We develop a machine learning (ML) approach to establish new insights into how memory affects ffnancial market participants’ belief formation processes in the field. Using analyst forecasts as proxies for market beliefs, we extract analysts’ mental contexts and recalls that shape forecasts by training an ML memory model. First, we find that long-term memories are salient in analysts’ recalls. However, compared to an ML benchmark trained to fit realized earnings, analysts pay more attention to distant episodes in regular times but less during crisis times, leading to recall distortions and therefore forecast errors. Second, we decompose analysts’ mental contexts and show that they are mainly shaped by past earnings and forecasting decisions instead of current firm fundamentals as indicated by the ML benchmark. This difference in contexts further explains the recall distortion. Third, our comprehensive memory model reveals the significance of specific memory features and channels in analysts’ belief formation, including the temporal contiguity effect and selective forgetting.

Exploiting ffnancial news stories data, we construct news-implied linkages and document a strong lead-lag effect of ffrms with shared news coverage in China’s stockmarket. The news-link momentum strategy generates a monthly return of 1.33% and a four-factor alpha (Liu et al., 2019) of 1.43%. While prior evidence on the attention dynamics among ffrms with joint news coverage is limited, we show that the momentum spillover of news-linked ffrms is largely driven by investor underreaction. The return predictability from news links is also robust to controlling for alternative economic linkages. The ffndings suggest that information diffuses sluggishly among news-connected ffrms, thereby providing new evidence on the implication of media coverage for pricing efffciency.

This study examines the relationship between analyst forecast dispersion or accuracy and supplier concentration of listed firms in China from 2008 to 2019. Our findings suggest that higher supplier concentration is associated with lower analyst forecast dispersion, which can be attributed to the increased attention it receives from analysts. Moreover, this effect is more pronounced when firms have less bargaining power and higher institutional ownership, indicating a greater reliance on the supply chain. Our study highlights the importance of disclosing supply chain information, which provides insight beyond traditional financial information.

This paper examines how globalization process shapes the corporate ESG efforts in emerging markets. Using a staggered difference-in-difference model based on the gradual inclusion of China's A-shares in the MSCI index, we find that public companies improved their ESG performance and disclosure quality after being included. The results are robust to propensity score matched sample. Notably, the impact on ESG disclosure was significantly greater than on ESG performance, and the effect is more pronounced for non-SOEs and firms with weak governance. The inclusion also leads to significant increasesin foreign holdings, the proportion of women directors, and analyst attention, which have promoting effects on corporate ESG performance and disclosure ratings. This study sheds light on the macro-level determinants of corporate ESG engagement.

Deep learning technology is rapidly adopted in financial market settings. Using a large data set from the Chinese stock market, we propose a return-risk trade-off strategy via a new transformer model. The empirical findings show that these updates, such as the self-attention mechanism in technology, can improve the use of time-series information related to returns and volatility, increase predictability, and capture more economic gains than other nonlinear models, such as LSTM. Our model employs Shapley additive explanations (SHAP) to measure the “economic feature importance” and tabulates the different important features in the prediction process. Finally, we document several economic explanations for the TF model. This paper sheds light on the burgeoning field on asset allocation in the age of big data.

We provide the first systematic analysis of the stock return lead-lag effect among firms connected through shared analyst coverage in China’s A-share markets. We measure the shared analysts-weighted average returns of connected firms (CF) and show that CF return is a significant positive predictor of future returns of the focal firms in the following one to 12 months. The CF-based long-short portfolio earns an abnormal return of 10% to 12% per year. The effect is robust to controls for the industry and geographic momentum effects. Further evidence shows that the CF momentum spillover effect is stronger when the focal firm shares more analysts with connected firms, is covered by more non-star analysts or analysts with lower levels of education, or is held by more stress-resistant institutional investors. Our findings contribute to the cross-asset momentum literature by documenting a new, strong, and long-lasting momentum spillover effect in the Chinese stock markets.

To explore what kinds of roles foreign investors take in a gradually opening financial market, we propose the abnormal holding value ratio (AHVR) of northbound investors among stocks through China’s Stock Connect Mechanism. We find that AHVR positively predicts the expected stock returns and significantly relates to firms’ quality-related fundamental information, especially profitability. Foreign investors learn the firm fundamentals before they invest in the Chinese market, which is different from the trading behavior of domestic individual investors. The AHVR premium is larger among firms with higher attention of analysts who focus on effective information and with lower attention of individual investors who have behavioral bias. In all, the northbound inflows are smart money, which will increase the efficiency of the Chinese market instead of simply chasing stars that only grab investors’ attention.

The pilot carbon emission trading schemes (ETSs) of China were created to combat climate change in a cost-effective and economically efficient manner, and their potential impact on regulated firms has drawn increasing attention. This study is conducted to provide empirical evidence on the effect of China’s pilot ETSs on firm-level financial performance during the period from 2008 to 2017. The empirical results show that the ETS pilots have a positive impact on firms’ profitability and value, and a negative impact on operational costs. We also find that the ETS pilots improve total factor productivity (TFP) but that changes in technology have an indirect suppressing effect on the relation between the ETS and short-term financial performance, providing support for the weak version of the Porter Hypothesis. Further, we show that the carbon emission price has a negative impact on firms’accounting-based performance but increases firms’ market value. Finally, we find evidence that, in contrast to state-owned enterprises (SOEs), non-SOEs do not experience significant improvements in their financial performance, led by the ETS pilots. Our findings have policy implications for firms’sustainable development and the transition to a low-carbon economy.

Using the staggered opening of governmental public data platforms in China, we employ the difference-in-difference approach to investigate how public data access affects stock price synchronicity. We find that stock price synchronicity significantly drops after the public data platform is established in a firm’s headquarters city. The underlying mechanism is reducing information acquisition costs rather than increasing market attention or corporate information disclosure quality. Furthermore, the informational role of public data platforms magnifies under higher informed trade risk, poorer corporate governance, or better regional economic and innovation capacity. We highlight the role of public data in facilitating financial market efficiency.

Recent financial frauds in China such as Kangmei Pharmaceuticals’ case have raised suspicion in the capital market and among academics about the reliability of accounting information of listed companies, and as a result, various non-financial information that is compulsory or encouraged to be disclosed by regulators and voluntarily disclosed by listed companies is gradually gaining attention from various stakeholders and academics. The corporate social responsibility (CSR) information is one of the most widely disclosed non-financial information by listed firms, but its reliability and motivation are also questionable, for example, is CSR commitment affected by firms’ financial information quality? Using China a-share listed companies that disclosed their corporate social responsibility reports from 2009-2019 as a sample, we investigate whether earnings management can influence corporate social responsibility by analysing the management’s motives embedded in earnings management, in order to further examine whether Chinese listed companies are morally motivated to undertake social responsibility or use social responsibility as a strategic tool to maintain the reputation of the firm and the management. The results of the study show that earnings management and CSR are positively correlated, and the finds continue to be robust when using 2SLS, Heckman two-step regression and propensity score matching to control for reverse causality and self-selection bias, proving that China's listed companies are ethically motivated to fulfil their social responsibility. Therefore, it is important to focus on the quality of earnings information in order to perceive the motivation of CSR when evaluating a company’s CSR commitment.