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.

Shadow financing through off-balance sheet wealth management products (WMPs) has become increasingly important besides deposits in China. We quantify the economic magnitude of the effect of WMPs on banking stability in an equilibrium model calibrated to Chinese banking sector data. Alternative equilibria emerge, which deviate substantially from the observed banking system and lead to severe financial distress and large welfare losses. Rollover costs from the WMP market and negative shocks to the asset market underlying WMPs can exacerbate banking instability. Moreover, we show that smaller and medium sized banks are comparably relevant for financial stability as the systemically important big 4 banks in China.

We investigate the impact of state ownership structure on asset prices and corporate policies. By primarily focusing on China’s corporations, we show that the relationship between expected returns and capital investment varies significantly across state owned enterprises (SOE) and private owned enterprises (POE). A portfolio that longs low investment and shorts high investment firms earns an average annual excess stock return of 5% in the SOE sector. In contrast, there is no relationship between investment and expected returns in the POE sector. We show that the difference in the link between expected returns and investment across SOE and POE firms is driven by their differential exposures to the debt issuance shocks, which captures the monetary supply shocks in China. As SOE firms have easier access to bank loans, the high investment firms in the SOE sector are more able to raise debt despite that debt supply is shrinking, and hence they are less risky. We develop a dynamic model with SOE and POE firms facing different frictions in debt markets. The economic mechanism emphasizes that heterogeneous access to the debt market is an important determinant of equilibrium risk premiums across sectors with different state ownership.

基于1980年-2017年42个经济体的政府、企业、居民部门杠杆率数据，本文考察了 杠杆增速、特别是各部门杠杆增速如何影响金融危机发生的概率。研究发现，杠杆增速比杠 杆水平对金融危机的影响更为显著，这意味着“管增速”优于“管上限”，即稳杠杆优于去 杠杆。同时，各部门的杠杆增速对金融风险具有异质影响:相比于政府部门，私人部门(居 民与企业)的相对杠杆增速越高，则一国发生金融危机的概率越大;进一步地，相比于企业 部门，居民部门的相对杠杆增速越高，则一国发生金融危机的概率越大。这是由于不同债务 主体的负债能力与杠杆使用效率均存在差异。这意味着“控部门”优于“控总量”，即结构 性去杠杆优于一刀切去杠杆。以上结果在不同设定下保持稳健。

This paper examines how FinTech can lower investment barriers and help households move toward optimal risk-taking, using a unique account-level data on consumption, investments, and FinTech usage from Ant Group. During our sample period, China ex- perienced a rapid increase in FinTech penetration in the form of offline digital payment, and our measure of FinTech adoption is constructed relative to this fast-developing trend of new technology. Taking advantage of our consumption data, we further infer individuals’ risk tolerance from their consumption volatility. We find that, while Fin- Tech adoption improves risk-taking for all, the more risk-tolerant individuals benefit more from FinTech advancement. The magnitude of FinTech improvement is further quantified relative to the optimal alignment of risk-taking and consumption prescribed by Merton (1971). Aggregating to the city-level, we find significant variations in Fin- Tech adoption across cities in China, owing to the gradual spread of the new technology from Hangzhou to inner China. Examining the enhancement in risk-taking across ge- ographical locations, we find that cities with low financial-service coverage benefit the most from FinTech penetration. Overall, our results show that, by unshackling the traditional constraints, FinTech improves risk-taking for individuals who need it the most.

We provide the first direct evidence on how issuers choose a credit rating agency (CRA). Using rating data from a leading CRA in China, we find that although in most cases the issuers publish more favourable ratings, in some cases issuers just select the ratings provided by CRAs they have business relationships with, especially when the more favourable ratings are above issuers’ prior ratings. Our further analysis suggests that this phenomenon is driven by the switching cost arising from the issuer being considered as a rating shopper when it obtains an upgrade from a CRA without a business relationship.

Over the past 150 years, finance has evolved into one of the world’s most globalized, digitized and regulated industries. Digitalization has transformed finance but also enabled new entrants over the past decade in the form of technology companies, especially FinTechs and BigTechs. As a highly digitized industry, incumbents and new entrants are increasingly pursuing similar approaches and models, focusing on the economies of scope and scale typical of finance and the network effects typical of data, with the predictable result of the emergence of increasingly large digital finance platforms. We argue that the combination of digitization, new entrants (especially BigTechs) and platformization of finance – which we describe as FinTech 4.0 and mark as beginning in 2019-2020 – brings massive benefits and an increasing range of risks to broader sustainable development. The platformization of finance poses challenges for societies and regulators around the world, apparent most clearly to date in the US and China. Existing regulatory frameworks for finance, competition, data, and technology are not designed to comprehensively address the challenges to these trends to broader sustainable development. We need to build new approaches domestically and internationally to maximize the benefits of network effects and economies of scope and scale in digital finance while monitoring and controlling the attendant risks of platformization of finance across the existing regulatory silos. We argue for a principles-based approach that brings together regulators responsible for different sectors and functions, regulating both on a functional activities based approach but also – as scale and interconnectedness increase – addressing specific entities as they emerge: a graduated proportional hybrid approach, appropriate both domestically in the US, China and elsewhere, as well as for cross-border groups, building on experiences of supervisory colleges and lead supervision developed for Globally Systemically Important Financial Institutions (G-SIFIs) and Financial Market Infrastructures (FMIs). This will need to be combined with an appropriate strategic approach to data in finance, to enable the maximization of data benefits while constraining related risks.