We study credit allocation across  rms and its real e ects during China's economic stimulus plan of 2009-2010. We match con dential loan-level data from the 19 largest Chinese banks with  rm-level data on manufacturing  rms. We document that the stimulus-driven credit expansion disproportionately favored state-owned rms and  rms with lower average product of capital, reversing the process of capital reallocation towards private  rms that characterized China's high growth before 2008. We argue that implicit government guarantees for state-connected  rms become more prominent during recessions and can explain this reversal.

Abstract: Traditional research about the passive investors and index were mainly focus on the tracking error and the performance of mutual funds. However, they ignored that, deceptive by name, the passive investors, such as index-tracking funds and ETFs, may have an active impact on the value of the company through large-scale transactions of these passive investors. Focused on the Chinese stock market, this paper investigates whether specific passive investors, the funds and ETFs that track MSCI China index, will actively influence the market valuation after MSCI Index Rebalance. When the passive shareholders, which are always the mutual funds, exceeds a threshold, I find that firms added to the index will have a significant positive return, about X%, to the index itself. Also, I find the firms eliminated out to the index have a significant negative return, about X%, to the index itself. One potential interpretation of these results is that index-rebalancing will lead the index-trackers to buy those stocks added to the index, and these transactions represent a large buy power that will lead the demanding of those stocks to exceed the selling power and this dynamic of trading plus the following transactions of other investors eventually cause a premium and positive return. The firm size will also have an impact on stock performance when the index get rebalanced, partially in that the weight of the index is calculated according to the market value, a calculate method that leads to the higher weight of large companies. If large companies are added to or removed from the index, the trading volume will be larger, causing more transactions dynamic on those stocks.

We provide nationwide causal estimates of digital financial inclusion’s (DFI) effect on air pollution in the short term for China from 2014 to 2018. Using distance to Xihu District as an instrument, 1% gain of DFI increases air pollution by 0.36%. The baseline result is strongly robust to various checks. The coverage breadth and usage depth of DFI increase pollution, with the elasticity of 0.39 and 0.37 respectively, whereas the digitization level of DFI lowers pollution, with the elasticity of -1.42. The heterogeneous short-run effect of DFI can be attributed to a multitude of channels, including pollution standard, geographical factors, population density, development gaps and international trade.

We examine the role of borrowers’ digital footprints in debt collection. Using a large sample of personal loans from a fintech lender in China, we find that the information acquired by the lender through borrowers’ digital footprints can increase the repayment likelihood on delinquent loans by 18.5%. The effect can be explained by two channels: bonding borrowers’ obligations with their social networks and locating borrowers’ physical locations. Moreover, the lender is more likely to approve loan applications from borrowers with digital footprints, even though these borrowers may occasionally have a higher likelihood of delinquency. The use of digital footprints can remain legitimate under stringent privacy protection regulations and fair debt collection practices. Our findings suggest that digital footprints, as a new type of collateral, can ultimately enhance financial inclusion by facilitating the lender’s collection of delinquent loans.

With growing awareness of sustainability, the field of Environmental, Social and Governance (ESG), has been attracting mainstream investors and researchers. Many previous studies have found inconclusive or mixed results on the relationship between ESG ratings and firms’ financial performance, which are mainly attributed to their varied markets, time horizons, and sources of ESG rating. Based on evidence from an emerging market, namely China, this paper examines whether ESG is an adequate indicator for firms’ future financial performance. Given the divergence in ESG rating methodologies, we use ESG data from two ESG rating agencies, one based in China (SynTao) and the other based in Switzerland (RepRisk), for robustness. Specifically, we investigate 377 China A-share companies covered by both agencies and find that ESG rating, albeit divergent due to disparate methodologies, is instrumental in predicting the trend of corporate financial performance (CFP). This work verifies that the forward-looking nature of ESG makes it crucial for firms’ long-term valuation and financial performance in emerging markets. Throughout the research, we observe four issues in the current ESG rating process: the opacity and inaccessibility of source data, the obscurity of ESG rating methodologies adopted by rating agencies, the lack of automated pipeline, and the unannounced historical data rewriting. We believe that the public blockchain ecosystem is promising to address these issues, and we propose future research on the ESG framework for blockchain to call for sustainability focus on this emerging technology.

We study how institutional investors utilize potentially biased information by analyzing the e ect of IPO underwriters' earnings forecasts on investors' bidding behaviors in Chinese IPO auctions. Despite the presence of upward biases in underwriters' earnings forecasts, we  nd that investors' bid prices are higher in IPOs with higher earnings forecasts. The investors' positive reaction to biased information can be explained in a rational expectation model where the underwriter has valuable information about the IPO but has a biased incentive in presenting the information to investors. Consistent with the model's predictions, we  nd that an investor's bid price is more sensitive to the underwriter's earnings forecast when the forecast bias is expected to be smaller, when the relative precision of the underwriter's information over the investor's information is higher, and when the investor has a higher valuation of the IPO.

We examine the influence of implicit government guarantees on the information content of credit ratings in China, guided by a theoretical credit rating game model in the presence of government guarantees. Using issuers’ controlling shareholder identity as the defining metric of implicit government guarantees, we document a less sensitive relationship between credit ratings and primary market offer yields for SOE bonds (i.e., bonds issued by firms controlled by government or government related agencies) than that for non SOE bonds. Moreover, ratings of non SOE bonds have a stronger predictive power on both future downgrades and a market based measure of issuer expected default probability than those of SOE bonds. These findings are robust to considering the u nobserved influence of the controlling shareholder identity on security pricing and bond default risk. Taken together, our empirical findings are consistent with the model’s prediction that government guarantees can dampen the incentives for credit rating agencies to acquire costly information, thus lowering the equilibrium informativeness of ratings for SOE bonds.

We use supervised machine learning to develop a Chinese language financial sentiment dictionary from 3.1 million financial news articles. Our dictionary maps semantically similar words to a subset of human-expert generated financial sentiment words. In article-level validation tests, our dictionary scores the sentiment of articles consistently with a human reading of full articles. In return validation tests, our dictionary outperforms and subsumes previous Chinese financial sentiment dictionaries such as direct translations of Loughran and McDonald’s (2011) financial words. We also generate a list of politically-related positive words that is unique to China; this list has a weaker association with returns than does the list of otherwise positive words. We demonstrate that state media exhibits a sentiment bias by using more politically-related positive and fewer negative words, and this bias renders state media’s sentiment less return-informative. Our findings demonstrate that dictionary-based sentiment analysis exhibits strong language and domain specificity.

Financial repression—regulating interest rates below the laissez-faire equilibrium—has historically impeded investment in developing economies. In China, bank deposits were long subject to binding interest rate caps. Using transaction and local penetration data from a leading FinTech payment company, we study the FinTech’s introduction of a money market fund (MMF) with deposit-like withdrawal features but uncapped interest rates aids in interest rate liberalization. In aggregate, MMF assets grow rapidly, and banks whose deposit base was more exposed to the payment app see greater outflows. These outflows are concentrated in household demand deposits, for which the MMF is the closest substitute. Contrary to regulator concerns, exposed bank profitability does not decline. Rather, exposed banks invest more in financial innovation and are more likely to launch competing funds with similar features. Our results highlight how FinTech competition stimulates interest rate liberalization among traditional banks by introducing competition for funding.

China repos trade in the over-the-counter interbank market as well as the stock exchange. This paper examines the behaviours, sources, and drivers of the spread between China’s exchange and interbank reporates from December 2006 to June 2018. After adjusting for different day-count quoting methods, I dissect the exchange to interbank repo spread into two components: cross-market segmentation between exchange and interbank markets for non-depository institutions (NDIs), and within-market counterparty segmentation between NDIs and depository institutions (DIs) in the interbank market. The 1-day repo markets are found to be more segmented, with the spread mainly driven by the cross-market segmentation for NDIs, reflecting the two different market mechanisms and trading frictions that prevent NDIs from effectively arbitraging across the two markets in the shorter tenor. On the other hand, the 7-day repo markets are found to be less segmented, with the spread mainly driven by the counterparty segmentation between NDIs and DIs within the interbank market, reflecting greater counterparty credit and liquidity risks for NDIs relative to DIs. Further analysis uncovers the impacts of quarter-end effect, monetary policies, and shadow banking activities on the cross-market and within-market segmentations in China’s repo markets.