Mutual fund ownership and foreign exchange risk in Chinese firms

J. Int. Financ. Markets Inst. Money 60 (2019) 169–192

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Journal of International Financial Markets, Institutions & Money j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / i n t fi n

Mutual fund ownership and foreign exchange risk in Chinese firms q Elaine Hutson a, Elaine Laing b, Min Ye c,⇑ a

Department of Banking and Finance, Monash Business School, Australia Trinity Business School, Trinity College Dublin, Dublin2, Ireland c Department of Finance, The School of Finance and Statistics, Hunan University, Changsha, China b

a r t i c l e

i n f o

Article history: Received 12 September 2017 Accepted 26 December 2018 Available online 28 December 2018 JEL classification: F31 G1 G3 Keywords: Mutual fund ownership Hedging Foreign exchange exposure China

a b s t r a c t We examine the effect of a potentially important external governance mechanism – mutual fund ownership – on the extent to which Chinese firms are exposed to exchange rate movements. Using a sample of 560 firms over the period 2005–2011, we show that greater mutual fund ownership is associated with lower exposure to the US dollar, and that this effect is greatest for firms with higher state ownership. We contend that these findings can be explained by mutual funds ensuring that Chinese firms limit their unhedged dollardenominated borrowing. This paper contributes to the literature on the benefits to Chinese firms’ shareholders of institutional ownership, and to the growing body of evidence on the favorable effects of good governance on firms’ risk management practices. Ó 2018 Elsevier B.V. All rights reserved.

1. Introduction Exposure to exchange rate movements is pervasive. All firms with expected future cash flows in foreign currencies are directly exposed, and most firms also bear indirect (or competitive) exposure. Foreign exchange risk is not confined to countries with floating exchange rate systems; firms tend to be more exposed in pegged than in floating exchange rate systems (Parsley and Popper, 2006; Ye et al., 2014). Managing foreign exchange risk is value-enhancing because it moderates cash flow volatility – mitigating underinvestment, reducing the likelihood of financial distress and expected financial distress costs, and lowering the cost of debt.1 Without the right monitoring and incentives, however, managers as agents of shareholders may fail to engage in optimal foreign exchange risk management. Several studies have shown that strong internal and external governance is associated with more effective hedging (Stulz, 1984; Smith and Stulz, 1985; Tufano, 1996; Graham and Rogers, 2002; Knopf et al., 2002; Borokhovich et al., 2004; Géczy et al., 2007; Allayannis et al., 2012; Lel, 2012; Kumar and Rabinovitch, 2013; Bartram, 2015) and lower foreign exchange exposure (Hutson and Stevenson, 2010).

q

This work was supported by the National Natural Science Foundation of China under Grant No. 71801087.

⇑ Corresponding author.

E-mail addresses: [email protected] (E. Hutson), [email protected] (E. Laing), [email protected] (M. Ye). Leland (1998); Stulz (1984), Smith and Stulz (1985), Froot et al. (1993), Allayannis and Weston (2001), Carter et al. (2006), Mackay and Moeller (2007), Disatnik et al. (2014), Campello et al. (2011). 1

https://doi.org/10.1016/j.intfin.2018.12.012 1042-4431/Ó 2018 Elsevier B.V. All rights reserved.

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Most studies of governance and risk management examine firms in developed countries with Anglo-American governance systems. China is a particularly interesting emerging market on which to focus because most of its listed firms are state-controlled, and Chinese state-owned enterprises (SOEs) tend to have weak governance structures (Xu and Wang, 1999; Sun and Tong, 2003; Cull and Xu, 2005; Zou and Adams, 2008). In this paper, we examine the effect of a potentially important external governance mechanism – mutual fund ownership – on the extent to which Chinese firms are exposed to exchange rate movements.2 Institutional investors provide critical monitoring and advisory functions (Shleifer and Vishny, 1986; Coffee, 1991; Jensen, 1993), and several studies have found that ownership by institutions is associated with a greater likelihood that firms hedge with currency derivatives (Géczy et al., 1997; Graham and Rogers, 2002; Tai et al., 2014). In China, mutual funds have a special role in listed firms. The Chinese authorities, since 2000, have encouraged mutual fund participation explicitly for the purposes of improving governance and assisting in protecting the interests of minority shareholders (Yuan et al., 2008; Firth et al., 2016). There is evidence that mutual funds in China provide a monitoring function, creating positive outcomes for firms (Yuan et al., 2008; Aggarwal et al., 2014; Wu et al., 2016). In this paper, we ask whether greater mutual fund participation in Chinese firms leads to reduced exposure to exchange rate movements. The Chinese authorities’ encouragement of mutual fund participation has in particular been driven by a desire to see improvements in governance at SOEs (Yuan et al., 2008; Firth et al., 2016). Chinese SOEs have access to financial support from the government (Yuan et al., 2008; Chen et al., 2016), and until recently were widely thought to operate under an implicit government guarantee against default (Fan et al., 2013; Jiang and Kim, 2015). As this perceived guarantee is likely to be associated with a reduced probability that the firm encounters financial distress, negative expected cash flow outcomes are truncated. This may be seen by SOE shareholders to obviate the need for hedging. Armed with this protection, SOE shareholders might prefer the greater upside volatility associated with not hedging. So might SOE managers, who are typically rewarded on the basis of profitability rather than firm value or stock price performance (Chen et al., 2006). Failing to hedge, however, involves considerable risk because the perceived implicit government guarantee might be withdrawn. The monitoring and advice provided by well-informed investors such as mutual funds, therefore, may be more critical for firms with higher levels of state shareholdings in ensuring that foreign exchange risks are optimally managed. To the extent that Chinese mutual funds do effectively monitor to mitigate exposure, a supplementary research question relates to whether mutual fund participation has a greater exchange rate risk-reduction effect for firms with a higher proportion of shares owned by the state. In addressing this question, we distinguish between shareholdings held directly by the state and those held by SOEs.3 Chen et al. (2009) make this distinction on the basis that managers in state-owned firms lack good management skills, and the state officials that oversee SOE managers often fail to properly monitor the firms under their control. In contrast, SOEs have the incentive and expertise to choose high-quality managers for the firms that they control, and to actively monitor and advise them (Chen et al., 2009). Using a data set comprising 560 Chinese firms (or 3048 firm years) with A-shares listed on the Shenzhen and Shanghai stock exchanges, we examine Chinese firms’ foreign exchange exposure over the period 2005–2011. In July 2005, the Chinese authorities abandoned the long-standing official dollar peg and moved to a de jure managed float against a basket of currencies. It soon became clear, however, that the Chinese renminbi (RMB) was being tightly controlled against the dollar rather than to a currency basket (see, for example, Frankel, 2009). Against a backdrop of surpluses on both current and capital account, the People’s Bank of China intervened regularly to limit the upward movement in the RMB’s value, allowing it to appreciate gradually against the dollar, and accumulating vast foreign currency reserves in the process. For a period of about two years beginning September 2008 – the worst phase of the financial crisis – the exchange rate regime reverted to a (de facto) dollar peg. These two regimes can be clearly seen in Fig. 1, which depicts the US dollar-renminbi (USD/RMB) exchange rate, as well as Euro-renminbi (EUR/RMB) rate and China’s nominal effective exchange rate (EER) for the period 2002–2011. It is important to make a distinction between exposure to the US dollar and to other currencies – against which the RMB essentially floated during our sample period. The tight coupling of the RMB to the US dollar constitutes a second government guarantee to Chinese firms: an implicit guarantee regarding the stability of the currency that is a feature of pegged or heavily managed exchange rate regimes (Eichengreen and Hausmann, 1999; Chang and Velasco, 2000; Burnside et al., 2001; Schneider and Tornell, 2004). In pegged systems, firms may not only fail to engage in foreign currency hedging; they can also be tempted to double down and borrow in the currency of the peg (Eichengreen and Hausmann, 1999). Due to obvious risks relating to currency mismatches, combined with the fact that exchange pegs tend not to last (Klein and Shambaugh, 2008), this behavior is highly risky. While the peg persists, however, such risks may be easy for firms to ignore, whereas exposure to non-US dollar currencies during our sample period would have been particularly salient to Chinese firms. For each of our sample firms, using daily data we estimate annual foreign exchange exposures to the USD/RMB, the EUR/ RMB and the EER, and then use these exposure estimates as the dependent variable in a series of panel regressions. In our research, two particular endogeneity problems need to be addressed. The first relates to simultaneity: mutual funds may be drawn to firms with better foreign exchange risk management practices and lower foreign exchange exposure. The second

2 We examine the firms’ foreign exchange exposure outcome as a proxy for their hedging activities mainly because data on the use of derivatives and other hedging tools are not readily available for Chinese firms. This approach has several advantages over the standard approach in risk management studies – whereby a dummy representing derivatives use or some measures of derivatives holdings is used. We discuss this further in Section 3. 3 Our data set provides us with an approximation to the distinction between state-owned and SOE-owned firms. We use percentage of shareholdings owned by the state as our measure of direct state ownership, and percentage of shareholdings owned by ‘legal persons’ – which are mostly SOEs (Chang and Wong, 2004; Zou and Adams, 2008; Aggarwal et al., 2014) – as our proxy for ownership by SOEs.

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USD/RMB 0.17 0.16 0.15 0.14 0.13 0.12 0.11 0.10

EUR/RMB 0.15 0.14 0.13 0.12 0.11 0.10 0.09 0.08 0.07 0.06

EER 125 120 115 110 105 100 95 90 85 80

Fig. 1. Exchange rates. This figure depicts daily values for three exchange rates over the period 2002 to 2011 inclusive: two bilateral rates USD/RMB and EUR/RMB, and China’s nominal effective exchange rate (EER). All three are expressed such that a rise indicates an appreciation of the RMB.

relates to the possibility of a dynamic relation between the explanatory and dependent variables. Standard fixed effects estimation techniques assume that current values of the explanatory variable are independent of past values of the dependent variable; if they are not independent, fixed effects estimation will yield inconsistent estimates (Wintoki et al., 2012). A dynamic relation is possible in our study. If mutual funds monitor firms to ensure hedging is undertaken, current estimates of exposure may be a function of past mutual fund holdings. To overcome these problems, we use a dynamic panel generalized method of moments (GMM) (Arellano and Bond, 1991) estimator. In addition to alleviating issues relating to simultaneity and the dynamic relation between dependent and independent variables, the dynamic panel GMM approach deals with endogeneity issues relating to unobservable heterogeneity and omitted variables (Wintoki et al., 2012). Dynamic panel GMM has been used in prior studies of governance in China (Conyon and He, 2012), and in risk management research (Kumar and Rabinovitch, 2013). We find that the higher the firm’s percentage of shares owned by mutual funds, the lower its exposure to movements in the US dollar vis-à-vis the RMB. Confirming our conjecture that SOE managers may prefer not to hedge, we also find that the greater the state’s shareholding in the firm, the higher its exposure to the dollar. When we interact mutual fund holdings

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with the percentage of shares held by the state, we find that the dollar exposure-reduction effect of mutual fund ownership is confined to firms with substantial state and legal person share ownership. In contrast to our strong findings on exposure to the US dollar, the percentage of shares held by mutual funds has no significant effect on exposure to the euro and the EER. When we re-estimate our models using the number of mutual funds rather than the percentage of shares held by mutual funds, however, we find a significant effect: the greater the presence of mutual funds, the lower the firm’s exposure to the euro and the EER. Further, when we interact the number of mutual funds with the state ownership variables, we show that exposure to the euro is confined to firms with greater state ownership. This suggests that mutual funds ensure that firms with state ownership hedge their salient exposures to major trading partners’ currencies, and this is consistent with prior findings that institutional ownership is associated with better risk management (Géczy et al., 1997; Graham and Rogers, 2002). Still, our findings on mutual funds’ effect on US dollar exposures are much stronger and more robust than those relating to non-dollar exposures. This apparent puzzle may be explained by US dollar-denominated borrowing amongst Chinese firms. Foreign currency borrowing can act as a hedge if the firm has underlying foreign currencies revenues (Kedia and Mozumdar, 2003), but otherwise it will heighten exposure. As long as the apparently inexorable rise in the RMB against the US dollar during our sample period continued, dollar borrowing would have enhanced profits because the value of principal and interest in RMB would have been expected to fall over time. The government guarantee against debt default for SOEs – protecting them from severe downside cash flow realizations – would have made dollar borrowing even more attractive to firms with substantial state shareholdings.4 A plausible explanation for our strong findings for US dollar exposures is that mutual funds monitor firms to restrain their unhedged dollar borrowing activities.5 Just as they should have anticipated an eventual end to the implicit government guarantee against default, well-informed investors such as mutual funds are likely to have been able to foresee that the upward path of the RMB against the dollar would eventually go into reverse.6 Chinese firms were certainly borrowing in US dollars, and the extent of dollar borrowing accelerated toward the end of our sample period. There is considerable anecdotal and other evidence that much of this was unhedged. Our own findings – which show a strong rise in the proportion of firms positively exposed to movements in the US dollar coincide with the documented rise in dollar borrowings – also provide supporting circumstantial evidence that Chinese firms were increasingly engaging in unhedged dollar borrowing. Our findings that mutual fund participation is associated with lower exposure to the US dollar is robust to an alternate measure of mutual fund participation: the number of mutual funds invested in the firm in lieu of percentage of shares held by mutual funds. In a second set of robustness tests, we show that our findings are similar when we use total exposure in place of the standard market-adjusted (or ‘residual’) approach to estimating exposure advocated by Jorion (1990). We also subject our findings to the critique that mutual fund holdings in China are so small that they are unlikely to provide a monitoring function (see, for example, Jiang and Kim, 2015). If our results are stronger when we use a measure that includes only large mutual funds stakes, this strengthens the argument that our findings are the result of mutual funds’ monitoring activities. When we replace the percentage of shares held by mutual funds with a measure that aggregates large mutual fund ownership stakes – those holding more than a 1 percent stake – we find that the magnitude of the relation between mutual fund holdings and dollar exposure is several times greater than in our base case specifications. Our US dollar findings are robust to three further tests. First, in order to address the concern that mutual funds like to invest in firms with good foreign exchange risk management practices, we match firms with and without mutual fund ownership using propensity score matching. Second, we show that ownership by dedicated mutual funds – those with a longer-term investment horizon – is associated with lower dollar exposure of their investee firms, relative to their counterparts with a more transient focus. Third, we find that the dollar exposure reduction effect is stronger for investee firms considered important by mutual funds – that is, where the mutual fund’s holding of a particular firm is a major investment of the fund. This research contributes to the literature on the benefits of mutual fund participation in listed firms in China (Yuan et al., 2008; Aggarwal et al., 2014; Wu et al., 2016), and to the wider literature on institutional monitoring as an important external governance mechanism (Chaganti and Damanpour, 1991; Wahal, 1996; Bushee, 1998; Hartzell and Starks, 2003; Grinstein and Michaely, 2005; Wei et al., 2005; Chen et al., 2007; Cornett et al., 2007; Ruiz-Mallorquí and Santana-Martín, 2011; Aghion et al., 2013; Luong et al., 2017). Our study also complements the literature on the beneficial effect of good corporate governance on firms’ financial risk management activities and outcomes (Tufano, 1996; Graham and Rogers, 2002; Knopf et al., 2002; Borokhovich et al., 2004; Géczy et al., 2007; Hutson and Stevenson, 2010; Allayannis et al., 2012; Lel, 2012; Kumar and Rabinovitch, 2013; Bartram, 2015). The remainder of the paper is organized as follows. Section 2 reviews the relevant literature and Section 3 discusses the data and the methodology. Section 4 presents summary information on our data set as well as the results of our multivariate

4 There is a body of evidence pointing to widespread unhedged dollar-denominated borrowing by Chinese firms during our sample period; this is discussed in detail in Section 5. 5 We are not able to demonstrate this empirically. Before 2007, Chinese firms were not required to provide information on the currency composition of their debt. The new ASBEs (Accounting Standards for Business Enterprises), effective Jan 1st, 2007, include a requirement to provide information on debt currency composition. If firms have dollar-denominated borrowings, therefore, this must be reported in a footnote. However, the CSMAR data on foreign currency borrowings are incomplete. In Section 5, we perform a simple test on a limited data set of firm years in which dollar-denominated borrowing data are available. 6 Both the ‘appreciation phase’ and the implicit government guarantee have since come to an end; the RMB has been depreciating against the dollar since 2014, and the Chinese authorities have presided over several SOE bond defaults in the last few years; see the references in footnote 7.

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analysis. Section 5 addresses the issue of whether our dollar exposure findings can be explained with reference to dollardenominated borrowing, and Section 6 presents the results of our robustness tests. Section 7 concludes.

2. Literature review 2.1. Foreign exchange exposure and governance The shareholder value theories of financial risk management suggest that in reducing the volatility of expected future cash flows, hedging creates value by mitigating underinvestment, reducing financial distress costs and the cost of debt, and enhancing debt capacity (Myers, 1984; Smith and Stulz, 1985; Froot et al., 1993; Leland, 1998). These theories have received considerable empirical support (Allayannis and Weston, 2001; Graham and Rogers, 2002; Carter et al., 2006; Mackay and Moeller, 2007; Bartram et al., 2011; Campello et al., 2011; Disatnik et al., 2014). There is a growing body of evidence showing that good internal and external governance is important in providing monitoring, advice and incentives regarding optimal hedging policies (Borokhovich et al., 2004; Géczy et al., 2007; Allayannis et al., 2012; Lel, 2012; Bartram, 2015). A firm is subject to foreign exchange exposure if changes in exchange rates impact expected future cash flows, and therefore firm value. This includes direct exposure, encompassing transaction exposure (involving known foreign currency receivables and payables) and expected future foreign currency cash flows. Indirect exchange exposure arises from the competitive environment in which the firm operates. A firm that manufactures and sells locally, for example, will be exposed to a strengthening domestic currency as competing imports become relatively cheap. Many studies examining the extent of foreign exchange exposure amongst firms in developed markets have found that few firms are significantly exposed to exchange rate movements (see Muller and Verschoor, 2006, for a review), and this is best explained by the fact that most firms hedge (Bartov and Bodnar, 1994; Dewenter et al., 2005; Hsin et al., 2007; Bartram et al., 2010; Ye and Hutson, 2011). Studies examining firms in developing and emerging markets (including China) report a greater proportion of firms significantly exposed (Dominguez and Tesar, 2001; Parsley and Popper, 2006; Chue and Cook, 2008; Aysun and Guldi, 2011; Lin, 2011). In the only study to have examined the exposure-governance nexus, Hutson and Stevenson (2010) use a large international data set to show that firms in countries with stronger shareholders’ and creditors’ rights experience lower foreign exchange exposures.

2.2. China’s SOEs China’s SOEs are likely to be more exposed to exchange rate movements than non-SOEs for two main reasons. First, SOEs’ governance is relatively poor. Early papers tend to blame Chinese firms’ inefficiency and poor management on China’s weak governance environment (Zheng et al., 1998; Xu and Wang, 1999; Zhang et al., 2001; Sun and Tong, 2003; Bai et al., 2004), and this is largely due to the pervasiveness of state and state-related controlling shareholders amongst China’s firms. Profitability is often sacrificed to personal ambition or political ends, and managers in Chinese SOEs tend to face weak or inconsistent incentives (Xu and Wang, 1999; Sun and Tong, 2003; Allen et al., 2005; Cull and Xu, 2005; Firth et al., 2006; Zou and Adams, 2008; Chen et al., 2016). Shares in Chinese firms are classified as state-owned, legal person-owned, and tradable (Xu and Wang, 1999; Firth et al., 2010). State-owned and legal person-owned shares were until recently not tradable, and the conflicts of interest between the shareholders of tradable and non-tradable shares have long been recognized as a problem in Chinese firm governance (Chen et al., 2006; Feinerman, 2007; Chen et al., 2009). To remedy this, the Chinese authorities, in a major reform to the ownership and governance of public firms, implemented the split share structure reforms (SSSR) 2005 and 2006 – whereby formerly non-tradable government owned shares become tradable (Firth et al., 2010; Chen et al., 2016; Li et al., 2011). There is evidence that SOEs’ governance has improved since the advent of the SSSR; Chen et al. (2016) show that better monitoring by state-owned controlling shareholders has led to a greater likelihood that they would replace managers engaging in fraud. Government ownership of firms in China has been linked to inferior firm performance (Wei and Varela, 2003; Bai et al., 2004; Kang and Kim, 2012; Yu, 2013; Haß et al., 2016), corporate litigation (Firth et al., 2011), earnings management (Yuan et al., 2008), lower share price informativeness (Ding et al., 2013), fraud (Aggarwal et al., 2014), and a weaker link between incentives – such as CEO compensation – and performance (Firth et al., 2006, 2007; Conyon and He, 2012). SOEs in China receive considerable government support, including preferential access to contracts and financial backing (Allen et al., 2005; Chen et al., 2016). Financial backing includes an implicit guarantee against default (Fan et al., 2013; Jiang and Kim, 2015), and bankruptcy is very rare (Allen et al., 2005; Li et al., 2009; Fan et al., 2013). The existence of this implied government guarantee during our sample period is likely to have had an important effect on SOE managers’ incentives to hedge. The value creation theories of financial risk management hinge on the ability of hedging to moderate cash flow volatility and reduce the likelihood that firms encounter financial distress or bankruptcy. A government guarantee has similar effects – it limits the downside of cash flow volatility, reducing the likelihood of the SOE becoming distressed. This is a clear example of moral hazard; protected against failure, SOE shareholders and managers may be incentivised to take more risk. As most hedging techniques limit upside as well as downside cash flow realizations, SOE managers may actively choose not to hedge.

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We distinguish direct state ownership from ownership by SOEs. Chen et al. (2009) argue that it is important to make this distinction because the two types of firm have disparate incentives, goals and levels of government support. Relative to stateowned firms, they argue, SOE-owned firms are better governed and their managers are more likely to be monitored. In regard to the implicit government guarantee, however, there does not appear to be any distinction between the two types of firms. Media articles reporting on SOE bond defaults in 2015 and 2016 suggest that firms which are directly state-owned and those controlled by SOEs have been subject to the implicit goverment guarantee.7 We consequently expect to see a stronger positive relation between the proportion of shares owned directly by the state and the level of foreign exchange exposure than that between exposure and ownership by SOEs. 2.3. Mutual fund ownership and risk management Shleifer and Vishny (1986) and Coffee (1991) suggest that institutional investors play an important role in monitoring managers. Jensen (1993) goes further and argues that active institutional investors are essential to a well-functioning corporate governance system. There is considerable academic evidence demonstrating the importance of institutional investors in contributing to improved levels of corporate governance and transparency (Karpoff et al., 1996; Gillan and Starks, 2003; Hartzell and Starks, 2003; Ferreira and Matos, 2008; Aggarwal et al., 2011; Chung and Zhang, 2011), as well as a range of other positive outcomes.8 There is limited evidence, however, on the role of institutional share ownership in motivating managers’ financial risk management practices. Géczy et al. (1997) and Graham and Rogers (2002) find that US firms with high institutional ownership are more likely to hedge with currency derivatives. More recently, Tai et al. (2014) show that institutional investors enhance the probability and extent of hedging amongst Taiwanese firms. In China, institutional investors play a potentially important governance role. Because of China’s unique share ownership structure, the interests of minority shareholders can often be neglected, so mutual funds have a unique role to play as the minorities’ champion. To this end – and to help improve governance and performance in Chinese firms more generally – the Chinese government has since 2000 actively developed mutual funds, and encouraged them to perform a monitoring and advisory role (Yuan et al., 2008; Firth et al., 2016). Mutual fund ownership in China has been found to be associated with greater firm value (Yuan et al., 2008) and a reduced likelihood that Chinese firms commit fraud (Aggarwal et al., 2014; Wu et al., 2016). Wu et al. (2016) find a particularly strong fraud-reduction effect of mutual fund participation in SOEs. These studies imply that Chinese mutual funds monitor managers, and that this behavior benefits shareholders. Mutual funds may therefore act to ensure foreign exchange hedging activities are undertaken, resulting in lower foreign exchange exposure. Mutual funds do not always act in shareholders’ interests. Firth et al. (2010) demonstrate that during the SSSR in 2005– 2006, mutual funds failed to act in their unitholders’ interests – and by extension, minority shareholders’ interest – by bowing to political pressure in helping to expedite the reform process and minimize the costs borne by SOEs.9 Jiang and Kim (2015) argue that mutual funds in China are unlikely to provide a monitoring function because they have a relatively short investment horizon and their holdings are too small to have any influence on firm decision-making and governance. Following this line of reasoning, mutual fund participation may have no effect on firms’ risk management practices. It is also possible that mutual funds in China monitor firms to ensure that they do not hedge. This is because many firms in are government-controlled and benefit from the implicit government guarantee against default; equity investors may therefore prefer to see cash flow volatility maximized. Some evidence for this position can be found in Erkens et al. (2012) who undertook multi-country study of bank performance around the 2007–2008 financial crisis. They find that banks with greater institutional ownership (as well as more independent boards) saw worse post-crisis return performance as a result of greater pre-crisis risk-taking, and they argue that this was due to the moral hazard associated with institutional arrangements such as deposit insurance. 3. Data and method Our sample comprises A-shares listed on Shanghai and Shenzhen stock exchanges that are traded in renminbi,10 for the period 2005 to 2011. Following Bekaert et al. (2007),11 we remove firms that are severely thinly traded. Specifically, firms that have more than 30 percent zero returns, or have no trading data for 22 consecutive days (one month) are eliminated. We also remove firms that have less than 252 (one year’s) observations. The initial sample consisted of 2359 actively traded A-shares on 7 See, for example, ‘‘SOE you’ve actually defaulted?” Financial Times, April 22, 2015. ‘‘China bond defaults by state-owned groups spook investors: Moral hazard is in retreat as government pulls back on bailouts and permits creditor losses” Reuters, April 13, 2016. ‘‘China state enterprises likely to suffer more defaults, S&P says” Bloomberg News, April 18, 2016. ‘‘It’s all suddenly gone wrong in China’s $3 trillion bond market” Bloomberg News, April 19, 2016. Several of these articles argue that these SOE defaults – which are selective rather than universal – imply an explicit change in government policy to allow some SOEs to fail. 8 For example, performance (Wahal, 1996; Yuan et al., 2008), value (Wei et al., 2005; Ruiz-Mallorquí and Santana-Martín, 2011), capital structure (Chaganti and Damanpour, 1991), operating cash flows (Cornett et al., 2007), executive compensation (Hartzell and Starks, 2003), mergers and acquisitions (Chen et al., 2007), R&D investment behaviour (Bushee, 1998), dividend policy (Grinstein and Michaely, 2005), and innovation (Aghion et al., 2013; Luong et al., 2017). 9 This is an example of the conflicts of interest that can arise between institutions and other shareholders discussed by Woidtke (2002). 10 We exclude B-shares from the sample as they are traded in US dollars and Hong Kong dollars on the Shanghai and Shenzhen stock exchange, respectively. 11 Bekaert et al. (2007) highlight the common problem with thin trading in emerging markets. They examined the liquidity of firms in 19 emerging market firms and found that the average proportion of zero daily returns across the 19 emerging markets was 31 percent. In our study, the highest proportion of zero daily returns is around 40 percent.

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both stock exchanges, and this was reduced to 1335 firms after screening. The institutional ownership and firm characteristics data are from the Resset Financial and CCER databases respectively. After retaining firms for which the institutional ownership and firm characteristics data are available, the final sample comprises 3048 firm years. The number of firms varies over time; in 2005 we have 360 firms and in 2011 there are 560. 3.1. Estimating foreign exchange rate exposure We use the firms’ foreign exchange exposures as a proxy for their hedging activities because data on the use of derivatives and other hedging tools are not readily available for Chinese firms. This approach can be considered preferable to using the standard proxy for risk management, the use of derivatives, because derivatives play a minor role in many firms’ currency hedging strategies (Guay and Kothari, 2003; Kedia and Mozumdar, 2003; Gamba and Triantis, 2014). There are many other approaches to managing foreign exchange exposures, including pass-through, operational hedging and foreign currency borrowing. Hutson and Stevenson (2010) argue that when estimating exposure using market-based models – which detect posthedging exposure – a finding of significant exposure constitutes evidence that the firm is incompletely hedged. As estimated foreign exchange exposure reflects all hedging techniques used by the firm, it provides a suitable proxy for the extent to which firms are hedged against exchange rate movements. Following Jorion (1990) and others, we use the augmented capital market model to estimate foreign exchange exposure in each year for all firms: X Rn;t ¼ an þ bm n Rm;t þ bn X t þ en;t

ð1Þ

where Rn;t and Rm;t denote respectively the return on day t of firm n’s share price and the return on the market portfolio m, and X t denotes the change in the exchange rate on day t. The coefficient bm n measures the nth firm’s sensitivity to the market, and bnX is firm n’s sensitivity to exchange rate movements. A value-weighted market return is constructed with weights proportional to the percentage of the respective market capitalization of the Shanghai and Shenzhen stock exchanges. We estimate exposure to the US dollar (USD/RMB) and to the euro (EUR/RMB), as well as to the JP Morgan nominal trade-weighted RMB effective exchange rate index (EER). All time-series data are daily, and were obtained from Datastream. To ensure that our results using the USD/RMB and the EUR/RMB exchange rate are directly comparable to those using the EER, we express the two bilateral exchange rates such that a rise indicates an appreciation of the RMB. To overcome the conditional heteroskedasticity problem,12 in cases when the residuals, en;t , in Eq. (1) exhibit time-varying heteroscedasticity, we add a GARCH (1, 1)13 process to incorporate conditional variance into the system, as follows: X Rn;t ¼ an þ bm n Rm;t þ bn X t þ en;t

with

ð1Þ0

qffiffiffiffiffiffiffiffi

en;t ¼ ln;t r2n;t

and

r2n;t ¼ c þ

Xp

s r2n;ti þ

Xq

i¼1 i

j¼1

uj e2n;tj

where i is the lag length, r2n;t denotes the conditional variance of the residuals and ln;t represents the white noise error term.

ð2Þ

en;t ; c, si and uj are unknown parameters14;

bnX is our estimate of foreign exchange exposure for firm n. The standard approach when using bnX as the dependent vari  able in cross-sectional or panel analysis is to first take the absolute value of the exposure coefficients,b X , because firm-level n

variables can assist in explaining only the magnitude rather than the direction of exchange rate changes. Because taking the absolute value imposes truncation bias which results in non-normal error terms, a further transformation involves taking the qffiffiffiffiffiffiffiffiffi square root of the absolute values bnX ; this leaves the error term normally distributed (Dominguez and Tesar, 2006; Hutson and Stevenson, 2010; Hutson and Laing, 2014). 3.2. Panel model specification We implement the following dynamic panel GMM model:

qffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffi  X   b  ¼ ant þ b1 b X  n n t

t1

þ b2 X nt þ b3 Z nt þ b4 Dnt þ nt

ð3Þ

12 Many studies (for example, Baillie and Bollerslev, 1989; Hsieh, 1989; Bollerslev et al., 1992; Tse, 1998, among others) have documented that conditional heteroskedasticity in asset returns may result in inefficient parameter estimates as well as biased test statistics in the ordinary least squares regression. We therefore checked whether the residuals in Eq. (1), en;t , exhibit time-varying heteroskedasticity by using the Lagrange multiplier test proposed by Engle (1982), and we found that en;t are heteroskedastic about 30 percent of the time. 13 We use the Akaike (1973) Information Criterion (AIC) and the Schwarz (1978) Information Criterion (SIC or BIC) to determine the optimal GARCH (p, q) model for each firm. Both criteria suggest GARCH (1, 1) as the optimal model in almost all cases. 14 The unknown parameters are estimated by maximum-likelihood and generated using the Berndt et al. (1974) algorithm.

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qffiffiffiffiffiffiffiffiffi  X b  is the square root of the absolute foreign exchange rate exposure coefficient estimated in Eq. (1)’ for firm n in n t qffiffiffiffiffiffiffiffiffi  X  year t, bn  is the square root of firm n’s absolute foreign exchange rate exposure coefficient in the prior year. Xnt are the where

t1

ownership structure variables: the percentage of shares held by mutual funds, the percentage of shares held by the state, and the percentage of shares held by legal persons. Znt are the corporate governance control variables, Dnt represents other controls, and nt is a time-varying unobservable. 3.3. The independent variables Our main independent variable is % mutual, which is the percentage of each firm’s total shares held by mutual funds. We have two state ownership variables: the percentage of shares held directly by the state (% state), and the percentage of shares owned by legal persons (% legal). The latter is our proxy for ownership by SOEs (Chen et al., 2009). Although the category ‘legal person’ (or ‘legal entity’) includes various bodies such as collectively owned enterprises and private companies (Zou and Adams, 2008), the vast majority of ‘legal person’ shareholders are SOEs (Chang and Wong, 2004; Aggarwal et al., 2014). 3.4. Corporate governance controls The internal governance mechanisms that we include as controls are ownership concentration, measured as the sum of 5 largest shareholdings expressed as a percentage of total shareholdings (top 5 holdings); the percentage of shares owned by top executives, which includes the CEO, the executive vice presidents, the chairperson, and the vice chairpersons of the board of directors (executive holdings); the number of directors (board size); the number of board meetings each year (board meetings); the percentage of directors who are independent (board independence); and a dummy representing instances in which the chief executive and chairman roles are held by the same person (CEO duality). Top 5 holdings. Concentrated ownership can mitigate agency problems within the firm, resulting in increased firm value (Jensen and Meckling, 1976; Shleifer and Vishny, 1986; Thomsen and Pedersen, 2000). In studies of Chinese firms, ownership concentration has been found to improve firm performance and value (Tian, 2001; Sharpe et al., 2013). Aggarwal et al. (2014) show that firms with greater ownership concentration are less likely to engage in fraudulent activities, and Conyon and He (2011) find that executive pay is lower in firms with high levels of ownership concentration. As many firms are controlled by SOEs, the presence of multiple large shareholders in Chinese firms is seen by the government as providing a check on controlling shareholders’ power (Jiang and Kim, 2015). Executive holdings. Jensen and Meckling (1976) show that shareholders can reduce agency costs by using managers’ remuneration packages that link pay and wealth to the value and performance of the firm. Regarding incentives to manage financial risks, Stulz (1984) and Smith and Stulz (1985) show that stock-based compensation may lead managers to neglect to hedge, because they seek the upside risk associated with not hedging. Jiang and Kim (2015), however, argue that few Chinese firms grant shares or other equity-related compensation to managers, and the weak findings of Bai et al. (2004) and Liu and Lu (2007) support this conjecture. In contrast, Haß et al. (2016) report a positive relation between the percentage of shares held by executives and operating performance persistence (which is associated with a lower cost of capital). Board size. Large boards can enable the CEO to obtain too much power and control, leading to board inefficiencies and inferior performance (Jensen, 1993; Yermack, 1996; Cornett et al., 2008). Adam et al. (2017) examine hedging in the US gold mining industry and report a positive relation between board size and the extent of selective hedging – which is essentially speculating and is likely to enhance rather than reduce financial risk. Studies examining the effect of governance on firm performance and fraudulent activity in Chinese firms (Chen et al., 2006; Wang, 2010; Aggarwal et al., 2014) find that board size is not significant. Board meetings. The number of board meetings held each year can provide insight into the monitoring, oversight and vigilance of the management team. If this is so, more board meetings would be expected to be associated with better monitoring and oversight, leading to positive outcomes for the firm. The evidence is in general not consistent with this ideal. Vafeas (1999) finds that board meeting frequency is associated with lower firm value, and this relation is largely driven by greater board activity following reductions in the share price – suggesting that boards meet more when the firm encounters problems or crises. Chen et al., (2006) and Aggarwal et al. (2014) find that a greater number of board meetings in Chinese firms is associated with a higher incidence of fraud. Their explanation for this finding is that if fraud is suspected, the board meets more often to discuss the issue. Board independence. There is a substantial literature showing that independent directors provide greater monitoring and oversight than executive directors (Fama and Jensen, 1983; Hermalin and Weisbach, 1988; Weisbach, 1988; Core, Holthausen and Larcker, 1999; Bhagat and Black, 2001;), and several studies report a link between the proportion of independent directors and operating performance (Rosenstein and Wyatt, 1990; Byrd and Hickman, 1992; Brickley et al., 1994; Dahya and McConnell, 2005; Peasnell et al., 2005). The evidence on board independence and risk management activity is mixed. Kumar and Rabinovitch (2013) show that more independent boards are associated with less hedging activity, and similarly, Huang et al. (2013) suggest that better monitoring by independent boards curtails excessive manageriallymotivated hedging. In contrast, Borokhovich et al. (2004) report that better monitoring by outside directors increases firms’ use of interest rate derivatives.

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In 2001 the China Securities Regulatory Commission (CSRC) introduced the Corporate Governance Code, aimed at aligning governance standards in China with the Anglo-American model of governance. One key area of focus in the code is the structure and independence of the board; the guidelines stipulate that company boards must consist of at least one-third independent directors.15 Consistent with US and European Studies, a greater proportion of independent directors has been associated with positive outcomes for Chinese firms. Fan et al. (2007) highlight that inside or executive directors are often bureaucrats appointed by the state or controlling shareholder, resulting in less effective managerial monitoring. Chen et al. (2006) find that a greater proportion of outside directors on Chinese boards is associated with a lower incidence of fraud; Firth et al. (2007) show that Chinese firms with a greater proportion of outside directors have better earnings informativeness and cleaner audit opinions; and Conyon and He (2011) find that Chinese firms with a higher proportion of independent directors have a greater pay-for-performance link. Jiang and Kim (2015), however, show that most Chinese firms have the bare minimum one-third of independent directors, and suggest that it is difficult to assess the effectiveness of independent directors given the low variation in the independence ratio. CEO duality. Another key area of focus in the 2001 governance reforms in China was encouraging firms to separate CEO (usually known in China as general manager) and chairman roles. The dual CEO/Chairman role increases CEO power and managerial discretion, and is associated with greater control of information flow to the board – resulting in reduced board effectiveness and lower firm value (Mallette and Fowler, 1992; Jensen, 1993; Yermack, 1996; Adams et al., 2005; Cornett et al., 2007). Consistent with the findings of US studies, Bai et al. (2004) and Chen et al. (2006) find that amongst Chinese firms, CEO duality is associated with lower firm value and greater likelihood of fraud, and Conyon and He (2011) report higher levels of executive pay when the CEO and chairman roles are combined. 3.5. Other control variables Hedging theories suggest that hedging is more valuable for firms with growth opportunities (Géczy et al., 1997). We control for growth opportunities using market-to-book value of equity (MTB). We use the quick ratio (Quick) as a measure for firm liquidity, as liquidity can be seen as a substitute for hedging (Nance et al., 1993). Economies of scale in hedging activities implies that large firms are likely to be less exposed to exchange rate fluctuations than small firms (Allayannis and Ofek, 2001; Dominguez and Tesar, 2006; Hutson and Stevenson, 2010; Hutson and Laing, 2014). We use total assets in millions of yuan as a proxy for firm size (Size). Highly indebted firms are more susceptible to potential underinvestment and more likely to encounter financial distress, so they should be more likely to hedge (Myers, 1977; Froot et al., 1993; He and Ng, 1998), and Nance et al. (1993) suggest that hedging becomes valuable when leverage rises. For Chinese firms, Yuan et al. (2008) suggest that leverage could proxy for the availability of state funding. We use the debt-to-asset ratio as the measure for leverage (DA). We control for industry using the CSRC industry classification codes and include 13 industry dummies. Industries are affected differentially by exchange exposure for several reasons. Some are more likely to engage in international transactions than others (Bodnar and Gentry, 1993; He and Ng, 1998; Griffin and Stulz, 2001); they are exposed to different levels of international competition (Bartram et al., 2010); and some industries are more likely to be multinational than others – and therefore are naturally hedged (Hutson and Laing, 2014). Lastly, we control for the de-facto peg period using a dummy variable equal to 1 for the year 2009, and zero otherwise. 4. Results 4.1. Summary statistics Table 1 summarises the volatility (as measured by the standard deviation of daily percentage exchange rate changes) for the USD/RMB and EUR/RMB bilateral exchange rates and the EER, over the period 2005–2011. It details annual volatility as well as volatility over three phases: the initial de jure managed float period, from 21st July 2005 to 12th September 2008; the de facto dollar peg of 15th September 2008 to 17th June 2010, and reversion to the managed float from 18th June 2010 to the end of 2011. It is clear that EER and the EUR/RMB are much more volatile than that of the USD/RMB, which is to be expected given that RMB during this period was tightly controlled against the dollar. The daily volatility of the USD/RMB during the two managed float sub-periods either side of the de facto dollar peg period, of 0.111 and 0.130 percent respectively, is more than twice as high as its volatility during the de facto peg period of 0.051 percent. Table 2 presents the annual exchange rate exposure estimates for our sample firms for the USD/RMB, EUR/RMB and the EER. For exposure to each currency, we report median absolute exposure, the proportion of the sample negatively exposed, the proportion significant (at the 5 percent level), the proportion significant and negative, and the proportion significant and positive. As well as for each year (columns [1] through [7]), we present summary statistics for the full period (column [8]), the de facto peg period, defined as 2009 (column [9]), the managed float period (in which the de facto peg period 2009 is removed from the full period) in column [10], and column [11] contains the z-statistic for a Wilcoxon rank sum test for dif15 This deadline for compliance with this rule was June 2003, which pre-dates our sample period. The corporate governance code defines director independence as: ‘‘The independent director shall be independent from the listed company that employs them and the company’s major shareholders.”

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Table 1 Exchange rate arrangements and exchange rate volatility.

2005 2006 2007 2008 2009 2010 2011

Exchange rate arrangements

USD/RMB

EUR/RMB

EER

Official dollar peg to managed float Managed float Managed float Managed float to de facto peg De facto dollar peg De facto peg to managed float Managed float Managed float, 21st July 2005 to 12th September 2008 De facto peg, 15th September 2008 to June 17th, 2010 Managed float, June 18th to end 2011

0.129 0.065 0.096 0.113 0.025 0.103 0.125 0.111 0.051 0.130

0.579 0.509 0.392 0.854 0.799 0.747 0.686 0.510 0.890 0.696

0.280 0.240 0.179 0.380 0.374 0.282 0.305 0.242 0.381 0.300

Notes: This table reports the standard deviation of the daily percentage change in the USD/RMB and the EUR/RMB bilateral exchange rates, and China’s nominal effective change rate (EER), for each year of our sample period 2005–2011, and for three sub-periods: the two ‘managed float’ periods either side of the de facto peg period, and the de facto peg period.

ference in medians during the de facto peg and managed float periods. In the de facto peg period, the median absolute exposure to the dollar, at 4.55, is over 4 times higher than during the rest of our sample period, at 1.00, and this difference is highly significant (z = 25.8). Our finding of higher exposure to the dollar during the peg regime is consistent with the findings of Parsley and Popper (2006) and Ye et al. (2014). In contrast, Chinese firms’ exposure to the EER and the euro is lower in the de facto peg than in the managed float period (0.25 versus 0.35 for the EER and 0.13 versus 0.19 for the Euro); these differences are also significant. It is because the exposures of our firms are so different during the de facto peg period relative to the rest of the sample period that we use the de facto peg dummy in our multivariate analysis. For exposure to the dollar, we find that a very high proportion of our sample firms have significant exposure – 51 percent on average – and these are mostly negative exposures. This is to be expected given the export focus of many Chinese firms. There is, however, a substantial reduction in the firms significantly negatively exposed to the dollar during the last two years of our sample period. In 2010, 55 percent of the firms were negatively exposed, compared to only 3 percent in 2011, and the proportion positively exposed to the dollar leaps from 2 percent in 2010 to 9 percent in 2011. This is probably related to dollar-denominated borrowing by our sample firms; we discuss this further in Section 5. The proportion of the firms significantly exposed to the EUR/RMB exchange rate is much lower (25 percent), and again these are dominated by negative exposures; there are very few instances of firms significantly positively exposed to the euro. Table 3 reports summary statistics for the ownership variables. For each year and for the full sample period, we report the proportion of shares owned directly by the state (state-held), by legal persons (legal person-held), and by mutual funds. We also report the number of mutual funds that have an ownership stake in our sample firms. Mean and median mutual fund ownership over the full period is 4.25 and 0.59 percent respectively. There is in general a rising trend in both percent held by mutual funds as well as number of mutual funds, although the rise is not monotonic. By 2011, the mean percentage of shares held by mutual funds is 5.17 percent, and the median is 1.72 percent. These are much higher than those reported by Jiang and Kim (2015) for mutual fund holdings in 2011: a mean of 0.382 percent and a median of 0.064 percent. Table 4 presents summary statistics for the control variables. As found in prior studies (Wei et al., 2005; Yuan et al., 2008; Zou and Adams, 2008; Jiang and Kim, 2015), executive holdings in our sample of Chinese firms are relatively small, with a mean of 2.74 percent and a median of zero. Mean board size is 6 people, and our sample firms’ boards meet on average 9 times a year. As market-to-book value of equity (MTB) and firm size by total assets (size) are highly right-skewed, we use the log of these variables in our multivariate analysis. Lastly, the CEO is also the chairman in 17 percent of cases (not reported in the table). Table 5 presents Spearman rank correlations for all of our variables. The table reveals little to be concerned about regarding multicollinearity, with the exception of the strong negative correlations between MTB and the quick ratio with the debtto-assets ratio (DA). However, all variables have a Variance Inflation Factor (VIF) of less than two. 4.2. Multivariate results Our findings for the GMM dynamic panel regression analysis (Eq. (3)) can be found in Table 6. Columns [1] to [3] contain our base specification findings for exposure to the US dollar, the euro and the EER. We find that mutual fund ownership is strongly and significantly negatively related to dollar exposure. For exposure to the euro and the EER, however, mutual fund holdings have no significant effect. On the impact of state shareholdings on Chinese firms’ foreign exchange exposure, both the percentage of shares held directly by the state (% state) and the percentage held by legal persons (% legal) – our proxy for ownership by SOEs – are highly significantly associated with greater exposure to the EER and the euro. However, state ownership is not significant for exposure to the dollar, and the relation is negative for % legal; that is, firms with greater legal person ownership have lower dollar exposure. Columns [4] to [6] in Table 6 present the specifications in which we interact percentage mutual fund holdings with the two state ownership variables. We find that mutual fund ownership is associated with lower dollar exposure for firms with

Table 2 Summary of annual absolute exposure estimates. [2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

2005 Peg to managed float (21 July)

2006 Managed float

2007 Managed float

2008 Managed float to peg (15 Sept)

2009 De facto peg

2010 Peg to managed float (18 June)

2011 Managed float

Full period

Peg period

Managed float periods

Peg vs managed float z-stat

USD/RMB Median absolute Propn. neg Propn. significant Propn. sig and neg Propn. sig and pos

0.53 0.91 0.65 0.64 0.01

2.48 0.72 0.58 0.52 0.06

1.60 0.59 0.35 0.32 0.03

1.00 0.92 0.75 0.75 0.00

4.56 0.80 0.63 0.62 0.01

0.99 0.83 0.57 0.55 0.02

0.59 0.45 0.12 0.03 0.09

1.18 0.74 0.51 0.48 0.03

4.55

1.00

25.8***

EUR/RMB Median absolute Propn. neg Propn. significant Propn. sig and neg Propn. sig and pos

0.16 0.94 0.20 0.20 0.00

0.21 0.56 0.02 0.02 0.00

0.35 0.19 0.07 0.00 0.07

0.13 0.96 0.11 0.11 0.00

0.18 0.98 0.29 0.29 0.00

0.15 0.95 0.46 0.46 0.00

0.12 0.98 0.44 0.44 0.00

0.16 0.82 0.25 0.24 0.01

0.13

0.19

10.1***

EER Median absolute Propn. neg Propn. significant Propn. sig and neg Propn. sig and pos

0.27 1.00 0.41 0.41 0.00

0.33 0.31 0.33 0.25 0.35 9.2*** 1.00 0.96 0.91 0.79 0.02 0.39 0.79 0.02 0.39 0.00 0.00 0.01  X Notes: This table reports summary statistics on the absolute values of estimates of foreign exchange exposure (bn  from Eq. (1)’) to the USD/RMB and the EUR/RMB bilateral exchange rates, and China’s nominal effective change rate (EER). We report summary statistics for each year of our sample period as well as the full period and the two exchange rate regimes: the peg period of 2009, and the managed float periods 2005–2008 and 2010–2011 inclusive. The last column provides the z-statistic for a Wilcoxon rank sum test for difference in median absolute exposure for the de facto peg period versus the managed float period. *** denotes significance at the 1 percent level. 0.58 0.91 0.22 0.22 0.00

0.69 0.43 0.14 0.10 0.04

0.27 0.99 0.55 0.55 0.00

0.25 0.99 0.51 0.51 0.00

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[1] Year Exchange rate regime

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greater levels of both state and legal person ownership (column [4]). The coefficient on % state becomes significant and positive, and the interaction term % state * mutual is strongly and significantly negative. State ownership is clearly associated with higher exposure to the US dollar, and a greater presence of mutual funds ameliorates the risk-enhancing effect of high state ownership. A similar effect is found for the percentage legal ownership-mutual fund interaction term (% legal * % mutual) in the dollar exposure specification – it is also negative and highly significant. As the coefficient on % mutual becomes insignificant in this specification, it is clear that the risk-reducing effect of mutual funds on dollar exposure is confined to firms with substantial state ownership – whether that ownership is directly by the state, or by SOE-owned firms. With regard to dollar exposures, therefore, mutual funds appear to be performing the monitoring role envisaged by the Chinese authorities. Our findings, however, cannot be solely explained with reference to mutual funds monitoring Chinese SOEs to ensure better risk management. While we find that greater state ownership is associated with higher exposure to the EER and the euro, we find no evidence that mutual funds have any effect on Chinese firms’ non-dollar exposures. This finding limits our ability to conclude that mutual funds in China monitor managers to ensure that they hedge their foreign exchange exposures. For such an explanation to be credible, mutual funds would surely ensure that the firms in which they invest also hedge their more salient exposures to the euro and other non-dollar currencies as well as dollar exposures. We advance the argument that this apparent conundrum may be explained by dollar-denominated borrowing, and we address this issue in detail in Section 5. 4.3. Control variable findings The de facto peg dummy. Consistent with our univariate results, we find that exposure to the dollar is significantly higher during the de facto peg period, whereas exposure to the EER and the euro is lower than during the rest of the period. Our finding of higher exposure to the dollar during the peg period is consistent with Parsley and Popper (2006) and Ye et al. (2014) – who in multi-country studies find that exposure to the currency of the peg rises following a switch to a peg. In this phase in particular, greater exposure to the dollar can be explained by heightened concerns about the future direction of the RMB against the dollar. The period of the de facto peg – which began around the time of the collapse of investment bank Lehman Brothers in September 2008 – was a particularly alarming time in world financial markets. In China, there would Table 3 Ownership summary statistics. 2005 Percentage state-held Mean 35.09 Median 40.98 Maximum 84.97 Skewness 0.17 Percentage legal person-held Mean 16.01 Median 2.42 Maximum 75.00 Skewness 1.17 Percentage held by mutual funds Mean 3.20 Median 0.09 Maximum 29.23 Skewness 2.26 Number of mutual funds Mean 10.20 Median 2.00 Maximum 128.00 Skewness 3.51

2006

2007

2008

2009

2010

2011

2005–2011

30.69 33.30 79.56 0.04

26.29 26.77 86.33 0.28

22.17 19.04 86.33 0.55

11.96 0.00 86.33 1.50

8.04 0.00 86.20 2.23

4.37 0.00 84.71 3.56

23.00 8.75 86.33 0.59

15.34 3.15 76.98 1.22

13.08 1.34 76.39 1.29

11.25 0.00 80.60 1.59

7.52 0.00 81.31 2.41

7.10 0.00 100.00 2.77

6.15 0.00 100.00 3.09

11.57 0.00 100.00 1.75

4.01 0.47 34.64 2.21

5.94 1.16 49.09 1.88

4.97 0.56 44.49 2.19

5.66 1.29 54.40 2.01

5.67 2.23 54.31 2.31

5.17 1.72 51.40 2.40

4.25 0.59 54.40 2.52

14.31 3.00 176.00 3.29

19.65 4.00 209.00 2.73

19.23 4.00 195.00 2.64

26.19 8.00 245.00 2.61

24.93 9.00 207.00 2.28

27.83 10.00 254.00 2.51

18.93 5.00 254.00 3.01

Notes. This table presents summary statistics – mean, median, maximum and skewness – for percentage of shares state-held, percentage held by legal persons, percentage held by mutual funds, and number of mutual funds with holdings in our sample firms. We report these statistics for each year and for our full sample period. The minimum value in all categories and years is zero.

Table 4 Summary statistics for control variables.

Min Mean Median Max Skew

Exec holdings

Top 5

Board size

Board meetings

Board independence

MTB

Quick

Size

DA

0.00 2.74 0.00 87.28 4.32

2.35 53.18 53.42 100.00 0.12

0.00 6.00 6.00 13.00 0.80

1.00 8.97 8.00 38.00 1.73

0.00 60.66 50.00 100.00 1.10

0.06 2.38 1.47 268.32 26.77

0.04 1.45 0.87 42.03 7.07

1340 13,400 2280 1,920,000 14.87

1.65 48.75 50.07 98.17 0.15

Notes: This table presents summary statistics for the control variables. Size (total assets) is denominated in millions of RMB. Please refer to the appendix for

Table 5 Correlations.

USD/RMB exposure EER exposure EUR/RMB exposure % mutual No. of mutual funds LMF % state % legal De facto peg dummy Top 5 holdings Executive holdings Board size Board meetings Board independence CEO duality MTB Quick Size DA

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

0.04 0.14* 0.01 0.03 0.03 0.07* 0.02 0.45* 0.01 0.03 0.00 0.07* 0.00 0.01 0.13* 0.04 0.05 0.04

0.27* 0.04 0.10* 0.02 0.03 0.08* 0.16* 0.02 0.00 0.01 0.01 0.02 0.01 0.09* 0.03 0.09* 0.02

0.01 0.06y 0.03 0.08* 0.05y 0.01 0.02 0.03 0.02 0.01 0.01 0.01 0.12* 0.04 0.06y 0.05y

0.81* 0.71* 0.03 0.03 0.08* 0.06y 0.04 0.06y 0.05 0.04 0.02 0.28* 0.06y 0.25* 0.02

0.37* 0.01 0.13* 0.07* 0.10* 0.02 0.08* 0.07* 0.01 0.01 0.19* 0.05y 0.55* 0.02

0.07* 0.02 0.04 0.08* 0.01 0.01 0.02 0.03 0.02 0.19* 0.04 0.04 0.01

0.20* 0.09* 0.19* 0.25* 0.26* 0.06y 0.05y 0.15* 0.10* 0.11* 0.12* 0.06*

0.09* 0.11* 0.12* 0.08* 0.06y 0.01 0.08* 0.07* 0.09* 0.18* 0.04

0.03 0.03 0.02 0.07* 0.02 0.02 0.11* 0.02 0.04 0.01

0.07* 0.00 0.05 0.02 0.05 0.21* 0.16* 0.05 0.14*

0.12* 0.05 0.02 0.27* 0.19* 0.22* 0.21* 0.18*

0.11* 0.40* 0.16* 0.10* 0.13* 0.15* 0.15*

0.08* 0.03 0.04 0.06y 0.18* 0.15*

0.07* 0.01 0.02 0.05 0.02

0.17* 0.14* 0.15* 0.17*

0.49* 0.43* 0.56*

0.38* 0.72*

0.46*

Notes. This table presents the Spearman rank correlation matrix for all dependent and independent   variables. *, and y indicate significance at the 1 and 5 percent levels respectively. USD/RMB exposure, EUR/RMB exposure, and EER exposure are absolute values of estimates of foreign exchange exposure (bnX from Eq. (1)’) to the USD/RMB and the EUR/RMB bilateral exchange rates, and China’s exchange rate index, respectively. Please see the appendix for detailed definitions of the control variables.

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

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Table 6 Dynamic panel GMM.

% mutual % state

[1]

[2]

[3]

[4]

[5]

[6]

USD 5.081 (0.00) 0.036 (0.93)

EUR 0.078 (0.47) 0.169 (0.00)

EER 0.087 (0.55) 0.226 (0.00)

1.766 (0.00)

0.161 (0.00)

0.270 (0.00)

0.784 (0.00) 11.147 (0.00) 0.749 (0.70) 0.093 (0.31) 0.074 (0.00) 0.880 (0.21) 0.265 (0.34) 0.515 (0.00) 0.036 (0.48) 1.149 (0.00) 3.006 (0.00) 22.368 (0.00)

0.030 (0.00) 0.308 (0.04) 0.170 (0.35) 0.004 (0.59) 0.004 (0.04) 0.039 (0.55) 0.012 (0.63) 0.110 (0.00) 0.002 (0.62) 0.039 (0.01) 0.365 (0.00) 1.181 (0.00)

0.131 (0.00) 0.733 (0.00) 0.027 (0.91) 0.016 (0.16) 0.006 (0.02) 0.044 (0.62) 0.044 (0.20) 0.122 (0.00) 0.011 (0.10) 0.027 (0.18) 0.339 (0.00) 1.150 (0.01)

USD 0.066 (0.96) 1.017 (0.02) 18.443 (0.00) 0.60 (0.30) 20.229 (0.00) 0.734 (0.00) 10.628 (0.00) 0.889 (0.65) 0.093 (0.31) 0.071 (0.00) 0.937 (0.18) 0.206 (0.45) 0.557 (0.00) 0.41 (0.43) 1.212 (0.00) 2.929 (0.00) 23.672 (0.00)

EUR 0.104 (0.45) 0.154 (0.00) 0.290 (0.43) 0.176 (0.00) 0.297 (0.55) 0.030 (0.01) 0.309 (0.00) 0.173 (0.34) 0.005 (0.59) 0.004 (0.04) 0.038 (0.56) 0.012 (0.63) 0.109 (0.00) 0.002 (0.61) 0.039 (0.01) 0.361 (0.00) 1.175 (0.00)

EER 0.180 (0.34) 0.187 (0.00) 0.732 (0.15) 0.293 (0.00) 0.477 (0.47) 0.130 (0.00) 0.732 (0.00) 0.026 (0.92) 0.016 (0.16) 0.006 (0.02) 0.047 (0.60) 0.046 (0.18) 0.120 (0.00) 0.011 (0.09) 0.026 (0.02) 0.332 (0.00) 1.142 (0.00)

% state * % mutual % legal % legal * % mutual De facto peg dummy Top 5 holdings Executive holdings Board size Board meetings Board independence CEO duality MTB Quick Size DA Constant

Notes. q ffiffiffiffiffiffiffiffiffi  This table provides the results for the dynamic panel GMM. The dependent variables are the square root of the absolute exposure coefficient b X for exposure to the USD/RMB and EUR/USD bilateral exchange rates, and China’s EER, as estimated via Eq. (1)’. Columns [1] to [3] present the results n for the base case models (Eq. (3)), and in columns [4] to [6] we add the interaction terms % state * % mutual and % legal * % mutual. Detailed definitions of the control variables are provided in the appendix. p-values are reported in brackets below the coefficients; bold indicates significance at the 5 percent level, and italics indicates significance at the 10 percent level.

have been heightened uncertainty as to the future direction of the formerly predictable USD/RMB exchange rate. During this time, small changes in the exchange rate may have presaged in investors’ minds a change in the direction of movement of the RMB against the dollar, or a change in policy relating to currency arrangements in China. Our finding of lower exposure for the EER and euro when the de facto dollar peg was in place is likely to be an artifact of EUR/RMB volatility. As seen in Table 1, daily EER volatility was at its highest during this period, and EUR/RMB volatility also surged. Given that the Chinese authorities reverted to a dollar peg, the volatility of the RMB against the euro would have been essentially the same as euro/dollar volatility. During the de facto peg period volatility across all financial markets – including currency markets – was unusually high. China’s stock market participants may have understood that this heightened volatility was likely to be short-lived. They would therefore have priced in a lower sensitivity to this amplified nondollar volatility – resulting in a lower estimated stock price sensitivity to exchange rate changes. Other controls. Two of our governance controls exhibit strong significance across all or most specifications – top 5 holdings and board meetings. For both of these variables, the signs on their coefficients are opposite in the dollar and non-dollar specifications. For top 5 holdings, the coefficients are positive for dollar exposure and negative for non-dollar. This is the only governance variable for which we find that apparently better governance – in the form of a bigger percentage of shares held by the largest 5 stockholders – is associated with lower exposure to the euro. This suggests that the oversight of large shareholders ensures that hedging is more likely to be undertaken against exposure to the euro. In contrast, the positive sign on the coefficient in the dollar specifications shows an apparent risk-enhancing effect of large shareholders on dollar exposure. In general, prior research on the relation between ownership concentration and various firm-level metrics shows a positive effect of ownership concentration – such as better performance and greater firm value (Xu and Wang, 1997; Sharpe et al., 2013), and a lower likelihood that management engages in fraudulent activities (Aggarwal et al., 2014), consistent with concentrated ownership mitigating agency problems. Our finding that top 5 holdings is associated with greater exposure to the dollar suggests otherwise. We revisit this issue in Section 5.

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Board meetings is significantly negative for exposure to the dollar and positive for non-dollar exposures; that is, more board meetings are associated with lower exposure to the dollar and higher exposure to the non-dollar currencies. There are two opposing explanations for the effect of more frequent board meetings and firm value: first, boards meeting more frequently is a proxy for the quality of the board’s vigilance and monitoring, and second, frequent board meetings provides evidence of stress or controversy (Vafeas, 1999; Chen et al., 2006). Our findings on dollar exposure therefore suggest that more effective board oversight is associated with better dollar risk management. In contrast, our finding of enhanced exposure to the euro and the EER as the number of board meetings rise suggests that boards dealing with other problems or crises are not focused on day-to-day foreign exchange risk management issues. In the risk management literature leverage is considered a proxy for financial risk, and theory suggests that more highly leveraged firms should be more likely to hedge. If they do not, however, they will be more exposed, because adverse exchange rate movements impair firms’ ability to meet interest payments (Hutson and Stevenson, 2010). Consistent with this latter argument, we find that firms with greater leverage (debt-to-assets ratio, or DA) are generally more exposed to movements in the EUR/RMB exchange rate. However, we find the opposite for exposure to the dollar: DA is strongly negatively related to exposure to the dollar across all specifications.16 Lastly, as expected we find that high market-to-book value of equity (MTB) firms are more exposed, as are smaller firms.17

5. The role of dollar-denominated debt We suggest that dollar-denominated borrowing provides a plausible explanation for our finding that a greater presence of mutual funds is more strongly associated with a reduction in exposure to the dollar than to exposure to the euro and to China’s EER. The moral hazard hypothesis of Eichengreen and Hausmann (1999) suggests that foreign currency borrowing is common in pegged and heavily managed exchange rate systems as a result of an implicit government guarantee (Eichengreen and Hausmann, 1999; Chang and Velasco, 2000; Burnside et al., 2001; Schneider and Tornell, 2004). Due to obvious risks relating to currency mismatches, however, foreign currency borrowing is not recommended unless it is used for hedging. Borrowing in foreign currencies is particularly risky in pegged regimes because when pegs break and currencies plummet, the local currency value of foreign borrowings skyrockets – as occurred, for example, during the Mexican and Asian crises of the 1990s. In this section, we advance the argument that mutual funds in China perform a monitoring role to ensure the firms in which they invest limit their unhedged dollar borrowing.18 The period under study – China’s ‘managed float’ period – was unusual in that unhedged dollar borrowings may have been seen not only as low-risk but also as profit-enhancing. This is because the Chinese authorities were allowing the RMB to rise gradually against the dollar, against a backdrop of very strong net inflows on current and capital account as well as intermittent pressure from the US to revalue the RMB. The future movement in the RMB during this period, therefore, would have been to a large extent predictable. Dollar borrowings would have made apparent financial sense because firms would not only have been paying a lower rate of interest in dollar terms,19 but their interest costs in RMB would have been expected to fall over time. Two questions arise. First, were Chinese firms borrowing dollar-denominated; and second, were they borrowing unhedged? Chinese firms were certainly borrowing substantial amounts in dollars during our sample period, according to a report from the Bank for International Settlements (BIS).20 This accelerated in the final few years of our sample period – rising from $270 billion in 2009 to $880 billion in March 2012, and the vast majority of this was denominated in dollars. It is highly likely that many Chinese firms were borrowing dollars unhedged. In an analysis of dollar-denominated debt issuance by non-US firms, Bruno and Shin (2015) show that for emerging market firms – but not for firms in developed countries – dollar debt is often raised for currency carry trade-like purposes. Another BIS report – on offshore securities issuance by emerging market firms – specifically mentions unhedged dollar borrowing amongst Chinese construction companies,21 as do Kofanova et al. (2015). There is also considerable anecdotal evidence that Chinese firms engaged in unhedged dollar borrowing. In the early years of the period of RMB depreciation against the dollar, which began in early 2014, several newspaper articles reported that Chinese firms were rushing to pay back their dollar-denominated debt. 22 This is clearly not an imperative for firms that had borrowed in dollars explicitly to hedge. 16

We revisit this issue in the next section. We have estimated parsimonious versions of all our regression analyses – that is, without the insignificant control variables executive holdings, board size, board independence and CEO duality, and the results are unchanged. 18 As discussed in footnote 5, we cannot demonstrate this definitively, because the CSMAR data on foreign currency borrowings are incomplete. Later in this section we examine a small set of available dollar-denominated debt data. 19 Chinese firms were in general paying a lower rate of interest on dollar vis-à-vis yuan-denominated debt; see for example ‘‘Chinese corporates reassess FX hedging” Euromoney, November 11, 2015. http://www.euromoney.com/Article/3505396/Chinese-corporates-reassess-FX-hedging.html. 20 The Telegraph, October 27, 2013. http://www.telegraph.co.uk/finance/china-business/10407625/BIS-sees-risk-of-1998-style-Asian-crisis-as-Chinese-dollardebt-soars.html. 21 ‘‘Emerging market debt securities issuance in offshore centres” Bank for International Settlements, September 15, 2013. http://www.bis.org/publ/qtrpdf/r_ qt1309w.html. 22 ‘‘Forex losses lay bare China’s lack of hedging expertise” Reuters, May 15, 2014. ‘‘China’s big dollar borrowers hold off on hedging foreign currency debt” Wall Street Journal, August 28, 2016. ‘‘Chinese corporates reassess FX hedging” Euromoney, November 11, 2015. ‘‘China Inc ‘bleeding’ from Yuan devaluation seeks hedging help” Bloomberg, March 23, 2016. ‘‘Currency hedging demand rises amongst Chinese firms” Financial Times, June 27, 2016. ‘‘Yuan swings hurt Chinese developers, but hedging can hurt more” South China Morning Post, June 27, 2016. 17

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Median USD absolute exposure

184

1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

Quanle 1

Quanle 2

Quanle 3

Quanle 4

US dollar debt / total assets Fig. 2. Median absolute dollar exposure vs US dollar debt. This figure depicts, for a small subset of our firms for which a breakdown of dollar-denominated borrowings is available (n=112 firm years), median absolute US dollar exposure by dollar debt-to-assets quantiles.

Our summary statistics on estimated exposure in Table 2 show a rising number of positive exposures in the latter part of the sample period. The proportion of firms that are negatively exposed to the dollar fell from 83 percent to 45 percent between 2010 and 2011 – which is the same as saying that there was a leap in positive exposures from 17 to 55 percent. Further, the percent of firms significantly positively exposed rose from 2 percent in 2010 to 9 percent in 2011. As the cost of servicing dollar-denominated debt falls as the RMB rises, this increase in positive exposure to the dollar is likely to be the result of a rise in unhedged dollar-denominated borrowing amongst our sample firms.23 In a final element of evidence in support of the argument that our findings on dollar exposure are the result of dollardenominated borrowing, we use a very small subsample of firms for which data on US dollar debt are available from CSMAR. We have data for 112 firm years. Average dollar-denominated debt is 4.5 percent of total assets, with the median being 1.9 percent and the maximum 32 percent. Fig. 2 depicts median absolute dollar exposure of the firms in this subsample, by dollar-denominated debt quartiles. A U-shaped relation is apparent. The highest median absolute exposure to the dollar is found amongst quartile 4 firms – those with the highest dollar debt relative to assets. For these firms, it is plausible that dollar-denominated debt is the source of their high exposure. Firms in the lowest quartile of dollar debt-to-assets (quartile 1) are also highly exposed to the dollar. These firms clearly use little if any dollar debt, and the source of their exposure is likely to be unhedged dollar revenues. The smaller median exposures for the firms in foreign debt quartiles 2 and 3 is best explained by a judicious quantity of dollar borrowings that act as a hedge against volatility in the RMB value of dollar-denominated revenues. It follows that some Chinese firms were using dollar debt for hedging purposes. Kofanova et al. (2015) find that many emerging market dollar issuers use dollar debt to at least partially hedge, and the BIS shows that foreign currency borrowings by several Chinese companies were used explicitly to create hedged positions when financing the acquisition of foreign assets.24 5.1. Top 5 holdings and DA Across all of our specifications we find a negative relation between leverage (DA) and exposure to the dollar, and a positive relation between DA and euro exposure. A positive relation is explained by leverage making firms more vulnerable to the cash flow shocks associated with currency movements (see, for example, Hutson and Stevenson, 2010). Our findings that firms with greater debt are more exposed to movements in the EUR/RMB exchange rate suggest that managers fail to recognize the dangers of not hedging this salient exposure when leverage is high. The negative relation for dollar exposures can be explained by Chinese firms recognizing the dangers of leverage in heightening dollar exposures, and therefore being more likely to hedge (He and Ng, 1998). If such firms use dollar-denominated borrowing to hedge their dollar revenues, the inverse relation between leverage and exposure to the dollar could, at least in part, be explained by a material proportion of their debt being dollar-denominated. Recall that we find a positive relation between top 5 holdings and exposure to the dollar, and a negative relation for EER and euro exposure. Contrary to the literature on multiple large shareholders, and to the hopes of the Chinese authorities that such shareholders provide a check on controlling shareholders (Jiang and Kim, 2015), the former finding suggests that large shareholders were championing dollar-denominated borrowing. In contrast, the inverse relation for EER and euro exposure suggests that large shareholders monitor to ensure firms hedge their salient exposures. These findings are not necessarily contradictory – if Chinese firms’ managers tend to focus on the short term. Profit-oriented managers may have considered the short-term earnings-enhancing benefits of unhedged dollar borrowing to outweigh the risks. For exposure to the non23

This of course assumes that investors were pricing in a negligible likelihood that the yuan would reverse trend and fall against the dollar in the short term. ‘‘Emerging market debt securities issuance in offshore centres” Bank for International Settlements, September 15, 2013. http://www.bis.org/publ/qtrpdf/r_ qt1309w.html. 24

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E. Hutson et al. / J. Int. Financ. Markets Inst. Money 60 (2019) 169–192 Table 7 Dynamic panel GMM: number of mutual funds.

No. of mutual funds % state

[1]

[2]

[3]

[4]

[5]

[6]

USD 0.007 (0.06) 0.122 (0.76)

EUR 0.001 (0.06) 0.154 (0.00)

EER 0.001 (0.09) 0.217 (0.00)

1.874 (0.00)

0.145 (0.00)

0.272 (0.00)

0.777 (0.00) 10.465 (0.00) 0.09 (0.97) 0.089 (0.33) 0.085 (0.00) 0.932 (0.19) 0.234 (0.40) 0.476 (0.00) 0.052 (0.33) 1.087 (0.00) 3.415 (0.00) 21.618 (0.00)

0.031 (0.01) 0.297 (0.00) 0.216 (0.31) 0.005 (0.53) 0.004 (0.04) 0.032 (0.62) 0.01 (0.69) 0.115 (0.00) 0.004 (0.46) 0.038 (0.02) 0.383 (0.00) 1.156 (0.00)

0.132 (0.00) 0.711 (0.00) 0.032 (0.91) 0.014 (0.22) 0.007 (0.01) 0.059 (0.51) 0.036 (0.30) 0.126 (0.00) 0.012 (0.07) 0.023 (0.30) 0.354 (0.00) 1.078 (0.03)

USD 0.000 (0.96) 0.755 (0.12) 0.030 (0.00) 0.726 (0.22) 0.063 (0.00) 0.796 (0.00) 9.838 (0.00) 0.122 (0.96) 0.078 (0.40) 0.083 (0.00) 0.973 (0.17) 0.199 (0.47) 0.547 (0.00) 0.054 (0.31) 1.045 (0.00) 3.427 (0.00) 20.861 (0.00)

EUR 0.000 (0.79) 0.212 (0.00) 0.002 (0.01) 0.232 (0.00) 0.005 (0.01) 0.029 (0.01) 0.341 (0.00) 0.215 (0.31) 0.006 (0.46) 0.004 (0.03) 0.038 (0.56) 0.009 (0.74) 0.120 (0.00) 0.004 (0.47) 0.036 (0.03) 0.388 (0.00) 1.108 (0.00)

EER 0.001 (0.07) 0.187 (0.00) 0.001 (0.39) 0.324 (0.00) 0.004 (0.11) 0.132 (0.00) 0.715 (0.00) 0.04 (0.89) 0.014 (0.21) 0.007 (0.01) 0.057 (0.52) 0.037 (0.29) 0.126 (0.00) 0.012 (0.06) 0.022 (0.34) 0.345 (0.00) 1.050 (0.04)

% state * no. of mutual funds % legal % legal * no. of mutual funds De facto peg dummy Top 5 holdings Executive holdings Board size Board meetings Board independence CEO duality MTB Quick Size DA Constant

Notes. This table provides the results for the dynamic panel qffiffiffiffiffiffiffiffiffi  GMM in which we use number of mutual funds in lieu of % mutual. The dependent variables are the square root of the absolute exposure coefficient bnX for exposure to the USD/RMB and EUR/USD bilateral exchange rates, and China’s EER, as estimated via Eq. (1)’. Columns [1] to [3] present the results for the base case models (Eq. (3)), and in columns [4] to [6] we add the interaction terms % state * % mutual and % legal * % mutual. Detailed definitions of the control variables are provided in the appendix. p-values are reported in brackets below the coefficients; bold indicates significance at the 5 percent level, and italics indicates significance at the 10 percent level.

dollar currencies – for which adverse changes in their value could occur at any time – there are clear and immediate benefits to the bottom line of hedging against movements in these volatile currency pairs. 6. Robustness tests 6.1. Number of mutual funds We repeat our main analysis using the number of mutual funds holding each firm’s shares rather than the percentage of shares held by mutual funds (Sias, Starks and Titman, 2006; Sharpe et al., 2013). These results can be found in Table 7 (which follows the structure of Table 6). For dollar exposures, our findings are essentially the same as those of our original specifications in Table 6. In one important respect, however, our number of mutual funds findings are stronger than those of our base specifications: while formerly we found no effect of mutual fund participation on exposures to the euro, here we find a (weakly) significant negative effect (column [2]; p = 0.06). This provides some evidence that a greater number of mutual fund voices – rather than a greater proportion of stock held by mutual funds – has the effect of monitoring to ensure that Chinese firms’ more salient exposures are hedged. For exposure to the euro we also find that the interaction terms are highly significant and negative (column [5]; p = 0.01 for both interaction terms) at the same time as the number of mutual funds term becomes insignificant. This suggests that the euro exposure risk-reduction effect of enhanced monitoring is confined to firms with greater state and legal person ownership that are known to have weaker governance and incentive structures. 6.2. Total exposure As seen in Fig. 1, during our sample period the RMB was closely tied to the dollar. It may be argued that the standard approach to estimating exposure using Jorion’s (1990) market-adjusted model, which estimates ‘residual’ exposure, is not

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Table 8 Dynamic panel GMM: total exposure.

% mutual % state

[1]

[2]

[3]

[4]

[5]

[6]

USD 6.453 (0.00) 0.703 (0.12)

EUR 0.044 (0.69) 0.048 (0.20)

EER 0.208 (0.23) 0.036 (0.55)

2.595 (0.00)

0.018 (0.72)

0.101 (0.18)

0.442 (0.00) 17.103 (0.00) 1.624 (0.48) 0.185 (0.08) 0.103 (0.00) 0.919 (0.26) 0.318 (0.32) 0.314 (0.01) 0.063 (0.30) 1.408 (0.00) 5.812 (0.00) 26.679 (0.00)

0.021 (0.07) 0.227 (0.02) 0.216 (0.25) 0.004 (0.62) 0.005 (0.02) 0.071 (0.29) 0.009 (0.74) 0.063 (0.00) 0.003 (0.49) 0.071 (0.00) 0.180 (0.04) 0.974 (0.00)

0.018 (0.30) 0.660 (0.00) 0.651 (0.03) 0.002 (0.90) 0.010 (0.00) 0.005 (0.96) 0.059 (0.15) 0.233 (0.00) 0.001 (0.89) 0.050 (0.04) 0.444 (0.00) 0.026 (0.96)

USD 0.299 (0.86) 0.424 (0.41) 21.022 (0.00) 1.005 (0.13) 27.798 (0.00) 0.385 (0.01) 16.360 (0.00) 1.758 (0.44) 0.183 (0.08) 0.099 (0.00) 0.976 (0.23) 0.234 (0.46) 0.362 (0.00) 0.068 (0.25) 1.485 (0.00) 5.703 (0.00) 28.327 (0.00)

EUR 0.206 (0.15) 0.080 (0.06) 0.587 (0.13) 0.056 (0.31) 0.673 (0.19) 0.019 (0.09) 0.206 (0.03) 0.221 (0.24) 0.004 (0.63) 0.005 (0.02) 0.069 (0.31) 0.007 (0.79) 0.061 (0.00) 0.003 (0.51) 0.073 (0.00) 0.174 (0.04) 1.016 (0.00)

EER 0.162 (0.48) 0.025 (0.72) 0.196 (0.75) 0.108 (0.22) 0.114 (0.89) 0.018 (0.31) 0.665 (0.00) 0.654 (0.03) 0.002 (0.90) 0.010 (0.00) 0.005 (0.96) 0.06 (0.15) 0.233 (0.00) 0.001 (0.89) 0.049 (0.05) 0.444 (0.00) 0.022 (0.97)

% state * % mutual % legal % legal * % mutual De facto peg dummy Top 5 holdings Executive holdings Board size Board meetings Board independence CEO duality MTB Quick Size DA Constant

Notes. q ffiffiffiffiffiffiffiffiffi  This table provides the results for the dynamic panel GMM. The dependent variables are the square root of the absolute total exposure coefficient b X for exposure to the USD/RMB exchange rate, the EER and the EUR/USD rate, as estimated via an amended version of Eq. (1)’ from which the market n term Rm,t has been removed. Columns [1] to [3] present the results for the base case models (Eq. (3)), and in columns [4] to [6] we add the interaction terms % state * % mutual and % legal * % mutual. Detailed definitions of the control variables are provided in the appendix. p-values are reported in brackets below the coefficients; bold indicates significance at the 5 percent level, and italics indicates significance at the 10 percent level. Detailed definitions of the control variables are provided in the appendix. P-values are reported in brackets below the coefficients; bold indicates significance at the 5 percent level or better, and italics indicates significance at the 10 percent level.

appropriate when exchange rates are heavily controlled. The market term in Eq. (1)’ is included on the basis that both exchange rates and stock prices are directly affected by the same macroeconomic shocks, such as shocks to interest rates and expected inflation. If the exchange rate is heavily controlled – as is the case with the RMB against the dollar – changes in the exchange rate are likely to be little affected by macroeconomic factors. The use of a market term may therefore introduce a bias into the exposure estimates. In our second set of robustness tests, we estimate total annual exposures for each firm as in Eq. (1)’ without the market term. The square root of the absolute value of these total exposure estimates are used as the dependent variable in the dynamic panel GMM regressions. Table 8 presents these results. It is clear that our original findings of an inverse relation between mutual fund holdings and exposure to the dollar are remarkably robust to the use of total exposure. There is a strong negative relation between the percentage of shares held by mutual funds and dollar exposure (column [1]), and when we include the interaction terms (column [4]), the dollar exposure-reduction effect of mutual funds is again confined to firms with high levels of state and legal person ownership. On exposure to the non-dollar currencies, our original results are also confirmed; mutual fund participation has essentially no impact on exposure to the euro and the EER. 6.3. Large mutual fund holdings Despite the evidence that the monitoring activity of Chinese institutions has various positive outcomes (Yuan et al., 2008; Aggarwal et al., 2014; Wu et al., 2016), Jiang and Kim (2015) argue that Chinese institutional shareholders do not monitor. They advance this assertion on the basis that institutions have very small stakes in Chinese firms, particularly relative to dominant state or family majority shareholders. Indeed, theory suggests that mutual funds are more likely to be active monitors when they have large shareholdings (Shleifer and Vishny, 1986). In this robustness test, we use a measure of mutual

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E. Hutson et al. / J. Int. Financ. Markets Inst. Money 60 (2019) 169–192 Table 9 Dynamic panel GMM: large mutual fund shareholdings.

% LMF % state

[1]

[2]

[3]

[4]

[5]

[6]

USD 19.343 (0.00) 0.030 (0.94)

EUR 0.282 (0.17) 0.173 (0.00)

EER 0.584 (0.03) 0.233 (0.00)

1.643 (0.00)

0.164 (0.00)

0.274 (0.00)

0.750 (0.00) 10.979 (0.00) 1.166 (0.55) 0.085 (0.34) 0.066 (0.00) 0.952 (0.17) 0.173 (0.53) 0.585 (0.00) 0.020 (0.69) 1.089 (0.00) 2.888 (0.00) 21.345 (0.00)

0.031 (0.01) 0.315 (0.00) 0.178 (0.33) 0.004 (0.61) 0.004 (0.06) 0.038 (0.56) 0.013 (0.60) 0.104 (0.00) 0.002 (0.66) 0.044 (0.00) 0.357 (0.00) 1.280 (0.00)

0.131 (0.00) 0.740 (0.00) 0.041 (0.87) 0.017 (0.15) 0.005 (0.04) 0.045 (0.60) 0.047 (0.17) 0.112 (0.00) 0.010 (0.11) 0.034 (0.09) 0.326 (0.00) 1.315 (0.00)

USD 0.838 (0.78) 1.553 (0.00) 69.785 (0.00) 0.165 (0.76) 56.291 (0.00) 0.699 (0.00) 10.088 (0.00) 0.854 (0.66) 0.100 (0.26) 0.062 (0.00) 0.926 (0.18) 0.100 (0.71) 0.610 (0.00) 0.026 (0.61) 1.134 (0.00) 2.581 (0.00) 22.034 (0.00)

EUR 0.119 (0.68) 0.141 (0.00) 1.534 (0.06) 0.135 (0.01) 1.155 (0.25) 0.030 (0.01) 0.290 (0.00) 0.174 (0.34) 0.005 (0.58) 0.004 (0.07) 0.038 (0.55) 0.014 (0.59) 0.103 (0.00) 0.002 (0.63) 0.043 (0.00) 0.347 (0.00) 1.270 (0.00)

EER 0.097 (0.80) 0.176 (0.00) 2.690 (0.01) 0.225 (0.00) 1.827 (0.18) 0.130 (0.00) 0.701 (0.00) 0.034 (0.89) 0.016 (0.15) 0.005 (0.05) 0.044 (0.61) 0.049 (0.15) 0.111 (0.00) 0.010 (0.10) 0.034 (0.09) 0.310 (0.01) 1.321 (0.00)

% state * % LMF % legal % legal * % LMF De facto peg dummy Top 5 holdings Executive holdings Board size Board meetings Board independence CEO duality MTB Quick Size DA Constant

Notes: This table provides the results for the dynamic panel GMM in which we use % LMF, defined as the percentage aggregate ownership of mutual funds that each hold 1 percent or more of the firm’s equity, in lieu of % mutual. The dependent variables are the square root of the absolute exposure coefficient q ffiffiffiffiffiffiffiffiffi  b X for exposure to the USD/RMB and EUR/USD bilateral exchange rates, and China’s EER, as estimated via Eq. (1)’. Columns [1] to [3] present the results n for the base case models (Eq. (3)), and in columns [4] to [6] we add the interaction terms % state * % mutual and % legal * % mutual. Detailed definitions of the control variables are provided in the appendix. p-values are reported in brackets below the coefficients; bold indicates significance at the 5 percent level, and italics indicates significance at the 10 percent level.

fund holdings that encompasses only large mutual fund holdings (LMF), defined as the percentage aggregate ownership of mutual funds that each hold 1 percent or more of the firm’s equity (Firth et al., 2016). If our findings on the dollar exposurereduction effect of mutual fund participation are stronger using LMF than using % mutual (as in our base specifications in Table 6), this would provide support for our argument that Chinese mutual funds monitor their investee firms to ensure that they curtail their unhedged dollar-denominated borrowing or hedge their dollar exposures. In 40 percent of our firm years LMF is positive, and the average mutual fund shareholding for these firms is 4.8 percent of total shares. Table 9 presents these findings for the GMM dynamic panel regression in which we replace the percentage of shares held by mutual funds with LMF. It is clear that the magnitude of the effect of mutual fund participation on our sample firms’ exposure to the dollar is much greater when we use LMF. For example, in column [1] of the table, the coefficient on LMF in the dollar exposure regressions is 19.3 (and this is highly significant (p = 0.00)); this is almost 4 times the magnitude of the equivalent coefficient in our base specifications (column [1] of Table 6). In the dollar exposure specification in which we include the interaction terms (column [4]), the coefficients on % state * LMF and % legal * LMF are negative and highly significant, and are about three times the size of the corresponding coefficients in Table 6. These findings provide further support for our contention that mutual funds monitor Chinese firms to ensure that they curtail their unhedged dollar borrowing. 6.4. Additional robustness tests Propensity score matching. Mutual funds may be drawn to firms with better foreign exchange risk management practices and therefore lower foreign exchange exposure. To alleviate concerns relating to selection bias, we repeat our main analysis using a propensity score matched sample. Using propensity scores, treatment firms (those with mutual fund ownership) and control firms (those without mutual fund ownership) are matched without replacement using similar several firm-level

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characteristics: firm size, quick ratio, market-to-book ratio, debt-to-assets ratio, executive holdings, top 5 holdings, board size, board meetings, and board independence. We impose common support in our propensity score distributions; we remove treatment firms with a propensity score higher than the largest propensity score of probable control firms. We also remove potential control firms that have a propensity score lower than the minimum score of treatment firms. The matched sample comprises 1008 firm year observations. In preliminary analysis, the average treatment effect on the treated (ATT) results suggest that treatment firms with mutual fund ownership exhibit significantly lower dollar exposure (at the 5 percent level). When we repeat our main dynamic GMM analysis using the matched sample, our findings are robust and similar to our earlier findings presented in Table 6.25 Dedicated vs. transient mutual funds. Mutual funds whose style is oriented toward long-term investment are believed to play a more pronounced monitoring role in their investee firms, compared to short-term investors (Chen et al., 2007). We distinguish between dedicated and transient mutual funds and examine whether dedicated mutual funds are more effective in reducing the foreign exchange exposure of their investee firms. Following Niu et al. (2013), we measure the investment stability of mutual fund i in firm n (IOSi,n) as the shareholding ratio in the current year divided by the standard deviation of the shareholding ratios in the previous three years. The larger the IOSi,n, the more stable is the investment of mutual fund i in firm n. To take into account differences across industries, we calculate the median value of the IOSi,n for each industry, IOSIm, and then compare IOSi,n with IOSIm. If IOSi,n is larger than (or equal to) its corresponding IOSIm, mutual fund i will be categorized as dedicated for firm n, otherwise it is classified as transient. Finally, we calculate the ratio (Rn) of the number of dedicated mutual funds to the total number of mutual funds in firm n. If Rn is larger than (or equal to) 0.5, the dedicated investors in firm n are in a dominant position; otherwise, it indicates predominant ownership by transient investors. Dividing sample firms into two groups – one with dedicated investors in a dominant position and the other with mostly transient investors – we find that the USD exposure for firms with dedicated mutual funds is 1.08, which is significantly lower (at the 1 percent level) than that for firms with transient mutual funds of 2.01. This finding is consistent with our argument that dedicated mutual funds may be more effective in strengthening Chinese firms’ risk management, resulting in lower exposure to the US dollar of their investee firms. To investigate this further, we incorporate an interaction term into our GMM model, as stated in the following Eq. (4).

qffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffi  X   b  ¼ ant þ b1 b X  n n t

t1

þ b2 X nt þ b3 X nt Dednt þ b4 Z nt þ b5 Dnt þ nt

ð4Þ

where Dednt = 1 if firm n is with dedicated mutual funds in dominant position, and = 0, otherwise. To interact mutual fund ownership Xnt with Dednt , so that the coefficient of the interaction term is supposed to capture the comparative strengthening/weakening effects of dedicated mutual funds compared to transient ones on their investee firms. The specifications of other variables are the same as in Eq. (3). Using this approach, we find that the coefficient of the interaction term is significantly negative for USD exposure. That is, the exposure-reducing effects of mutual fund ownership are more pronounced when the mutual funds are dedicated.26 The most vs. least important investees. The strength of monitoring effect of mutual funds on the investee firms in their portfolio may vary. A low level of mutual fund ownership does not necessarily imply little monitoring. For instance, the shareholding in firm A of mutual fund B may be small but account for a significant proportion of mutual fund B’s assets. Firm A can therefore be considered an important investee for mutual fund B, and it is reasonable to expect that mutual fund B is more willing to monitor firm A even though it is not considered a big investor in firm A. To examine whether mutual funds exert a more pronounced monitoring effect on their important investees, we distinguish between the most important and the least important investees in each fund’s portfolio. Specifically, we identify the most important 3 (Top 3) and the least important 3 (Bottom 3) investees in each fund’s portfolio in each year 2005–2011. We also identify the most important mutual funds for the Top 3 firms, and the least important mutual funds for the Bottom 3 firms. We calculate the total shareholdings of the most and least important mutual funds for these Top 3 and Bottom 3 firms, and divide our sample firms into two groups – one that comprises firms that are prioritized by their institutional investors, and the other includes firms that are less important to their investors. We find that the USD exposure for firms that are prioritized by their investors is 1.12 – significantly lower than that for firms that are not as important (1.87). To investigate this further, we incorporate an interaction term into our GMM model, as follows:

qffiffiffiffiffiffiffiffiffi qffiffiffiffiffiffiffiffiffi b X  ¼ ant þ b1 b X  n n t

t1

þ b2 X nt þ b3 X nt Topnt þ b4 Z nt þ b5 Dnt þ nt

ð5Þ

where Topnt = 1 if firm n is in the Top 3 important firms in their mutual funds’ portfolios, and 0 otherwise. In interacting mutual fund ownership Xnt with Topnt the coefficient of the interaction term should capture whether mutual funds exert larger exposure-reducing effects on their most important investee firms compared to less important investee firms in their portfolio. The specifications of other variables are the same as in Eq. (3). We find that the coefficient on the interaction term is significantly negative for USD exposure.27 This finding supports our argument that mutual funds exert greater monitoring 25 26 27

For the sake of brevity we do not report the results but are available upon request to the corresponding author. For the sake of brevity we do not report the results but are available upon request to the corresponding author. For the sake of brevity we do not report the results but are available upon request to the corresponding author.

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effort on their most important investee firms, improving their risk management practices and helping to lower their foreign exchange exposure. Weekly exposure estimates. In our main analysis, we estimated the exchange exposure coefficients using daily data. A potential shortcoming of this approach is that there may be a timing difference if the stock market close does not coincide with the daily exchange rate. To address this possibility, and to exclude ‘artificial’ stock prices in daily data that may arise from thin trading, we re-run the regression analysis with the exposure coefficients estimated via Eq. (1) using weekly data. Unreported results support our main findings that mutual fund ownership is associated with lower exposure to the US dollar in firms with higher state ownership.28 7. Conclusion Using a sample of 560 firms over the period 2005–2011, we examine the extent to which mutual funds perform an effective monitoring role that mitigates the foreign exchange exposures of Chinese listed firms. Using dynamic panel GMM, we find that the greater the proportion of shares held by mutual funds, the lower the exposure to the dollar, and this relation is confined to firms with high levels of state and legal person ownership. We contend that the most plausible explanation for this finding is that mutual funds warn firms about the dangers of unhedged dollar-denominated borrowing, and ensure that they limit the extent of this activity. When we use the number of mutual funds rather than percent held by mutual funds, we find the same relation for exposure to China’s EER and the EUR/RMB exchange rate, suggesting that mutual funds also ensure that firms hedge their salient exposures. Our findings are consistent with research on financial risk management in developed countries – that institutions advise and monitor firms to ensure appropriate hedging is conducted (Géczy et al., 1997; Graham and Rogers, 2002). They also complement recent studies that show mutual fund participation yields positive outcomes for the shareholders of Chinese firms (Yuan et al., 2008; Aggarwal et al., 2014; Wu et al., 2016). This paper contributes more generally to the body of evidence demonstrating that institutional ownership provides material benefits to shareholders. Appendix:. Independent variable definitions

Variable

Definition

% mutual % state % legal Number of mutual funds % LMF

Mutual institutional ownership is the percentage of total shares held by mutual funds State ownership is the percentage of shares held by the state Legal person ownership is the percentage of shares by legal entities The number of mutual funds with a holding (of any size) in the firm

De facto peg Top 5 holdings Executive holdings Board independence Board size Board meetings CEO duality MTB Quick DA Size

Industry

28

Large mutual funds is the percentage aggregate ownership of mutual funds that each hold 1 percent or more of the firm’s equity A binary variable equal to 1 for the year 2009 – the full year in which the de facto dollar peg was in place – and zero otherwise The sum of the largest 5 largest shareholdings expressed as a percentage of total shareholdings The proportion of shares held by top executives. The holdings of directors and supervisors are excluded The percentage of independent directors defined as the number of independent directors divided by the total number of directors on board The number of directors on the board The number of meetings held each year A binary variable assigned a value of one if the CEO also holds the position of board chair and zero otherwise Market-to-book value of equity is defined as the market value of the ordinary (common) equity divided by the balance sheet value of the ordinary (common) equity The quick ratio (also referred to as the liquidity ratio) is defined as (cash & equivalents + receivables (net))/current liabilities The debt-to-assets ratio is long-term total debt divided by total assets Size is proxied by total assets, which is the sum of total current assets, long term receivables, investments in unconsolidated subsidiaries, other investments, net property, plant and equipment, and other assets Using the China Securities Regulatory Commission (CSRC) industry classification codes, we include 13 industry dummies

For the sake of brevity we do not report the results but are available upon request to the corresponding author.

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