While observational studies have long suggested a positive correlation between social relationships and online transactions, surprisingly little research demonstrates a causal link. Effects identified in observational data generally conflate the Information effect, which bears the counterfactual causal interpretation, with the Homophily/environment effect. Against this background, this study conducted a pioneering a randomized field experiment design to isolate the Information effect of friendship disclosure from confounding homophily factors. We exploit a rare opportunity to conduct a field experiment on a large Chinese online second-hand platform, in which we manipulate buyer and seller’s awareness of their preexisting friendship ties. We provide the first empirical evidence that the effect of revealing friendship information between transaction parties turns out to be insignificant. We demonstrate that reliance on observational estimates of the “total effect” of friendship significantly overstates the benefits of providing friendship information in online marketplaces. Our findings contribute to a better understanding of social commerce and highlight the potential fallacy of relying on observational data in business studies.

We revisit cross-asset time-series momentum (XTSM) and single-asset time-series momentum (TSM) in China's stock and bond markets. With a fixed-effects model, we find a positive momentum from bonds to stocks and a negative momentum from stocks to bonds, with both momentum persisting for no more than six months. By employing a cross-grouping method, we find that the choice of lookback periods and asset signals impacts the performance of XTSM and TSM. A comparison between XTSM, TSM, and time-series historical (TSH) portfolios reveals that XTSM outperforms in small/midcap stocks and government bonds, while its performance is weak in large-cap stocks and corporate bonds. A spanning test confirms that XTSM generates excess returns that other pricing factors can not explain. XTSM is more prone to momentum crashes. Increased market stress has similarly adverse effects on XTSM and TSM. Furthermore, Market illiquidity, IPO counts, new investor accounts, and consumer confidence index positively correlate with the returns of XTSM and TSM portfolios, while IPO first-day return and turnover rate correlate negatively. The effects of these sentiment indicators exhibit heterogeneity.

The co-holding of mutually interdependent pairs of heterogeneous blocks can provide firms with stable financial support. The interdependence of heterogeneous blockholders’ investment decision has become an important frontier in the financial literature on large shareholders. In this paper, we study the interdependence of heterogeneous blocks in China. We find significant positive interdependence among blockholders of the same type. In heterogeneous block pairs, the financial–private pair shows positive interdependence. The findings are in contrast to those observed in the US. Under the mixed-ownership reform in China, our findings suggest the potential for cooperation among multiple blocks of the same type rather than between heterogeneous strategic partners.

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.

The role of institutional investors in the development of Environmental, Social, and Governance (ESG) criteria lacks consensus in the academic community. This study utilizes a quasi-natural experiment involving Chinese mutual funds that have signed the United Nations Principles for Responsible Investment (UNPRI) to investigate whether institutional Investors’ ESG investment commitments can significantly reduce ESG rating disagreement among the companies in their portfolios. We first find that companies held by ESG commitment institutional Investors exhibit less disagreement in ESG rating compared to those held by Non-ESG commitment institutional Investors. we then show that institutional Investor’ ESG investment commitment influence ESG rating disagreement by enhancing the quality of ESG disclosure and attracting external ESG attention. We further discover that institutional investors’ ESG investment commitments significantly mitigates the ESG rating disagreement among domestic ESG rating agencies and firms with a higher level of corporate governance.

Green bonds, pivotal to green finance, draw growing attention from scholars and investors. Social media’s proliferation has amplified the influence of investor sentiment, necessitating robust analysis of its market impact. However, general sentiment lexicons often fail to capture domain-specific slang and nuanced expressions unique to China’s bond market, leading to inaccuracies in sentiment analysis. Thus, this study constructs a specialized sentiment lexicon for the green bond market, namely the COBC (Chinese online bond comments sentiment lexicon), to dissect bond market slang and investor remarks. Compared to three general lexicons (Textbook, SnowNLP, and VADER), it improves the average prediction accuracy by approximately 87.2% in sentiment analysis of Chinese online language within the green bond domain. Sentiment scores derived from COBC-based dictionary analysis are systematically integrated as predictive features into a two-stage hybrid predictive model is proposed integrating Support Vector Machine (SVM), Auto-Regressive Moving Average (ARMA), Bidirectional Long Short-Term Memory Networks (BiLSTM), and Attention Mechanisms to forecast China's green bond market, represented by the China Bond 45 Green Bond Index. First, ARMA-SVR is employed to extract residuals and statistical features from the green bond index. Then, the BiLSTM-Attention model is applied to assess the impact of investor sentiment on the index. Empirical results show that incorporating investor sentiment significantly enhances the predictive accuracy of the green bond index, achieving an average of 67.5% reduction in Mean Squared Error (MSE), and providing valuable insights for market participants and policymakers.

This paper examines the impact of low-cost carriers (LCCs) on the pricing strategies of full-service carriers (FSCs) in the Chinese airline market. We first analyze the effect of LCC presence and find that LCCs exert significant downward pressure on FSC fares, with the magnitude of this impact varying across carriers and routes. Next, we explore the dynamic responses of incumbent FSCs to the entry and the threat of entry by LCCs. Our findings reveal that FSCs begin lowering fares well in advance of LCC entry, with fare reductions of approximately 11%–18% occurring as early as the 8th quarter before entry. The fare reductions intensify as the entry date approaches and persist beyond it. On the other hand, FSCs do not seem to respond to LCC entry threats. Our analysis highlights the importance of considering the dynamic pricing responses of FSCs rather than relying solely on LCC presence which is commonly used in the literature studying Chinese LCCs.

We combine a new collection of microdata from China with a natural policy experiment to investigate the extent to which reductions in rural-urban migration barriers affect flows of trade and investments between cities and the countryside. We find that increases in worker eligibility for urban residence registration (Hukou) across origin-destination pairs increase rural-urban exports, imports, capital inflows and outflows, both in terms of bilateral transaction values and the number of unique buyer-seller matches. To quantify the implications at the regional level, we interpret these estimates through the lens of a spatial equilibrium model in which migrants can reduce buyer- seller matching frictions. We find that a 10% increase in a rural county’s migration market access on average leads to a 1.5% increase in the county’s trade market access and a 2% increase in investment market access. In the context of China’s recent Hukou reforms, we find that these knock-on effects on market integration were on average larger among the urban destinations compared to the rural origins, reinforcing incentives for rural-urban migration.

The influence of informal institutions such as history and culture on corporate behavior has been widely recognized, but few studies have been analyzed from the perspective of the ruling party culture. Based on the data of the old revolutionary base areas (ORBA) in China, this paper makes an empirical test on the role of Red Culture in promoting enterprises green innovation. First, this paper finds that the stronger the Red Culture in the region where the enterprise is located, the higher the level of green innovation.Secondly, in the samples with high political sensitivity and less cultural conflict, the promoting effect of Red Culture is more obvious. This paper not only expands the relevant literature on the influence of informal system on enterprise green innovation, but also enriches the research on the influence of Chinese unique culture on enterprise management decision-making.

This paper develops an SOE (small open economy) dynamic general equilibrium model to study the impact of climate change on the current account. By calibrating the model to Chinese economy, we find the following results. First, the current account-output ratio improves in the first decade following an increase in global temperature caused by climate change. It then deteriorates in the following next three decades. Second, the overall current account-output ratio dynamics in response to climate change is neither affected by the types and stringency of climate policies, nor by the levels and growth rates of temperature increases. Third, the impact of an increase in temperature from 1.28 ℃ to 1.5 ℃ relative to the pre-industrial periods (1850-1900) on the current account-output ratio is equivalent to that of an approximate 0.14% permanent decline in TFP. Finally, although the current account-output ratio is likely to deteriorate in the first year when temperature increases instantly, it might not be true if the coefficient of relative risk aversion, or interest rate premium is larger, or debt sensitivity to interest rate is smaller.