed an innovative WTC-LSTM-ASSA-NSTformer framework for gold price forecasting. The model integrates Wavelet Transform Convolution, Long Short-Term Memory networks (LSTM), and an improved Nyström Spatial-Temporal Transformer (NSTformer) based on Adaptive Sparse Self-Attention (ASSA), effectively capturing the multi-scale features and long- and short-term dependencies of gold prices. Additionally, for the first time, various financial and economic uncertainty indices (including VIX, GPR, EPU, and T10Y3M) are innovatively incorporated into the forecasting model, enhancing its adaptability to complex market environments. An empirical analysis based on a large-scale daily dataset from 1990 to 2024 shows that the model significantly outperforms traditional methods and standalone deep learning models in terms of MSE and MAE metrics. The model’s superiority and stability are further validated through multiple robustness tests, including varying sliding window sizes, adjusting dataset proportions, and experiments with different forecasting horizons. This study not only provides a highly accurate tool for gold price forecasting but also offers a novel methodological pattern to financial time series analysis, with important practical implications for investment decision-making, risk management, and policy formulation.

This study explores the anchoring effect of previous bid premiums on acquirers’ bidding behavior in serial acquisitions. We demonstrate that, after controlling for deal characteristics, learning, and unobserved factors, the current bid premium is positively correlated with the acquirer’s previous bid premium. The strength of this anchoring effect diminishes with longer time intervals between acquisitions and increases with the industry similarity of targets. Notably, it remains unaffected by the acquirer’s state ownership or acquisition frequency. Additionally, the anchoring effect is less pronounced during periods of high economic uncertainty and can reverse following a change in the acquirer’s CEO. Our findings suggest that serial acquisitions are interrelated events, challenging the notion that each bid is an isolated occurrence. This research provides insights into the underperformance of serial acquirers compared to single acquirers and the declining trend in announcement returns across successive deals.

We introduce a deep visualization (DV) framework that turns conventional commodity data into images and extracts predictive signals via convolutional feature learning. Specifically, we encode futures price trajectories and the futures surface as images, then derive four deep‑visualization (DV) predictors, carry ($bs_{DV}$), basis momentum ($bm_{DV}$), momentum ($mom_{DV}$), and skewness ($sk_{DV}$), each of which consistently outperforms its traditional formula‑based counterpart in return predictability. By forming long–short portfolios in the top (bottom) quartile of each DV predictor, we build an image‑based four‑factor model that delivers significant alpha and better explains the cross‑section of commodity returns than existing benchmarks. Further evidence shows that the explanatory power of these image‑based factors is strongly linked to macroeconomic uncertainty and geopolitical risk. Our findings reveal that transforming conventional financial data into images and relying solely on image-derived features suffices to construct a sophisticated asset pricing model at least in commodity markets, pioneering the paradigm of image‑based asset pricing.

Motivated by the growing importance of the Chinese domestic economy for the global economic condition, we test whether the consumption risk of China matters for the cross-section of U.S. equity returns. We find that the two-factor international assetpricing model with both U.S. and Chinese consumption risk explains 40% of the crosssectional variation in U.S. equity returns. We also find a sizable risk premium of 7.08% per annum. This finding is robust to different estimation approaches, portfolio groups, controlling for other equity factors, and using individual equities. For economic mechanism, we find that it is the discount rate channel that is related to investors’ risk aversion, sentiment, and economic uncertainty through which Chinese consumption matters for the U.S. equity returns. Also, the result is not entirely driven by Chinese investors participating in the U.S. Overall, we present equity market-based novel evidence of the importance of Chinese macro fundamentals for the U.S.

From the perspective of corporate labor employment, we examine whether debt pressure on local governments prompts them to shift part of their social responsibilities to local firms. We conduct an analysis on Chinese local government debt (LGD) data and find that when LGD is higher, local firms are less likely to cut labor costs when their sales decrease, indicating greater labor cost stickiness. We attribute this to the responsibility-shifting effect, i.e., with heavier debt burdens, local governments intervene more in corporate labor decisions by restricting employee layoffs. Consistent with this argument, we find that the effect of LGD on labor cost stickiness is more pronounced for state-owned and politically connected firms; in regions with lower marketization levels and government fiscal self-sufficient capacities; and when regional unemployment rates, macroeconomic uncertainty, and political risk are higher. We show that through responsibilityshiftingamid high LGD, local governments benefit from a reduction in social expenditures. However, firms with stickier current labor costs will have lower subsequent productivity and market value, despite local governments reciprocating with more subsidies. Overall, LGD not only adversely impacts firm financing through the crowding-out effect but also erodes firm value through the responsibility-shifting effect.

Purpose – The aim of this paper is to examine the effect of information disclosure by unlisted bond issuers on the stock price informativeness of listed firms in the same industry. Design/methodology/approach – This paper takes advantage of information disclosure during the bond issuance and examines the spillover effect of unlisted bond issuers’ information disclosure on listed firms in the stock market. The sample is composed of A-share firms listed on the Shanghai and Shenzhen stock exchanges from 2007 to 2018. All the data are obtained from the China Stock Market and Accounting Research and WIND databases. The impact of bond market information disclosure on price informativeness of listed firms in the same industry is identified through multivariate regression analyses. Findings – Empirical results show that price informativeness of listed firms has a significantly positive association with the information disclosure of same-industry unlisted bond issuers. Further analyses show that the above finding is more significant when information disclosure of bond issuers is a more important channel for acquiring industry information (i.e. when industry is more concentrated, when economic uncertainty is high, and when industry information is less transparent) and understanding the industry competitive landscape (i.e. when bond issuers are relatively large, when bond issuers and listed firms have more direct product competition, when bond issuance firms are large-scale state-owned business groups), and when there are more cross-market information intermediaries (i.e. more cross-market institutional investors and more sellside analysts).This paperindicates that information disclosure of bond issuers has a positive spillover effect on the stock market. Originality/value – The novelty of the research is that the authors examine industry information spillover from unlisted firms to listed firms leveraging on unlisted firms’ information disclosure in bond markets.

This paper examines whether sell-side analysts struggle to cope with macroeconomic uncertainty. We find that analysts issue more accurate earnings forecasts when facing higher economic policy uncertainty, which conflicts with the conclusions in the US. We provide a novel explanation for this finding and exclude the view that forecast accuracy improvement comes from analysts’ efforts to actively collect private information through site visits. Further evidence supports that heuristic cognitive bias and emotional framing effect hold back analysts’ tendency to optimism in China, resulting in higher forecast accuracy. As to why Chinese analysts do not work harder but issue more accurate forecasts, we suggest that it is mainly due to the different market regimes faced by analysts in the two countries. Our study sheds light on how macroeconomic uncertainty affects analysts’ unethical behavior and explains the cognitive processes involved.