To conduct this research, datasets from several different sources are merged for use in the final analysis. The main research examines the hypotheses from section 2.5.3 on the relationship between firm characteristics and shadow-banking activities and the differences that arose in this relationship when the financial market environment changed due to the credit stimulus policy.
As commonly defined, “shadow banking ... comprises a diverse set of institutions and markets that, collectively, carry out traditional banking functions – but do so outside, or in ways only loosely linked to, the traditional system of regulated depository institutions”
(BErnAnkE, 2012). In brief, shadow banks are nonbank financial intermediaries that do not adhere to central bank deposit reserve requirement regulations. This definition covers many channels of lending and borrowing, including individuals, nonfinancial firms, trust funds, private equities, etc. One of the biggest categories in the Chinese shadow bank-ing system is company-to-company entrusted loans. As shown in figure A.7, aggregated entrusted loans in China reached an all-time high of 13.8 trillion RMB (approximately 2 trillion USD1) in August 2017; this figure was less than 2 trillion RMB before 2008, according to the People’s Bank of China. Compared with bank credit to nonfinancial corporations in figure A.6, entrusted loans have already surpassed 10% of the volume of normal commercial loans.
Although no database covers all Chinese shadow banking or entrusted loan activities, we collect a subsample dataset at the transaction level with respect to both the source of funding (from banks to listed firms) and usage of funding (from listed firms to related companies) to represent the Chinese shadow banking system.
2.6.1 Firm level loan data
In the literature, Qian and Yeung, 2015b access a private and detailed sample of loans from one Chinese national bank whose borrowers are mainly private firms. However, to examine firm characteristics, financial statements of borrowing firms are required. Bailey et al., 2011balso tries to search for all bank loan-related announcements from the website of the China Securities Regulatory Commission (CSRC). Although the China Stock Mar-ket and Accounting Research (CSMAR) Database, from a Shenzhen-based data vendor (GTA), has built a database of these public announcements, it is missing a large number of observations. First, regulations on disclosure only require listed firms to announce major events that may significantly affect their stock prices. Listed firms therefore only
1Chinese Companies Rush In With Nearly $2 Trillion Where Bankers Fear to Lend, Wall Street Jour-nal, https://www.wsj.com/articles/chinese-companies-rush-in-with-nearly-2-trillion-where-bankers-fear-to-lend-1486636204
selectively announce loans above a certain level. Second, some firms’ corporate gover-nance policies allow the CEO or chair to sign loan contracts without shareholder approval as long as the amount is below a certain threshold. Third, most lorelated public an-nouncements state the overall amount of the loan or credit line after the signing of an agreement with a bank, but the number in the announcement is not the annually real-ized value of the commercial loan. In the absence of a Chinese corporate loan database analogous to the Loan Pricing Corporation’s (LPC’s) DealScan database for US firms, we designed a computer program to extract information on loans from firms’ annual financial reports. Listed firms are encouraged but not required to disclose their top 5-10 short-and long-term borrowings by loan size, including not just bank loans but also loans from trusts and even persons. In addition, most firms also report new commercial loans in the notes of their financial statements. From both sources, we extract bank-loan observations with information such as the issuing bank’s name, loan size, loan tenor, interest rate or the rules for calculating interest, and loan currency. Most firms summarize the borrowing amount for each loan class, i.e., collateralized, guaranteed, or credit loans. Some firms further provide details, e.g., collaterals used in a loan contract or guarantors of a loan.
These variables are not always available for each observation, and until recent years, firms’
annual reports differed from each other, with no consistent format, which creates tremen-dous difficulty in collecting the data. Overall, this sample contains over 100 thousand observations for nearly 3000 listed firms from 2001 to 2015. To the best of our knowledge, this is the first work to collect and study these data.
2.6.2 Measures of shadow-banking activities
The literature has used two sets of measurements for shadow-banking activities in China.
In an early study, G. Jiang et al., 2010b detected that a substantial portion of “other receivables” items in financial statements are directly traceable to intercorporate loans towards the largest shareholder and its affiliates. Later, Jia et al., 2013arevealed that the CSMAR database collects data on direct shadow-banking activities, namely, related-party transactions (RPTs), which the CSRC mandates all publicly listed firms disclose. This chapter uses this RPT database from CSMAR. The database captures all information on each related-party transaction and includes detailed profiles of the transacting parties, including their name and relation with the listed firm (parent firm, subordinate, sibling firm under same parent firm, etc.) Since transactions with subordinates are included in the listed firm’s consolidated financial statements, earnings or losses will be distributed among all shareholders equally, and this relationship trading is not counted in shadow-banking activity. Only off-balance-sheet transactions between parent and sibling firms involve the transfer of financing resources, with one party acting as a financial intermediary. Thus, this research only takes into account transactions between parent and sibling firms.
The details on each transaction in this dataset include the transaction amount, cur-rency, interest rates if applicable, duration, date, and category. This latter variable iden-tifies the characteristics of each transaction as a loan, guarantee, or operational exchange.
This chapter specifically focuses on RPT loans, which represents observed shadow-banking activities. We should point out that RPT loans involve many entrusted loans but are not limited to this format. They also include direct firm funding transactions without banks as trustees.
Figure A.4 illustrates the evolution of the aggregate amount of RPT loans. The percentage of listed firms directly lending intercorporate loans to other business group
affiliates rose from 18% in 2007 to 39% in 2015. The total value of RPT loans reached 1.1 trillion yuan (approximately 160 billion USD) in 2015, up from only 0.1 trillion yuan (15 billion USD) in 2007. By aggregating firm-year transactions under the RPT loan category, we construct the dependent variable, named “RPT loans”, to measure the value of shadow banking by taking the natural logarithm of the aggregate amount of RPT loans plus one.
Another serious issue linked to shadow banking relates to the two most booming industries in China after 2008, the real estate and finance sectors. Even when a listed firm does not belong to either industry, the business group behind it will surely not miss out on the opportunity to reap enormous profits from investments in these industries.
Many business groups have subordinates specializing in real estate and finance and may force the listed subsidiary to leverage and transfer bank resources to these sibling firms.
This might be one of the drivers of the Chinese real-estate market bubble and shadow banking. To address this issue, an algorithm is designed to identify the industries of related parties by detecting keywords in the names of the transacting firms. We sum the RPT transaction amounts destined for these two sectors and create the “RPT in real estate” and “RPT in finance” dependent variables to analyze the resources injected into these specific industries.
2.6.3 Firm characteristics
All accounting control variables for the listed firms are obtained from the RESSET database. After comparing several databases with true financial reports, we found the RESSET database to be the most accurate. To analyze the impact of shadow-banking behaviors on interest rates and default risks, it is necessary to control for several firm-level characteristics that might indirectly relate to the degree of tunneling and corporate governance. To make variables cross-sectionally comparable, we scale them by total as-sets at the beginning of the year (t−1). Size is the natural logarithm of the book value of lagged assets. Size squared is the square of size. Leverage is the ratio of long-term debt over lagged assets. Cash is total cash in proportion to lagged assets. Capital expen-diture is the capital expenexpen-diture in cash-flow statements divided by lagged assets. The market-to-book ratio is the stock price at the end of the year multiplied by total shares and divided by total book equity. The Kaplan-Zingales (KZ) index is constructed follow-ing the methodology of Baker et al., 2003 to measure the likelihood that the firm faces financial constraints. Relative firm age is the firm age standardized by the age of other firms in the same industry, where age is calculated as the number of years since the firm was founded.
Controlling for these firm characteristics helps to isolate the direct impact of shadow-banking behaviors on interest rates and default risks from the effects of other factors, e.g., asymmetric information arising from size, leverage, and profitability (Murray Z Frank and Vidhan K Goyal, 2003b) or extra investment opportunities arising from cash and capital expenditure. In addition, year dummies and 23 industry dummies are used to further capture fixed effects. Firm industry information is obtained from RESSET’s basic firm information section. The industry definitions follow the Global Industry Classification Standard (GICS) categories.
Finally, the sample of interest is nonfinancial listed firms in business groups. The firm is defined as affiliated with a business group when the group holds a controlling share in the firm. To analyze the policy impact of the 2008 stimulus package, we further limit the
dataset to firms existing both before and after policy. This rules out approximately 2000 observations.
2.6.4 Summary statistics
The summary statistics of the key variables for the analysis are presented in table A.1.
Panel A illustrates the distribution of the shadow-banking activities that this chapter will discuss, i.e., RPTs. Compared with the amounts of intercorporate loans inferred from “other receivables” items (G. Jiang et al., 2010b), the amounts of realized RPTs are much larger, albeit across fewer observations. Across more than 27,000 firm-year observations, 25% involve intercorporate loans, 40% have internal guarantees, 77% have operational tunneling transactions, and 99.9% contain other receivables. In terms of mean size, RPT loans are not far from RPT guarantees and represent approximately half of operational RPTs; however, the median size of guarantees is three times that of intercorporate loans and operations. The totals for all RPT item categories are at least 4 times larger than that for other receivables. The operational RPTs category also has the largest standard deviation among all the RPT variables. These results demonstrate that the distributions of all RPT variables are very skewed, and thus they require log-transformation. In addition, RPT flows to financial firms are twice as large as direct transfers to real-estate firms, which demonstrates a preference for acting in the role of shadow bank rather than being directly involved in a specific industry.
Panel B summarizes the basic characteristics of Chinese listed firms. On average, they present higher levels of cash resources, at 26.5% (16% at median) of total assets, than of external financing, which only accounts for 14% of total assets (7% at median). The firms utilize their financing channels to invest 8% of their value in long-term assets (CAPEX), which yields a 6% return on assets for the investors. These create market value that is 7 times the size of book value. In terms of loan size, interestingly, the amount of new borrowing is close to the total debt at both the mean and the median, but net borrowing after repayment represents less than 1/10 of new borrowing at the mean and 1/20 at the median. This implies that many listed firms survive on new loans to replace the old ones.
2.6.5 T-test comparison
The t-test of the differences between the two intervals in table 2.1 assesses fundamental changes in the key variables before and after the credit supply boom in 2008. Shadow-banking behaviors through corporate tunneling explode across all dimensions in panel A.
Except operational RPTs and other receivables, intercorporate activities after 2008 are at least double the amount before 2008. All firm variables in panel B increase significantly.
While the size of firms enlarges almost threefold after 2008, leverage still increases by 4%
and translates to 11% more cash, 1% more investment, a 2% higher ROA and a 64% higher market-to-book ratio. Meanwhile, it also pushes up the KZ index, which measures the possibility of bankruptcy, and it raises default risk. Due to the level of competition in the banking industry after the policy change, the sizes of all loan categories almost triple, and average interest rates also rise by 5% in panel C. The tables with the summary statistics and t-tests of the two intervals vividly portray the rapid explosion in the financing market, which in turn activates a burgeoning market in shadow banking transactions. Moreover, in panel D, the competitiveness of the banking industry measured by the Herfindahl-Hirschman Index (HHI) decreases across the board, whether the market share of banks
is defined by their total assets, total loans or net loans. This offers evidence in support of prediction 1 in section 2.5.3 that the level of competitiveness in the banking sector increases significantly.