The flow-performance relation in China’s mutual fund market differs from that in developed markets (e.g., the U.S.). We find that investors actively allocate capital to poorly performing funds, generating a negative relation at the bottom of return distribution. These flows are driven mainly by increased purchases rather than reduced redemptions. We then examine the mechanisms behind this anomaly. First, investors act on rational expectations of performance reversals, with this pattern being more pronounced among funds with higher activeness. Second, product differentiation attracts heterogeneous investors when performance is weak. Third, marketing and fund family effects serve as simple signals that amplify inflows. Overall, our study provides new empirical evidence on fund investor behavior and its economic consequences in an emerging market context.

This study theoretically and empirically examines the impact of intangible capital on firm markups. The current research follows Altomonte et al. (2021) and first establishes a theoretical framework of intangible capital affecting firm markups. Accordingly, this study finds that an increase in intangible capital results in an increase in firm markups via the “production efficiency” channel but a decrease in firm markups via the “market-based pricing” channel. We use the data of Chinese manufacturing firms to further empirically study the influence of intangible capital on firm markups and its influencing mechanism. After a series of robustness and endogeneity tests, this research finds that intangible capital is conducive to increasing firm markups. Results of the empirical analysis also reveal that the positive impact of an increase in intangible capital on the markups of Chinese manufacturing firms via the “production efficiency” channel are higher than the negative impact of an increase in intangible capital via the “market-based pricing” channel. Moreover, the impact on the markups of different types of firms are not the same, with significant heterogeneity characteristics. This study provides micro evidence from a large developing country on how intangible capital affects the change in firm markups, thereby providing a new perspective on the economic effects of intangible capital.

We develop a deep-visualization framework for timing the factor zoo. Historical factor return trajectories are converted to two complementary image representations, which are then learned by convolutional neural networks (CNNs) to generate factor-specific timing signals. Using 206 equity factors, our CNN-based forecasts deliver significant economic gains: timed factors earn an average annualized alpha of about 6\%, and a high-minus-low strategy yields an annualized Sharpe ratio of 1.22. The outperformance is robust to transaction costs, post-publication decay, and factor category-level analysis. Interpretability analyses reveal that CNNs extract predictive signals from path boundaries and regime shifts, capturing patterns orthogonal to investor attention.

This paper examines how China allocates its newly accumulated foreign assets by analyzing the long-run relationship between net national savings, foreign exchange reserves, and outward direct investment (ODI). Using quarterly data from 2005 to 2023, a cointegrated vector autoregression framework shows that ODI—particularly through state-owned enterprises— has emerged as an important channel for recycling national savings abroad. Although short-run reserve fluctuations persist, sustained reserve accumulation has become less central to China’s external asset management. This study contributes to the literature by highlighting the institutional role of state ownership in shaping cross-border investment patterns and by identifying ODI as a strategic mechanism for channeling national savings internationally. The findings shed new light on China’s evolving approach to external asset allocation and its broader economic and geopolitical implications.

This study examines the economic consequences of China’s Three-Red-Lines policy, introduced in 2021 to cap real estate developers' leverage by imposing strict thresholds on debt ratios and liquidity. Developers breaching these thresholds experienced sharp declines in financing, land acquisitions, and financial performance. Privately owned developers(POE) are hit harder than state-owned firms (SOE), with larger drops in sales and higher default risk. Using granular project-level data, we show that the policy reduces developer sales primarily by curtailing new-project supply: breached developers launch fewer projects. On the demand side, homebuyers reallocate purchases from privately owned developers to SOEs, further widening the POE-SOE gap. The policy also reduced local governments’ land-transfer revenues and increased reliance on local government financing vehicles (LGFVs) for land purchases. These LGFV-acquired parcels exhibit very low subsequent development rates, which may increase local governments’off-balance-sheet debt risks.

In the Chinese context, the term “overseas” refers to countries and regions outside the sovereignty and jurisdiction of China. Overseas listing is an important strategy for firms to integrate into global capital markets and enhance their corporate investment efficiency. Using data from 600 Chinese companies listed exclusively overseas and 860 domestically listed firms for the period 2009–2023, this study analyzes the impact of overseas listing on corporate investment efficiency using empirical research methods, underlying mediating mechanisms, and the moderating role of economic policy uncertainty. The findings show that overseas listing improves Chinese firms’ investment efficiency. Compared to listing on the United States securities market (Nshares), listing on the Hong Kong securities market, (H-shares) has a pronounced effect on enhancing investment efficiency. Enhanced information disclosure quality improves the investment efficiency of Chinese enterprises listed overseas. Economic policyuncertainty can strengthen the positive impact of overseas listing on corporate investment efficiency. This study shows that overseas listing improves investment efficiency of firms in developing countries and offers new insights into advancing micro-level opening-up in these countries.

This study proposes an innovative CPO-VMD-PConv-Informer framework to forecast the KBW Nasdaq Financial Technology Index (KFTX). The framework comprehensively incorporates the effects of eight representative uncertainty indicators on KFTX price predictions, including the Economic Policy Uncertainty Index (EPU) and the Geopolitical Risk Index (GPR). The empirical findings are as follows: (1) The proposed CPO-VMD-PConv-Informer framework demonstrates superior predictive performance across the entire sample period, achieving R² values of 0.9681 and 0.9757, significantly outperforming other commonly used traditional machine learning and deep learning models. (2) By integrating VMD decomposition and CPO optimization, the model effectively enhances its adaptability to extreme market volatility, maintaining stable predictive accuracy even under structural shocks such as the COVID-19 outbreak in 2020. (3) Robustness tests show that the proposed model consistently delivers strong predictive performance across different training-testing data splits (9:1, 8:2, and 6:4), with the MAPE remaining below 2%. These findings provide methodological advancements for forecasting in the KFTX market, offering both theoretical value and practical significance.

We propose a novel dual-screening classification framework for fund selection designed to align statistical objectives with investor goals. Testing on the Chinese mutual funds market, a Gradient Boosting model implementing our framework generates a statistically and economically significant 14.65% annual risk-adjusted alpha, substantially outperforming identical models trained under a standard regression framework. Feature importance analysis confirms that fund-level momentum and flows are the most significant predictors of performance in this market. Our findings provide a robust and practical framework for active management, demonstrating that modelling both upside potential and downside risk is critical for superior performance.

Purpose The study investigates the impact of government intervention policy of capital markets (“National Team”) on firms’ sustainable management, i.e., green mergers and acquisitions (GMAs) in China, aiming to understand how such interventions influence corporate investment activities amidst a growing focus on green transition. Design/methodology/approach The research employs a dynamic analysis of quarterly data from Chinese companies (2014 Q1 to 2022 Q4), utilizing identified strategies, such as double machine learning-DID and multiple panel data regressions to assess the effects of government intervention on GMAs, and examines potential economic channels like liquidity, market stabilization, and informativeness. Findings The study finds that increased government intervention via direct stock purchases significantly boosts both the number and amount of GMAs, with economic significance of 23% and 45%, respectively. It identifies liquidity, market stability, and informativeness efficiency as underlying economic channels for this effect. Practical implications The findings suggest that government interventions can enhance corporate investment in green sectors, guiding firms to align strategies with sustainability goals. This can inform policymakers regarding the effectiveness of direct stock purchases in fostering a green economy, especially for large emerging countries. Social implications By promoting GMAs, government interventions contribute to green innovation and energy transition, ultimately benefiting society through enhanced environmental sustainability and compliance with eco-friendly regulations. Originality/value This research uniquely documents the direct effects of government stock purchases on corporate green financial activities, particularly GMAs, in a Chinese context characterized by tight credit, thereby expanding the understanding of government intervention in emerging markets.

We construct a Housing Purchase Intention Index (HPII) using the Baidu Search Index, which captures online search behavior directly reflecting households’ housing purchase intentions. We assess the predictive power of the HPII for the growth rate of housing transaction volume and further examine factors influencing housing purchase intention. The results show that the HPII has significant predictive ability and enhances real-time forecasting accuracy, highlighting the role of search behavior as a behavioral signal in the housing market. We also find that housing purchase intention is shaped by policy, economic, demographic, and supply factors. Specifically, purchase restriction policies exhibit an inverted U-shaped effect; moderate mortgage-rate hikes dampen purchase intention, while persistent increases may induce anticipatory buying. In addition, rising wages, increasing population concentration, and expanded residential land supply consistently strengthen housing purchase intention. These findings provide new behavioral evidence on the drivers of housing demand and underscore the value of search-based indicators for understanding household decision-making in the real estate market.