We study how bank liberalization affects analyst forecast accuracy using two interest rate deregulations in China—the removal of the cap on bank lending rates in 2004 and the removal of the floor in 2013—as quasi-natural experiments. Our results show that the analyst forecast accuracy for high-risk firms decreases significantly after the removal of the lending rate cap, whereas analyst forecast accuracy for low-risk firms increases significantly after the removal of the lending rate floor. Moreover, interest rate liberalization affects forecast accuracy through operational risk and information asymmetry channels. Furthermore, the impact was concentrated on firms whose actual performance fell short of performance expectations and those that received more bank loans. Our findings imply that interest rate liberalization policies may have unintended consequences for analyst forecasts.

Utilizing the removal of the bank lending interest rate floor (IRFD) in China as an exogenous shock of banking liberalization, we find that IRFD leads to a significant rise in firms’ cost of equity capital, which is consistent with the prediction from the MM theory. The identified effects are more pronounced among firms with weaker ex-ante corporate governance and more severe ex-ante agency problems. We also find that IRFD witnesses an increase in the amount of acquired bank loans, a decrease in the average interest rate, and an increase in free cash flow. Further evidence also suggests IRFD provokes a drop in firms’ investment quality. Overall, our findings highlight an unexplored role of banking sector deregulation on firms’ cost of equity capital.

Using a proprietary bank-loan-level dataset, we find a surprising negative relation between loan spreads and minimum wage. We propose a stylized model to explain the relation: banks filter out the low-quality borrowers after the wage shocks, resulting in a separating equilibrium. Our evidence is consistent with the model’s predictions: (1) city-level and firm-level evidence shows that an increase in minimum wage is negatively associated with the likelihood of obtaining bank loans, especially for labor-intensive borrowers, (2) deal-level evidence shows that both the average default rate and loan spreads decrease when minimum wage rises, and (3) subsequently, labor intensive firms that are still able to obtain bank loans when minimum wage rises outperform their peers. Our findings suggest that as more credit resources are allocated to better quality firms and leave other firms far more behind, the existence of such credit reallocation effects can exacerbate the divergence between higher and lower quality firms induced by an increase in minimum wages.

Using plant-level pollution data from Environmental Survey and Reporting Database in China, we analyze how the government's 2007 environmental policy aiming to curb pollution from two-high industries (i.e., high-polluting and high energy-consuming) affect real firm pollution activities across the supply chain. We employ the Differences-in-Differences approach and find that following the 2007 environmental policy, firms in two-high industries that are mostly in the upstream of the supply chain indeed reduce emissions of air and water pollutions significantly (e.g., sulfur dioxide (SO2), chemical oxygen demand (COD), and wastewater). Such reductions are mainly due to higher pollution removal and the increased investments in treatment facilities that are funded by subsidized bank loans such as the China Development Bank (CDB). By contrast, following the 2007 policy, firms in the downstream of two-high industries increase their production levels without any increases in their pollution treatment facilities and capabilities, which leads to increased pollution levels such as SO2 emissions and coal consumption. Furthermore, such adverse spillover effects in downstream industries can be alleviated by CDB loans, which help finance the investments in pollution treatments for these downstream firms. Our findings about direct and unintended spillover effects of the environmental policy suggest that policymakers should carefully consider all potential impacts across the supply chain when designing the package of environmental policies to mitigate unintended adverse consequences.

This paper studies the effect of top management team (TMT) network centrality on bank loan contracts. We show that firms with high TMT network centrality obtain bank loans with lower loan spreads, larger loan size, longer maturity, and fewer collateral requirements. From the mediating effect analysis, we find that TMT interlocking networks affect loan pricing by reducing agency costs, improving the quality of accounting information, expanding resource channels, and enhancing the credibility of companies. In addition to easing financial constraints, TMT network centrality is also beneficial to investment efficiency and innovation output of corporates, but it will decrease firm performance.

This paper uses a loan transactions dataset from China to identify whether shadow loans cost more than formal bank loans even with collateral. This motivates us to explore the reasons as to why a listed firm would opt for such loans. Using propensity-score matched data, we find that privately-owned firms with shadow loans are forced to obtain these loans since they are politically discriminated following a regulation change in 2009 that favoured state-owned firms. However, state-owned firms obtain shadow loans due to their inferior firm characteristics. Further, we employ a Difference-in-Differences methodology to uncover that privately-owned firms experience a decline in their performance, investment growth and an increase in default probability following their high dependence on shadow loans when they are excluded from the formal loan market. The above results survive various robustness checks, including doubly-robust inverse-probability weighted Difference-in-Differences regressions.

The finance–growth nexus has been a central question in understanding the unprecedented success of the Chinese economy. With unique data on all the registered firms in China, we build extensive ownership networks, reflecting firm-to-firm equity investment relationships, and show that thesenetworks have been expanding rapidly since the 2000s, with more than five million firms in at least one network by 2017. Entering a network and increasing network centrality, both globally and locally, are associated with higher firm growth. Such positive network effects tend to be more pronounced for high productivity and privately owned firms. The RMB 4 trillion stimulus, mostly in the form of newly issued bank loans and launched by the Chinese government in November 2008 in response to the global financial crisis, partially ‘crowded out’ the positive network effects. Our analysis suggests that equity ownership networks and bank credit tend to act as substitutes for state-owned enterprises, but as complements for privately owned firms in promoting growth.

China’s shadow banking system has experienced surprisingly high growth since the global financial crisis. We develop a model to understand this puzzling phenomenon. With local government interventions in bank loans for low-quality projects and information asymmetry between banks and regulators, a policy combination of tightening formal banking and loosening shadow banking can reduce inefficiency, because the higher funding liquidity risk of shadow banking incentivizes banks to be more disciplined about the quality of projects. We find consistent empirical evidence that when on-balance-sheet financing was constrained by regulators, banks primarily shifted high-quality projects into their controlled shadow banking system.

In this paper, I build a continuous-time macro-finance model in which firms can access both bond credit and bank credit. The model captures the simple idea that the presence of bond financing increases the price elasticity of demand for bank loans. I find that the optimal capital adequacy ratio is quantitatively sensitive to the presence of bond financing and that models would overstate the banking sector's recovery rate if they omit bond financing. Furthermore, the model highlights that an economy's optimal capital requirement highly depends on the efficiency of its bankruptcy procedure and the risk profile of its real sector.

By analyzing a hand-collected transaction-level dataset on the finance leases of China’s public firms for the period 2007-2019, this paper sheds light on China’s financial leasing industry. We find that banks use their affiliated leasing firms to provide credit to constrained clients in order to circumvent the government’s targeted monetary tightening policy. Finance Lease offsets the expected decline in traditional bank loans in affected industries, and therefore hampers the effectiveness of the monetary policy. Although this regulatory arbitrage may accumulate systemic risk at the macro level, bank-affiliated leasing firms charge lower leasing rate and exert tighter risk control than non-bank-affiliated leasing firms at the micro level. This finding indicates that banks use finance leases as a channel to keep low-risk clients rather than to make excessive profit.