This paper represents the first attempt to show how heterogeneous shock experiences help explain the enduring scars on household future behaviors. Using a large-scale household survey with 15,652 observations combined with geospatial transportation big data, we identify a novel belief-updating mechanism through which crises may exert prolonged impacts on household asset allocation and consumption patterns. An increase in the duration of previous lockdown experience is associated with a 10.52% escalation in enhanced anxiety for future precautionary saving motivations. This experience-based learning perspective supports the resolution of long-lasting overreactions to negative shocks via belief revisions and extends to households’ consumption behaviors. The lingering effects continue to skew households' beliefs even when conditions improve. Additionally, households with different individual-based shock experiences may exhibit varying perceptions of external shocks, resulting in disparate belief revision processes.

Using a comprehensive factor zoo, we document a notable factor MAX premium in the Chinese market. Factors with high maximum daily returns consistently outperform those with low maximum returns by 0.82% per month in the future, on a risk-adjusted basis. This premium remains robust controlling for various factor characteristics, and is not sensitive to the selection of factors. The factor MAX anomaly stands apart from lottery-type stock anomalies and contributes to elucidate most of these anomalies. The factor MAX premium concentrates in high-eigenvalue principal component factors, shedding light on the prevalent lottery preferences for factor investing in China’s A-share market. We document pronounced existence of factor MAX anomaly in the United States and other G7 countries.

Using a comprehensive factor zoo, we document a notable factor MAX premium in the Chinese market. Factors with high maximum daily returns consistently outperform those with low maximum returns by 0.82% per month in the future, on a risk-adjusted basis. This premium remains robust controlling for various factor characteristics, and is not sensitive to the selection of factors. The factor MAX anomaly stands apart from lottery-type stock anomalies and contributes to elucidate most of these anomalies. The factor MAX premium concentrates in high-eigenvalue principal component factors, shedding light on the prevalent lottery preferences for factor investing in China’s A-share market.

A lack of sufficient information about potential borrowers is a major obstacle to access to financing from the traditional financial sector. In response to the need for better information to prevent fraud, to increase access to finance and to support balanced sustainable development, countries around the world have moved over the past several decades to develop credit information reporting requirements and systems to improve the coverage and quality of credit information. Until recently, such requirements mainly covered only banks. However, with the process of digital transformation in China and around the world, a range of new credit providers have emerged, in the context of financial technology (FinTech, TechFin and BigTech). Application of advanced data and analytics technologies provides major opportunities for both market participants – both traditional and otherwise – as well as for credit information agencies: by utilizing advanced technologies, participants and credit reporting agencies can collect massive amounts of information from various online and other activities (‘Big Data’), which contributes to the analysis of borrowing behavior and improves the accuracy of creditworthiness assessments, thereby enhancing availability of finance and supporting growth and development while also moderating prudential, behavioral and conduct related concerns at the heart of financial regulation. Reflecting international experience, China has over the past three decades developed a regulatory regime for credit information reporting and business. However, even in the context of traditional banking and credit, it has not come without problems. With the rapid growth and development of FinTech, TechFin and BigTech lenders, however, have come both real opportunities to leverage credit information and data but also real challenges around its regulation. For example, due to fragmented sources of borrower information and the involvement of many players of different types, there are difficulties in clarifying the business scope of credit reporting and also serious problems in relation to customer protection. Moreover, inadequate incentives for credit information and data sharing pose a challenge for regulators to promote competition and innovation in the credit market. Drawing upon the experiences of other jurisdictions, including the United States, United Kingdom, European Union, Singapore and Hong Kong, this paper argues that China should establish a sophisticated licensing regime and setout differentiated requirements for credit reporting agencies in line with the scope and nature of their business, thus addressing potential for regulatory arbitrage. Further, there is a need to formulate specific rules governing the provision of customer information to credit reporting agencies and the resolution of disputes arising from the accuracy and completeness of credit data. An effective information and data sharing scheme should be in place to help lenders make appropriate credit decisions and facilitate access to finance where necessary. The lessons from China’s experience in turn hold key lessons for other jurisdictions as they move from credit information to credit data regulation in their own financial systems.

We propose a theory of corporate social responsibility (CSR) by linking it to a firm’s product market. In our model, the firm’s product exhibits network effects whereby its value increases with the number of consumers who purchase it. Moreover, with advancements in technology and big data, the firm can adopt personalized pricing for each consumer. We show that such a firm could use CSR as a commitment device for low product prices, which helps overcome the coordination problem among consumers and increases firm profits, thus supporting the notion of “doing well by doing good.”

The improvement of production efficiency and the change of business model brought about by the deep integration of the digital economy and the real economy have become an important driving force for industrial transformation and upgrading. This paper explains the necessity of digital transformation of manufacturing, the trends, paths and six technologies of financial digital transformation. In the digital era, relying on data, scenarios, and algorithms to explore the essential logic of business, make predictions and decisions based on business insights, and put forward higher requirements for financial empowerment business. As an important way for enterprise management transformation and upgrading, the core goal of financial digital transformation is to take "data-driven" as the main line, promote transformation based on the two principles of industry-finance integration and in-depth scenarios, and build "value-creating" finance, that is, based on the integrated application of digital technology, so that finance can expand the functions of supporting strategy, assisting decision-making, empowering business, preventing and controlling risks, lean management, operational excellence, quality and efficiency, compliance supervision and other functions on the basis of basic transaction accounting functions, and promote and even lead the value creation functions of enterprises. The article points out that the manufacturing industry should take enhancing competitiveness as the direction, financial management as the center, and improving quality and efficiency as the goal to accelerate digital transformation. Introduced Midea Group's financial digital transformation practices and results. It is proposed that enterprises should vigorously promote the deep integration of big data, Internet, cloud computing, Internet of Things, artificial intelligence, blockchain and the real economy, accelerate the digital transformation of finance, and inject new impetus into the high-quality development of enterprises.

The improvement of production efficiency and changes in business models brought about by the deep integration of the digital economy and the real economy have become an important driving force for industrial transformation and upgrading. This paper expounds the necessity of digital transformation of manufacturing industry and its path selection. The important role of industrial Internet and Internet platforms is pointed out, and the characteristics of industrial Internet platforms such as Midea Group and Siemens MindSphere and their effects on industrial transformation are analyzed. Enterprises should vigorously promote the deep integration of big data, the Internet, cloud computing, the Internet of Things, artificial intelligence, blockchain and the real economy, accelerate the digital transformation of the manufacturing industry, and inject new impetus into the high-quality development of the manufacturing industry.

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.

In the era of big data, asset pricing is influenced by various factors, which are extracted from multi-source heterogeneous information, such as high frequency market and sentiment information, low frequency firm characteristic and macroeconomic information. Especially, low frequency policy information plays a significant role in the long-term pricing in China but it is barely investigated due to its textual form. To this end, we first extract policy variables from major national development plans (“Five-Year Plans”, “Government Work Reports”, and “Monetary Policy Reports”) using Natural Language Processing (NLP) technique and Dynamic Topic Model (DTM). However, traditional models are inadequate for mixed frequency data modeling and feature extraction. Then, we propose a mixed frequency deep factor asset pricing model (MIDAS-DF) that solves the asset pricing problems under the mixed frequency data environment through mixed data sampling (MIDAS) technique and deep learning architecture. Time-varying latent factors and factor loadings can be modeled from mixed frequency data directly in a nonlinear and data-driven way. Thus, the MIDAS-DF model is able to learn the nonlinear joint-patterns hidden in multi-source heterogeneous information. Our empirical studies of 4939 stocks on the Chinese A-share market from January 2003 to July 2022 demonstrate that low frequency policy information has profound impacts on asset pricing, which anchors the long-term pricing direction, and high frequency market and sentiment information have significant influences on stock prices, which optimize the short-term pricing accuracy, they together enhance the pricing effects. Consequently, pricing effects the MIDAS-DF model outperform the five competing models on individual stocks, various test portfolios, and investment portfolios. Our research about heterogeneous information provides implications to the government and regulators for decision-support in policy-making and our investment portfolio is of great importance for investors’ financial decisions.

Although big data technologies such as digital contact tracing and health certification apps have been widely used to combat the COVID-19 pandemic, little empirical evidence regarding their effectiveness is available. This paper studies the economic and public health effects of the "Health Code" app in China. By exploiting the staggered implementation of this technology across 322 Chinese cities, I find that this big data technology significantly reduced virus transmission and facilitated economic recovery during the pandemic. A macroeconomic Susceptible-Infectious-Recovered (SIR) model calibrated to the micro-level estimates shows that the technology reduced the economic loss by 0.5% of GDP and saved more than 200,000 lives by alleviating informational frictions during the COVID-19 outbreak.