Related party transactions and stock price crash risk ......China offers an interesting setting in which to explore this research question, because of its unique institutional features

2018-89 4/9/2018

Related party transactions and stock price crash risk: Evidence from China*

Ahsan Habib** School of Accountancy

Massey University Albany Private Bag 102904

Auckland 0745 New Zealand

Email: [email protected]

Haiyan Jiang

Waikato Management School Waikato University

Hamilton, New Zealand Email: [email protected]

&

Donghua Zhou School of Accounting

Jiangxi University of Finance and Economics Jiangxi 330013, China

Email: [email protected]

*The paper benefited comments from the conference participants at the 2016 Accounting Conference at Temple University, Philadelphia. We also thank Guochang Zhang for helpful comments. **Contact author

mailto:[email protected]



2018-89 4/9/2018 Abstract: This paper investigates the association between related party transactions (RPTs) and crash risk in China. RPTs may violate the arm's-length assumption of regular market-based transactions, impairing the representational faithfulness and verifiability of accounting data and, consequently, increase the risk of future price crash. We first document a significant positive effect of RPTs on crash risk, and then identify tunneling and propping as the two mechanisms through which RPTs increases the risk of price crash. Our analyses also support the propositions that the effect of tunneling and propping on crash risk is more pronounced when listed parents conduct RPTs with their subsidiaries, and that tunneling incrementally explains crash risk when listed firms use it to meet profitability thresholds. Finally, we conduct additional test to differentiate normal component of RPTs from abnormal ones in our analysis of the effect of RPTs on crash risk. The analyses reveal that abnormal RPTs increase the risk of price crash, whereas normal RPTs are negatively associated with the crash risk. Our main results remain robust to the use of alternative proxies of tunneling and propping, and to the propensity score matching and 2SLS regression procedures designed to address potential endogeneity between RPTs and crash risk. Overall, our evidence highlights the risks of opportunistic use of RPTs, and contributes to the financial reporting, corporate governance and crash risk literature. Keywords: Related party transactions, crash risk, propping, tunneling, earnings management

2018-89 11/1/2018 (Revision 1) Original submission=4/9/2018

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1. Introduction This paper examines empirically the effects of related party transactions (hereafter RPTs) on

future stock price crash (hereafter crash risk) in China. A related party transaction is a transfer of

resources, services, or obligations between related parties, regardless of whether or not a price is

charged (International Auditing Standards 24.9), where a related party is a person or entity

related to the entity preparing its financial statements. Our empirical investigation is motivated

by the competing arguments as to the beneficial versus the detrimental roles of RPTs, which

suggest countervailing effects of RPTs in accentuating or attenuating crash risk. The efficiency-

enhancing theory suggests that imperfect markets increase transaction costs, and that RPTs can

be used within corporate groups as a way of optimizing internal resource allocation, reducing

transaction costs, and improving return-on-assets (Khanna & Palepu 2000). For instance, related

party sales can be used to prop up firms’ performance when significant negative events are

encountered. Related party sales may also leverage earnings in the current year as well in the

subsequent periods (Jian & Wong 2010). In line with this argument, the stock price crash risk

might be mitigated by beneficial RPTs that are conducted by firms in order to lower transaction

costs and maximize shareholder values.

On the other hand, abundant evidence shows that RPTs are commonly used for

opportunistic purposes. Opportunistic RPTs, designed to expropriate external stakeholders’

funds, can be conducted in different forms, such as by trading products and services at distorted

prices, and by the use of intercorporate loans, among others. Opportunistic RPTs are likely to

increase stock price crash risk for two reasons. First, RPTs driven by opportunistic motives are

not arm’s length transactions and distort firms’ operating, financing and investing decisions:

actions that damage shareholders’ values. Thus, opportunistic RPTs constitute risk factors


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leading to a potential stock price crash, owing to the operational risks associated with conducting

RPTs. Second, the RPTs can increase the risk of stock price crash if firms manipulate financial

statements to obfuscate poor performance emanating from the opportunistic RPTs. Literature

shows that firms inflate their financial performance using RPTs (Gordon & Henry, 2005;

Hwang, Chiou, & Wang, 2013; Kohlbeck & Mayhew, 2010; Jian & Wong 2010), and this is

especially so in China partially due to the stock listing thresholds set up by stock exchange

regulators (Chen & Yuan, 2004; Haw, Qi, Wu, & Wu, 2005). 1 Such earnings manipulation

reduces financial reporting quality which increases the risk of price crash, because it allows

managers to withhold bad news for an extended period. When the accumulation of bad news

passes a threshold, and is revealed to the market at once, there will be a large drop in stock price

(Hutton, Marcus, & Tehranian, 2009; Jin & Myers, 2006). Therefore, we argue that opportunistic

RPTs allow managers to conceal negative information, thus, increasing crash risk.

China offers an interesting setting in which to explore this research question, because of

its unique institutional features and the volatility of its stock market. First, RPTs are substantial

in China. CSMAR (China Stock Market & Accounting Research) statistics show that the value

of RPTs conducted by Chinese listed companies amounted to 2.353 trillion Renminbi in 2015

(equivalent to $354 billion USD) and, on average, 86.23% of listed companies have conducted

RPTs over the period 2000-2015 (CSMAR, 2015). The mechanisms through which RPTs are

conducted in China are diverse and complex. Twenty-one types of RPT have been identified as

common business practices in China. The pervasiveness of RPTs conducted in China prompts

our inquiry about whether all categories of RPTs have the same implications for stock price

1 The Chinese Securities Regulatory Commission (CSRC) requirement for meeting certain profitability thresholds in order to qualify for raising capital from the market, motivated many firms to carry out transfers through propping and earnings manipulation, so that these requirements could be met (Chen & Yuan, 2004; Haw et al., 2005).


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crash risk. Second, the rich RPT information in China provides an opportunity for archival

investigation. Chinese listed companies are required to disclose RPTs according to both the old

Chinese Accounting Standards, and the new accounting standards that took effect from January

2007. The Shenzhen Stock Exchange Listing Rules also stipulate disclosure requirements for

firms conducting RPTs. 2 Meanwhile, RPT information disclosed is recorded by several

databases enabling mass download. In contrast, RPT data in other countries are not machine-

readable and, thus, have to be manually collected, thereby restricting large-sample empirical

testing. Third, anecdotal evidence strongly suggests a link between RPTs and the risk of stock

price crash for firms in China.3 Amid a current stock market crash, pondering its causes and

proposing RPTs as one of the determinants, is timely and potentially insightful. Taken together,

given the pervasive and complex nature of its RPTs and the significance of China to other

economies, researching RPT issues in China offers rich implications.

Using a large sample of listed Chinese industrial firm-year observations, we find that

RPTs increase subsequent crash risk. A one standard deviation increase in RPTs as a proportion

of total assets (RPT/TA), increases crash risk by about 2.5%. We identify operating RPTs and

RP loans as the two main types of RPTs, and find that both are related to crash risk positively

and significantly. To test the competing arguments of efficiency-enhancing versus opportunistic

2 The Shenzhen Stock Exchange Listing Rules (10.2.3-10.2.5) require firms’ immediate public announcement of RPTs exceeding CNY 300,000 when the RPs are individuals. Timely public disclosure is also required if a RPT is above CNY3,000,000 and accounts for more than 0.5% of audited net assets if the RP is a registered company. When the RPT is above CNY 30 million and accounts for more than 5% of audited net assets, besides the required disclosure, the transaction and pricing must be appraised by a certified broker and approved by shareholders (The Shengzhen Stock Exchange Listing Rules Amended Version, 2014). 3 A recent example of opportunistic use of RPTs in China is a Chinese Solar maker company, Hanergy Thin Film Solar Group, whose stock price crashed in May, 2015. Specifically, the firm lost nearly $19 billion of market value within 24 minutes of trading on May 20. Among other things, the media reports that, according to the listed company’s 2014 financial report, more than 60% of Hanergy’s sales come from its Beijing-based parent Hanergy Holding Group (booked as “trade and other receivables”). It appears that the firm’s management inflated financial performance of the firm using related party transactions, which may lead to its stock price crash (Wesoff, 2015).


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motives for RPTs, we decompose total RPTs as well as total operating RPTs and RP loans, into

their normal and abnormal components, and find that normal (abnormal) RPTs decrease

(increase) price crash risk. Thus, the evidence suggests the positive relationship between RPTs

and crash risk is driven by the opportunistic RPTs rather than by the RPTs conducted to benefit

stakeholders. Furthermore, financial tunneling and financial propping, two proxies for

opportunistic RPTs, increase crash risk. Our additional analyses demonstrate a positive

association between crash risk and the opportunistic RPTs when listed parent firms conduct

tunneling and propping with their subsidiaries. Finally, we reveal that propping via RPTs in

order to meet profitability thresholds mandated by Chinese regulations explains the incremental

increase in price crash risk. In order to alleviate possible endogeneity concerns between RPTs

and crash risk that could arise from model misspecifications, reverse causality or omitted

variable concerns, we conduct propensity score matching as well two-stage least square (2SLS)

regressions. Our results remain robust after controlling for endogenous relationships.

Although a number of corporate governance factors (e.g., CEO equity incentives,

corporate tax-avoidance, business strategies, and financial reporting quality), have been

examined as the determinants of crash risk 4 , the effect of RPTs is understudied. To our

knowledge, this study is one of the few, to examine the association between RPTs and crash risk.

Shen, Jiang and Chen (2014) explore how opportunistic RPTs increase crash risk in China. They

find that tunneling through intercorporate loans by large shareholders increases crash risk.

However, our paper differs from that of Shen et al. (2014) in several important ways.

First, our study conducts in-depth analyses on the effect of RPTs on crash risk by

considering not only opportunistic RPTs, including financial tunneling and financial propping,

4 Habib, Hasan and Jiang (2018) provide a comprehensive review of the literature on the determinants of stock price crash risk.


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but also beneficial RPTs conducted to enhance corporate operational efficiency. We decompose

RPTs into normal and abnormal components to differentiate between efficiency-driven versus

opportunistic RPTs, and show how this distinction differentially affects crash risk. The finding

of the differential effects of RPTs adds new evidence to the long-lasting debate on the value-

enhancing versus value-destroying nature of RPTs. In addition, our analysis of the RPT impact

on crash risk also extends to the following aspects: a focus on two major types of RPT, including

operating RPTs that involve sales and purchase as well as RP loans, tunneling and propping

conducted for opportunistic purposes; transactions with unlisted subsidiaries by listed parents;

transactions conducted to meet regulatory thresholds through financial propping. Thus, our study

provides an in-depth investigation into the economic consequences of RPTs. This is important,

given the pervasiveness of RPTs conducted by companies in many countries, and the increasing

regulatory and academic attention paid to RPT disclosures. The findings also have implications

for market participants’ decisions on investment in firms controlled by government and related

shareholders.

The remainder of the paper proceeds as follows. Section 2 reviews relevant literature and

develops the hypotheses. Section 3 describes the research design issues. Sample selection and

descriptive statistics are presented in Section 4. The following section provides the main test

results, and Section 6 concludes the paper.

2. Literature review and hypotheses development The role of RPTs within business groups is widely discussed in the literature. There are

two opposing arguments concerning RPTs popularized in the literature. The efficiency-

enhancing theory suggests that imperfect markets increase transaction costs, and RPTs can be

used within corporate groups as a way to optimize internal resource allocation, reduce


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transaction costs, and improve return-on-assets (Khanna & Palepu, 2000). Chen, Wang and Li

(2012) argue for, and find evidence consistent with, the hypothesis that normal RPTs reduce

transaction costs and, thus, mitigate the potential bankruptcy risk in competitive, rather than non-

competitive industries. Liu and Liu (2007) find that related party sales and purchases of goods

and services have been effective in reducing transaction costs and sales expenses.

In contrast, agency theory argues that RPTs can be used opportunistically with the intent

of expropriation by tunneling resources from listed firms (Chang & Hong, 2000). Cheung, Rau

and Stouraitis (2006) present evidence that RPTs were used in Hong Kong to expropriate wealth

from public firms. Such tunneling effects were more prevalent among firms whose ultimate

ownership could be traced to China. Abusive use of RPTs has been found to affect future

performance and firm values adversely (Chen, Cheng, & Xiao, 2011; Kohlbeck & Mayhew,

2010; Ryngaert & Thomas, 2012; Zhu & Zhu, 2012). Wang and Yuan (2012) find that earnings

are less informative for firms with a high level of involvement in RPTs. Auditors charge higher

audit fees because of increased audit risks emanating from opportunistic RPTs (Habib, Jiang, &

Zhou, 2015). Beuselinck and Deloof (2014) document that earnings management is facilitated,

in particular, through intra-group transactions in Belgium. Gordon and Henry (2005) find that

managers engaging in RPTs manage earnings through discretionary accruals. Owing to the

existence of corporate opportunistic motives, regulators have considered some RPTs detrimental

to minority shareholders’ interests. The Financial Accounting Standards Board (FASB) has long

expressed concerns about the possible negative impact of RPTs on financial information, in

terms of both its representational faithfulness and the reliability of reported values (SFAS 57,

15). RPTs have been portrayed as a mechanism for self-dealing by company executives, enabling

them to expropriate minority shareholders’ wealth (Chang & Hong, 2000).


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Given the routine nature of RPTs in many countries globally, it is important to investigate

how such transactions impact price crash: an outcome of direct economic consequences. Stock

price crash risk at the firm level refers to the likelihood of observing extreme negative values in

the distribution of firm-specific returns after adjusting for the return portions that co-move with

common factors (Jin & Myers, 2006; Kim, Li, & Zhang, 2011a,b). Jin and Myers (2006) predict

that crash risk results from managerial incentives for withholding bad news for an extended

period. Hutton et al. (2009) test this proposition directly by using absolute accruals as a proxy for

opaque financial reporting, and find that reporting opaqueness increases crash risk. A vast body

of literature has investigated the various determinants of crash risk including tax avoidance (Kim

et al., 2011a), managerial equity incentives (Kim et al., 2011b), real earnings management

(Francis, Hasan & Li, 2016), short interest (Callen & Fang, 2015), and institutional investor

stability (Callen & Fang 2013). Prior research on stock price crash risk in China has investigated

excess perks in SOEs (Xu, Li, Yuan, & Chan, 2014), analyst optimism (Xu, Jiang, Chan, & Yi,

2013), analyst herding (Xu, Jiang, Chan, & Wu, 2017), and political incentives (Piotroski, Wong,

& Zhang, 2015) as some of the determinants of crash risk. Xu et al. (2013), for example, find

that stock price crash risk increases with an increase in a firm's analyst coverage, and becomes

more pronounced when analysts are more optimistic and are affiliated with investment banks and

brokerage firms.

RPTs can take many different forms including operating RPTs and intercorporate loans,

sales of non-monetary assets, leases, franchises and administrative overheads and others. There

are twenty-one types of RPTs conducted by Chinese listed companies that have been recorded by

the database. Among them, operating RPTs and intercorporate loans are the most frequently

conducted RPTs and are quite sizable in monetary values. Thus, they constitute the focus of our


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inquiry. Operating RPTs involve sales and purchases of goods and services, and consist mainly

of the trade relationship, together with some minor items, such as the sales of non-monetary

assets, leases, franchises and administrative overheads. Despite the benefits associated with

operating RPTs as described in the preceding section, prior literature reveals opportunistic use of

operating RPTs (McCahery & Vermeulen 2005). Empirical evidence also shows that Chinese

firms with high levels of intercorporate loans demonstrate significantly poor future performance,

including sharp declines in profitability, a higher likelihood of receiving Special Treatment (ST)

status5, and a higher likelihood of entering financial distress in the future (Jiang, Lee, & Yue,

2010), compared to firms with low levels of intercorporate loans. Thus, an intercorporate loan

can have significantly adverse effects on future performance.

Opportunistic RPTs could accentuate crash risk for at least two reasons. First, where a

non-arm’s length transaction occurs, the buyers and sellers do not act independently in the best

interests of their own shareholders. The transactions often distort economic reality for the self-

interest of executives, or of certain controlling shareholders of one party, at the expense of its

remaining shareholders or other parties’ shareholders. Such RPTs, therefore, cause inefficiency

and reduce shareholders’ wealth. The operational risks will be high if such RPTs are conducted

frequently, resulting in a high risk of stock price crash. Second, in the presence of frequent and

large RPTs, information asymmetry is severe owing to managers’ reluctance to disclose detailed

information associated with certain RPTs, especially when the detailed disclosures of those RPTs

may reveal managerial opportunism. As is well known, accumulation of negative news over an

5 To preserve the listing status, listed firms must meet certain requirements. Special treatment (ST) is used as an instrument of delisting procedures. The ST designation could be a result of measurable financial statement issues or of auditor decisions. Once designated as a “special treatment” firm by the stock exchange, the firm must place a special designation on its ticker symbol as a warning to investors. The firm is on probationary status for 1 year after the designation. The trading will be suspended in the second year if the firm has not taken steps to rectify the conditions leading to the designation. As a result of continuous Special Treatment status, the firm will be delisted.


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extended period increases price crash risk. Although there are competing arguments regarding

the association between RPTs and price crash, the empirical evidence on the abusive use of RPTs

in China is abundant. We therefore hypothesize the following:

H1A: There is a positive association between RPTs and price crash.

H1B: Operating RPTs increase crash risk.

H1C: RP loans increase crash risk.

The preceding hypotheses are based on aggregate RPT values. However, RPT

components could have different implications for crash risk. Many RPTs take place in the

ordinary course of the business. Such normal RPTs can be used within corporate groups as a way

of optimizing internal resource allocation, reducing transaction costs, and improving return-on-

assets (Khanna & Palepu 2000). When a related party possesses an in-depth knowledge of firm-

specific activities and an expertise that the company demands, then the service can be provided

to the company more effectively by the related party than by an outsider (Gordon & Henry

2005), which in turn should lower a company’s operational risk leading to a lower crash risk. In

addition, RPT disclosures in financial statements are likely to be informative about firm value

and the substance of the transactions (Lo & Wong, 2016). The disclosures of beneficial RPTs

should reduce information asymmetry and enhance transparency. As a result, normal RPTs

should be related to low likelihood of price crash. In contrast, abnormal RPTs, i.e., RPTs that

deviate from the expected RPTs estimated based on firms’ fundamentals, reflect managerial

opportunism, increase firm’s operational and informational risks, and are the catalyst for price

crash. Based on the preceding argument we develop the following two hypotheses:

H2A: Normal RPTs (total RPTs, operating RPTs, and RP loans) decrease price crash risk. H2B: Abnormal RPTs (total RPTs, operating RPTs, and RP loans) increase price crash risk.


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Then, we focus on intercorporate loans (RP loans) only and identify ‘tunneling’ and

‘propping’ using RP loans as two mechanisms through which opportunistic RPTs increase crash

risk. With respect to tunneling, we consider other receivables as a proportion of total assets as

our direct proxy for financial tunneling. Other receivables present ‘non-operating fund

occupation’ that is irrelevant to daily business, and are found to be used as vehicle for tunneling

(Berkman et al., 2009; Jiang et al., 2010). It is reported that RPTs are utilized by controlling

shareholders as tools for tunneling in many countries (Cheung, Jing, Lu, Rau, & Stouraitis, 2009;

Johnson, La Porta, Lopez-de-Silanes, & Schleifer, 2000), and there is a negative effect of

tunneling on firms’ future performance (Jiang et al., 2010). In addition, firms involved in

opportunistic RPTs tend to manage earnings to conceal such expropriation of resources (Jian &

Wong, 2010; Habib et al., 2017). Given the significantly adverse effect of this type of RPT on

future performance, and managers’ motivation to delay the disclosure of bad news to the market,

and/or engagement in earnings management to mask this poor performance, tunneling is

expected to increase the risk of subsequent crash.

Propping, mainly conducted through borrowings from related parties, might also increase

crash risk, since propping is temporary and is used strategically in China to cover up a propped

company’s bad operating performance. Jian and Wong (2010) find that propping via RPTs is

used by financially troubled listed firms to meet earnings thresholds, and is often accompanied

by significant cash tunneling afterwards. With respect to propping, we note that in China, a large

number of Chinese listed firms have been restructured from existing SOEs through “carve-outs”,

and they retain many transactions with members of their business groups. SOEs, as controlling

shareholders, have incentives to engage in propping activities in order to qualify for refinancing

(Jiang & Wong, 2010; Ying & Wang, 2013). Jian and Wong (2010) find that abnormal RP sales


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are used opportunistically to prop up firms’ performance when listed firms experience difficulty

in meeting earnings targets set by the CSRC, and this effect is more pronounced in regions with

weak economic institutions. As a result, propping effectively obfuscates the true picture of firm

performance and misleads shareholders. Jian and Wong (2010) provide further evidence that

there is significant cash transfer via related lending, as a form of tunneling from listed firms back

to their related parties after propping. In addition, Cheung et al. (2006) fail to find a positive

market reaction to propping transactions that are likely to benefit the listed firms. They report

that the cash receipts as a result of propping are associated with value losses of 39 cents. Cheung

et al. (2006) find that propping does not earn listed firms significant excess return over the post-

announcement period. In general, their findings suggest that propping is not successful. Due to

the high level of opacity associated with propping using RPTs, firm’s future crash risk is

accentuated. We hypothesize the following:

H3A: Tunneling through RPTs increases crash risk. H3B: Propping through RPTs increases crash risk. 3. Research design and sample selection 3.1 Stock price crash risk

In this study, two measures of firm-specific crash risk are used, consistent with Chen,

Harrison and Stein (2001). These measures are based on the firm-specific weekly returns

estimated as the residuals from the market model. This ensures that our crash risk measures

reflect firm-specific factors rather than broad market movements. Specifically, we estimate the

following expanded market model regression:

𝑟𝑟i,t = 𝛼𝛼𝑗𝑗 + 𝛽𝛽1,𝑗𝑗𝑟𝑟𝑚𝑚,𝑡𝑡−2 + 𝛽𝛽2,𝑗𝑗𝑟𝑟𝑚𝑚,𝑡𝑡−1 + 𝛽𝛽3,𝑗𝑗𝑟𝑟𝑚𝑚,𝑡𝑡 + 𝛽𝛽4,𝑗𝑗𝑟𝑟𝑚𝑚,𝑡𝑡+1 + 𝛽𝛽5,𝑗𝑗𝑟𝑟𝑚𝑚,𝑡𝑡+2 + 𝜀𝜀𝑖𝑖,𝑡𝑡 (1)


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where r,j,t is the return of firm j in week t, and rm,t is the return on CRSP value-weighted

market return in week t. The lead and lag terms for the market index return is included, to allow

for non-synchronous trading (Dimson, 1979). The firm-specific weekly return for firm j in week

t (W j,t) is calculated as the natural logarithm of one plus the residual return from Eq. (1) above.

In estimating equation (1), each firm-year is required to have at least 26 weekly stock returns.

Our first measure of crash risk is the negative conditional skewness of firm-specific weekly

returns over the fiscal year (NCSKEW). NCSKEW is calculated by taking the negative of the third

moment of firm-specific weekly returns for each year and normalizing it by the standard

deviation of firm-specific weekly returns raised to the third power. Specifically, for each firm j in

year t, NCSKEW is calculated as:

𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 = −�𝑛𝑛(𝑛𝑛 − 1)3 2⁄ ∑𝑤𝑤3𝑖𝑖,𝑡𝑡�/ �(𝑛𝑛 − 1)(𝑛𝑛 − 2)�∑𝑤𝑤2

𝑖𝑖,𝑡𝑡�3/2� (2)

Our second measure of crash risk is the down-to-up volatility measure (DUVOL) of the crash

likelihood. For each firm j over a fiscal-year period t, firm-specific weekly returns are separated

into two groups: ‘‘down’’ weeks when the returns are below the annual mean, and ‘‘up’’ weeks

when the returns are above the annual mean. The standard deviation of firm-specific weekly

returns is calculated separately for each of these two groups. DUVOL is the natural logarithm of

the ratio of the standard deviation in the ‘‘down’’ weeks to the standard deviation in the ‘‘up’’

weeks:

𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡 = 𝑙𝑙𝑙𝑙𝑙𝑙��(𝑛𝑛𝑢𝑢 − 1)∑ 𝑤𝑤2𝑖𝑖,𝑡𝑡𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 (𝑛𝑛𝑑𝑑 − 1)∑ 𝑤𝑤2

𝑖𝑖,𝑡𝑡𝑈𝑈𝑈𝑈� �� (3)

A higher value of DUVOL indicates greater crash risk. As suggested in Chen et al. (2001),

DUVOL does not involve third moments and, hence, is less likely to be overly influenced by

extreme weekly returns.

3.2 Empirical model


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3.2.1 RPTs and crash risk

To test the hypotheses, we employ the following regression equation.

𝑁𝑁𝐶𝐶𝐶𝐶𝑁𝑁𝐶𝐶𝑖𝑖,𝑡𝑡 = 𝛼𝛼0 + 𝛽𝛽1𝑁𝑁𝐶𝐶𝐶𝐶𝑁𝑁𝐶𝐶𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽2𝐶𝐶𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽3𝑅𝑅𝐷𝐷𝐶𝐶𝑁𝑁𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽4𝐶𝐶𝑁𝑁𝑅𝑅𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽5𝑁𝑁𝐷𝐷𝐶𝐶𝑁𝑁𝑅𝑅𝑖𝑖,𝑡𝑡−1

+ 𝛽𝛽6𝑁𝑁𝑆𝑆𝑆𝑆𝑁𝑁𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽7𝑀𝑀𝑀𝑀𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽8𝐷𝐷𝑁𝑁𝐷𝐷𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽9|𝐷𝐷𝐶𝐶𝑁𝑁|𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽10|𝐶𝐶𝑁𝑁𝑀𝑀|𝑖𝑖,𝑡𝑡−1+ 𝛽𝛽11𝐷𝐷𝑁𝑁𝑁𝑁𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽13𝐶𝐶𝑁𝑁𝐶𝐶𝐷𝐷𝐴𝐴𝑁𝑁𝑅𝑅𝑖𝑖,𝑡𝑡−1 + 𝜀𝜀𝑖𝑖,𝑡𝑡 (4)

where CRASH risk is proxied by the NCSKEW and DUVOL measures following equations (2)

and (3) above. RPT represents several measures for the stated hypotheses. For H1A, it is the sum

of total RPTs divided by total assets and is denoted as RPT; for H1B, it is operating RPTs (the

sum of RP sales and RP purchases) divided by total revenue (OPRPT); for H1C, it is RP loan

(the sum of RP lending to, RP borrowing from, and loan guarantee provided to related parties)

divided by total assets (RP_LOAN). We expect the coefficients on γ2 to be positive and

significant which will support H1A, H1B and H1C.

To test our H2A and H2B, we decompose total RPT (RPT), operating RPT (OPRPT) and

RP loan (RP_LOAN) into their normal and abnormal components. Following Jian and Wong

(2010), we regress the respective RPT measures on SIZE (natural log of total assets), LEV (sum

of short and long-term debt over total assets), and MB (market value of equity divided by book

values of equity). The predicted values from this regression are our proxy for normal RPTs. The

residual, therefore, proxies for abnormal RPTs. We expect the coefficients on normal (abnormal)

RPTs to be negative (positive) respectively.

In order to tests for the effect of tunneling and propping through RPTs on crash risk as

predicted in H3A and H3B, we replace the RPT in equation (4) with direct proxies for tunneling

and propping in the regression specifications. Specifically, Tunneling (TUNEL_FIN) is the ratio

of other receivables to total assets. Propping (PROP_FIN) is the sum of borrowings from related


14

parties, loans and other payables to related parties divided by total assets. We also expect the

coefficients on TUNEL_FIN and PROP_FIN to be significantly positive as well which will

support H3A and H3B.

We cluster the standard errors by firms in order to control for potential heteroskedasticity

and autocorrelation problems and to provide robust standard error estimation with reliable t-

statistics. The independent variables including the control variables are calculated using data

from the preceding year, as is consistent with the crash risk literature (e.g., Chen, Kim, Li and

Liang, 2018). We first control for the lag value of CRASH in order to account for the potential

serial correlation of NCSKEW or DUVOL for the sample firms. Inclusion of the control variables

follows prior literature on the determinants of crash risk (Hutton et al., 2009; Kim et al.,

2011a,b). TURN is the average monthly share turnover for the current fiscal year minus the

average monthly share turnover for the previous fiscal year, where monthly share turnover is

calculated as the monthly trading volume divided by the total number of shares outstanding

during the month. Chen et al. (2001) indicate that this variable is used to measure differences of

opinion among shareholders, and it is positively related to crash risk proxies. Chen et al. (2001)

show that negative skewness is larger in stocks that have had positive stock returns over the prior

36 months. To control for this possibility, we include past one-year weekly returns (RET).

SDRET is the standard deviation of firm-specific weekly returns over the fiscal year, and denotes

stock volatility, as stocks that are more volatile are likely to be more crash prone. Chen et al.

(2001) also demonstrate that negative return skewness is higher for larger firms. To control for

the size effect, we add SIZE measured as the natural log of total assets. The variable MB is the

market value of equity divided by the book value of equity. Both Chen et al. (2001) and Hutton

et al. (2009) show that growth stocks are more prone to future crash risk. LEV is the sum of short


15

and long-term debt over total assets, and is shown to be associated with future crash risk

negatively (Kim et al., 2011a, b). OWN is the proportion of institutional shareholdings over total

outstanding shares. ANALYST is the natural log of number of analysts following a firm.

|DAC| are the absolute discretionary accruals calculated using the Modified Jones model

controlling for firm performance (Dechow, Sloan, & Sweeny, 1995; Kothari, Leone, & Wasley,

2005), and should be associated with crash risk positively (Hutton et al., 2009). We estimate the

following Equation for all firms in the same industry (using the SIC two-digit industry code)

with at least eight observations in an industry in a particular year:

𝐶𝐶𝑁𝑁𝑁𝑁𝑖𝑖,𝑡𝑡𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1

= 𝛾𝛾0 �1

𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1� + 𝛾𝛾1 �

∆𝑁𝑁𝐶𝐶𝐷𝐷𝑁𝑁𝑁𝑁𝑖𝑖,𝑡𝑡 − ∆𝐶𝐶𝑁𝑁𝑁𝑁𝑁𝑁𝑆𝑆𝐷𝐷𝐶𝐶𝑀𝑀𝐷𝐷𝑁𝑁𝑖𝑖,𝑡𝑡𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1

� + 𝛾𝛾2 �𝑅𝑅𝑅𝑅𝑁𝑁𝑖𝑖,𝑡𝑡𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1

� + 𝛾𝛾3�𝐶𝐶𝐷𝐷𝐶𝐶𝑖𝑖,𝑡𝑡−1�

+ 𝜀𝜀𝑖𝑖,𝑡𝑡 … … … … … … … (5)

where ACC is total accruals calculated as earnings before extraordinary items and discontinued

operations minus operating cash flows; TA is total assets in year t-1; ΔSALES is change in sales

from year t-1 to year t; ∆RECEIVABLE is change in accounts receivable from year t-1 to year t;

PPE is gross property plant and equipment; ROA is return on assets measured as earnings before

extraordinary items and discontinued operations for the preceding year, divided by total assets

for the same year.. The coefficient estimates from Equation (5) are used to estimate the non-

discretionary component of total accruals (NDAC) for our sample firms. The discretionary

accruals is then the residual from equation (5), i.e. DAC=ACC-NDAC.

|REM| is the absolute values of real earnings management, which has been shown to be

another determinant for crash risk along with DAC (Francis et al., 2016). We follow prior

literature in developing our REM proxies (Cohen & Zarowin, 2010; Garcia Lara, Osma, &

Penalva, 2009; Gunny, 2010; Roychowdhury, 2006; Zang, 2012); abnormal levels of cash flow

from operations (ACFO); abnormal production costs (APROD); and abnormal discretionary


16

expenses (ADISX) by estimating the following regression model within each two-digit SIC

industry and year:

𝑁𝑁𝐶𝐶𝐷𝐷𝑖𝑖,𝑡𝑡 𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1⁄ = 𝛼𝛼0 + 𝛼𝛼1 ∗ �1 𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1⁄ � + 𝛼𝛼2 ∗ �𝑁𝑁𝐶𝐶𝐷𝐷𝑁𝑁𝑁𝑁𝑖𝑖,𝑡𝑡 𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1⁄ � + 𝛼𝛼3 ∗ �∆𝑁𝑁𝐶𝐶𝐷𝐷𝑁𝑁𝑁𝑁𝑖𝑖,𝑡𝑡 𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1⁄ � + 𝜀𝜀𝑖𝑖,𝑡𝑡 (6.1) 𝑅𝑅𝐶𝐶𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡 𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1⁄ = 𝛽𝛽0 + 𝛽𝛽1 ∗ �1 𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1⁄ � + 𝛽𝛽2 ∗ �𝑁𝑁𝐶𝐶𝐷𝐷𝑁𝑁𝑁𝑁𝑖𝑖,𝑡𝑡 𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1⁄ � + 𝛽𝛽3 ∗ �∆𝑁𝑁𝐶𝐶𝐷𝐷𝑁𝑁𝑁𝑁𝑖𝑖 ,𝑡𝑡 𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1⁄ � + 𝛽𝛽4 ∗ �∆𝑁𝑁𝐶𝐶𝐷𝐷𝑁𝑁𝑁𝑁𝑖𝑖,𝑡𝑡−1 𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1⁄ �+ 𝜀𝜀𝑖𝑖,𝑡𝑡 (6.2) 𝐷𝐷𝑆𝑆𝑁𝑁𝐷𝐷𝑖𝑖 ,𝑡𝑡 𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1⁄ = 𝛾𝛾0 + 𝛾𝛾1 ∗ �1 𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1⁄ � + 𝛾𝛾2 ∗ �𝑁𝑁𝐶𝐶𝐷𝐷𝑁𝑁𝑁𝑁𝑖𝑖,𝑡𝑡 𝑅𝑅𝐶𝐶𝑖𝑖,𝑡𝑡−1⁄ � + 𝜀𝜀𝑖𝑖,𝑡𝑡 (6.3)

Where 𝑁𝑁𝐶𝐶𝐷𝐷𝑖𝑖,𝑡𝑡 is operating cash flow in period t. 𝑅𝑅𝐶𝐶𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡 is the change of inventory in

period t plus the cost of sales. 𝐷𝐷𝑆𝑆𝑁𝑁𝐷𝐷𝑖𝑖,𝑡𝑡 is the discretionary expenses which is equal to operating

expenses plus general and administrative expenses. For each firm-year, ACFO is the actual CFO

minus the “normal” CFO calculated using estimated coefficients from the corresponding industry

year model and the firm-years’ sales and lagged assets. Following the same logic, we then

calculate the APROD and ADISX. Considering the firms may use all of the three real transactions

to manipulate firm earnings, we build a comprehensive proxy for the real transactions-based

earnings management (Cohen et al., 2010): REMi,t =(-1)*ACFO + APROD + (-1)*ADISX.

4. Sample selection and descriptive statistics

We retrieve firms’ RPT information, annual financials and ownership information from

the CSMAR and WIND databases. Our sample is based on all shares listed on the Chinese A-

share market (i.e., A, AH, AB and AHB shares). The sample period is from 2001 to 2013. Our

sample period ends in 2013 because the data for calculating TUENL_FIN and PROP_FIN

became unavailable from 2014 and onward from the WIND database.6 Table 1, Panel A shows

our sample selection procedure. We start with a large number of RPTs (300,395 RPTs) over our

sample period. Individual companies reporting multiple RPTs in the same fiscal year with

6 The format has been changed from the account-based dataset (e.g., account like other receivables), to transaction-based dataset with 21 types of RPTs.


17

different parties, or even with the same party, explain the large number of transactions. We

summarize the value of RPTs occurring for the same company in the same year in order to obtain

firm-year observations, resulting in 20,182 firm-year observations for matching with the

variables required to estimate the crash risk variable. Due to unavailable data related to crash risk

and associated control variables, we delete a further 5,527 observations, resulting in 14,655 firm-

year observations for conducting the baseline regression of RPTs on crash risk. Not all firms

conduct operating RPTs or intercorporate loans. Therefore, we use 9,984 (10,056) firm-year

observations for operating (loan) RPTs, respectively. A second RPT database, WIND, provided

the required data for measuring financial tunneling (TUNEL_FIN) and financial propping

(PROP_FIN) directly because RPT data in WIND is recorded based on ledger accounts. Among

14,655 firm-year observations with different types of RPTs, a total of 7,951 and 9,991 firm-year

observations were related to TUNEL_FIN and PROP_FIN respectively.

Panel B presents descriptive statistics for the variables used in the regression variables.

The mean (median) values of NCSKEW are -0.06 (-0.04) respectively. The corresponding values

for DUVOL are 0.03(-0.02). The RPTs ratio, i.e., RPT, is 38%. Operating RPTs (OPRPT) and RP

loan (RP_LOAN) have mean values of 0.15 and 0.27 respectively. The mean of TUNEL_FIN and

PROP_FIN are 0.04 and 0.03 respectively. Sample firms are larger and have growth

opportunities (a mean MB ratio of 3.09), but are low-leveraged (mean leverage of 0.09).

Absolute DAC is 0.07 and absolute REM is 0.10 of lagged total assets. Institutional owners own

28% of total outstanding shares and a firm is, on average, followed by three analysts. Six percent

of the firm-year observations report an average ROE that is within the 6% to 7% range. The

industry distribution of sample companies is presented in Panel C of Table 1, revealing that the

machinery, equipment and instrument industry accounts for 16% of the total sample


18

observations, followed by the petroleum, chemical and rubber, and the metal and non-metal

industries, with 11% and 9% of sample observations respectively.

[TABLE 1 ABOUT HERE]

Correlations among the variables are presented in Table 2. Both crash measures are

correlated with one another positively and significantly. The correlation among the crash

measures and RPT is significantly positive (correlation of 0.02 and 0.03 for NCSKEW and

DUVOL measures respectively, significant at p<0.01). The correlation between RP_LOAN with

both the crash measures is positive and significant at p<0.01. The correlation between

TUNEL_FIN and PROP_FIN with both the crash measures, too, are positive and significant at

p<0.01. Overall, our correlation analysis provides preliminary evidence that higher levels of

RPTs increase crash risk. However, univariate correlation does not provide a definitive answer,

because it does not control for other related independent variables that affect crash risk. We

therefore turn our attention to regression analysis as explained below.


5. Regression results 5.1 RPTs, operating RPTs, RP loans and crash risk

Table 3, Panel A presents the regression results of the gross RPTs and RPT components

on the risk of stock price crash. The dependent variable CRASH is proxied by the NCSKEW and

DUVOL measures respectively. The coefficients on RPTs are both positive and significant for

the two crash risk measures (0.035 and 0.041 respectively, both significant at p<0.01) as reported

in Columns (1) and (2). In terms of economic significance the reported result shows that a one

standard deviation increase in RPT leads to an approximately 37 percent ([0.035*0.64]/0.06)


19

increase in NCSKEW in year t+1 around its mean. The corresponding value for the DUVOL

measure is approximately 87 percent ([0.041*0.64]/0.03). We therefore conclude that the

association between RPTs and crash risk is not only statistically significant but economically

significant as well. This supports H1A, revealing that crash risk increases with an increase in

RPTs. Columns (3) to (6) report the effects of RPT components on crash risk. We find consistent

evidence that both OPRPT (coefficients of 0.08 and 0.07, both significant at p<0.05) and

RP_LOAN (coefficients of 0.09 and 0.10, both significant at p<0.01) increase crash risk, thus

supporting H1B and H1C.

Among the control variables, the coefficient on average returns (RET) is positive, and

that on return volatility (SDRET) is negative, suggesting that firms with better stock and

accounting performance and lower volatility are more likely to experience crashes. This also

suggests that crashes are unlikely to be a manifestation of declining business conditions,

continuation of poor stock performance (i.e., negative stock momentum), and/or high stock

volatility. Trading volume (TURN) increases crash risk consistent with findings in Chen et al.

(2001). We find that the firm size (SIZE) variable is negative and significant, which is in contrast

to the findings in the literature, i.e., larger Chinese firms, on average, tend to experience lower

price crash risk compared to their small firm counterparts. Earnings management proxied by

absolute DAC is negatively associated with crash proxies, a finding that is counter-intuitive. One

of the reasons could be the way accruals are defined. Hutton et al. (2009) used a three-year

moving average of accruals as their earnings management proxy while, like most other studies,

we use abnormal accruals calculated annually using a cross-sectional regression analysis

(Kothari et al., 2005) (using a moving average reduces our sample size substantially). The

coefficient on ANALYST is positive and significant across all the specifications. Although analyst


20

following is likely to attenuate crash risk because of analysts’ monitoring expertise, Xu et al.

(2013) find that price crashes increase with an increase in a firm's analyst coverage in China, and

this is more pronounced when analysts are more optimistic, and affiliated with investment banks

and brokerage firms. The coefficient on institutional ownership (OWN) becomes significantly

negative in the fixed effects regression suggesting that crash risk decreases for firms with more

institutional shareholdings (An & Zhang, 2013; Callen & Fang, 2013).

5.2 Normal and abnormal components of RPTs and crash risk

Panel B of Table 3 reports regression results for the normal and abnormal components of

RPTs and price crash (test of H2A and H2B). We find evidence consistent with the hypothesized

positive association between abnormal RPTs and its components and price crash. For example

the coefficients on the abnormal OPRPT are 0.071 (p<0.05) and 0.056 (p<0.10) for the NCSKEW

and DUVOL measures respectively. The corresponding coefficients for abnormal RP_LOAN are

0.081 p<0.01) and 0.088 (p<0.01) for the two crash proxies. The coefficients on the normal

OPRPT and RP_LOAN, on the other hand, are negative and statistically significant at p<0.01, for

both the crash risk proxies. These findings, therefore, suggest that abnormal RPTs, rather than

normal RPTs, reflect managerial opportunism and accentuate price crash.

5.3 Tunneling, propping, and stock price crash risk

We now discuss the possible mechanisms through which RPTs might affect crash risk.

We hypothesized that tunneling trough RP transactions (denoted as TUNEL_FIN) and propping

through RP transactions (denoted as PROP_FIN) are the mechanisms through which firm-level

RPTs may affect crash risk. TUNEL_FIN is the ratio of other receivables to total assets.

PROP_FIN is the sum of borrowings from related parties, loans and other payables to related


21

parties divided by total assets. Results are reported in Columns (1) to (4) in Table 3. The

coefficients on both the TUNEL_FIN and PROP_FIN are positive and significant across both the

crash measures. For example the coefficients are 0.47 (p<0.01) and 0.45 (p<0.01) respectively

for the NCSKEW measures. The corresponding coefficients for the DUVOL measure are 0.34

(p<0.01) and 0.35 (p<0.01) in columns (2) and (4) in Table 3. Our results, therefore, support both

H3A and H3B.

The above analysis uses a separate sample for financial tunneling and financial propping

respectively, i.e., many of the firm-year observations are non-overlapping. This is justified,

because we have developed individual hypotheses for financial tunneling and financial propping.

Also, financial propping has been used to later tunnel resources in China. In order to investigate

which of the two mechanisms is more responsible for price crash, we create an overlapping

sample, i.e., we retain only those observations that have both non-missing tunneling and

propping values for the same firm. A total of 6,404 firm-year observations meet this criterion.

We then include both TUNEL_FIN and PROP_FIN in the same regression model. Columns (5)

and (6) provide convincing evidence that both tunneling and propping increase crash risk

although the coefficients on TUNEL_FIN is much higher than the coefficients on PROP_FIN

(e.g., 0.59 and 0.20, significant at p<0.01 and p<0.05 respectively, for the NCSKEW measure).

The corresponding coefficients are 0.57 (p<0.01) and 0.22 (p<0.10) for DUVOL measure.


5.4 Additional tests 5.4.1 Mediating effects of financial reporting quality on the association between RPTs and price crash


22

So far, we have presented results indicating a significant positive relation between RPTs and

price crash. A related issue, is the extent to which RPTs affect price crash directly (i.e., without

mediation), and through its effect on financial reporting quality, the so-called mediation effect.

We follow the mediation test approach of Baron and Kenny (1986) who propose that a

mediation effect exists when the following three conditions are fulfilled: (1) Path A: Variations

in the levels of the independent variable (i.e., RPTs in our study)) account significantly for

variations in the proposed mediator (i.e., |DAC|) (Equation 7.1 below); (2) Path B: Variations in

the proposed mediators account significantly for variations in the dependent variable (CRASH)

(Equation 7.2 below); and (3) Path C: The significant relationship between RPT and CRASH

(Equation 4) becomes insignificant once Paths A and B are controlled (full mediation); or the

significant relation is reduced once Paths A and B are controlled (partial mediation) (Equation

7.3 below). The following set of equations is developed to conduct the mediation tests:

|𝐷𝐷𝐶𝐶𝑁𝑁| = 𝛼𝛼0 + 𝛼𝛼1 ∗ 𝐶𝐶𝑅𝑅𝑅𝑅 + ∑𝑁𝑁𝑙𝑙𝑛𝑛𝐶𝐶𝑟𝑟𝑙𝑙𝑙𝑙𝐶𝐶 + ∑𝑆𝑆𝑛𝑛𝐼𝐼𝐼𝐼𝐶𝐶𝐶𝐶𝑟𝑟𝐼𝐼 + ∑𝐴𝐴𝑌𝑌𝑌𝑌𝑟𝑟 + 𝜀𝜀𝑖𝑖,𝑡𝑡……(7.1)

𝑁𝑁𝐶𝐶𝐶𝐶𝑁𝑁𝐶𝐶 = 𝛽𝛽0 + 𝛽𝛽1 ∗ |𝐷𝐷𝐶𝐶𝑁𝑁| + ∑𝑁𝑁𝑙𝑙𝑛𝑛𝐶𝐶𝑟𝑟𝑙𝑙𝑙𝑙𝐶𝐶 + ∑ 𝑆𝑆𝑛𝑛𝐼𝐼𝐼𝐼𝐶𝐶𝐶𝐶𝑟𝑟𝐼𝐼 + ∑𝐴𝐴𝑌𝑌𝑌𝑌𝑟𝑟 + 𝜀𝜀𝑖𝑖,𝑡𝑡 …….(7.2)

𝑁𝑁𝐶𝐶𝐶𝐶𝑁𝑁𝐶𝐶 = 𝛾𝛾0 + 𝛾𝛾1 ∗ 𝐶𝐶𝑅𝑅𝑅𝑅 + 𝛾𝛾2 ∗ |𝐷𝐷𝐶𝐶𝑁𝑁| + ∑𝑁𝑁𝑙𝑙𝑛𝑛𝐶𝐶𝑟𝑟𝑙𝑙𝑙𝑙𝐶𝐶 + ∑𝑆𝑆𝑛𝑛𝐼𝐼𝐼𝐼𝐶𝐶𝐶𝐶𝑟𝑟𝐼𝐼 + ∑𝐴𝐴𝑌𝑌𝑌𝑌𝑟𝑟 + 𝜀𝜀𝑖𝑖,𝑡𝑡……(7.3)

The total effect of RPT on CRASH can be decomposed into direct and indirect effects.

The direct effect is γ1 from Equation (7.3) above, while the indirect effect is α1*γ1 for the

financial reporting quality channel. To test for the indirect effect, the null hypothesis may be set

as follows: Ho: α1*γ1=0. For the above estimation we use the 2SLS method as a simultaneous

equation model, which controls for the endogeneity problem. We tabulate the direct and indirect

effects of RPTs on price crash for the pooled sample in Table 4.

With respect to results relating to Equation (7.1), we find that various forms of RPTs

affect financial reporting quality adversely. Coefficients on RPT (0.005, p<0.01), TUNEL_FIN


23

(0.086, p<0.01) and PROP_FIN (0.034, p<0.01) are all positive and significant in columns (1),

(4) and (7), respectively. Importantly, we find the coefficients on all the RPT proxies are positive

and significant for price crash, even after controlling for the effects of |DAC|. For example, the

coefficients on RPT are 0.035 (p<0.01) and 0.041 (p<0.01) for the NCSKEW and DUVOL

measures in columns (2) and (3), respectively. When we isolate direct and indirect effects of

RPTs on price crash, we find that RPTs increase price crash directly and the direct effects

constitute the bulk of the total effects. Thus, this evidence supports our theoretical argument that

RPTs increase operational risk, which, consequently, increases price crash as well.

[INSERT TABLE 4 ABOUT HERE]

5.4.2 Relational analysis, RPTs, and crash risk

One of the interesting aspects of RPTs in China is the large number of parties with whom

RPTs are conducted. However, the transaction between parents and subsidiaries constitute the

bulk of the transactions. Table 5, Panel A presents regression results for Equation (4) for

observations whereby parents conduct RPTs with their unlisted subsidiaries. Prior evidence

suggests that such transactions expose parents to significant operational risk because of their

subsidiaries’ poor performance, high debt to asset ratios and inefficient use of internal loans

(Jiang et al., 2010). As subsidiaries fail to repay the loan in time, the parent company suffers

from working capital and capital investment constraints, with an adverse effect on parent

financial performance. Managers of parent companies will therefore have incentives to withhold

bad news from the market for the longest period possible. Once such bad news is revealed in the

market, price crash occurs. Columns (1) and (2) in Table 5 show that the coefficients on

TUNEL_FIN and PROP_FIN are positive and significant across both crash risk measures

(coefficients 0.552 (p<0.01) and 0.596 (p<0.01) for NCSKEW and DUVOL measures


24

respectively). The corresponding coefficients for PROP_FIN are 0.458 (p<0.1) and 0.524

(p<0.01) for the two crash measures. Taken together, both financial tunneling and financial

propping by parents to their unlisted subsidiaries does appear to increase crash risk.

5.4.3 RPTs, profitability thresholds, and crash risk

Our next set of analyses examines the effect of RPT-induced earnings management on

crash risk. Prior evidence on the association between RPTs and earnings management has found

that managers use RPTs to manipulate earnings (e.g., Aharony, Wang, & Yuan, 2010; Gordon &

Henry, 2005). To achieve specific ROE targets and avoid losses, Chinese firms both engage in

accruals-based earnings management and use financial propping via RPTs to meet earnings

targets and avoid losses (Aharony et al., 2010; Chen et al., 2011; Jian & Wong, 2010). Therefore,

the opportunistic use of RPTs to meet profitability thresholds in China provides an interesting

context for examining the effect of RPTs on crash risk. We test whether managers engage in

opportunistic propping to increase earnings so that the ROE-based regulatory thresholds can be

met. We include SUSPECT, an indicator variable coded 1 if the average ROE for the preceding

three years lies between 6% and 7% (both inclusive)7, and 0 otherwise, and interact PROP_FIN

with SUSPECT to assess the RPT-induced crash risk for the SUSPECT group. Results are

reported in panel B of Table 5. We are primarily interested in the interactive coefficient on

7 The CSRC has set and changed the standards required for listed firms to issue rights shares quite frequently in the past. In 1993, firms were required to have only two consecutive years of profits before they could issue rights. In September 1994, the CSRC specified, for the first time, that a firm must have an average ROE of more than 10% in the previous three years before it could issue rights. In January 1996, the CSRC toughened this requirement, stating that a firm must have more than 10% ROE for each of the previous three years. CSRC then lowered the standard in March 1999, requiring that firms have an average ROE above 10% in the past three years, but not lower than 6% in any of these years. In March 2001, the CSRC further lowered the standard, stating that firms must have an average ROE above 6% in the past three years. Managers would, therefore, have had a strong incentive to manipulate their earnings, to meet the rights issue thresholds of three-year average ROE being above 6%.


25

PROP_FIN*SUSPECT. The coefficients on the interactive variable are 0.065 (p<0.01) and 0.032

(p<0.05) for NCSKEW and DUVOL measures respectively. Thus, we find that Chinese firms use

financial propping to meet the regulatory earnings thresholds, and this practice increases the risk

of subsequent price crash.


5.4.4 Is the association between crash risk and RPTs driven by endogeneity problem?

Firms may non-randomly choose the level of RPTs to be conducted based on a

consideration of the outcomes. Selection bias arises when the propensity to engage in RPTs is

correlated with the error term of the crash model. Such a selection bias may violate the standard

OLS assumptions, and the least squares coefficients of the RPT variables could be biased.

Putting it differently, it is not clear whether RPTs lead to crash risk, or some common factors

affect both RPTs and the risk of price crash. For example, it may be that lack of transparency due

to poor information disclosure increases both RPTs and crash risk.

The propensity score matching (PSM) methodology (Rosenbaum & Rubin, 1983, 1985) is a tool

for controlling the potential self-selection problem by matching sample firms with control firms

with similar characteristics according to a function of covariates. We select the optimal match

based on the average treatment effect (ATE) and nearest neighbor (NN) techniques of the

propensity score matching procedure. In our setting, the basic approach to PSM is to first model

the variation across RPTs on the underlying firm-specific determinants (e.g., firm size, leverage,

growth, profitability, and government ownership). We divide our sample into two groups based

on the yearly mean level of both TUNEL_FIN and PROP_FIN. We consider the groups with

RPTs above (below) the yearly mean as the treated (control) group. In this way, we match the


26

firms from the treatment group, which has a high propensity to conduct RPTs, with firms from

the control group, which has a low propensity to conduct RPTs.

To this end, we include a set of firm characteristics that may explain the likelihood that a

given firm will be involved in RPTs. Importantly, inclusion of these controls ensures proper

balance between treated and untreated subjects in the matched sample, which is a key criterion

for PSM (Austin, 2011). One important aspect of propensity score matching is to examine the

distribution of measured baseline covariates between treated and untreated subjects within the

propensity score matched sample. If, after conditioning on the propensity score, no systematic

differences exist in baseline covariates between treated and untreated subjects, this indicates that

the propensity score model has been correctly specified (Austin, 2011). In Table 6 Panel A, none

of the included covariate is significantly different between the treated and the control sub-groups.

Panel B shows the regression results for the PSM analysis. Coefficients on TUNEL_FIN and

PROP_FIN are all positive and significant in across both PSM techniques and two crash risk

measure. For example the coefficient on TUNEL_FIN is 0.27 (p<0.05) and 0.26 (p<0.05) for

NCSKEW and DUVOL measures of crash risk proxies respectively when NN technique is used.

The coefficients on PROP_FIN are 0.33 (p<0.05) and 0.39 (p<0.01) for NCSKEW and DUVOL

measures of crash risk respectively for NN technique.

However, a shortcoming of the PSM approach is that it does not resolve the selection

issue due to unobservable factors. Therefore, we use an instrumental variable technique in Panel

C of Table 6 to validate our interpretation of the results documented in Table 3. This should also

alleviate any concerns with reverse causality or model misspecification in the OLS (Wooldridge,

2002). We use the industry average tunneling (TUNEL_IND) and propping (PROP_IND) as the

instruments for our 2SLS analysis. We expect these variables to be positively and significantly

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associated with firm-level tunnel and propping values but have no reason to believe that these

industry-level measures are directly related to firm-level crash risk or are only related through

firm-level measures.

Section I in panel C, Table 6 reports that the coefficients on the instrumental variables are

highly positive and significant (p<0.01). Results in Section II suggest that the relationship

between RPTs and crash risk remains robust after accounting for the endogenous relationship

between RPTs and crash risk. For example, the estimated coefficients of TUNEL_FIN is 0.935

(P<0.10) and 1.112 (P<0.05) for the NCSKEW and DUVOL measures of crash risk respectively.

The corresponding coefficients for the PROP_FIN is 0.933 (p<0.10) for DUVOL measure. The

coefficient, however, is insignificant for the NCSKEW risk measure. These results, thus, suggest

that endogeneity cannot explain away the documented relationship between RPTs and crash risk.

In terms of the validity of the chosen instrument, under-identification test results (LM

statistic) in Section II, Table 6, reveal that the excluded instruments are “relevant” because the

Kleibergen-Paap rk LM statistic is significant at p<0.001. The weak instrument test results show

that the excluded instruments are correlated with the endogenous regressors, because the Cragg-

Donald Wald F statistic is greater than the Stock and Yogo (2010) critical value (i.e., 16.38) at

10%. Results, therefore, suggest that the instruments are uncorrelated with the error term and are

correctly excluded from the second stage regression, which reflects the validity of the

instruments used for the 2SLS regression.


6. Conclusion

This study investigates whether and how RPTs influence the stock price crash risk, using data

from listed Chinese companies. Although RPTs are prevalent and influential in many countries,


28

the extant RPT literature is inconclusive about whether they are efficient or detrimental. The

results of our study confirm a positive association between future stock price crash and RPTs in

general and, more specifically, two categorical RPTs, including operating RPTs and RP loans.

More importantly, our results provide corroborative evidence on the countervailing effects of

beneficial versus opportunistic RPTs, in that normal (abnormal) RPTs are negatively (positively)

associated with future price crash. Therefore, we add to the theoretical debate from the

perspective of the economic consequences of RPTs.

Our study proposes RPTs as a distinct determinant of stock price crash risk based on two

theoretical underpinnings. On one hand, firms that conduct opportunistic RPTs, distort economic

reality and expropriate minority shareholder values, are often associated with high operational

risks and subsequent price crash. On the other hand, informational risks resulting from

opportunistic RPTs are also high, because firms tend to obfuscate information in order to

disguise the detrimental nature of the RPTs, leading to future price crash. Our evidence suggests

that the operational risk associated with RPTs is the primary determinant of price crash.

Our results also have practical implications. The adverse economic consequence of

opportunistic RPTs, justifies the regulatory scrutiny on RPTs and the disclosure requirements

imposed on companies. The findings may have particular implications for small shareholders

who have limited access to inside information and, thus, have little knowledge of the true

economic incentives for RPTs, especially those conducted by state-controlled companies. Our

study is a timely addition to our understanding of the current worldwide stock market tumble

ignited by the Chinese stock market crash. Although the triggers in the case of China are

perplexing, identifying the ‘Red Flags’ for a crisis is a continuous effort for academicians and

practitioners.


29

Appendix A. Variable definitions

Dependent variables

NCSKEW The negative coefficient of skewness captured by the negative of the third moment of firm-specific weekly returns for each year and separating it by the standard deviation of firm-specific weekly returns raised to the third power. See Eq. (2) for details.

DUVOL The down-to-up volatility. Trading weeks being classified based on down (firm-specific weekly returns below the annual mean) and up weeks (firm-specific weekly returns above the annual mean). We then calculate the standard deviation for both subsamples separately. We use the natural logarithm of the ratio of the standard deviation of the down weeks to the standard deviation of the up weeks. See Eq. (3) for details.

Independent variables

RPT Total RPTs divided by total assets of the firm. OPRPT Operating RPTs (the sum of RP sales and RP purchases) divided by total revenue. RP_LOAN Related party loans (the sum of RP lending to, RP borrowing from, and loan guarantees provided

to, a related party) divided by total assets. TUNEL_FIN Ratio of other receivables to total assets. PROP_FIN Sum of borrowings from related parties, loans and other payables to related parties divided by

total assets. Firm-level control variables TURN Average monthly share turnover during fiscal year t, minus the average monthly share turnover

during previous fiscal year t - 1, where monthly share turnover is calculated as monthly trading volume divided by total number of shares outstanding during the month

RET The mean of firm-specific weekly returns over the fiscal year t SDRET The standard deviation of firm-specific weekly returns over the fiscal year t SIZE The natural logarithm of total assets MB The market-to-book ratio of firm i in year t, calculated as [(market price at the end of fiscal year

× number of shares outstanding )/book value of equity] LEV Sum of short and long-term debt over total assets |DAC| The absolute discretionary accruals calculated using the Modified Jones model controlling for

firm performance (Dechow et al., 1995; Kothari et al., (2005).


30

|REM| The absolute value of total REM calculated as REM= (-1) *ACFO + APROD + (-1) *ADISX (Franics et al., 2016).

OWN Proportion of institutional shareholdings over total outstanding shares ANALYST The natural log of number of analysts following a firm. SUSPECT A dummy variable coded 1 if the average of the prior three years’ ROE falls within the range of

6%-7% (both inclusive), and 0 otherwise

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TABLE 1: Sample selection procedure and descriptive statistics PANEL A: Sample selection procedure Selection process Observations Initial RPT observations including multiple observations for one firm over the period 2001-2013 (Many firms transact with a number of related parties and/or even with the same party in a single year)

300,395

Number of firm-year observations with total RPT values 20,182 Less: Missing data for calculating crash measures and other control variables (5,527) Firm-year observations with total RPT and complete data for conducting baseline regression of crash risk on gross RPTs

14,655

Firm-year observations with data on operating RPTs 9,984 Firm-year observations with data on RPTs related to intercorporate loans and transfers 10,056 Firm-year observations with data on financial tunneling 7,951 Firm-year observations with data on RPTs related to financial propping 9,991 PANEL B: Descriptive statistics

Variables N Mean SD 1st qrt Median 3rd qrt NCSKEWt 14,655 -0.06 0.85 -0.59 -0.04 0.49 DUVOLt 14,652 0.03 0.73 -0.48 -0.02 0.47 NCSKEWt-1 14,655 -0.02 0.86 -0.56 -0.01 0.54 DUVOL t-1 14,652 0.03 0.75 -0.47 0.00 0.51 RPT t-1 14,655 0.38 0.64 0.04 0.15 0.41 OPRPT t-1 9,984 0.15 0.23 0.01 0.05 0.18 RP_LOAN t-1 10,056 0.27 0.39 0.03 0.12 0.32 TUNEL_FIN t-1 7,951 0.04 0.11 0.00 0.00 0.02 PROP_FIN t-1 9,991 0.03 0.07 0.00 0.00 0.02 TURN t-1 14,655 0.02 0.19 -0.07 0.01 0.10


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RET t-1 14,655 0.00 0.02 0.00 0.00 0.01 SDRET t-1 14,655 0.06 0.05 0.05 0.06 0.08 SIZE t-1 14,655 21.78 1.16 21.00 21.69 22.46 MB t-1 14,655 3.09 3.29 1.35 2.09 3.71 LEV t-1 14,655 0.09 0.11 0.01 0.05 0.14 |DAC t-1| 14,655 0.07 0.08 0.02 0.05 0.09 |REM t-1| 14,655 0.10 0.11 0.03 0.06 0.12 OWN t-1 14,655 0.28 0.25 0.03 0.23 0.48 ANALYST t-1 14,655 1.09 1.18 0.00 0.69 2.08 SUSPECT t-1 11,105 0.06 0.24 0.00 0.00 0.00 PANEL C: Industry distribution Industry Observations Percentage A: Farming, Forestry, Animal Husbandry &Fishery 300 0.02 B: Mining and Quarrying 315 0.02 C0: Food and Beverage. 659 0.05 C1: Textile, Clothing, Fur 611 0.04 C3: Papermaking, Printing 279 0.02 C4: Petroleum, Chemical, Rubber, Plastic 1,656 0.11 C5: Electronic 652 0.04 C6: Metal, Non-metal 1,304 0.09 C7: Machinery, Equipment, Instrument 2,389 0.16 C8: Medicine, Biologic Products 917 0.06 C9: Other manufacturing 94 0.01 D: Production & Supply Of Power, Gas & Water 664 0.05 E: Construction 302 0.02 F: Transportation, Storage 602 0.04 G: Information Technology Industry 883 0.06 H: Wholesale and Retail Trades 891 0.06 J: Real Estate 844 0.06 K: Social Services 458 0.03 L: Transmitting, Culture Industry 44 0.00 M: Integrated 791 0.05 Total 14,655 1.00


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TABLE 2: Correlation analysis

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) NCSKEWt (1) -

DUVOLt (2) 0.92 -

RPTt-1 (3) 0.02 0.03 -

OPRPTt-1 (4) 0.02* 0.01 0.43 -

RP_LOANt-1 (5) 0.05 0.06 0.75 0.04 -

TUNEL_FINt-1 (6) 0.10 0.10 0.16 -0.04 0.19 -

PROP_FIN t-1 (7) 0.02 0.02 0.24 -0.02 0.30 0.22 -

TURN t-1 (8) 0.12 0.12 -0.09 0.00 -0.08 0.00 -0.06 -

RET t-1 (9) 0.07 0.08 -0.15 -0.01 -0.16 -0.13 -0.11 0.56 -

SDRET t-1 (10) 0.08 0.10 -0.05 0.00 -0.07 0.00 0.01 0.23 0.42 -

SIZE t-1 (11) -0.11 -0.12 -0.07 0.08 -0.18 -0.31 -0.16 0.02 0.23 -0.01 -

MB t-1 (12) -0.03 -0.04 -0.15 -0.03 -0.17 -0.09 -0.06 0.12 0.35 0.29 0.14 -

LEV t-1 (13) -0.01 -0.01 0.10 0.02* 0.16 0.02 0.13 -0.03 -0.02* -0.03 0.21 -0.06 -

|DACt-1| (14) -0.01* -0.02 0.05* -0.02* 0.04 0.12 0.08 -0.02 0.05 0.08 -0.03 0.08 0.00 -

|REM t-1| (15) -0.02* -0.02* 0.02* -0.05 -0.01 -0.05 0.06 -0.06 0.03 0.10 0.09 0.11 0.02* 0.32 -

OWN t-1 (16) -0.11 -0.12 -0.02 0.01 -0.05 -0.22 -0.03 -0.12 -0.02* -0.07 0.64 0.11 0.20 -0.05 0.10 -

ANALYST t-1 (17) -0.05 -0.08 -0.01 0.03 -0.09 -0.19 -0.10 -0.07 0.05 -0.04 0.72 0.03 0.18 -0.07 0.09 0.63 - Note: Italicized and bold-faced correlations are significant at p<0.01. * represents statistical significance at p<0.05. Variables are defined in the Appendix.


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TABLE 3: RPTs and stock price crash risk Panel A: Total RPTs, operating RPTs, RP loans and stock price crash risk (H1A, H1B and H1C)

(1) (2) (3) (4) (5) (6) Variables NCSKEW DUVOL NCSKEW DUVOL NCSKEW DUVOL NCSKEW t-1 -0.008 - 0.013 - -0.010 - [-0.95] [1.28] [-0.92] DUVOL t-1 - -0.029*** - -0.010 - -0.032*** [-3.48] [-0.97] [-3.02] RPTt-1 0.035*** 0.041*** - - - - [3.29] [4.47] OPRPTt-1 0.081** 0.065** - - [2.29] [2.16] RP_LOANt-1 - - 0.091*** 0.100*** [4.03] [5.13] TURN t-1 0.641*** 0.615*** 0.669*** 0.629*** 0.598*** 0.612*** [13.21] [15.44] [11.58] [13.24] [10.85] [13.20] RET t-1 8.259*** 8.343*** 9.390*** 9.688*** 9.098*** 9.272*** [7.46] [7.79] [6.36] [6.65] [6.47] [6.76] SDRET t-1 -2.075*** -2.131*** -2.351*** -2.432*** -2.583*** -2.847*** [-5.04] [-5.17] [-4.34] [-4.48] [-4.09] [-4.38] SIZE t-1 -0.114*** -0.107*** -0.082*** -0.077*** -0.090*** -0.083*** [-11.37] [-12.19] [-7.06] [-7.48] [-7.42] [-7.90] MB t-1 -0.018*** -0.022*** -0.027*** -0.029*** -0.020*** -0.022*** [-6.01] [-8.31] [-6.82] [-8.38] [-5.95] [-7.78] LEVt-1 0.135* 0.120** 0.190** 0.168** 0.107 0.089 [1.90] [2.08] [2.24] [2.41] [1.24] [1.27] |DACt-1| -0.140 -0.181** -0.200* -0.241** -0.216* -0.290*** [-1.46] [-2.21] [-1.66] [-2.28] [-1.91] [-2.96] |REM t-1| 0.103 0.045 0.138* 0.079 0.129 0.098 [1.58] [0.84] [1.71] [1.24] [1.60] [1.52] OWN t-1 -0.016 -0.028 -0.091** -0.095** -0.031 -0.035 [-0.38] [-0.82] [-1.98] [-2.42] [-0.69] [-0.93] ANALYST t-1 0.043*** 0.018** 0.033*** 0.007 0.038*** 0.010 [4.91] [2.46] [3.22] [0.83] [3.79] [1.13] Constant 2.348*** 2.220*** 2.076*** 1.985*** 2.259*** 2.165*** [10.52] [11.36] [8.11] [8.66] [8.54] [9.23] Industry Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Adjusted R2 0.09 0.14 0.09 0.14 0.09 0.14 Observations 14,655 14,652 9,984 9,984 10,056 10,056


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39

Panel B: Normal vs. abnormal RPTs and stock price crash risks (H2A and H2B)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) NCSKEW NCSKEW DUVOL DUVOL NCSKEW NCSKEW DUVOL DUVOL NCSKEW NCSKEW DUVOL DUVOL Variables Normal Abnormal Normal Abnormal Normal Abnormal Normal Abnormal Normal Abnormal Normal Abnormal RPTt-1 -0.178 0.035*** 1.621*** 0.041*** - - - - - - - - [-0.30] [3.33] [3.46] [4.50] OPRPTt-1 - - - - -7.356*** 0.071** -6.937*** 0.056* - - - - [-6.97] [2.07] [-7.46] [1.93] RP_LOANt-1 - - - - -5.506*** 0.081*** -4.073*** 0.088*** [-9.70] [3.91] [-8.63] [4.91] Other control variables

YES YES YES YES YES YES YES YES YES YES YES YES

Industry YES YES YES YES YES YES YES YES YES YES YES YES Year YES YES YES YES YES YES YES YES YES YES YES YES Adjusted R2 0.09 0.09 0.13 0.14 0.09 0.09 0.14 0.14 0.09 0.09 0.09 0.14 Observations 14,655 14,655 14,652 14,652 9,984 9,984 9,984 9,984 10,056 10,056 10,056 10,056


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PANEL C: Tunneling and propping using RPTs and stock price crash risk (H3A and H3B)

Variables (1) (2) (3) (4) (5) (6) TUNEL_FIN TUNEL_FIN PROP_FIN PROP_FIN TUNEL_FIN &

PROP_FIN TUNEL_FIN & PROP_FIN

NCSKEW DUVOL NCSKEW DUVOL NCSKEW DUVOL NCSKEW t-1 -0.018 -0.012 - - -0.013 - [-1.59] [-1.13] [-1.00] - DUVOL t-1 - - -0.041*** -0.030*** - -0.032*** [-3.84] [-3.10] - [-2.62] TUNEL_FIN t-1

0.470*** 0.335*** - - 0.594*** 0.574*** [5.18] [3.01] [6.10] [6.94] PROP_FIN t-1 - - 0.454*** 0.351*** 0.203** 0.219* [6.08] [3.79] [2.30] [1.68] TURN t-1 0.693*** 0.589*** 0.677*** 0.571*** 0.636*** 0.626*** [9.22] [9.99] [10.48] [11.65] [7.72] [8.58] RET t-1 9.057*** 8.289*** 9.048*** 8.347*** 8.125*** 8.235*** [5.46] [5.84] [5.51] [6.03] [4.40] [4.49] SDRET t-1 -2.334*** -2.097*** -2.511*** -2.123*** -2.090*** -2.169*** [-3.34] [-3.99] [-3.85] [-3.99] [-2.76] [-3.19] SIZE t-1 -0.105*** -0.108*** -0.099*** -0.097*** -0.017* -0.008 [-8.00] [-9.06] [-8.72] [-9.23] [-1.89] [-1.08] MB t-1 -0.011*** -0.016*** -0.016*** -0.020*** -0.011*** -0.017*** [-2.96] [-4.74] [-5.15] [-6.92] [-2.67] [-4.58] LEVt-1 0.275*** 0.102 0.213*** 0.099 0.229** 0.165* [2.94] [1.20] [2.69] [1.41] [2.20] [1.79] |DACt-1| -0.213* -0.245** -0.272** -0.259*** -0.271* -0.280** [-1.65] [-2.23] [-2.46] [-2.74] [-1.89] [-2.17] |REM t-1| 0.065 0.121 0.023 0.055 0.107 0.042 [0.71] [1.52] [0.30] [0.85] [1.03] [0.48] OWN t-1 -0.080 -0.046 -0.056 -0.056 -0.135** -0.115** [-1.38] [-0.94] [-1.16] [-1.32] [-2.14] [-2.11] ANALYST t-1 0.061*** 0.055*** 0.031*** 0.028*** 0.021 -0.010 [4.84] [5.05] [2.96] [2.99] [1.62] [-0.90] Constant 2.092*** 2.234*** 2.040*** 2.045*** 0.205 0.118 [7.09] [8.30] [7.95] [8.57] [0.95] [0.67] Industry YES YES YES YES YES YES Year YES YES YES YES YES YES Adjusted R2 0.09 0.08 0.14 0.13 0.08 0.12 Observations 7,951 7,951 9,991 9,991 6,404 6,404

Note: Panel A of this table reports results from the OLS regressions of stock price crash risk on total related party transactions (RPTs), operating RPTs, and RP loan and control variables for Chinese stock exchange-listed industrial firms from 2001 to 2013. Panel B replaces the aggregated measures of RPTs with normal and abnormal components of RPT, OPRPT, and RP_LOAN. Following Jian and Wong (2010), we regress the respective RPT measures on SIZE (natural log of total assets), LEV (sum of short and long-term debt over total assets), and MB (market value of equity divided by book values of equity). The predicted values from this regression are our proxy for normal RPTs. The residual, therefore, proxies for abnormal RPTs. Continuous variables are winsorized at their 1st and 99th percentiles. Panel C reports results from OLS regressions estimating stock price crash risk as a function of tunneling and propping using related party transactions (RPTs) and control variables for Chinese stock exchange-listed industrial firms from 2001 to 2013. Continuous variables are winsorized at their 1st and 99th percentiles. Robust t-statistics in brackets. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels respectively (two-tailed test). Variables are defined in the Appendix.


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TABLE 4: Mediation test using financial reporting quality proxies (1) (2) (3) (4) (5) (6) (7) (8) (9) Variables |DACt-1| NCSKEW DUVOL |DACt-1| NCSKEW DUVOL |DACt-1| NCSKEW DUVOL Eq. (7.1) Eq. (7.3) Eq. (7.3) Eq. (7.1) Eq. (7.3) Eq. (7.3) Eq. (7.1) Eq. (7.3) Eq. (7.3) NCSKEW t-1 - -0.013 - - -0.018* - - -0.012 - [-1.59] [-1.66] [-1.19] DUVOL t-1 - -0.034*** - - -0.041*** - - -0.030*** [-4.30] [-3.93] [-3.17] RPT t-1 0.005*** 0.035*** 0.041*** - - - - - [4.75] [3.24] [4.52] TUNEL_FIN t-1 - - - 0.086*** 0.470*** 0.454*** [11.58] [5.40] [6.25] PROP_FIN t-1 - - - - - - 0.034*** 0.335*** 0.351*** [3.23] [2.83] [3.54] |DACt-1| - -0.142 -0.187** - -0.213* -0.272** - -0.245** -0.259*** [-1.50] [-2.35] [-1.65] [-2.52] [-2.18] [-2.76] Other control variables Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry Yes Yes Yes Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 14,520 14,520 14,518 7,951 7,951 7,951 9,991 9,991 9,991 Adjusted R2 0.09 0.09 0.14 0.17 0.10 0.14 0.17 0.09 0.13 Direct effect - 0.035*** 0.041*** 0.470*** 0.454 0.335*** 0.351*** Indirect effect - -0.00064 -0.00084** -0.0183 -0.0234** -0.0083* -0.0087** Total effects - 0.0344*** 0.040*** 0.4517*** 0.4306*** 0.3267*** 0.3423*** Note: This table presents OLS regression results of the mediation tests. We use financial reporting quality (|DAC|) as the mediator. Eq. (7.1) is the regression of the mediating variable, i.e., |DAC| on RPTs; whilst Eq. (7.3) is the regression of stock price crash risk on RPTs and the mediator. Continuous variables are winsorized at their 1st and 99th percentiles. Robust t-statistics are in brackets. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels respectively (two-tailed test). Variables are defined in the Appendix.


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Table 5: Additional Analysis Panel A: Relational analysis

TUNEL_FIN PROP_FIN Variables (1) (2) (3) (4) NCSKEW DUVOL NCSKEW DUVOL NCSKEW t-1 -0.038* - -0.058*** - [-1.68] [-2.72] DUVOL t-1 - -0.049** - -0.068*** [-2.30] [-3.52] RPT t-1 0.552*** 0.596*** 0.458* 0.524*** [3.11] [3.86] [1.89] [2.63] Other control variables YES YES YES YES Industry YES YES YES YES Year YES YES YES YES Adjusted R2 0.10 0.15 0.09 0.14 Observations 1,826 1,826 2,203 2,201 Note: This table reports results from OLS regressions relating the related party transactions (RPTs) to the stock price crash risk measures and control variables for Chinese stock exchange-listed industrial firms from 2001 to 2013. Continuous variables are winsorized at their 1st and 99th percentiles. Robust t-statistics in brackets. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels respectively (two-tailed test). Variables are defined in the Appendix. PANEL B: Propping through RPTs for meeting or beating regulatory earnings thresholds and stock price crash risk Variables (1) (2) PROP_FIN PROP_FIN NCSKEW DUVOL NCSKEW t-1 -0.012 - [-1.02] DUVOL t-1 - -0.043*** [-3.50] RPT t-1 0.001** 0.001*** [2.55] [4.70] SUSPECT t-1 -0.024 -0.034 [-0.95] [-1.45] RPT*SUSPECT t-1 0.065*** 0.032** [3.90] [2.34] Other control variables YES YES Industry YES YES Year YES YES Adjusted R2 0.10 0.15 Observations 7,781 7,781 Note: This table reports results from OLS regressions estimating the crash risk effect of the propping through RPTs for meeting or beating regulatory earnings thresholds for Chinese stock exchange-listed industrial firms from 2001 to 2013. SUSPECT is a dummy variable coded 1 if the average of the prior three years’ ROE falls within the range of 6%-7% (both inclusive), and 0 otherwise. Continuous variables are winsorized at their 1st and 99th percentiles. Robust t-statistics in brackets. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels respectively (two-tailed test). Variables are defined in the Appendix.


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TABLE 6: Endogeneity Test Panel A: Covariate matching for the Propensity score matching (PSM) test

Variables Treated Control t-statistic SIZE 20.84 20.90 -0.97 MB 1.91 1.83 0.64 LEV 0.09 0.09 0.67 OWN 0.116 0.121 0.52 ANALYST 0.382 0.414 -0.73 GOVT 0.617 0.513 1.51 AGE 17.09 11.66 1.59 SGROW 0.40 0.27 0.54 BSIZE 9.13 9.18 -0.43 CEO_OWN 0.0017 0.0010 0.27 TOP1 0.374 0.372 0.18 Panel B: Regression results using PSM sample NN NN ATE ATE NN NN ATE ATE (1) (2) (3) (4) (5) (6) (7) (8) NCSKEW t-1 -0.075*** - -0.065*** - -0.038* - -0.049*** - [-2.80] [-3.34] [-1.73] [-3.13] DUVOL t-1 - -0.101*** - -0.089*** - -0.036* - -0.058*** [-4.09] [-4.91] [-1.81] [-3.93] TUNEL_FIN t-1 0.266** 0.259** 0.276** 0.249** - - - - [2.02] [2.39] [2.39] [2.56] PROP_FIN t-1 - - - - 0.326** 0.386*** 0.316** 0.353*** [2.15] [2.96] [2.29] [3.06] Other control variables

YES YES YES YES YES YES YES YES

Industry YES YES YES YES YES YES YES YES Year YES YES YES YES YES YES YES YES Adjusted R2 0.09 0.13 0.10 0.15 0.08 0.10 0.09 0.12 Observations 1,616 1,616 5,633 5,633 2,658 2,658 7,297 7,297 Note: This table reports results from OLS regressions estimating stock price crash risk as a function related party transactions (RPTs) and control variables for Chinese stock exchange-listed industrial firms from 2001 to 2013. The estimations follow the propensity-matching technique (Rosenbaum & Rubin 1983, 1985). We select the optimal match based on the average treatment effect (ATE) and nearest neighbor (NN) techniques. All specifications include year and industry fixed effects. Continuous variables are winsorized at their 1st and 99th percentiles. Robust t-statistics in brackets. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels respectively (two-tailed test). GOVT is government shareholding measured as the percentage of government shareholding; AGE is the natural logarithm of the number of years since listing on the stock exchanges; SGROW is annual sales growth measured as SALESt-SALESt-1/SALESt-1; BSIZE represents board size, which is measured as the natural logarithm of number of board of directors; CEO_OWN measures shareholding of the CEOs; TOP1 is the shareholding of largest shareholder. Other variables are defined in the Appendix.


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Panel C: Two-stage least squares (2SLS) test Section I: First-stage regressions TUNEL_FIN PROP_FIN

Explanatory Variable (1) (2) (3) (4) Instrument TUNEL_IND

0.859*** (6.26)

0.858*** (6.25)

- -

PROP_IND - - 0.831*** (6.85)

0.831*** (6.85)

All Variables in Main Test Yes Yes Yes Yes Year FE Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Observation (N) 7,948 7,948 9,990 9,990 Adjusted R2 0.09 0.12 0.07 Underidentification Test Kleibergen-Paap rk LM statistic 35.23 35.13 52.69 34.85 p-value 0.000 0.000 0.000 0.000 Weak Identification Test Corrected Cragg-Donald Wald F statistic

223.72 223.18 441.09 76.29

Stock and Yogo (2005)10% maximal IV size (Critical Value)

16.38 16.38 16.38 16.38

Section II: Second-Stage Regressions

(1) NCSKEW

(2) DUVOL

(3) NCSKEW

(4) DUVOL

Potentially Endogenous Instrumented Variable RPTt-1 0.935* 1.112** 0.124 0.933*

(1.79) (2.56) (0.19) (1.64) All Variables in Main Test Yes Yes Yes Yes Year FE Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Note: Continuous variables are winsorized at their 1st and 99th percentiles. Robust t-statistics in brackets. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels respectively (two-tailed test). Variables are defined in the Appendix.

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Related party transactions and stock price crash risk ......China offers an interesting setting in which to explore this research question, because of its unique institutional features