The COVID-19 epidemic has accelerated the digital economy’s pervasiveness throughout the Chinese economy, leading to a sharp rise in demand for “contactless” services in the financial industry. We examine the digital transformation of the Chinese banking industry using the DEA–Malmquist index method, supplemented by a distance function and time to compare the dynamic changes of productivity. Our paper then conducts an empirical study on the digital transformation of Chinese commercial banks based on their improvements in efficiency. We analyze banks with superior efficiency in science and technology investment and evaluate their digital maturity and digital transformation experience. Results show that digitalization investment has contributed to substantial production efficiency improvement for commercial banks; however, heterogeneity exists across banks. We further advocate a path for banks’ digital transformation based on theoretical research and empirical digital transformation experience in this area.

In this article, the authors use machine learning tools to analyze industry return predictability based on the information in lagged industry returns. Controlling for post-selection inference and multiple testing, they find significant in-sample evidence of industry return predictability. Lagged returns for the financial sector and commodity- and material-producing industries exhibit widespread predictive ability, consistent with the gradual diffusion of information across economically linked industries. Out-of-sample industry return forecasts that incorporate the information in lagged industry returns are economically valuable: Controlling for systematic risk using leading multifactor models from the literature, an industry-rotation portfolio that goes long (short) industries with the highest (lowest) forecasted returns delivers an annualized alpha of over 8%. The industry-rotation portfolio also generates substantial gains during economic downturns, including the Great Recession. TOPICS:Big data/machine learning, analysis of individual factors/risk premia, portfolio construction, performance measurement

Financial inclusion is a crucial link in achieving the Sustainable Development Goals (SDGs). Strengthening the capability of financial institutions to provide inclusive financial services can help to narrow the inequality gap and increase access to opportunities. Digitalization, with its competitive advantages and rapid growth, may be a powerful tool to foster financial inclusion and sustainable development. This paper examines the effects of bank digitalization on sustainable financial inclusion and explores two underlying incentive mechanisms in banks: profit driven and risk aversion. We construct a basic model and a mechanism model and exploit a nonlinear attempt, heterogeneous estimation as well as supplementary variable and instrument variable methods for a robustness test. The results of the basic model demonstrate that bank digitalization has significant positive effects on financial inclusion and the current financial inclusive effects are sustainable. The mechanism models designed as the mediation effect panel model suggest that digitalization enables banks to expand the business probability frontier of profit-driven behavior and pursuit of credit portfolio diversity in risk aversion behavior, thereby promoting sustainable financial inclusion. As a result of digitalization, vulnerable groups can benefit from sustainable financial inclusion, while financial inclusion feeds back into banks’ sustainable development. This paper conforms to the trend of the development of digitalization and provides theoretical and empirical support for banks to build digitalization and realize sustainable financial inclusion, which contributes to the “triple-win” financial ecology for improving banks’ performance, increasing the rights of vulnerable groups and promoting sustainable development throughout society.

Rapid urbanization and industrialization in Chinese cities have substantially elevated carbon emissions, and transportation plays a major role in these emissions. Due to data availability, research on the impact of both high-speed rail (HSR) and other urban transportation modes on urban carbon emissions is rare. Using a relatively large panel of 194 Chinese cities from 2008–2013, we examine the impact of HSR, conventional rail, bus, roads, and subways on urban carbon emissions. We further document the interaction of these transport modes with geo-economic variables, and more accurately measure HSR’s impact on emissions using a comprehensive accessibility metric. During this time, China developed, constructed and began to operate an extensive HSR network. Our results show that increases in HSR lead to rises in carbon emissions, emissions per GDP unit and per capita. We also find that transportation’s impact on carbon emissions differs by city size and region, and transportation modes significantly interact with GDP, population and urban area to affect carbon emissions. These interactions imply that the government’s promotion of HSR over conventional rail may have unintended consequences and boost urban carbon emissions.