International Review of Economics and Finance 31 (2014) 205–217

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International Review of Economics and Finance

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Causes and consequences of corporate asset exchanges bylisted companies in China

Fang Lou a,1, Jiwei Wang b,⁎, Hongqi Yuan c,2

a School of Economics, Shanghai University of Finance and Economics, Chinab School of Accountancy, Singapore Management University, Singaporec School of Management, Fudan University, China

a r t i c l e i n f o

⁎ Corresponding author. Tel.: +65 68280616.E-mail addresses: loufang@mail.shufe.edu.cn (F. Lo

1 Tel.: +86 21 65903193.2 Tel.: +86 21 25101113.3 Although we could not obtain any statistics on bar

in that country.

http://dx.doi.org/10.1016/j.iref.2014.02.0041059-0560/© 2014 Elsevier Inc. All rights reserved.

a b s t r a c t

Article history:Received 3 May 2012Received in revised form 4 February 2014Accepted 5 February 2014Available online 13 February 2014

China's listed companies often exchange corporate assets with their parent companies. Wefind that listed companies that have been incompletely restructured from former state-ownedenterprises and in sound financial condition tend to exchange higher quality assets for lowerquality assets (i.e., tunneling). However, when there is a need to avoid reporting a loss and toraise additional capital, listed companies tend to exchange lower quality assets for higher qualityassets (i.e., propping). We also find that the market reacts indifferently to asset exchangeannouncements. Finally, we find asset exchanges motivated by a tunneling (propping) incentiveto be associated with poorer (improved) post-exchange stock performance and financialperformance. In summary, this study contributes to the corporate asset literature by providingtwo new incentives: tunneling and propping.

© 2014 Elsevier Inc. All rights reserved.

JEL classification:G14G15G34

Keywords:Asset exchangeTunnelingPropping

1. Introduction

The literature on corporate assets focuses on transactions involving payment in the form of cash, equity, and/or futureconsiderations (Slovin, Sushka, & Poloncheck, 2005) rather than barter-type asset exchanges.3 In China, however, many listedcompanies barter by exchanging corporate assets with such related parties as parent companies and sister companies undercommon control. This paper addresses the reasons for and consequences of the exchange of corporate assets by listed companiesin China.

We identify two possible nonexclusive incentives for asset exchanges. The first is the incentive for related parties to reclaimhigher quality assets (i.e., assets characterized by better investment opportunities and greater profitability) and inject lowerquality assets, thereby resulting in the expropriation of minority shareholders. We label this incentive the tunneling incentive inthe spirit of Johnson, La Porta, Lopez-de-Silanes, and Shleifer (2000). The second is the incentive for related parties to exchangehigher quality assets for lower quality assets to help the listed firm to boost operating performance. We label this the proppingincentive in the spirit of Friedman, Johnson,and Mitton (2003). We argue that both the tunneling and propping incentives exist in China's particular institutional setting.

u), jwwang@smu.edu.sg (J. Wang), yuanhq@fudan.edu.cn (H. Yuan).

ter-type asset exchanges in the United States, we conjecture that this type of asset transaction is quite rare

206 F. Lou et al. / International Review of Economics and Finance 31 (2014) 205–217

Most of China's listed companies have been restructured from state-owned enterprises (SOEs), typically through one of thethree following restructuring processes. First, an existing SOE may peel off part of its operating assets to form a listed company,and it remains the parent of that listed company. Second, an SOEmay be fully integrated with a listed company, and a governmentagency or equivalent acts as the latter's parent. Finally, several SOEs may be bundled to form a listed company, and a governmentagency or equivalent acts as its parent. We classify the first type as incomplete restructuring because only part of the existing SOEhas been transformed into a listed company. We treat the other two types as complete restructuring because the existing SOE(s)has been integrated into a listed company in its entirety. In an incomplete restructuring process, to help the to-be-listed firm to gopublic, the unlisted parent company tends to carve out higher quality assets to boost pre-IPO performance (Aharony, Wang, &Yuan, 2010). The remaining lower quality assets and other non-operating assets such as schools and hospitals become a financialburden, and hence the parent has a strong incentive to exchange lower quality assets for higher quality assets from the listedcompany in the post-IPO period. In addition, Peng, Wei, and Yang (2011) find that when listed companies in China are financiallyhealthy, their controlling shareholders are more likely to conduct connected transactions to expropriate the minorityshareholders of the listed companies. Following the description of tunneling put forward by Johnson et al. (2000), i.e., “thetransfer of assets and profits out of firms for the benefit of those who control them,” we label a parent company's incentive toexchange lower quality assets for higher quality assets from a listed company under its control the “tunneling incentive.” Thus, weexpect listed firms that have undergone an incomplete restructuring process and in sound financial condition to exchange higherquality assets for lower quality assets from their related parties.

Unlike the securities regulators in more developedmarkets, those in China have established two bright-line earnings targets toregulate firm listings: a firm must report at least 0% return on equity (ROE) to maintain its listing status and 10% (6% after 2001)ROE to issue new shares. Although these bright-line rules have some benefits, such as reducing adverse selection problems (Chen& Wang, 2007), the ROE targets provide listed firms' managers with opportunistic earnings manipulation incentives (Chen &Yuan, 2004) and parent companies with incentives to assist listed firms in boosting their ROE. We thus expect that listedcompanies that need to avoid reporting a loss and to raise additional capital through equity offerings are likely to exchange lowerquality assets for higher quality assets with their related parties. Following the description of propping put forward by Friedmanet al. (2003), i.e., “transferring private resources into firms that have minority shareholders,” we label a parent company'sincentive to exchange higher quality assets for lower quality assets from a listed company under its control the “proppingincentive.” Accordingly, we expect listed firms with the intention to avoid reporting a loss and to raise additional capital toexchange lower quality assets for higher quality assets from related parties.

Owing to the limited information available on exchanged assets, we were unable to measure asset quality directly. In an arm'slength exchange, the valuation of the assets exchanged should be equivalent. If a manager wants to exchange lower quality assetsfor higher quality assets without extra compensation, he or she is likely to opportunistically manipulate the valuation of the lowerquality assets to match that of the higher quality assets, thus indicating that the former should have a higher abnormal valuation.Accordingly, we should be able to infer asset quality through comparison of the abnormal valuation rates of the exchanged assets.When the abnormal valuation of the assets surrendered by a listed company is higher than that of the assets it acquires, we inferthat the quality of the surrendered assets is lower than that of the acquired assets and vice versa.

We examine a sample of 305 asset exchanges made by 229 listed companies on the Shanghai and Shenzhen Stock Exchangesfrom 2000 to 2006. We present a model for determining asset valuation and measure the abnormal asset valuation rate by themodel's residual (the model is discussed in Section 5). The difference between the abnormal surrendered asset valuation rate andabnormal acquired asset valuation rate is then used as a proxy for the quality of the assets exchanged. If the abnormal valuation ofthe surrendered assets is higher than that of the acquired assets, we assume the quality of the surrendered assets to be lower thanthat of the acquired assets. Thus, a greater difference in the abnormal asset valuation rate indicates that there is a greaterlikelihood that the firm has exchanged lower quality assets for higher quality assets (i.e., the propping incentive) and vice versa(i.e., the tunneling incentive).

We then provide empirical evidence to show that firms characterized by incomplete restructuring during the IPO process andin sound financial condition are associated with a lower abnormal asset valuation difference. The evidence indicates that this typeof firms exchanges higher quality assets for lower quality assets, which is consistent with the tunneling incentive. Firms thatintend to avoid reporting a loss and to raise additional capital through equity offerings, in contrast, are associated with a higherabnormal asset valuation difference. The evidence suggests that these firms are more likely to exchange lower quality assets forhigher quality assets, which is consistent with the propping incentive. These results hold even after we control for other firm-levelfactors, such as return on assets (ROA), firm size, past stock return, market-to-book ratio of equity, cash holdings, leverage ratio,and growth rates in sales and gross property, plant, and equipment (PPE). Tunneling and propping behavior should have differenteffects on investors, and we would thus expect investors to exhibit different reactions to asset exchange announcementsdepending on their motivating factor. However, we find the market to react indifferently to such announcements, which may castdoubt on the Chinese capital markets' reputation for semi-strong efficiency.

We also examine the consequences of barter-type asset exchanges. In the presence of the tunneling incentive, managers inlisted companies exchange higher quality assets for lower quality assets. Hence, we expect long-term financial and stockunderperformance in these companies. The propping incentive, in contrast, results in managers exchanging lower quality assetsfor higher quality assets. Hence, we expect long-term improvement in both financial performance and stock performance forthese firms. Accordingly, we use stock performance (12- and 24-month post-exchange buy-and-hold abnormal return [BHAR])and financial performance (1- and 2-year average post-exchange ROA) to test the consequences on post-exchange performance.As predicted, we find a positive association between both performance measures and a difference in abnormal asset valuation,

207F. Lou et al. / International Review of Economics and Finance 31 (2014) 205–217

thus indicating that asset exchanges motivated by the propping incentive result in improved post-exchange firm performanceand those motivated by the tunneling incentive in poorer post-exchange performance. We also control for other factors that mayaffect firm performance, such as current ROA, firm size and leverage ratio, and find these results to hold.

This paper contributes to the corporate asset literature in a number of ways. First, it identifies a sample of firms that engage inbarter-type asset exchanges. The existing literature focuses on asset sales and purchases inmonetary terms alone (e.g., Maksimovic &Phillips, 2001; Warusawitharana, 2008). Second, to the best of our knowledge, we contribute new incentives for asset sales andpurchases, i.e., the tunneling and propping incentives, to the corporate asset literature. The existing literature examines corporateasset transactions from either the investment efficiency incentive (e.g., John & Ofek, 1995; Maksimovic & Phillips, 2001;Warusawitharana, 2008) or financing incentive perspectives (e.g., Asquith, Gertner, & Scharfstein, 1994; Brown, James, &Mooradian,1994; Lang, Poulsen, & Stulz, 1995). Finally, we use asset valuation information to infer the quality of surrendered and acquired assets,which to date has gone unexamined in the literature.

This study also extends the body of research on the expropriation and propping of minority shareholders by controllingshareholders. Johnson et al. (2000) conjecture that controlling shareholders have an incentive, legally or illegally, to expropriate(or “tunnel”) minority investors when the legal environment and corporate governance system are weak. Friedman et al. (2003)extend the findings of Johnson et al. to show that managers (or controlling shareholders) may also have incentives to transfertheir private resources to benefit minority shareholders. Using a sample of connected transactions in China, Peng et al. (2011)provide empirical support for both Johnson et al. (2000) and Friedman et al. (2003) by furnishing clear evidence of propping andtunneling occurring in the same company. Su, Fung, Huang, and Shen (2014) find that firms that pay less in cash dividends areassociated with more related-party transactions, which represents wealth expropriation from general stockholders. In this paper,in contrast, we present the barter-type asset exchanges that take place in China as direct evidence of tunneling and propping byexamining various aspects of the country's corporate restructuring processes and securities regulations that have previously beenunexamined in the literature.

This paper also contributes to the asset valuation literature in the accounting field. Jarrell (1979) finds that utility companiesovervalue their assets to increase the price of their products. Our research complements that study by showing that themanipulation of asset valuation can be used to achieve various goals, such as propping and tunneling in our context.

The remainder of the paper is organized as follows. Section 2 reviews the asset sale and purchase literature. Section 3 outlinesChina's particular institutional background and develops our hypotheses. In Section 4, we describe our sample and data, followedby presentation of our empirical results in Section 5. Finally, our conclusions are presented in Section 6.

2. Review of asset exchange literature

The existing literature on corporate assets focuses on transactions involving payment in the form of cash, equity, and/or futureconsiderations (Slovin et al., 2005) rather than barter-type asset exchanges. The overall market for corporate assets includesmergers, acquisitions, and partial asset sales. Asset exchanges are related to partial asset sales. Alexander, Benson, andKampmeyer (1984) and Jain (1985) were among the first to show the valuation consequences of asset sell-offs. Using a sample ofmore than 1000 voluntary sell-off announcements, Jain (1985) shows that there is a positive effect on the shareholders of boththe sellers and buyers.4 Subsequent studies offer various theories to explain the motives and valuation consequences of partialasset sales by corporations.

The efficiency hypothesis is the dominant theory, but there are various views of efficiency. It is generally argued that managersreallocate resources efficiently through asset sales and purchases. Managers may sell assets if they discover that another party canmanage them more efficiently. Hite, Owers, and Rogers (1987) investigate the valuation consequences of voluntary proposals tosell part of a corporation's assets. They find that both successful and unsuccessful sellers reap statistically significant abnormalreturns from initial proposal announcements but that unsuccessful sellers lose the initial gain at the offer termination. Theyinterpret these findings as evidence that asset sales are associated with the movement of resources to higher-value uses. Therationale is that asset sales are in the best interests of stockholders if and only if the net sale proceeds exceed the present value ofthe net future cash flows from continued ownership and operation. Thus, potentially productive gains can be realized only by thetransfer of the target assets from their current use to the buyer's control.

John and Ofek (1995) offer an alternative view of efficiency. They argue that the divested asset's interference with the seller'sother operations provides the motive for selling it. Hence, selling an unrelated asset leads to an increase in focus and moreefficient operation of the core business. Using several accounting performance measures, such as operating margin and ROA, theyfind that the firm's remaining assets are more profitable after the sell-off.

Maksimovic and Phillips (2001, 2002) provide both a theoretical model and empirical results to support the efficiency view.Their intuition is that some firms are more productive and can produce more than other firms from a given number of plants.They argue that firms adjust in size until the marginal benefit is equal to the marginal cost of production. As output pricesincrease, more productive firms realize a larger gain in value from the assets they control. As a result, they find it optimal toacquire plants from less productive firms in the industry. By the same token, a positive shock in an industry increases theopportunity cost of operating as an inefficient producer in that industry. Thus, industry shocks alter the value of assets and createincentives for transfers to more productive uses. Their empirical results show that assets are more likely to be sold (1) when

4 Alexander et al. (1984) find similar results but with a much smaller sample (53 announcements).

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the economy is undergoing positive demand shocks, (2) when the assets are less productive than their industry benchmarks,(3) when the selling division is less productive, and (4) when the selling firm has more productive divisions in other industries.

Warusawitharana (2008) develops a model that links asset purchases and sales to the fundamental properties of avalue-maximizing firm. The key economic idea of this model is that firms engage in asset purchases and sales to move the firmtoward its optimal size, which varies with profitability. Their empirical results show ROA to be strongly predictive of when firmswill purchase or sell assets. In response to improved profitability, firms have the option of growing externally through assetpurchases. Firms with a low degree of profitability can improve their average productivity of capital via asset sales. In summary,the foregoing studies characterize asset sales and purchases as a process that efficiently reallocates corporate resources.

The existing literature also suggests alternative explanations for asset sales. The financing hypothesis, for example, argues thatmanagement values firm size and control and is thus reluctant to sell assets for efficiency reasons alone. A more compellingmotivation for selling assets is to obtain funds when alternative sources of financing are too expensive. This hypothesis alsoargues that the completion of an asset sale signals good news about the value of the asset because if its value were low, then thesale would not have taken place. Lang et al. (1995) provide empirical results to support the financing hypothesis. They find thatfirms selling assets tend to be poor performers and/or to have high degrees of leverage, even when bankrupt firms and those indefault are excluded. This result suggests that the typical firm selling assets is motivated to do so by its financial situation ratherthan a desire to efficiently reallocate corporate resources. Lang et al. (1995) also report that the stock-price reaction to asset salesis significantly positive for firms that are expected to use the proceeds to pay down debt, but negative and insignificant for thosethat are expected to keep the proceeds within the firm, which is also inconsistent with the efficiency hypothesis. Asset sales mayalso be an important way of resolving financial distress. Asquith et al. (1994) find that asset sales are a way of avoiding Chapter 11but are limited by industry factors, with firms in distressed and highly leveraged industries less prone to engage in such sales.Brown et al. (1994) find significantly lower returns to shareholders when asset sale proceeds are used to repay debt than whenthey are retained by the firm.

3. Institutional background and hypotheses

The peculiar institutional background of Chinese firms renders the motives for asset exchanges different from those discussedin the foregoing section.

3.1. The restructuring process of China's listed companies

In the transition from a centrally planned economy to a market economy, the Chinese government has adopted a gradualapproach by introducing private ownership to wholly state-owned enterprises without selling any state-owned assets (Chan,Fung, & Thapa, 2007;Wei, Xie, & Zhang, 2005). Existing SOEs are first restructured into corporations, which then go public to raiseprivate capital. There are three types of restructuring: peel-off, integration, and bundling.

An existing SOE may peel off part of its operating assets to form a new independent corporation, a process that can be termedincomplete restructuring. The existing SOE becomes the parent of the new independent corporation and retains ownership of allof the peeled-off assets. Peel-off restructuring differs from the typical carve-out or spin-off. It differs from the typical carve-out inthat the parent company sells no existing assets to other investors, and hence there is no cash flow effect on the parent. It differsfrom a typical spin-off in that the newly independent corporation gains new investors through issuing new shares in the IPOprocess. As Aharony, Lee, and Wong (2000) point out, to render the new corporation more marketable and to attract publicinvestors, parent companies have strong incentives to peel off only their most profitable business units for public offering andretain the nonproductive and unprofitable units. Another important incentive arises from the strict IPO quota system establishedby the Chinese government (Aharony et al., 2010). Prior to 1999, the total annual number of IPOs was subject to a quota system,meaning that the central government set a quota for the entire capital value of shares to be issued each year. This total amountwas then allocated among local governments, which in turn were directed to identify key industries and nominate worthycompanies for listing on the local stock exchanges. Thus, parent companies also had the incentive to strengthen the to-be-listedcompanies by peeling off the higher quality assets. Although this quota system was eliminated in 1999, the first aforementionedincentive (Aharony et al., 2000) still exists.

Such incomplete peel-off restructuring leaves most of the financial and social burden with the remaining parent company,which may reduce that burden by improving the operating efficiency of its remaining assets. Another option is to reclaim thesuperior assets injected into the listed subsidiary during the restructuring process. One feasible way of doing so is to exchangelower quality assets for higher quality assets.5 In addition, a sound financial condition in the listed subsidiary serves to exaggeratethe tunneling incentives of its controlling shareholder (Peng et al., 2011). This discussion leads us to the following hypothesis,which is stated in alternative form.

Hypothesis 1. Listed companies produced by incomplete restructuring and in sound financial condition are more likely toexchange higher quality assets for lower quality assets (i.e., the tunneling hypothesis).

5 Aharony et al. (2010) also find that Chinese parent companies expropriate their listed affiliates through the non-repayment of corporate loans.

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3.2. Influence of bright-line regulations on firm listings

We consider two situations in which parent companies have the incentive to prop up their listed affiliates by injecting higherquality assets to replace lower quality assets. The former assets may come from the parent companies themselves or from othercompanies. Although Chen and Yuan (2004) show that regulation based on accounting numbers, such as ROE, triggersopportunistic earnings management, the China Securities Regulatory Commission (CSRC) is notable for its use of bright-lineregulations to monitor firm listings.6

The first situation involves the incentive to avoid reporting a loss by the listed company. According to guidelines introduced bythe CSRC in 1998, a listed firm will be designated a special treatment (ST) firm if it reports a net loss for two consecutive years. AnST firm's semi-annual report must be audited. If it reports a net loss for three consecutive years, it is suspended from normaltrading, and investors can trade only under a particular transfer (PT) arrangement. If a PT firm has not become profitable by thefollowing year, it is completely delisted. Although the literature considers reporting loss avoidance to be an important incentivefor earnings management (Degeorge, Patel, & Zeckhauser, 1999), China's institutional setting gives managers even strongerincentive to engage in such management to avoid government scrutiny and delisting.

The other situation in which a parent company has a strong incentive to engage in asset exchange to prop up its listed affiliate isduring rights offerings (ROs) and seasoned equity offerings (SEOs). In the 1990s, listed companieswere able to issue additional sharesonly through preemptive rights offered to their existing shareholders. Because of the lack of any other means for listed companies toraise capital and the insatiable demand for stocks from the investing public in China in the early 1990s, ROs were excessively abusedby listed companies (Chen & Yuan, 2004). To curb this excessive activity, the CSRC adopted a minimum ROE of 10% (6% after 2001).7

Since 2002, a similar threshold (10% of ROE) has regulated SEOs. As ROs and SEOs are the primary channels by which Chinese listedcompanies raise capital, qualification for such offerings is an important objective for parent companies. Li and Zhou (2005) also arguethat listed companies are better able than unlisted companies to relieve unemployment problems and enhance the infrastructuredevelopment of the ministries to which they belong or the regions in which they operate. Thus, both the central and localgovernments that act as the ultimate controlling owners of these firms have strong incentives to help them to maintain their listingstatus and qualify to raisemore funds.We thus predict parent companies to have strong incentives to replace listed companies' lowerquality assets with higher quality assets in the two aforementioned situations, as summarized in the following hypothesis.

Hypothesis 2. Listed companies with the intention to avoid reporting a loss and to raise additional capital are more likely toexchange lower quality assets for higher quality assets (i.e., the propping hypothesis).

If the market were efficient, then investors would be able to recognize the tunneling and propping behavior involved in assetexchanges. Cheung, Rau, and Stouraitis (2006) find that listed firms in Hong Kong announcing asset sales that are a priori likely toresult in the expropriation of minority shareholders earn significantly negative abnormal returns in the days following theexchange announcement. Hence, we expect investors in Shanghai and Shenzhen to react negatively to asset exchanges arisingfrom the tunneling incentive and positively to those arising from the propping incentive if the Chinese market is as efficient asHong Kong's. We summarize these predictions in the following hypothesis.

Hypothesis 3. Investors react negatively to asset exchanges motivated by the tunneling incentive and positively to thosemotivated by the propping incentive surrounding the asset exchange announcement date.

Given the differing quality of the exchanged assets, we predict differences in post-exchange firm performance, as summarizedin the following hypothesis.

Hypothesis 4. Listed companies that exchange higher (lower) quality assets for lower (higher) quality assets experience aperformance decline (improvement) in the post-exchange period.

4. Asset exchange data description

We hand-collected all 305 public announcements of asset exchanges between parent companies and other parties released by229 listed companies on the Shanghai and Shenzhen Stock Exchanges during the 2000–2006 period. Our sample period begins in2000 because there were very few asset exchanges (only five in total) prior to that year. We also hand-collected IPO restructuringdata from each company's IPO prospectus. Other data such as stock return and financial performance data were obtained from theChina Stock Market & Accounting Research (CSMAR) database.8

Table 1 presents the sample composition for each year from 2000 to 2006, classified by 10major industry categories (two-digitSIC code). The original industry classifications were first obtained from the CSRC, and we then reclassified the industries into 10categories based on Campbell (1996). As there are only three firms in the petroleum industries (SIC codes 13 and 29), we combine

6 Chen and Wang (2007) show that, in China, bright-line rules may serve to reduce adverse selection problems.7 Table 1 in Chen and Wang (2007) summarizes the regulations on ROs and SEOs in China.8 The CSMAR is a leading data vendor that provides financial accounting and stock price data on all listed companies in China. It also offers corporate

governance and mergers and acquisitions databases. The CSMAR database can be obtained from Wharton Research Data Services.

Table 1Sample composition of asset exchange firms.The table shows the sample composition by year and by industry, classified by ten major industries (two-digit SIC code). The sample consists of 305 assetexchanges by 229 listed Chinese firms on the Shanghai Stock Exchange and Shenzhen Stock Exchange from 2000 to 2006. The industry classification is based onCampbell (1996). The equivalent two-digit SIC codes are: food and tobacco (1, 2, 9, 20, 21, 54); basic industries including petroleum (10, 12, 13, 14, 24, 26, 28, 29,33); construction (15, 16, 17, 32, 52); textiles and trade (22, 23, 31, 51, 53, 56, 59); consumer durables (25, 30, 36, 37, 39, 50, 55, 57); capital goods (34, 35, 38);transportation (40, 41, 42, 44, 45, 47); utility (46, 48, 49); services (60–69 for financial services, 72, 73 75, 76, 80, 82, 87, 89); and there are no specific SIC code forconglomerate. As the number of firms in the petroleum industries (SIC code 13, 29) is small (only one exchange in 2001, 2005 and 2006 respectively), wecombine them with the basic industries.

Industry Year

2000 2001 2002 2003 2004 2005 2006 Subtotal Percentage

Food and tobacco 2 0 2 1 7 3 2 17 5.6%Basic industries including petroleum 1 2 4 6 16 2 7 38 12.5%Construction 1 0 3 3 2 3 4 16 5.2%Textiles and trade 0 5 0 3 11 3 2 24 7.9%Consumer durables 1 2 7 15 11 6 12 54 17.7%Capital goods 1 2 5 14 7 8 11 48 15.7%Transportation 1 2 0 6 5 0 0 14 4.6%Utility 1 2 1 7 1 1 6 19 6.2%Services including financial services 1 3 4 5 5 5 2 25 8.2%Conglomerate 1 8 5 19 3 7 7 50 16.4%Entire sample 10 26 31 79 68 38 53 305 100%

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themwith basic industries. Financial services industries (SIC codes 60–69) are included in the services category. As Table 1 shows,four industry groups, the basic (including petroleum), consumer durables, capital goods, and conglomerate categories, had ahigher proportion of asset exchanges during the sample period (ranging from 38 to 54 cases, or 12.5% to 17.7%) than theremaining industry categories (ranging from 14 to 25 cases, or 4.6% to 8.2%). The table also shows that, in general, there werefewer asset exchanges in the early period than in the later period (the fewest cases, 10, were in 2000, and the most, 79, in 2003).

We present the types of exchange parties and types of assets exchanged in Table 2. Exchange parties are generally classified asrelated parties and non-related parties. Listed companies disclose this classification in their asset exchange announcements, andthe definition of related parties should follow Chinese Accounting Standard 36 (CAS 36, issued in 2006), which is the same asInternational Accounting Standard 24 (IAS 24, revised in 2011). Related parties consist of the parent companies (i.e., the largestcorporate shareholders) of listed companies, other large corporate shareholders, sister companies under common control withthe listed companies, and others.9 As shown in Panel A of Table 2, the majority of asset exchanges are between listed companiesand their parent companies (219 cases, or 71.8% of the sample). Non-related parties account for just 49 cases (16.1%). However,non-related parties may be de facto related to listed companies because they are under the common control of the government,but they are not treated as related parties by CAS 36 or IAS 24. Because all of the non-related parties in our sample are non-listedcompanies, we cannot identify whether they are de facto related to listed companies.10 As shown in Panel B of Table 2, five typesof assets were exchanged in our sample: asset groups, equity shares, receivables, PPE, land and other tangible assets, andintangibles. An asset group is a group of assets and liabilities such as a production line and an operating unit. Equity shares refer toa company's equity ownership in a separate entity.11 Receivables are exchanged assets that consist primarily of receivables. PPE,land, and other tangible assets include PPE, land, or other tangible assets such as inventories or a combination thereof. Intangiblesrefer to exchanged assets that primarily comprise intangible assets. Panel B shows that, of the assets surrendered, equity sharesand receivables are the most popular types (accounting for 113 [37.0%] and 110 [36.1%], respectively). In contrast, more than halfof all acquired assets are equity shares (176, or 57.7%), with about one quarter PPE, land, and other tangible assets (73, or 23.9%).

Table 3 reports the end-of-fiscal-year summary statistics for firms that exchanged assets in the subsequent year (thus, theperiod for the statistics reported is 1999–2005, i.e., one year ahead of the sample period). For comparison, we also present thestatistics for all listed firms with sufficient data during the same period. As Table 3 shows, the sample firms tend to have a lowerROA. The mean ROA for the sample firms and all firms is 0.8% and 3.4%, respectively. The t-statistic for the test of mean differencesis 5.80, with a statistical significance level of 1%. We observe the same pattern for the other profitability and performancemeasures, such as stock returns (SRET), cash holdings (CASH), and sales growth (SALESG), which suggests that the managers offirms with a low ROA, low stock returns, low cash holdings, and low sales growth are more likely to engage in asset exchangeswith their parent companies. This finding is consistent with Warusawitharana's (2008) observation concerning corporate assetsales in the United States. Table 3 also shows that the other statistics of our sample firms, such as firm size (SIZE), market-to-bookratio (MTB), LEVERAGE, and PPE growth (PPEG), are similar to those of average listed firms. The deal value reported in the third tolast row reveals the average andmedian values of assets exchanged to be about RMB270 million and RMB98 million, respectively.

9 The “others” category is disclosed as “other related parties” by the listed company, and we do not know their specific relationship with it.10 Our research results remain qualitatively the same when we exclude the 49 assets exchanges with non-related parties.11 The split share structure reform in 2005, which was designed to convert non-tradable shares to tradable shares, should have no effect on equity shares in oursample because they refer to the company's equity ownership in a separate entity, rather than the company's own stocks.

Table 2Description of asset exchanges.

Panel A: Type of exchange parties

The panel shows the number of exchanges by the type of exchange parties. Exchange parties are generally classified as related parties and non-related parties.Related parties consist of parent companies (or the largest corporate shareholders) of listed companies, other large corporate shareholders, sister companieswhich are under the common control with listed companies, and others. Non-related parties have no relation with listed companies. The type of exchangeparties is disclosed by listed companies in their asset exchange announcements.

Related parties Non-related parties Total

Parent companies Other shareholders Sister companies Others

Number of exchanges 219 (71.8%) 17 (5.6%) 8 (2.6%) 12 (3.9%) 49 (16.1%) 305 (100%)

Panel B: Type of asset exchanged

The panel shows the number of exchanges by the type of asset exchanged. Asset group means that the exchanged asset includes a group of assets andliabilities such as a product line and an operating unit. Equity shares mean that the exchanged asset is the equity ownership in another entity. Receivablesmean that the exchanged asset mainly consists of receivables. PPE, land and other tangible assets mean that the exchanged asset is either PPE (property, plantand equipment), or land, or other tangible assets such as inventories, or a combination of tangible assets. Intangibles mean that the exchanged asset mainlyconsists of intangible assets.

Asset group Equity shares Receivables PPE, land and othertangible assets

Intangibles Total

Assets surrendered 32 (10.5%) 113 (37.0%) 110 (36.1%) 43 (14.1%) 7 (2.3%) 305 (100%)Assets acquired 15 (4.9%) 176 (57.7%) 11 (3.6%) 73 (23.9%) 30 (9.9%) 305 (100%)

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5. Empirical results

Our empirical implementation tested the four hypotheses derived in Section 3. In this section, we discuss our research designfor testing these hypotheses and present our empirical results.

5.1. Abnormal asset valuation

During the asset exchange process, firms need to hire professional valuers to value both the surrendered and acquired assets. In anarm's length transaction, the valuation of the two should be equivalent and approximate the fair value of the assets. However, inChina, professional valuers are not properly regulated and are typically not independent. If a manager wants to exchange lowerquality assets for higher quality assets, he or she can colludewith professional valuers to opportunisticallymanipulate the valuation ofthe two to match. We use the following numeric example to illustrate the concept of abnormal asset valuation.

Table 3Sample descriptive characteristics.The table reports end of fiscal year summary statistics for listed firms that exchange assets during the next year. For comparison, it also reports the statistics for alllisted firms during the same period (1999–2005, i.e., one year ahead of the sample period). Return on assets (ROA) is the operating income before depreciationscaled by book value of total assets at the beginning of the fiscal year. SIZE measures the nature logarithm of book value of total assets at the end of fiscal year.Stock return (SRET) is computed over the fiscal year. Market-to-book (MTB) ratio of equity is computed as the ratio between the market value of equity and thebook value of equity at the end of the fiscal year. CASH denotes cash and short-term investments scaled by the book value of assets at the end of fiscal year.LEVERAGE denotes book value of debt scaled by book value of total assets at the end of the fiscal year. Sales growth (SALESG) and PPE growth (PPEG) measuregrowth in net sales and net plant, property, and equipment, respectively, over the previous fiscal year. All the above statistics are winsorized at the 1st and 99thpercentiles. The deal value is reported in millions of Chinese Renminbi (RMB). The assets-out value and assets-in value are the valuation of surrendered andacquired assets in millions of RMB, respectively. The N denotes the number of observations and Std denotes the standard deviation. The last column reportsstatistics for test of differences in means and medians of the two groups. Bold statistics denote statistical significance at the 1% level.

All listed firms Sample firms Test of difference

N Mean Median Std. N Mean Median Std. t-Stat (z-stat)

ROA 6917 0.034 0.038 0.073 305 0.008 0.014 0.059 5.80 (8.22)ROE 6917 0.057 0.073 0.180 305 −0.011 0.026 0.217 5.40 (7.85)ROS 6908 0.048 0.077 0.336 305 −0.012 0.035 0.258 3.12 (6.51)SIZE 6917 21.004 20.929 0.885 305 20.927 20.882 0.819 1.42 (1.20)SRET 6921 −0.010 −0.107 0.492 305 −0.142 −0.150 0.314 4.10 (3.27)MTB 6750 4.150 3.150 3.812 305 4.227 3.090 3.451 −0.42 (−0.45)CASH 6917 0.158 0.129 0.119 305 0.135 0.113 0.101 3.04 (2.75)LEVERAGE 6917 0.078 0.040 0.099 305 0.072 0.042 0.087 0.82 (0.19)SALESG 6908 0.215 0.114 0.587 305 0.096 0.066 0.515 3.20 (3.40)PPEG 6915 0.178 0.044 0.496 305 0.158 0.011 0.596 1.04 (2.64)Deal value – – – – 305 270.145 97.764 1,272.466 –

Assets-Out value – – – – 305 251.991 87.939 1,264.481 –

Assets-In value – – – – 305 256.471 95.201 1,267.095 –

212 F. Lou et al. / International Review of Economics and Finance 31 (2014) 205–217

Suppose asset A's book value (BV) is $100 and fair value (FV) is $150, its market to book ratio (FV/BV) is 1.5. Asset B's bookvalue is $100 and fair value is $200, its market to book ratio is 2.0. Since market to book ratio is a good proxy of investmentopportunities and profitability, we infer that asset A has lower quality than asset B. If a listed company wants to exchange A for Bwithout paying extra compensation, the company may value asset A at $200 (the transaction price which is denoted as Phereafter). The difference between transaction price ($200) and fair value ($150) is the abnormal valuation ($50). For asset B,assume the company fairly values it at $200 and then its abnormal valuation is $0. Thus the company can exchange A for Bwithout any additional compensation. The difference in abnormal valuation of A and B is positive ($50). The example illustrates that ahigher abnormal valuation difference between surrendered asset (A) and acquired asset (B) indicates that the quality of surrenderedasset (A) is lower than the quality of acquired asset (B). Intuitively, the abnormal valuation differencemay indicate whether the bookvalue of the exchanged assets is high or low relative to the transaction price, which indicates whether the assets are of high or lowquality and whether the company is “stretching” the valuation in order to accomplish its tunneling or propping aims.

The challenge is that the fair value of exchanged assets is unobservable. Based on the information disclosed in the assetexchange announcements, we employ the following model to determine the fair value of the assets. The abnormal valuation ofthe asset is the estimated residual of the model.

12 Alteconsiste

OUTVAL or INVALð Þ ¼ a1OUTBOOK or INBOOKð Þ þ a2OUTFIX or INFIXð Þ þ a3OUTINT or ININTð Þ þ a4RPTþ a5AUDIT

þ a6INDDIR þ a7FINCONþ industry dummiesþ eð1Þ

We run separate ordinary least square (OLS) regressions based on this model (without intercept) for surrendered and acquiredassets. OUTVAL (INVAL) is the valuation of the surrendered (acquired) assets disclosed in the asset exchange announcements of listedcompanies. Book value is an important determinant of asset valuation, and hence the model includes OUTBOOK (INBOOK), which isthe book value of the surrendered (acquired) assets. OUTFIX (INFIX) takes a value of one if the surrendered (acquired) assets includefixed assets, and zero otherwise. Fixed assets are carried at their historical cost under the current CAS, and hence fixed assets aresubject to a higher valuation. OUTINT (ININT) takes a value of one if the surrendered (acquired) assets include intangible assets, andzero otherwise. We include this variable because intangible assets are more difficult to revalue. RPT takes a value of one if theexchange party is a related party of the sample firm, and zero otherwise. We believe that it is easier for managers to collude with arelated party in manipulating asset valuation. AUDIT takes a value of one if the exchange transaction is audited, and zero otherwise.INDDIR takes a value of one if the exchange transaction is supported by independent directors, and zero otherwise. FINCON takes avalue of one if the exchange announcement is accompanied by an independent financial consulting report. The three aforementionedvariables are introduced to control for themonitoring effects of auditors, independent directors, and professional consultants on assetvaluation. Finally, we introduce nine industry dummies in the model.

Panel A of Table 4 reports the OLS regression results. As expected, the book value of assets (OUTBOOK and INBOOK) issignificantly positively associated with the valuation of both surrendered and acquired assets. In general, the other independentvariables have no significant effects on asset valuation. The adjusted R-squares for both regressions are above 90%, indicating thatthe model has very high predictive power. The abnormal asset valuation rate is the residual obtained from the regressions inPanel A scaled by the book value of exchanged assets. We then take the difference between the abnormal surrendered assetvaluation rate and the abnormal acquired asset valuation rate to infer the quality of the exchanged assets. As previously noted, agreater abnormal valuation difference indicates that the surrendered assets are lower in quality than the acquired assets, which isindicative of possible propping behavior by the exchange parties. Panel B of Table 4 presents the descriptive statistics of theabnormal valuation rates. We find that of the surrendered assets to be significantly higher than that of the acquired assets. Themean and median abnormal valuation rate differences are 13.5% and 10.8%, respectively, both significant at the 1% level. We nowpresent the results of further empirical tests to explain this asymmetry in the valuation rates.

5.2. Tunneling and propping incentives for asset exchanges

Hypotheses 1 and 2 posit that firms with tunneling and propping incentives behave differently when exchanging assets. Weemploy the following model to investigate the two types of incentive.

ABVALDIF ¼ b0 þ b1TUNNELINGþ b2PROPPINGþ b3ROAþ b4SIZEþ b5SRETþ b6MTBþ b7CASHþ b8LEVERAGE

þ b9SALESGþ b10PPEGþ industry dummiesþ year dummiesþ eð2Þ

The dependent variable, ABVALDIF, is the abnormal valuation difference between surrendered and acquired assets obtainedfrom the regressions in model (1) and reported in Table 4. The independent variables are as follows.

(1) TUNNELING: This variable takes a value of one if the listed firm has met both of the following two characteristics, and zerootherwise: (i) the listed firmwaspeeled-off froman existing SOEduring its IPOprocess, and (ii) its previous ROEwas over 10%. Aswe discussed, firmswith incomplete restructuring and healthy financial condition aremore likely to have tunneling incentives.12

rnatively we define TUNNELING as “ROE N 10%” only or “ROE N 10% and firm does not raise capital” and the results remain qualitatively the same. This isnt with the findings in Peng et al. (2011).

Table 4Abnormal asset valuation.

Panel A: Asset valuation prediction models

This panel presents the ordinary least square (OLS) regression results using exchanged asset valuation as dependent variable and OUTBOOK (or INBOOK),OUTFIX (or INFIX), OUTINT (or ININT), RPT, AUDIT, INDDIR, FINCON and the industry dummies (see Table 1 for SIC equivalence) as independent variables.OUTBOOK (or INBOOK) is the book values of surrendered (or acquired) assets. OUTFIX (or INFIX) takes value of one if surrendered (or acquired) assetsinclude fixed assets and zero otherwise. OUTINT (or ININT) takes value of one if surrendered (or acquired) assets include intangible assets and zerootherwise. RPT takes value of one if the exchanged party is a related-party of sample firm and zero otherwise. AUDIT takes value of one if the exchangetransaction is audited and zero otherwise. INDDIR takes value of one if the exchange transaction is supported by independent directors and zero otherwise.FINCON takes value of one if the exchange announcement is accompanied with an independent financial consulting report. ⁎⁎ and ⁎⁎⁎ denote statisticalsignificance at the 5% and 1% level, respectively.

Asset valuation (surrendered) Asset valuation (acquired)

OUTBOOK 0.979*** –

(46.86)OUTFIX 619.965 –

(0.42)OUTINT 1,884.030 –

(0.52)INBOOK – 1.010***

(140.64)INFIX – 2,289.565

(0.94)ININT – −1,125.477

(−0.46)RPT 2,800.283 910.464

(1.38) (0.35)AUDIT 734.405 3,137.953

(0.43) (1.47)INDDIR −1,815.391 −2,138.486

(−1.11) (−1.04)FINCON 1,553.945 2,881.994

(0.99) (1.48)Food and tobacco −165.592 4,466.724

(−0.05) (0.94)Basic industries including petroleum −1,777.515 −4,257.482

(−0.61) (−1.10)Construction −2,841.194 649.546

(−0.76) (0.13)Textiles and trade −2,571.573 −3,595.501

(−0.79) (−0.84)Industry Dummies Consumer durables −976.891 1,096.899

(−0.36) (0.30)Capital goods −1,972.734 −3,919.787

(−0.69) (−1.06)Transportation 8,279.583** 7,547.296

(2.08) (1.50)Services including financial services −2,887.399 −4,325.598

(−0.65) (−0.77)Conglomerate −2,005.478 −2,749.125

(−0.79) (−0.82)N 305 305Adjusted R2 0.912 0.986

Panel B: Abnormal asset valuation descriptive statistics

This panel reports the abnormal asset valuation for surrendered and acquired assets. The residuals obtained from the regressions in Panel A scaled by bookvalue of exchanged assets are used to proxy for abnormal assets valuation. The difference between the abnormal surrendered asset valuation and abnormalacquired assets valuation is used as a proxy for the quality of exchanged assets in later analyses. The abnormal asset valuation measures are winsorized at the1st and 99th percentiles. *** denotes statistical significance at the 1% level.

Min 25th Median 75th Max Mean Std

Abnormal asset valuation (surrendered) −3.852 −0.252 −0.049 0.069 3.119 −0.085 0.853Abnormal asset valuation (acquired) −7.278 −0.658 −0.141 0.134 7.220 −0.253 1.627Abnormal valuation difference (surrendered–acquired) −7.179 −0.120 0.108*** 0.469 4.635 0.135*** 1.415

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(2) PROPPING: This variable takes a value of one if the listed firm has at least one of the following characteristics, and zerootherwise: (i) it had a previous net loss; (ii) it has a current net loss; (iii) its current ROE is lower than 1.5%; (iv) it is raisingadditional capital in the current year; and (v) it intends to raise additional capital in the next two years but does notcurrently fulfill the ROE threshold (either 10% or 6%). These characteristics are indications of propping incentives.

(3) ROE, ROA, SIZE, SRET, MTB, CASH, LEVERAGE, SALESG, PPEG: These variables are as defined in Table 3.(4) Nine industry dummies and six year dummies.

214 F. Lou et al. / International Review of Economics and Finance 31 (2014) 205–217

Table 5 presents the OLS regression results of the foregoing model. Regression 1 includes only the tunneling incentiveindicator (TUNNELING). The estimated coefficient on TUNNELING is −0.389, which is statistically significant at the 5% level. Asdiscussed in our hypothesis development, a lower valuation difference implies that the quality of the surrendered assets is higherthan that of the acquired assets. Thus, these results show that firms that have undergone incomplete restructuring and are in soundfinancial condition tend to exchange higher quality assets for lower quality assets (i.e., display a lower valuation difference), which isconsistent with the tunneling hypothesis (Hypothesis 1). Regression 2 includes only the propping indicator (PROPPING). Theestimated coefficient is 0.583, which is statistically significant at the 5% level. Thus, the results indicate that firms with an intention toavoid a loss or raise additional capital tend to exchange lower quality assets for higher quality assets, which is consistent with thepropping hypothesis (Hypothesis 2). Regression 3 includes both TUNNELING and PROPPING. We find the estimated coefficients ofboth TUNNELING and PROPPING to remain significant at the 5% level.

Of the control variables, only ROA and CASH have a significant effect on the valuation difference. In all three regressions, theestimated coefficients of ROA are negative and significant at the 5% level. Hence, highly profitable firms tend to exchange profitableassets for poorer quality assets, which is consistentwith tunneling behavior. When listed companies aremore profitable, their parentcompanies have more excuses to tunnel assets back. The same logic holds for CASH: the more cash the company holds, the moreprofits the parent company is able to tunnel. None of the other control variables has a significant influence on asset exchangevaluation. The adjusted R-squares of the regressions range from 8.3% to 11.0%.

As a robustness test, we use the observable transaction price (P) and book value (BV) to proxy for the abnormal valuation. InRegression 4 in Table 5, ABVALDIF is defined as the difference in book value of acquired and surrendered assets, i.e., (BV(acquired) − BV (surrendered)) / BV (surrendered). In Regression 5, we define ABVALDIF as the difference in price-to-bookratio, i.e., P/BV (surrendered) − P/BV (acquired). Unlike the measure of abnormal valuation difference derived from regression

Table 5Tunneling and propping incentives of asset exchange.This table presents the OLS regression results using abnormal valuation difference (ABVALDIF) as dependent variable and TUNNELING, PROPPING, ROA, SIZE,SRET, MTB, CASH, LEVERAGE, SALESG, PPEG, and the industry and year dummies as independent variables. In the first three regressions, ABVALDIF is calculatedbased on regression model (1) reported in Table 4. In Regressions 4, ABVALDIF is defined as (BV (acquired) − BV (surrendered)) / BV (surrendered). InRegression 5, ABVALDIF is defined as P/BV (surrendered) − P/BV (acquired). TUNNELING takes value of one if the firm is incompletely restructured during IPOprocess and the firm's previous ROE is over 10%, and zero otherwise. PROPPING equals one when the firm has the intention to avoid losses (it has previous one ortwo years' losses) or raise additional capital (either rights offering or seasonal equity offering) in the following year and zero otherwise. Return on assets (ROA) isthe operating income before depreciation scaled by book assets at the beginning of the fiscal year. SIZE measures the log book value of total assets in millions ofChinese Renminbi (RMB). Stock return (SRET) is computed over the fiscal year. Market-to-book (MTB) ratio is computed as the ratio between the market value ofequity and the book value of equity. CASH denotes cash and short-term investments scaled by the book value of assets. LEVERAGE denotes book value of debtscaled by book value of debt plus equity. Sales growth (SALESG) and PPE growth (PPEG) measure growth in net sales and net plant, property, and equipment,respectively, over the previous fiscal year.

Regression 1 Regression 2 Regression 3 Regression 4 Regression 5

ABVALDIF(based on model 1)

ABVALDIF(based on model 1)

ABVALDIF(based on model 1)

ABVALDIF(based on BV)

ABVALDIF(based on P/BV)

TUNNELING −0.389⁎⁎ −0.262⁎⁎ −0.179⁎ −0.186⁎

(−2.38) (−2.03) (−1.81) (−1.90)PROPPING 0.583⁎⁎ 0.495⁎⁎ 0.342⁎ 0.296⁎

(2.28) (2.06) (1.92) (1.70)ROA −4.354⁎⁎ −5.321⁎⁎ −5.376⁎⁎ −4.685⁎⁎ −5.368⁎⁎

(−2.15) (−2.48) (−2.38) (−2.26) (−2.29)SIZE −0.040 −0.119 −0.049 −0.112 −0.059

(−0.29) (−0.82) (−0.37) (−0.83) (−0.39)SRET 0.441 0.245 0.331 0.224 0.332

(0.95) (0.53) (0.71) (0.51) (0.72)MTB 0.013 0.012 0.013 0.009 0.013

(0.42) (0.40) (0.39) (0.38) (0.38)CASH −1.686⁎ −2.158⁎⁎ −1.945⁎⁎ −1.757⁎ −1.625⁎

(−1.70) (−2.10) (−1.96) (−1.80) (−1.86)LEVERAGE −0.499 −0.419 −0.615 −0.345 −0.627

(−0.47) (−0.40) (−0.56) (−0.39) (−0.60)SALESG −0.053 −0.125 −0.131 −0.139 −0.109

(−0.30) (−0.66) (−0.65) (−0.67) (−0.63)PPEG 0.226 0.172 0.217 0.189 0.216

(1.25) (0.95) (1.12) (0.97) (1.12)Industry dummies Included Included Included Included IncludedYear dummies Included Included Included Included IncludedIntercept 0.924 2.574 1.372 2.645 2.138

(0.31) (0.93) (0.48) (0.91) (0.59)N 305 305 305 305 305Adjusted R2 0.083 0.093 0.110 0.072 0.071

⁎ Denote statistical significance at the 10% level.⁎⁎ Denote statistical significance at the 5% level.

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model (1) in Table 4, these two measures do not factor in any other factors in model (1) which may affect the valuation ofexchange assets. As expected, the results reported in Regression 4 and Regression 5 are weaker but still significant at 10% level. Itindicates that regression model (1) for estimating abnormal valuation has incremental explanation power.

5.3. Market reaction to asset exchanges

Hypothesis 3 states that if listed firms exchange higher quality assets for lower quality assets, then investors should reactnegatively, and vice versa. To test the market reaction to such exchanges, we employ the event-study methodology summarizedby Campbell, Lo, and MacKinlay (1997). The event date (day zero) is defined as the date the firm announces an asset exchange.For each company, we use an event period of 300 days (starting with day −279 and ending with day +20 relative to dayzero). The first 259 days of this period (−279 through −21) are designated the “estimation period,” and the following 41(−20 through +20) the “event period.” We run OLS regressions using a security's daily return as the dependent variable andthe market daily return as the independent variable. The abnormal daily return is obtained from the regression model's residual.

Table 6 reports the 3-, 5-, and 11-day cumulative abnormal returns (CARs) surrounding asset exchange announcements. Wealso divide sample firms by the tunneling and propping incentives. As Table 6 shows, all of the average CARs are positive. Forexample, the average 3-day window CAR is 0.706%, and the average 11-day window CAR is 0.947%. However, there are nosignificant differences between the subgroups for any of the window periods. For example, for the 3-day window, the averageCAR for firms with tunneling incentives is 0.856% and that for firms without is 0.731%, but the difference is insignificant. Otherwindows, such as 1-, 2-, 5-, and 10-day windows, produce similar results to those in Table 6.

We also regress the various CARs on the abnormal valuation difference (ABVALDIF) and other factors such as return on assets,firm size, market-to-book ratio, cash holding, leverage ratio and industry effect. However, we do not find significant associationbetween CAR and ABVALDIF. We thus conclude that investors in China are unable to recognize the various incentives driving assetexchanges, which casts doubt on the efficiency of China's capital markets. These findings also suggest that the mainland Chinesemarket is less efficient than Hong Kong's, where Cheung et al. (2006) found investors able to see through tunneling behavior.

5.4. Post-exchange firm performance

Although investors cannot see the profitability of exchanged assets in the short term, we expect that such assets will affectfirm performance in the long term. Wemeasure post-exchange performance by both stock returns and financial performance. Weemploy the following model to test our last hypothesis.

Table 6CumulaThis tabregressinto twmean d

ThreeFull sTUNNTUNNPROPPROP

Five-dFull sTUNNTUNNPROPPROP

ElevenFull sTUNNTUNNPROPPROP

BHAR or AROAð Þ ¼ c0 þ c1ABVALDIFþ c2ROAþ c3SIZEþ c4MTBþ c5CASHþ c6LEVERAGEþ industry dummiesþ e ð3Þ

tive abnormal returns surrounding asset exchange announcements.le reports the 3-day, 5-day and 11-day cumulative abnormal returns surrounding asset exchange announcements. The normal return is predicted by aion using security's daily return on market daily return. Abnormal return is then obtained from the residual of the regression model. The sample is dividedo groups based on either TUNNELING or PROPPING. TUNNELING and PROPPING are defined in Table 5. The last column reports the t-statistics for test ofifference and z-statistics for test of median difference.

Number of events Average CAR (%) Median CAR (%) t-Statistics (z-statistics)

-day window CAR (−1, +1)ample 305 0.706 0.052ELING = 1 31 0.856 0.024ELING = 0 274 0.731 0.101 0.24 (−0.57)PING = 1 125 1.021 0.072PING = 0 180 0.386 −0.003 1.19 (0.57)

ay window CAR (−3, +1)ample 305 0.829 0.151ELING = 1 31 0.901 0.123ELING = 0 274 0.867 0.215 0.18 (−0.56)PING = 1 125 1.109 0.015PING = 0 180 0.645 0.143 0.71 (−0.53)

-day window CAR (−9, +1)ample 305 0.947 0.566ELING = 1 31 1.234 0.616ELING = 0 274 0.857 0.578 0.45 (0.58)PING = 1 125 0.855 0.121PING = 0 180 1.021 0.633 −0.21 (−0.46)

216 F. Lou et al. / International Review of Economics and Finance 31 (2014) 205–217

BHAR is the firm's 12-month (or 24-month) post-exchange buy-and-hold abnormal return starting one month after the assetexchange announcement month. AROA is 1-year (or 2-year) average ROA in the two years after the announcement year. Theindependent variables are as follows.

(1) ABVALDIF: the abnormal valuation difference between surrendered and acquired assets obtained from regression model (1).(2) ROA, SIZE, MTB, CASH, and LEVERAGE: defined in the same way as in Table 3, but calculated in the year of the asset

exchange in this regression. These variables are included to control for factors that may affect firm performance.

Table 7 reports the results of the foregoing regression model. In Regressions 1 and 2, when 12- and 24-month BHAR are usedas the dependent variables, the estimated coefficients on ABVALDIF are 0.039 and 0.051, with a significance level of 5%. Thepositive association between a valuation difference and long-term stock return performance indicates that firms outperform themarket if they exchange lower quality assets for higher quality assets with their parent companies, which is consistent withHypothesis 4. This result is intuitive because when higher quality assets are acquired, the firm earns more income and thusachieves better stock performance in the future. We find the same results in Regressions 3 and 4 when 1- and 2-year average ROA,respectively, are used as the dependent variable. These consistent results show that firms that exchange more (less) profitableassets for less (more) profitable assets will see poorer (improved) stock and financial performance in the long term.

In both regressions, ROA in the exchange year has significantly positive effects on firms' future performance. Firm size (SIZE)has a positive effect on future financial performance but not on stock performance. Consistent with the existing literature, MTB isnegatively associated with future stock performance but not with financial performance. Cash holdings (CASH) have a positiveassociation with future financial performance but not with stock performance. Finally, we find firm leverage (LEVERAGE) to haveno effect on either stock performance or financial performance. The adjusted R-squares are about 11% for the stock performanceregressions and 39% for the financial performance regressions.

6. Summary and conclusion

This paper examines the causes and consequences of a sample of asset exchanges carried out by listed companies in China. Ourdataset is unique because it is very rare for U.S. firms to engage in barter-type asset exchanges.

Contrary to the efficiency and financing hypotheses examined in our review of the asset sale and purchase literature,we identify two other reasons for asset exchanges in China: tunneling and propping incentives. When firms are restructured

Table 7The association between abnormal asset valuation difference and post-exchange firm performance.This table reports the results for regressions using post-exchange firm performance as dependent variables and ABVALDIF (based on residual frommodel 1), ROA,SIZE, MTB, CASH, LEVERAGE and industry dummies as independent variables. Post-exchange firm performance is measured by both stock returns and financialperformance. 12-Month (24-month) post-exchange BHAR is the firm's 12-month (24-month) post-exchange buy-and-hold abnormal return (BHAR) starting onemonth after the asset exchange announcement month. One-year post-exchange ROA is the firm's ROA (operating income scaled by beginning-of-year totalassets) in the fiscal year immediately after the asset exchange fiscal year. Average post-exchange ROA is calculated as the average of two years' ROA after the fiscalyear of asset exchange. The independent variable of ROA is the firm's ROA in the fiscal year of asset exchange. SIZE measures the log book value of total assets atthe end of exchange year. Market-to-book (MTB) ratio is computed as the ratio between the market value of equity and the book value of equity at the end ofexchange year. CASH denotes cash and short-term investments scaled by the book value of assets at the end of exchange year. LEVERAGE denotes book value ofdebt scaled by book value of total assets at the end of exchange year. t-Statistics are reported in parentheses.

Regression 1 Regression 2 Regression 3 Regression 4

Explanatory variables 12-Month post-exchangeBHAR

24-Month post-exchangeBHAR

One-year post-exchangeROA

Average two-year post-exchangeROA

Intercept 2.362⁎⁎ 2.532⁎⁎ −0.220⁎⁎ −0.214⁎⁎

(2.05) (2.27) (−2.26) (−2.14)ABVALDIF 0.039⁎⁎ 0.051⁎⁎ 0.016⁎⁎ 0.015⁎⁎

(2.01) (2.26) (2.11) (2.01)ROA 1.316⁎⁎ 1.325⁎⁎ 0.419⁎⁎⁎ 0.376⁎⁎⁎

(2.00) (1.98) (8.95) (8.47)SIZE 0.071 0.063 0.011⁎⁎ 0.010⁎⁎

(1.58) (1.56) (2.05) (2.03)MTB −0.057⁎⁎⁎ −0.056⁎⁎⁎ −0.000 −0.000

(−5.16) (−5.23) (−0.26) (−0.25)CASH 0.032 0.033 0.072⁎⁎ 0.082⁎⁎

(0.09) (0.09) (2.26) (2.37)LEVERAGE 0.319 (0.98) 0.409 (0.99) 0.029 (0.78) 0.028 (0.77)Industry dummies Included Included Included IncludedN 305 305 305 305Adjusted R2 0.113 0.115 0.393 0.387

⁎⁎ Denote statistical significance at the 5% level.⁎⁎⁎ Denote statistical significance at the 1% level.

217F. Lou et al. / International Review of Economics and Finance 31 (2014) 205–217

incompletely from existing enterprises and are in sound financial condition, they tend to exchange higher quality assets for lowerquality assets to help their unlisted parent companies. When the intention is to avoid a loss and raise additional capital, incontrast, the listed companies tend to exchange lower quality assets for higher quality assets with their parent companies. Wefind empirical evidence consistent with our hypotheses.

We further examine whether investors can recognize the different incentives for asset exchanges in the short term, and theresults suggest that they cannot, possibly because of the inefficiency of China's capital markets. In the long term, however, theasymmetry in asset valuation during the exchange does have an effect on firm performance. If higher quality assets are exchangedfor lower quality assets, firms tend to underperform in the long run, and vice versa.

Our results have a number of practical implications. For example, we reveal that there are additional investment risks for bothdomestic and foreign investors in China's capital markets. In addition, Chinese firms that are cross-listed on non-Chinese stockexchanges and engage in asset exchanges also pose added risk. According to our results, both domestic and foreign investors needto pay special attention to the restructuring history of listed firms and determine whether there is any intention to avoid a loss orraise additional capital when these firms exchange assets with their parent companies.

Like most papers, ours is not without limitations. For example, we examine only one special type of related party transaction,i.e., barter-type asset exchange. Hence, our tunneling and propping results are limited to this type of transaction. As transferpricing and other related party transactions are very popular in business groups, our results are unable to determine whetherthere is a net profit transfer between a parent company and a listed affiliate. Another limitation is that, owing to limitedinformation disclosure, we can infer the quality of exchanged assets only from abnormal asset valuation. The noise resulting fromour estimation of such valuation may have introduced bias.

Acknowledgments

We are particularly grateful to the Editor (Carl R. Chen) and two anonymous reviewers for their insightful and constructivesuggestions. We also appreciate the helpful comments from Kevin Chen, Herbert Lam, Stefan Trück, Changyun Wang, T. J. Wong,Chu Zhang and seminar participants at the 18th Annual Conference on PBFEAM, the EFM 2011 Symposium on Asian FinancialManagement in Beijing and the 2011 China International Conference on Finance in Wuhan. Wang acknowledges the researchgrant (08-C206-SMU-010) from the Office of Research, Singapore Management University and the Seedcorn Research Grant fromthe Institute of Singapore Chartered Accountants. Yuan acknowledges the support of the National Natural Science Foundation ofChina (NSFC No. 71272074).

References

Aharony, J., Lee, C. -W. J., & Wong, T. J. (2000). Financial packaging of IPO firms in China. Journal of Accounting Research, 38, 103–126.Aharony, J., Wang, J., & Yuan, H. (2010). Tunneling as an incentive for earnings management during the IPO process in China. Journal of Accounting and Public

Policy, 29, 1–26.Alexander, G. J., Benson, P. G., & Kampmeyer, J. K. (1984). Investigating the valuation effects of announcements of voluntary corporate selloffs. Journal of Finance,

39, 503–517.Asquith, P., Gertner, R., & Scharfstein, D. (1994). Anatomy of financial distress: An examination of junk bond issuers. Quarterly Journal of Economics, 109, 625–658.Brown, D. T., James, C. M., & Mooradian, R. M. (1994). Asset sales by financially distressed firms. Journal of Corporate Finance, 1, 233–257.Campbell, J. Y. (1996). Understanding risk and return. Journal of Political Economy, 104, 298–345.Campbell, J. Y., Lo, A., & MacKinlay, A. C. (1997). The econometrics of financial markets. Princeton, N.J.: Princeton University Press.Chan, K., Fung, H., & Thapa, S. (2007). China financial research: A review and synthesis. International Review of Economics and Finance, 16, 416–428.Chen, K., & Wang, J. (2007). Accounting-based regulation in emerging markets: The case of China's seasoned equity offerings. International Journal of Accounting,

42, 221–236.Chen, K., & Yuan, H. (2004). Earnings management and capital resource allocation: Evidence from China's accounting-based regulation of rights issues. The

Accounting Review, 79, 645–666.Cheung, Y. L., Rau, P. R., & Stouraitis, A. (2006). Tunneling, propping, and expropriation: Evidence from connected party transactions in Hong Kong. Journal of

Financial Economics, 82, 343–386.Degeorge, F., Patel, J., & Zeckhauser, R. (1999). Earnings management to exceed thresholds. Journal of Business, 72, 1–33.Friedman, E., Johnson, S., & Mitton, T. (2003). Propping and tunneling. Journal of Comparative Economics, 31, 732–750.Hite, G. L., Owers, J. E., & Rogers, R. C. (1987). The market for interfirm asset sales. Journal of Financial Economics, 18, 229–253.Jain, P. C. (1985). The effect of voluntary sell-off announcements on shareholder wealth. Journal of Finance, 40, 209–223.Jarrell, G. A. (1979). Pro-producer regulation and accounting for assets: The case of electric utilities. Journal of Accounting and Economics, 1, 93–116.John, K., & Ofek, E. (1995). Asset sales and increase in focus. Journal of Financial Economics, 37, 105–126.Johnson, S., La Porta, R., Lopez-de-Silanes, F., & Shleifer, A. (2000). Tunneling. American Economic Review, 90, 22–27.Lang, L. H. P., Poulsen, A. B., & Stulz, R. M. (1995). Asset sales firm performance and the agency costs of managerial discretion. Journal of Financial Economics, 37,

3–37.Li, H., & Zhou, L. A. (2005). Political turnover and economic performance: The disciplinary role of personnel control in China. Journal of Public Economics, 89,

1743–1762.Maksimovic, V., & Phillips, G. (2001). The market for corporate assets: Who engages in mergers and asset sales and are there efficiency gains? Journal of Finance,

56, 2019–2065.Maksimovic, V., & Phillips, G. (2002). Do conglomerates allocate resources efficiently? Journal of Finance, 57, 721–767.Peng, Q., Wei, K. C. J., & Yang, Z. (2011). Tunneling or propping: Evidence from connected transactions in China. Journal of Corporate Finance, 17, 306–325.Slovin, M. B., Sushka, M. E., & Poloncheck, J. A. (2005). Methods of payment in asset sales: Contracting with equity versus cash. Journal of Finance, 60, 2385–2407.Su, Z., Fung, H., Huang, D., & Shen, C. (2014). Cash dividends, expropriation, and political connections: Evidence from China. International Review of Economics and

Finance, 29, 260–272.Warusawitharana, M. (2008). Corporate asset purchases and sales: Theory and evidence. Journal of Financial Economics, 87, 471–497.Wei, Z., Xie, F., & Zhang, S. (2005). Ownership structure and firm value in China's privatized firms: 1991–2001. Journal of Financial and Quantitative Analysis, 40,

87–108.