Foreign direct investment and downside risk: Evidence from Taiwan

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Foreign direct investment and downside risk: Evidence from Taiwan Li-Hsun Wanga, , Chu-Hsiung Linb, Hung-Gay Fungc, Tzu-Chuan Kaod ⁎

a

Department of International Business Administration, Wenzao Ursuline University of Languages, Taiwan, 900 Mintzu 1st Rd, Kaohsiung City 80793, Taiwan Department of Finance, National Kaohsiung University of Science and Technology, Taiwan, 2 Jhuoyue Rd, Kaohsiung City 81164, Taiwan c College of Business Administration, University of Missouri-St. Louis, One University Blvd, St. Louis, MO 63121, USA d Department of Financial Management, Kun Shan University, 195, Kunda Rd, Tainan City 71070, Taiwan b

ARTICLE INFO

ABSTRACT

Keywords: Downside Risk FDI Agency cost Earnings Management

Foreign direct investment (FDI) has become highly popular in recent decades. This study investigates how FDI by Taiwanese firms affects downside risk by considering the fact that FDI firms increase both the revenue and risk in their expedition. The results indicate that downside risk is positively associated with FDI. Further analyses show that downside risks do not change for FDI in Mainland China but increases in other areas. The FDIs in areas other than Mainland China are primarily in tax haven countries and are positively related to agency cost and earnings management, increasing the downside risk. In addition, this study provides evidence that corporate governance reduces downside risk by restricting FDI to less opaque markets.

JEL Classification: F21 G32 G34

1. Introduction Foreign direct investment (FDI) is quite pervasive and attracts the attention of many economists (Sun et al. 2002; Davletshin et al. 2015). A substantial number of studies focus on the effect of FDI on economic growth (e.g., Davletshin et al. 2015), the improvement in the productivity of the host country (e.g., Fujimori and Sato 2015), the determinants of FDI decisions (e.g., Kinuthia and Murshed 2015), the location choice (Hao and Lahiri 2009), and the wage improvement within target firms (Wang and Wang 2015). This study focuses on whether the FDI affects the downside risk of the parent company (hereafter FDI firm). It is often observed that firms extend their production bases to foreign countries in search of cheap labor and large markets (Sanjo 2015). Iamsiraroj (2016) presents a significant growth of FDI after the 1990s and documents that FDI offers positive results ranging from a higher degree of stability, financial resource augmentation, positive productivity, and access to foreign markets. The characteristics of more financial resources, lower costs, and diversified markets contribute to moderate operation, consequently reducing the impact of unexpected negative shock. As a result, lower downside risks1 are expected for FDI firms. Essentially, it is difficult for stakeholders of FDI firms to monitor FDI, exacerbating the managerial agency's problems and Corresponding author. E-mail addresses: [email protected] (L.-H. Wang), [email protected] (C.-H. Lin), [email protected] (H.-G. Fung), [email protected] (T.-C. Kao). 1 Downside risk refers to the condition of firms' negative stock returns in this study (e.g., Wang et al. 2015). One of the most popular tradition risk measures, the volatility of stock returns, concerns the distribution of stock returns to a specific asset. Contrarily, the downside risk only sheds light on the distribution of negative counterparts. That is, the volatility of stock returns contains both upside and downside risks. The former is welcomed and the latter is detested by investors. ⁎

https://doi.org/10.1016/j.pacfin.2019.01.010 Received 2 May 2018; Received in revised form 10 November 2018; Accepted 26 January 2019 0927-538X/ © 2019 Published by Elsevier B.V.

Please cite this article as: Wang, L.-H., Pacific-Basin Finance Journal, https://doi.org/10.1016/j.pacfin.2019.01.010

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information asymmetry. Lee and Kwok (1988) argue that geographical constraints, culture, language and legal differences, multicountry financial statements, and multi-country auditors make it difficult to monitor managers in international markets. Additionally, Singhal and Zhu (2013) argue that managers may make value-destroying diversification decisions to derive and preserve private benefits, such as enhanced status, high perquisites, future employment prospects, and reduced employment risk. That is, FDI provides opportunities for managers to pursue personal interests. In line with the potential agency problem, managers also have more liberty to manipulate financial reports and increase the degree of information asymmetry in FDI. Hsu and Liu (2016) argue that FDI extends the organizational structure of the corporation and increases the information asymmetry. Therefore, FDI firms might be associated with more agency problems and information asymmetry. Their shareholders encounter a huge stock price slump at the time the agency problems or information asymmetry break out, implying a higher downside risk. In view of the goal of shareholder protection, investigating downside risk is critical for three reasons. First, downside risk provides intuitive suggestions to shareholders, describing a potential loss of stock return at specified probability conditions for firms' foreign extension decisions. Second, identifying whether the shareholders face more or less downside risk at the time they are benefitted by the extension activity discovers the nature of FDI. Finally, recognizing whether the downside risk is affected by the hidden agency problems and information asymmetry is urgent when firms' operations are distanced from shareholders' arm-length monitoring. In Taiwan, the authority set different regulations to the investment in Mainland China and non-Mainland China areas. Firms who have FDI in Mainland China have to report the operation contents to public and the requirement is immunized to FDIs in nonMainland China areas. That is, the information contents are different for the FDIs in these two destinations and so are to the downside risk. Wang et al. (2015) find the corporate governance, which is a sound mechanism to protect shareholders, can reduce downside risk. Hence, it is critical to examine whether the difference on regulations affects the downside risk and whether the corporate governance works to the opaque investment. This study investigates three questions. First, the study tests how FDI affects downside risk based on the fact that FDI can enhance revenue at the same time it increases agency costs and information asymmetry. Second, it examines whether the different opportunities caused by regulations lead to different effects on FDI downside risk. If so, what is the supporting rationale? Finally, it investigates whether the corporate governance protects shareholders in the cases of FDI that are difficult to monitor. This study uses examples from Taiwan to investigate the downside risk of FDI firms. Using data from the Taiwan Economics Journal (TEJ), this study collects 1360 samples from 2010:Q1 to 2017:Q3 and obtains 39,634 samples of quarterly data. Among the 1360 firms, 1154 firms engaged in FDI at third quarter 2017. Following Wang et al. (2015), the downside risk is measured by the Value-at-risk at both 95% (VaR95) and 99% (VaR99) confidence levels, and the expected shortfall at a 95% (ES95) confidence level. The dynamic panel regression analysis shows a positive relationship between downside risks and the degree of FDI engagement, which is measured as the capital amount of foreign investment to the firm's total assets. With the argument that the information is different for firms that invest in Mainland China and in non-Mainland China areas, this study segments the samples by their destination countries. Of the 1154 FDI firms, 1015 of them invest in Mainland China, 1121 firms invest in non-Mainland China areas, and 982 firms invest in both areas. This study finds a positive effect only for FDI in non-Mainland China areas, while considering that they might also invest in Mainland China simultaneously and vice versa. Our results remain solid when Heckman's (1979) two-stage model is applied. Such empirical results indicate that not all of the FDIs increase downside risk. FDI in Mainland China, which is required to make more transparent disclosure and is more easily monitored, does not affect the downside risk. Contrarily, the FDI in non-Mainland China areas increases the downside risk. It is plausible that most of them are less transparent and invest in tax haven countries, providing opportunities for managers to pursue specific interests. It is important to explore rationales behind downside risk increase with capital remitting in non-Mainland China areas. This study demonstrates that the investment amount is positively associated with the agency cost and earnings management. These two factors are related to the transparency of multinational firms and information is unlikely to be revealed to stockholders freely; thus, they help link the scales and practices of foreign investments to downside risk. Consequently, this study examines the relationship of corporate governance upon opaque investment. Using the internal governance mechanisms suggested by Tomassen et al. (2012), the empirical results document a negative relationship between the amount of foreign investment and the quality of corporate governance. That is, restricting the amount of investment to less opaque areas is one of the ways that corporate governance mitigates downside risk in foreign investment. This study yields three important results. First, it shows that FDI activity does not reduce the downside risk. A plausible rationale is that profit from FDI might be abundant, but the FDI might leave space to management to manipulate information. Second, it shows that FDI might increase downside risk. Some countries, especially the tax havens, attract firms that pursue private interests. The lower level of monitoring provides managers with more opportunity to derive personal benefits, leading to higher downside risk. Finally, corporate governance works in the case of FDI. Since investment in a foreign country is associated with increased agency problems, internal governance mechanisms restrict the capital outflow to opaque areas. The remainder of the paper is organized as follows. Section 2 presents the background of FDI in Taiwan and develops the hypotheses. Data and the methodology are revealed in Section 3. The empirical results are reported in Section 4. Section 5 presents the study's conclusions. 2. FDI in Taiwan and hypotheses development 2.1. FDI in Taiwan The lack of natural resources and limited market size force Taiwanese firms to depend heavily on international trade. In recent 2

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Table 1 Foreign direct investment in Mainland China and non-Mainland China areas. This table reports the foreign direct investment of Taiwanese firms in Mainland China (MC) and non-Mainland China areas (NMC). Panel A presents the number of firms who invest directly in MC, NMC, or both areas and the number of subsidiaries in foreign countries. The investment amount is computed as billion of US. dollar. Panel B presents the top 10 most popular investment area of Taiwanese firms based on the number of subsidiaries and the amount of investment. The amount of investment is computed as billion of US. dollar. The data is collected at the third quarter in 2017 from the Market Observation Post System (MOPS). Panel A Destination

n

subsidiary

investment amount

MC NMC Both

1320 1386 1210

6104 8857 14,644

74,038 218,370 278,774

Panel B Rank Area 1 British Virgin Island 2 Samoa 3 Hong Kong 4 U.S.A. 5 Cayman Islands 6 Singapore 7 Japan 8 Panama 9 Vietnam 10 Malaysia

n 1611 1222 1203 881 493 293 289 240 219 214

% 18.22% 13.82% 13.61% 9.97% 5.81% 3.13% 3.27% 2.70% 2.48% 2.38%

cum. % 18.22% 32.05% 45.66% 55.62% 61.20% 64.51% 67.78% 70.49% 72.98% 75.40%

Rank 1 2 3 4 5 6 7 8 9 10

Area British Virgin Island Cayman Islands Hong Kong Samoa Singapore U.S.A. Bermuda Malaysia Vietnam Panama

investment 62,275 39,150 29,213 19,411 13,738 11,176 5363 5236 4280 3210

% 28.52% 17.93% 13.38% 8.89% 6.29% 5.12% 2.46% 2.40% 1.96% 1.47%

cum. % 28.52% 46.45% 59.82% 68.71% 75.00% 80.12% 82.58% 84.98% 86.94% 88.41%

decades, increasing production costs, the practice of environmental protection regulation, and limitations on international trade have driven many Taiwanese firms to go abroad to seek an efficient production base. These firms extend their current business to Mainland China or other developing countries, such as Vietnam, Thailand, or Indonesia, to obtain competitive advantages and a better trading platform to access global customers. The Financial Supervisory Commission in Taiwan requests public firms to disclose information regarding their foreign investment in Mainland China and other countries on the Market Observation Post System (MOPS) every quarter. Data from MOPS shows that Taiwanese firms invest directly in 95 countries. Mainland China is the most popular choice because of its lower production cost, friendly political policy, same language, and short geographic distance since the open policy in 1987. At the third quarter of 2017, there are about 1651 public firms in Taiwan and 1320 of them invest in Mainland China with a total of 6104 subsidiaries. The total amount of investment is approximately 0.74 trillion U.S. dollars. Among these firms, Hon Hai Precision Industry Company, an electronics products manufacturer, has 153 subsidiaries in Mainland China. Pou Chen Group, a footwear manufacturer, has 104 subsidiaries. Uni-President Enterprise Corp., a food manufacturer, has 97 subsidiaries. Most firms declare that they extend their production and sales activities to Mainland China, as shown on the MOPS. On average, FDI firms own 4.8 subsidiaries. Alternatively, there are 1386 firms that invest in areas other than Mainland China; they own 8857 subsidiaries in total. Wisdom Marine Group, a dry bulk vessel shipping company, has 136 oversea subsidiaries. Neo Solar Power Corp. has 106 oversea subsidiaries and Ennoconn Corp., an innovative member of Foxconn, has 82 subsidiaries abroad. Panel A in Table 1 summarizes the information on foreign direct investment in Mainland China (MC), areas other than Mainland China (NMC), and both areas. An interesting finding is that there are 1210 firms investing in both MC and NMC with a total of 14,644 subsidiaries. These firms display aggressive intension to develop on the global stage. About 73.28% of public firms invest in more than one country, raising the issue of shareholders protection in Taiwan. Panel B shows the top 10 most popular host countries in the NMC by either the number of subsidiaries or the total amount of investment in U.S. dollars. The British Virgin Islands attract the most Taiwanese firms. Apparently, most of the top 10 countries are known as tax havens, which help firms to circumvent income tax. Approximately 45.66% of foreign subsidiaries are located in the top three most popular host areas; 59.825% of foreign capital investment is allocated to the British Virgin Islands, Cayman Islands, or Hong Kong. The foreign direct investment to countries other than Mainland China might not necessarily be a strategy of globalization.2 As observed, most of the markets are regarded as tax havens. Almost every firm that invests in NMC has at least one subsidiary in one of the tax havens.3 The less transparency makes both authority and shareholders hard to monitor firms' operations. Taiwan authorities regularly request that FDI firms disclose their information on the MOPS. Such regulation aims to protect shareholders by compelling information disclosure. However, the information is different for firms who invest in Mainland China versus other areas. Firms that invest in Mainland China must state the business activities of each subsidiary, while firms that invest in other areas are exempted. A primary reason that Taiwan authorities put close confinement on the FDIs in Mainland China is that

2 Firms who invest in foreign countries (excluding in Mainland China) are not requested to report their operational activities on the Market Observation Post System, making it difficult to know the real intention behind FDI. 3 Of the 1386 firms, only 23 (1.65%) of them do not invest in tax haven.

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moving core operation units and capital to Mainland China leads to deindustrialization. For the avoidance of industrial hollowingout, the authorities request additional information disclosure. In addition, investors in Taiwan are more familiar with Chinese markets due to similar language, culture, and geographic advantage; the more transparent information allows stakeholders to more easily oversee whether firms focus on their stated business and react to the changes of economic factors. By contrast, stakeholders are less capable of monitoring the operation in non-Mainland China areas mainly due to language and legal differences. In addition, a significant proportion of firms in Taiwan invest in tax havens (see Table 1) that are on the OECD (The Organisation for Economic Cooperation and Development) list and have received much attention in recent years. Since Taiwan authorities do not request disclosure of business activity, these firms, especially firms that invest in tax havens, are less regulated, more opaque, and more difficult to monitor. Stakeholders do not even know the operational scope of the subsidiary. Therefore, these firms have a higher likelihood of sudden stock price shocks caused by the exposure of negative events; consequently exacerbating downside risk. This study argues that regulation on FDIs leaves different space for managers to shun surveillance, leading to greater impact on downside risk. Hence, examination of the Taiwan data proves to be interesting, especially for the FDI in tax havens. 2.2. Hypotheses development FDI is a double edge sword to shareholders since it generates better returns meanwhile raises agency problem. Prior studies (e.g., Iamsiraroj 2016) have provided evidence that FDI is beneficial to firms. However, the geographical constraints, the culture, language and legal differences, multi-country financial statements, and multi-country auditors make shareholders hard to monitor firms' operations (Lee and Kwok 1988). Moreover, FDI is also a diversification decision and extends firm's organizational structure. Singhal and Zhu (2013) argue the diversification leaves more space for managers to pursue personal interests. Hsu and Liu (2016) argue the extension of organizational structure increases the information asymmetry. Therefore, the characteristics of less incapable of monitoring, potential agency problem and conceivable information asymmetry in FDI make shareholder protection being critical. This study argues the FDI activities increase the probability of negative stock price shock since the agency problems or the information asymmetry might expose to public eventually. The impacts are harmful to shareholders and can be directly observed from the downside risk measurements. Since the more the firms engaged in FDI, the more hidden negative issues might burst, leading to a higher negative stock return shock. Our first hypothesis then is developed as follow. H1. : The downside risk associates positively with firms' engagement in foreign direct investment. An interesting issue about the regulation in Taiwan is the effect from the difference on the information disclosure of FDI in MC or in NMC. Taiwan authority regulates these two FDIs differently on the information disclosure requirements. Firms who have FDI in NMC expose less about their FDI activities. As such, the less information that FDI firms are requested to report, the more space is left for management to manipulate the financial reports or to pursue personal interest. Therefore, the second hypothesis of this study is developed as follow. H2. : The downside risk is higher for NMC firms than for MC firms. Foreign direct investment, in which managers shift a firm's resources to other countries for pursuing the goal of long-term profit, is difficult for stakeholders to monitor. A critical argument in this study is the potential agency problem and information asymmetry lower the quality of firms' FDI decision, consequently raises the downside risk. Due to the engagement in FDI increasing the downside risk, our third hypothesis argues the agency problems and information asymmetry are the rationales to the degree of FDI engagement. The more agency cost and information asymmetry the firms have, the more abuse of foreign investment the management will take. H3a. : The agency cost and information asymmetry associate positively with the engagement in FDI. Balachandran and Faff (2015) propose that corporate governance improves the timeliness of financial information, helps combat accounting fraud, and enhances transparency in reporting. Wang et al. (2015) use Taiwan data and find the good corporate governance reduces downside risk because the decision-making process is more transparent in better governed firms. Therefore, this study further examines whether corporate governance affects the foreign investment decision that is supposed to increase a firm's downside risk. If the corporate governance functions well to the transparency, we expect that foreign investment is less abused by management and then develop the hypothesis as follow. H3b. : Corporate governance associates negatively with the FDI engagement. 3. Data and methodology This study uses the Taiwanese data from the 2010:Q1 to 2017:Q3, which is free from the 2008 financial crisis, to investigate the downside risk of FDI firms. In Taiwan, there are about 1651 public firms as reported by the authority4 in 2017. Public firms that are listed on the two stock exchanges (the Taiwan Stock Exchanges and Taipei Exchange) are included. We obtained financial data from Taiwan Economics Journal (TEJ) and the FDI data from MOPS. The measurement on downside risk presents the situation of potential negative return for specific asset by disclosing the financial 4

https://data.gov.tw/dataset/ 4

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risk of losses. Investors who care about the risk minimization would weight more on asset's downside risk at the time of making investment decision. Basel Committee on Banking Supervision has promoted the Value-at-Risk as a risk management tool for banks as an international standard since 2004. The Value-at-Risk measures the risk of loss for investments with a given probability. The higher Value-at-Risk of an asset presents a longer tail of the return distribution on the left hand side and the higher loss is expected. Since the FDI is evidenced to have better profit, investigation on the downside risk provides more constructive suggestion to investors than on either the upside or the total risk. In addition, the Value-at-Risk measures provide numerical information to investors that are easy to be understood. They are presented in a form of loss on return, helping investors to compare with the expected return in decision making process. This study uses three downside risk measurements of Wang et al. (2015): the expected shortfall at a 95% confidence level (ES95), the Value-at-Risk at a 95% confidence level (VaR95), and the Value-at-Risk at a 99% confidence level (VaR99). VaR95 (VaR99) is the fifth (first) percentile of the return distribution, which is estimated using the daily returns of a firm over a four-year period prior to a specific point in time. ES95 is estimated as the average of the daily returns that are less than the VaR95 in the return distribution. The higher value represents higher potential loss at a selected significant level of confidence of the firms at a given point of time. We use panel data to study the relationship between three downside risk measures and the degree of FDI engagement. The panel regression analysis captures the nature of oversea investment in this study. Firms may engage in or drop out of investment during the investigation period depending on their purposes. For example, there are 91 firms that decided to invest in China and 63 firms that drew their investment from Mainland China in the time span of 2010:Q1 to 2017:Q3. Therefore, the panel data analysis can capture the changes on foreign investment over the investigation time horizon of individual firm. Considering that risk measures are correlated and the effect from FDI stands over time, this study uses dynamic panel regression analysis with first-difference general method of moments (GMM) estimator of Arellano and Bond (1991) to examine the effect of FDI on downside risk. We also account for a cross-section fixed effect in empirical corporate finance by Arellano-Bond multiple-step estimation. The dynamic regression model is set as follow to examine the first hypothesis.

Downside Riski, t = + +

Downside Riski, t 1 + 2 INVTAi, t + 3 ROAi, t Volumei, t + 4 LnSizei, t + 5 Debti, t + 6 MarketPi, t 7 CapEx i, t + 8 Agei, t + 9 VROAi, t + i, t

1

3

(1)

The Downside Risk is one of the three measures (ES95, VaR95, or VaR99) and the lagged downside risk is the respective risk measure in the prior quarter. This study uses the ratio of investment amount in foreign countries to total assets (INVTA) to evaluate the degree of FDI involvement. The higher the INVTA, the more involved the firm is in FDI. If the coinsurance effect of the global diversification decision works, a negative coefficient should be observed. Either way, the FDI might associate with the downside risk. The other control variables are used similarly to those in the regression model of Wang et al. (2015). ROA (return-on-assets), which is the net income before extraordinary items divided by firm's average total assets, measures the profitability of the firm. A negative relation is expected between ROA and downside risk since profitable firms are more capable of dealing with negative impacts like loan default. Volume is the quarterly trading volume. Since a higher Volume implies a higher degree of opinion inconsistency to current stock price (Hong and Stein 2003), it is used as a proxy of information asymmetry and is expected to positively associate with the downside risk. Lnsize is the natural logarithm of a firm's market value. Larger firms obtain more resources and are more capable of handling unexpected shocks. Debt is the ratio of debt to total assets. A higher Debt implies a higher probability of insolvency. MarketP, the sales of a firm to the total sales of its respective industry, measures the firm's market power. CapEx is the capital expenditure, or changes in a firm's book value of fixed assets scaled by its total assets in the respective quarter. Higher CapEx indicates less financial slack and thus higher downside risk. Age is the number of years from the time the firm has been listed on the exchange to the observation quarter. It represents the degree of information transparency, since the longer the firm operates, the more familiar investors are with it. VROA, which measures the standard deviation of the prior 24 quarterly ROAs, represents the variability of profitability. It is expected to be positively associated with downside risk. This study obtains 31 samples of quarterly data for each firm from 2010:Q1 to 2017:Q3 from TEJ. Essentially, firms that invest in MC might also invest in NMC, causing the effects from regulation more complex. Of the 1154 FDI firms analyzed in this study, 1015 of them invest in MC and 1121 of them invest in NMC. That is, 982 firms invest in MC and NMC simultaneously. This study modifies Eq. (1) to address the potential combined effect as Eqs. (2):

Downside Riski, t = + +

Downside Riski, t 1 + 2 INVTACi, t + 3 DCi, t INVTAOi, t 4 ROAi, t + 5 Volumei, t + 6 LnSizei, t + 7 Debti, t 8 MarketPi, t + 9 CapEx i, t + 10 Agei, t + 11 VROAi, t + i, t

1

(2)

INVTAC is defined as a firm's amount of capital investment to Mainland China in relation to its total assets. DC is a dummy variable that equals one if a firm invests directly in Mainland China and zero elsewhere. INVTAO is defined as a firm's amount of investment to NMC in relation to its total assets. DO is defined as a dummy variable that equals one if a firm invests in NMC and zero if it invests elsewhere. This model captures the effect of a firm's investment in MC while taking into consideration its investment in NMC. Similarly, we develop model (3) to capture the effect of FDI in NMC to downside risk.

Downside Riski, t = + +

Downside Riski, t 1 + 2 INVTAOi, t + 3 DOi, t INVTACi, t 4 ROAi, t + 5 Volumei, t + 6 LnSizei, t + 7 Debti, t 8 MarketPi, t + 9 CapEx i, t + 10 Agei, t + 11 VROAi, t + i, t

1

5

(3)

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The first-difference GMM estimator with multiple-step estimation is again used for estimating the dynamic panel regression model. This study argues the agency problem and information asymmetry are the rationale to the abuse of FDI. To this, we use the SMTH and Corr of An et al. (2016) as earnings management to proxy the information asymmetry in firms. The SMTH is computed as the standard deviation of a firm's operating income scaled by the standard deviation of the operation's cash flow over the last five quarters, multiplied by −1. SMTH captures the reduction in variance of earnings due to accrual alternation. In addition, the Corr is computed as the correlation between changes in accruals and changes in cash flow from operations over the last five quarters, multiplied by −1. It measures the extent that insiders disguise any surprises in cash flows by using their accounting discretion. The higher SMTH or Corr implies a higher level of earnings management. We expect the SMTH and Corr associating positively with the degree of engagement in FDI (INVTA). In addition, this study uses the rate of total asset turnover (TATURN) of Singhal and Zhu (2013) to proxy firms' agency cost, which is computed as sales to total assets, adjusted by the industry average to proxy a firm's agency problems. The higher TATURN implies a lower agency cost. We expect the TATURN associates negatively with INVTA. We also argue the quality of governance making the decision process of foreign investment more transparent and examine the issue by model (4). Follow Wang et al. (2015), we use the composed corporate governance index (CG) to examine whether the better governed firms restrict the abuse in FDI. CG is computed as the sum of four standardized internal governance variables: blockholder ownership, managerial ownership, institutional ownership, and independent directors on the board. Blockholder ownership is the percentage of shares owned by the largest 10% of outside shareholders. Managerial (institutional ownership) is the percentage of shares owned by management (institutional investors). Finally, independent directors on the board is the proportion of independent directors on the board. We expect the CG associating negatively with the degree of engagement in FDI (INVTA). This study also uses ROA, LnSize, Debt, VROA, MarketP, FIO, RD, as control variables as in the model (4). FIO is the percentage of shareholding by foreign institutional investors. This study argues that higher foreign ownership increases the likelihood of FDI due to better international connections. RD is the R&D expenditure to firm's revenue. We also include the number of subsidiaries (Nfirm) in the extended analysis. The Nfirm is the number of subsidiaries that firms own in foreign countries. The more subsidiaries a firm has, the more capital is distributed in foreign countries. We again use the first-difference GMM estimator with multiple-step estimation. The regression model is presented as:

INVTAi, t = +

ROAi, t + 2 LnSizei, t + 3 Debti, t + 4 VROAi, t + 5 MarketPi, t 6 TATURNi, t + 7 FIOi, t + 8 RDi, t + 9 Nfirmi, t + 10 EMi, t

+

11

1

CGi, t +

(4)

i, t

EM is the earnings management variable. It can be either the SMTH or the Corr in the model. 4. Empirical results 4.1. Descriptive statistics The estimation of the downside risk requires four-year financial data prior to the estimation date. This study identifies 1360 firms that have sufficient financial data from TEJ. Of those, 1154 firms engage in FDI as reported in MOPS, indicating that a significant portion of Taiwanese firms have developed their business abroad and supporting the importance of this study. Among the 1154 FDI Table 2 Descriptive statistics. This table reports the descriptive statistics of 1360 Taiwan list firms that have complete data within the period of 2010:Q1 to 2017:Q3. In sum, 39,623 quarterly firm data is obtained. We use three variables to proxy downside risk. ES95 is the 95% confidence level of expected shortfall and VaR95 (VaR99) is the 95% (99%) confidence level of Value-at-Risk. INVTA is the investment amount to total assets. ROA, Volume, LnSize are the return-on-asset, trading volume of stocks, and the natural logarithm of firm's market value. DEBT and MarketP are the debt ratio, the product competition that is evaluated as the quarterly sales of firm to the average quarterly sales of its respective industry. CapEx, Age, and VROA is the capital expenditure, firm age, and the volatility of the ROAs that estimated by the 4-year ROA data prior to the evaluation quarter. All of the variables are obtained from TEJ and reported on quarterly basis.

ES95 (%) VaR95 (%) VaR99 (%) INVTA (%) ROA (%) Volume (%) Lnsize DEBT (%) MarketP (%) CapEx (%) Age VROA (%)

Mean

Median

Maximum

Minimum

St. Dev.

5.1353 3.7875 5.9768 32.2224 1.7783 12.5255 8.2503 43.0139 1.0619 0.0000 13.3544 2.1691

5.2312 3.5914 6.6038 20.5196 1.75 5.6423 8.0728 42.3900 0.2626 −0.0006 11.8333 1.6985

14.0813 9.6006 17.9499 1941.64 92.94 468.6288 15.5408 102.7900 65.9087 0.8391 55.5833 26.9205

0.2575 0 0.9581 0 −102.69 0.0009 3.6889 0.5400 0 −0.8964 4 0.0167

1.3896 1.4191 1.4058 49.72 3.2540 19.5982 1.4809 19.1004 3.2247 0.0346 8.9201 1.8702

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Table 3 Correlation of coefficient. This table reports the correlation of coefficient of variables used in this study. All the financial data is obtained from TEJ in the time span of 2010:Q1 to 2017:Q3 with a total number of 39,623 quarterly firm data. The variables are defined as same as reported in Table 2. Figure in bold are statistically significant at 1% level.

VaR99 ES95 INVTA ROA Volume Lnsize DEBT MarketP CapEx AGE VROA

VaR95

VaR99

ES95

INVTA

ROA

Volume

Lnsize

DEBT

MarketP

CapEx

AGE

0.7909 0.9387 0.1065 −0.1416 0.1928 −0.2587 0.0275 −0.0856 −0.0360 −0.2438 0.4339

0.9335 0.1083 −0.1048 0.2283 −0.2592 −0.0213 −0.1063 −0.0247 −0.2955 0.3369

0.1081 −0.1294 0.2268 −0.2748 −0.0027 −0.1047 −0.0299 −0.2949 0.4044

−0.1005 0.0005 −0.1573 −0.0412 −0.0427 −0.0622 −0.0348 0.0675

0.1046 0.2712 −0.0798 0.1001 0.0249 −0.0557 −0.1005

0.0703 −0.0699 −0.0341 0.0342 −0.1709 0.0207

0.1655 0.5163 0.0683 0.2569 −0.2014

0.1523 −0.0070 0.0926 −0.0973

0.0143 0.1692 −0.0858

−0.0164 0.0032

−0.1575

firms, 1015 firms invest in MC, 1121 invest in NMC, and 982 firms invest simultaneously in both areas. Table 2 presents descriptive statistics of variables within the period of 2010:Q1 to 2017:Q3. The average of ES95 is found to be 5.1353% for all 1360 public Taiwanese firms that have complete financial data in TEJ. The average VaR99 is found to be 5.9768%, which is about 1.5 time higher that the VaR95 (3.7875%). Such finding indicates a long left tail of return distribution for Taiwanese public firms in average. The long tail shows the importance on the study of downside risk as well as to the shareholders protection. INVTA is approximately 32.22% on average, with a maximum of 1941.64%. Such extremely high ratios indicate that some firms shifted their operation largely to foreign countries. Additionally, there are 148 firms whose capital investment in foreign countries is more than their current total assets. That is, approximately 10% of public firms take a broad view of the world and highly divert their operation (separate off). An interesting finding is that the average CapEx (capital expenditure) is found to be about 0%, which implies that most Taiwanese firms do not extend domestic business significantly. The book value of fixed assets does not change in home country. Taiwanese firms extend their operation in foreign countries, leaving less capital to invest in domestic market. Our data somewhat differs from the study of Wang et al. (2015) who use the Taiwan data from 2002 to 2012. According to the data form National Statistics, R.O.C. (Taiwan), the average economic grow rate is about 4.48% in the period of 2002 to 2012 and now goes down to the 3.47% in the period of 2010 to 2017. The downward economic growth thrusts firms to focus on international extension, leading to the reduction on the investment in home country. Both the high INVTA and low CapEx indicate that Taiwanese firms tend to develop business globally. Using 1360 firms that have complete financial data within our investigation period and are listed on either the Taiwan Stock Exchanges or Taipei Exchange as examples, this study applies dynamic panel regression analysis with quarterly date for 39,623 firms to examine whether FDI reduces downside risk. The coefficient of correlation to the variables is presented in Table 3. A positive correlation can be found between the INVTA and those three downside risk measures, initially providing support to our first hypothesis that downside risk associating positively with the degree of FDI engagement. Also, the ROA, Lnsize, MarketP, CapEx, and Age associate negatively with the downside risk measures, whereas the Volume and VROA associate positively with the downside risk measures. All of the relations consist with our expectation. 4.2. Univariate analysis To understand better whether firms engage in FDI associated with higher downside risk, we initially employ univariate analysis. Firms which do not involve in FDI are classed as non-FDI group. Firms that are ranked as the top 30% FDI involvement firms (measured by INVTA) are classed as High FDI group. We use the yearly data and conduct difference tests on the three downside risk measures between the High FDI and non-FDI groups over the years. The results are reported in Table 4. Differences on the downside risks measures are not significant between the High FDI and non-FDI groups in the year of 2010 and 2011. It is possible that the impact from 2008 financial crisis remains to make the indifference on the downside risks. However, the results turn to be significant in the period of 2012 to 2017. Such findings indicate that firms who engage more in FDI have longer left tail return distribution and shareholders face a higher potential negative stock price shock at the time the firms trying to be benefited more by firms' FDI decision. This result conforms the risk-return relation but contradicts the idea that firms can diversify operational risk by FDI. It is possible that Taiwanese firms aim primarily at profit making rather than at operational diversification in making que FDI decision. 4.3. Downside risk and FDI engagement Our first hypothesis argues the downside risk associates with the degree of engagement in FDI. Using 39,623 firm quarterly data, results from the panel regression analysis with first-difference GMM method of Arellano and Bond (1991) provide empirical support to it (Table 4). The INVTA, which is used to capture the degree of engagement in FDI, is positively associated with all downside risk measurements. As suggested by Wang and Wang (2015), it is possible that FDI firms allocate their limited resources to oversee their 7

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Table 4 Univariate analysis. Using the samples that do not engage in and the samples that involve highly in FDI activity, this table reports the tests on the difference of the three risk measures (VaR95,VaR99, and ES95) between those two groups over the years. The High FDI represents the firms that are the top 30% of firms who have higher degree of FDI involvement (with higher INVTA). Contrarily, the Non-FDI represents the firms that do not involve in FDI activity. The observations (Obs.) for the two groups in each year are also reported. Year 2010 2011 2012 2013 2014 2015 2016 2017

High FDI Non-FDI Difference High FDI Non-FDI Difference High FDI Non-FDI Difference High FDI Non-FDI Difference High FDI Non-FDI Difference High FDI Non-FDI Difference High FDI Non-FDI Difference High FDI Non-FDI Difference

VaR95

VaR99

ES95

Obs.

5.0199 5.0860 −0.0661 5.0572 4.9659 0.0913 4.6560 4.4114 0.2446 3.9257 3.7074 0.2183 3.6007 3.4041 0.1966 3.4724 3.1607 0.3117 3.3764 3.0345 0.3419 3.3368 2.9544 0.3824

6.8286 6.7601 0.0685 6.8587 6.7378 0.1209 6.7095 6.4295 0.2800 6.3019 5.8882 0.4137 6.0316 5.4865 0.5451 5.8841 5.2140 0.6701 5.8670 5.1162 0.7508 5.9129 5.0874 0.8255

6.1977 6.1791 0.0186 6.2482 6.1382 0.1100 5.9787 5.7195 0.2592 5.3521 5.0522 0.2999 5.0230 4.6858 0.3372 4.9182 4.4454 0.4728 4.8887 4.3658 0.5229 4.9112 4.3068 0.6044

358 201

** ** * *** *** ***

* *** *** *** *** *** ***

308 193

*** *** *** *** *** ***

319 183 333 185 342 184 347 184 349 190 341 197

*, ** and *** significance at the 10%, 5%, and 1% level, respectively.

subsidiaries. Such resource allocation increases the risk exposure with the increase in its investment abroad. It is also possible that firms might not remit revenue back to the parent firm because of tax avoidance, further extension in the host country, currency depreciation, or regulation.5 Yet, this study argues the downside risk is raised by the agency problem and information asymmetry and will be examined in next section. All of the control variables perform as expected. The Volume, DEBT, and VROA relate positively to downside risk. Higher trading volume indicates more inconsistence on the stock price (Hong and Stein 2003), leading to larger price adjustment and negative stock price changes. Also, higher debt ratio represents higher pressure on solvency. Higher volatility on profitability (VROA) increases the probability of default. Meanwhile, LnSize and AGE associate negatively to downside risk. Since larger firms have more tradable shares in the market, the changes on stock price tend to be fewer than smaller firms in general. Finally, elder firms usually are better known by the investors. The familiarity diminishes the probability of misunderstanding on the stock price. In sum, results from Table 5 show the downside risk associates with the degree of FDI engagement. The FDI not only increases firms' revenue as stated by prior studies but also the downside risk. We again show the FDI in Taiwan relates less to the operational diversification. Shareholders share the risk and profit with firms in the FDI decision in the Taiwanese case. This study also applies a structure of lagged variable to deal with the potential causality problem as a robustness check to the results in Table 5. Eq. (1) is modified as follow.

Downside Riski, t = + + +

Downside Riski, t 1 + 2 INVTAi, t 1 + 3 ROAi, t 1 3 Volumei, t 1 + 4 LnSizei, t 1 + 5 Debti, t 1 6 MarketPi, t 1 + 7 CapEx i, t 1 + 8 Agei, t 1 + 9 VROAi, t

1

1

i, t

(5)

The results from Table 6 show a similar finding to Table 5 that the lagged FDI engagement variable (INVTAt-1) associates positively with the downside risks in this setting. Such findings provide an additional support to the first hypothesis. Of the 1360 public firms, 206 of them do not invest in foreign country. We further apply paired sample tests to investigate if the firms that engage in foreign direct investment (FDI) have higher downside risk than firms without FDI. This study collects match firms to the non-FDI firms in two ways. First, firms who have similar firm size and engage in FDI are included. Second, FDI firms that are in the same industry and have similar firm size are chosen. Results for the paired sample test are reported in Table 7. Panel A (B) shows the size (size and industry) match firms have higher downside risk than those of 206 non-FDI firms. 5

For example, some countries might limit or tax on the remittance backing to parent firms. 8

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Table 5 The relation between FDI and downside risk: Dynamic panel regression. This study uses 1360 Taiwan list firms and dynamic panel regression model to investigate the relation between foreign direct investment and downside risk. Of them, 1154 firms engage in FDI activities. We use ES95, VaR95, and VaR99 to proxy downside risk. INVTAt represents the percentage of foreign direct investment to the total assets, which is used to proxy firms' degree of FDI involvement, at quarter t. ROAt, Volumet, LnSizet are the return-on-asset, trading volume of stocks, and the logarithm of firm's market value at quarter t. DEBTt and MarketPt are the debt ratio and the product competition that is evaluated as the quarterly sales of firm to the average quarterly sales of its industry. CapExt, Aget, and VROAt is the capital expenditure, firm age, and the volatility of the ROAs that estimated by the 4-year ROA data prior to the quarter t. Considering endogenous issues in a panel-data analysis, this table reports the regression results using first-difference GMM method of Arellano and Bond (1991) with multiple-step estimation. We also report the p-value of the Sargan test and the tests for residual autocorrelation, the AR(1) and AR(2). All of the data are obtained from TEJ from 2010:Q1 to 2017:Q3. VaR95 Riskt-1 INVTAt ROAt Volumet LnSizet DEBTt MarketPt CapExt Aget VROAt P(J-statistic) AR(1) AR(2)

0.5097 (0.0000) 0.0561 (0.0130) −0.0022 (0.4003) 1.0457 (0.0000) −0.0049 (0.0000) 0.0025 (0.0000) −0.0044 (0.0109) −0.0030 (0.9391) −0.1456 (0.0000) 0.0171 (0.0500) (0.2688) (0.0001) (0.9400)

VaR99 *** **

*** *** *** **

*** * ***

0.5093 (0.0000) 0.0437 (0.0146) −0.0001 (0.8720) 0.8637 (0.0000) −0.0041 (0.0000) 0.0013 (0.0204) −0.0046 (0.0043) −0.0078 (0.8114) −0.1188 (0.0000) 0.0167 (0.0061) (0.7783) (0.0000) (0.2875)

ES95 *** **

*** *** ** ***

*** *** ***

0.5171 (0.0000) 0.0594 (0.0081) 0.0006 (0.4049) 1.0523 (0.0000) −0.0047 (0.0000) 0.0020 (0.0002) −0.0038 (0.0202) −0.0061 (0.8641) −0.1365 (0.0000) 0.0197 (0.0044) (0.1670) (0.0000) (0.5011)

*** ***

*** *** *** **

*** *** ***

*, ** and *** significance at the 10%, 5%, and 1% level, respectively.

4.4. Downside risk and information disclosure Firms that invest directly in MC show a clear intention of expanding current operations, whereas the intentions of firms who create subsidiaries in NMC are not clear. It is partially caused by the Taiwanese regulation that cautiously audits the projects of public firms that intend to invest in MC and intervenes less with firms that invest in NMC. Obviously, many firms that invest in NMC go to tax havens with poor information disclosure requirements. Since stakeholders have less monitoring power over the FDI in NMC, this study argues that FDI in different areas acts differently regarding downside risk. In this section, we investigate whether the difference on the information disclosure matters to the downside risk. The results are reported in Table 8. A surprise finding from Table 8 Panel A is that FDI in MC does not affect the downside risk. The INVTAC has no significant relationship with the three downside risk measures. However, the FDI in NMC associates positively with the downside risk with firms that have simultaneously invested in MC (DCt×INVTAOt). In other words, the FDI in MC does not change, but the FDI in NMC increases the downside risk. This result also appears in the dual regression analysis. The investment in NMC (INVTAO) is positively associated with the downside risk, but no significant relationship is found when the FDI also invests in MC (DOt×INVTACt). In summary, the results indicate that the positive effect to the downside risk is driven by the FDI investments in NMC and provide support to our second hypothesis. Giaccotto and Krapl (2014) document that as firms expand internationally, the cash flow effect, which the firms are expected to benefit from foreign market opportunities, economies of scale, location-specific advantages, and synergy effects, presents good news to the market. We should expect the cash flow effect to reduce downside risk without market friction. For FDI in Mainland China, the tax rate is 10% if subsidiaries remit profits back to FDI firms. Such additional cost might limit the investment to FDI firms; in turn, there is no significant reduction of the downside risk. By contrast, the intention of investing in tax havens commonly relates to tax avoidance. Firms with such an intention hide information regarding their operation to avoid monitoring, especially since the authorities currently do not intervene much. Empirical results show that the FDI affects the downside risk conditionally. Our model (2) mainly examines the firms that invest in MC and model (3) investigates sample mainly invest in NMC. Even results from Tables 4 and 7 show the non-FDI firms have lower downside risk, a further quantitative analysis should also be done to examine whether the non-FDI firms have lower downside risk by using panel data. This study follows Eq. (1) and use INVTAD, which equals to 1 for firms who engage in FDI and zero elsewhere, to replace INVTA. Differing from the setting in models (2)–(3), all the firms are included in the regression analysis. The results are reported in Table 8 Panel B. 9

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Table 6 The relation between FDI and downside risk: Lagged variables. This table reports the results of dynamic panel regression that follows Wang et al. (2015) to use the lagged dependent variables to recheck the relation between foreign direct investment and downside risks. All the variables are as same as those used in Table 5. Considering endogenous issues in a panel-data analysis, this table reports the regression results using first-difference GMM method of Arellano and Bond (1991) with multiple-step estimation. We also report the p-value of the Sargan test and the tests for residual autocorrelation, the AR(1) and AR(2). VaR95 Riskt-1 INVTAt-1 ROAt-1 Volumet-1 LnSizet-1 DEBTt-1 MarketPt-1 CapExt-1 Aget-1 VROAt-1 P(J-statistic) AR(1) AR(2)

0.4949 (0.0000) 0.5318 (0.0151) −0.0038 (0.3350) −0.0042 (0.0063) 0.0866 (0.5167) 0.0142 (0.0924) −0.0271 (0.7238) 0.1525 (0.0135) −0.0730 (0.0049) 0.0708 (0.1941) (0.3657) (0.0030) (0.2042)

VaR99 *** **

***

*

** ***

***

0.3750 (0.0000) 0.4171 (0.0234) −0.0062 (0.4458) −0.0009 (0.5623) 0.7449 (0.0035) 0.0319 (0.0001) −0.0187 (0.7997) 0.3299 (0.0167) −0.1059 (0.0027) 0.2506 (0.0019) (0.9293) (0.0002) (0.1244)

ES95 *** **

*** ***

** ***

***

0.4329 (0.0000) 0.6184 (0.0152) −0.0036 (0.0835) −0.0065 (0.0030) 0.1618 (0.2234) 0.0123 (0.0027) −0.0733 (0.3342) 0.2236 (0.0009) −0.0603 (0.0006) 0.0193 (0.7679) (0.1017) (0.0000) (0.2269)

*** ** * ***

***

*** ***

***

*, ** and *** significance at the 10%, 5%, and 1% level, respectively. Table 7 Paired sample tests to non-FDI firms. This study uses the paired sample tests to investigate whether the firms who do not engage in foreign direct investment have lower downside risks. There are 206 firms that do not invest in any foreign country in our investigation period. Panel A reports the results that use samples with similar firm size as match firms. Panel B reports the results using samples that are in the same industry and also have similar firm size as match firms. The pvalue for the paired test is reported. Panel A VaR95 Average Statistics p-value n Panel B Average Statistics p-value n

0.4798 4.0881 (0.0000) 206 VaR95 0.8684 9.2651 (0.0000) 206

VaR99 ***

***

0.6172 4.3256 (0.0000)

VaR99 0.7275 8.9132 (0.0000)

ES95 ***

***

0.5444 4.1020 (0.0000)

ES95 0.7813 9.2124 (0.0000)

***

***

***significance at the 1% level.

As can be seen, the INVTAD has no significant relation with the downside risk measures. This finding yields two implications. First, in line with the results from Table 5, it implies that the decision on FDI (INVTAD) does not relate to the downside risk but the degree of engagement in FDI (INVTA) does. Alternatively, associating with results from Panel A, it indicates the FDI location (in NMC), not the FDI decision (INVTAD), matter to the downside risk. Therefore, the FDI in Taiwan does not deviate from the goal of operational diversification. Shareholders can share more benefit by the FDI decision meanwhile do not take more downside risk if firms make right decision on the investment location. A firm's decision on foreign direct investment may be non-random and therefore raises self-selection bias. This study uses the Heckman two-step sample selection model as a robustness check. We estimate a probit model with a binary FDI dummy (INVTAD), which equals one if the firm engages in FDI at that quarter and 0 elsewhere, as a dependent variable. Similarly, the dependent variable can be the INVTACD (INVTAOD) when testing the models (2) and (3). The INVTACD (INVTAOD) equals one if the firms have FDI in MC (NMC). The specification of the probit model is as follows. 10

11

−0.0023 (0.3919) 1.0405 (0.0000) −0.0049 (0.0000) 0.0024 (0.0000) −0.0044 (0.0110) 0.0175 (0.6949) −0.1462 (0.0000) 0.0169 (0.0579) (0.2644) (0.0002) (0.9688)

0.5077 (0.0000) 0.0341 (0.2422) 0.0574 (0.0407)

***

*

***

**

***

***

***

**

***

−0.0001 (0.8899) 0.8599 (0.0000) −0.0041 (0.0000) 0.0013 (0.0249) −0.0046 (0.0042) 0.0275 (0.5560) −0.1187 (0.0000) 0.0165 (0.0091) (0.7751) (0.0000) (0.2932)

0.5092 (0.0000) 0.0345 (0.1768) 0.0388 (0.0591)

***

***

***

***

**

***

***

*

***

0.0006 (0.3875) 1.0491 (0.0000) −0.0047 (0.0000) 0.0020 (0.0002) −0.0038 (0.0202) 0.0193 (0.6894) −0.1366 (0.0000) 0.0195 (0.0057) (0.1580) (0.0000) (0.5032)

0.5170 (0.0000) 0.0408 (0.1849) 0.0656 (0.0024)

*, ** and *** significance at the 10%, 5%, and 1% level, respectively.

P(J-statistic) AR(1) AR(2)

VROAt

Aget

CapExt

MarketPt

DEBTt

LnSizet

Volumet

ROAt

INVTADt

DOt×INVTACt

INVTAOt

DCt×INVTAOt

INVTACt

Riskt-1

***

***

***

**

***

***

***

***

***

−0.0023 (0.3849) 1.0438 (0.0000) −0.0049 (0.0000) 0.0025 (0.0000) −0.0043 (0.0110) 0.0199 (0.6536) −0.1465 (0.0000) 0.0167 (0.0610) (0.2709) (0.0002) (0.9380)

0.0723 (0.0127) 0.0353 (0.2524)

0.5080 (0.0000)

VaR95

***

*

***

**

***

***

***

**

***

−0.0001 (0.9027) 0.8621 (0.0000) −0.0041 (0.0000) 0.0013 (0.0238) −0.0046 (0.0042) 0.0289 (0.5346) −0.1189 (0.0000) 0.0163 (0.0100) (0.7702) (0.0000) (0.2823)

0.0480 (0.0829) 0.0358 (0.1875)

0.5094 (0.0000)

VaR99

***

***

***

***

**

***

***

*

***

0.0006 (0.3810) 1.0535 (0.0000) −0.0047 (0.0000) 0.0020 (0.0002) −0.0039 (0.0202) 0.0221 (0.6465) −0.1369 (0.0000) 0.0193 (0.0065) (0.1741) (0.0000) (0.4654)

0.0819 (0.0017) 0.0416 (0.2015)

0.5174 (0.0000)

ES95

***

***

***

**

***

***

***

***

***

−0.0141 (0.7819) −0.0025 (0.3498) −0.0048 (0.0000) 1.0338 (0.0000) 0.0024 (0.0000) −0.0043 (0.0108) −0.0109 (0.7819) −0.1455 (0.0000) 0.0165 (0.0640) (0.2511) (0.0002) (0.9386)

0.5075 (0.0000)

VaR95

ES95

VaR95

VaR99

Panel B

Panel A

***

*

***

**

***

***

***

***

0.0100 (0.6971) −0.0002 (0.8098) 0.8560 (0.0000) −0.0041 (0.0000) 0.0013 (0.0267) −0.0046 (0.0042) −0.0145 (0.6560) −0.1183 (0.0000) 0.0164 (0.0079) (0.7908) (0.0000) (0.3032)

0.5090 (0.0000)

VaR99

***

***

***

***

**

***

***

***

0.0015 (0.9469) 0.0005 (0.4902) 1.0425 (0.0000) −0.0046 (0.0000) 0.0019 (0.0003) −0.0039 (0.0201) −0.0152 (0.6664) −0.1357 (0.0000) 0.0193 (0.0059) (0.1658) (0.0000) (0.4660)

0.5167 (0.0000)

ES95

***

***

***

**

***

***

***

***

Table 8 The relation between FDI and downside risk: Dummy Variables. This table examines the relation between FDI and downside risks by using dummy variables. DC (DO INVTAD) is the dummy variable that equals to one if firms have FDI in MC (have FDI in NMC, have FDI) and to zero elsewhere. All of the other variables are as same as those in Table 5. The regression results using GMM (general method of moments) with multiple-step estimation to estimate the coefficients are reported. This study again reports the p-value of the Sargan test and the test for zero autocorrelation in first-differenced errors, the AR(1) and AR(2).

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0.9725 (0.0000) 0.0168 (0.0000)

12

−0.0009 (0.0058) 0.0015 (0.0000) −0.0050 (0.0000) 0.0006 (0.0000) −0.0017 (0.0001) 0.0066 (0.8284) −0.0010 (0.0000) 0.0068 (0.0000)

***

***

***

***

***

***

***

***

***

−0.0013 (0.0101) 0.0019 (0.0000) −0.0116 (0.0000) 0.0004 (0.0000) −0.0018 (0.0010) −0.1049 (0.0193) −0.0008 (0.0000) 0.0106 (0.0000)

0.9800 (0.0000) 0.0176 (0.0000)

***

***

**

***

***

***

***

**

***

***

−0.0009 (0.0123) 0.0017 (0.0000) −0.0082 (0.0000) 0.0005 (0.0000) −0.0016 (0.0001) −0.0222 (0.4677) −0.0005 (0.0000) 0.0069 (0.0000)

0.9807 (0.0000) 0.0184 (0.0000)

***

***

***

***

***

***

**

***

***

*, ** and *** significance at the 10%, 5%, and 1% level, respectively.

VROAt

Aget

CapExt

MarketPt

DEBTt

LnSizet

Volumet

ROAt

DOt×INVTACt

INVTAOt

DCt×INVTAOt

INVTACt

INVTAt

Riskt-1

−0.0009 (0.0066) 0.0015 (0.0000) −0.0052 (0.0000) 0.0006 (0.0000) 0.0017 (0.0001) 0.0022 (0.9421) −0.0009 (0.0000) 0.0069 (0.0000)

0.0037 (0.3555) 0.0239 (0.0000)

0.9727 (0.0000)

VaR95

ES95

VaR95

VaR99

Model (2)

Model (1)

***

***

***

***

***

***

***

***

***

−0.0012 (0.0145) 0.0019 (0.0000) −0.0118 (0.0000) 0.0004 (0.0000) 0.0018 (0.0012) −0.1108 (0.0135) −0.0008 (0.0000) 0.0109 (0.0000)

−0.0056 (0.3454) 0.0347 (0.0000)

0.9801 (0.0000)

VaR99

***

***

**

***

***

***

***

**

***

***

−0.0008 (0.0177) 0.0017 (0.0000) −0.0084 (0.0000) 0.0005 (0.0000) 0.0017 (0.0001) −0.0281 (0.3580) −0.0005 (0.0002) 0.0071 (0.0000)

−0.0021 (0.6055) 0.0318 (0.0000)

0.9809 (0.0000)

ES95

***

***

***

***

***

***

**

***

***

0.0265 (0.0000) 0.0032 (0.4216) −0.0009 (0.0107) 0.0015 (0.0000) −0.0050 (0.0000) 0.0006 (0.0000) 0.0017 (0.0001) 0.0030 (0.9203) −0.0010 (0.0000) 0.0067 (0.0000)

0.9727 (0.0000)

VaR95

Model (3)

***

***

***

***

***

***

**

***

***

0.0335 (0.0000) −0.0055 (0.3551) −0.0012 (0.0179) 0.0019 (0.0000) −0.0116 (0.0000) 0.0004 (0.0000) 0.0018 (0.0012) −0.1114 (0.0130) −0.0008 (0.0000) 0.0105 (0.0000)

0.9803 (0.0000)

VaR99

***

***

**

***

***

***

***

**

***

***

0.0331 (0.0000) −0.0026 (0.5251) −0.0008 (0.0269) 0.0017 (0.0000) −0.0082 (0.0000) 0.0005 (0.0000) 0.0016 (0.0001) −0.0279 (0.3622) −0.0005 (0.0001) 0.0067 (0.0000)

0.9810 (0.0000)

ES95

***

***

***

***

***

***

**

***

***

Table 9 The relation between FDI and downside risk: Heckman Two-step Sample Selection Model. This study uses the Heckman two-step sample selection model to recheck the model (1) and (2). We use the model (5), the probit model, as the first-test estimation to generate inverse Mills ratio and include it into model (1), (2) or (3). The current model (5) associates with less overfitting problem since it generates the lowest AIC in our several possible tests.

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INVTADi, t = +

ROAi, t + 2 LnSizei, t + 3 Debti, t + 6 TATURNi, t + 7 FIOi, t + 8 RDi, t +

1

4

VROAi, t +

5

MarketPi, t (6)

i, t

The ROA, LnSize, Debt, VROA, MarketP are the same as defined in model (1) and might relate to a firm's FDI decision. Managers in firms that have more agency problems might pursue personal interests, as argued by Singhal and Zhu (2013). This study uses the rate of total asset turnover (TATURN) of Singhal and Zhu (2013) to proxy a firm's agency cost, which is computed as sales to total assets, adjusted by the industry average to proxy a firm's agency problems. FIO is the percentage of shareholding by foreign institutional investors. This study argues that higher foreign ownership increases the likelihood of FDI due to better international connections. RD is computed as a firm's R&D expense to its firm's revenue. We expect that the R&D increases a firm's competitive advantage and FDI intention. The inverse Mills ratio is generated from Eq. (6) and is then included in the second-step model to control for the potential sample selection bias. The second-step model can be model (1), (2) or (3). Table 9 reports the regression results of the Heckman model.6 The Heckman two-step sample selection model provides confirmation of the results presented in Tables 5 and 8. The INVTA is shown to positively associate with three downside risk measures in the model (1) setting. In addition, the FDI in NMC (DCt×INVTAOt and INVTAOt) in either model (2) or (3) relates positively with the downside risks. The results reported in Tables 5 and 8 are solid when self-selection bias is considered. 4.5. Agency problem and information asymmetry in FDI The positive relationship between the FDI engagement and downside risk indicates that the more the FDI firms invest in NMC, the greater the downside risk they have. This study first argues that agency problem and information asymmetry matter, since managers in firms that have more agency problems might pursue personal interests (Singhal and Zhu 2013). This study uses TATURN to proxy a firm's agency cost and either the SMTH or the Corr of An et al. (2016) to proxy the information asymmetry. Firms with higher TATURN have fewer agency problems and managers have less intention to extract personal benefits. Therefore, they are expected to have less investment in opaque markets and consequently have less downside risk. Moreover, firms with less SMTH or Corr engage more in earnings management and then have severe information asymmetry. The more the firms invest in foreign countries, the more space the management has to manipulate the information. In this section, we use the samples that invest in NMC to examine the relation between the FDI in NMC and both the agency cost and information asymmetry. In addition, this study argues the quality of corporate governance makes the decision process more transparent, restricting the abuse of FDI. Table 10 reports the results. Results from Table 10 show the agency cost (TATURN) relates negatively to the foreign investment in NMC (INVTAO). This finding indicates that firms with higher agency problems carry more resources to NMC. Additionally, the earnings management variables (SMTH and Corr) are positively associated with INVTAO. The positive relationship indicates that firms that engage more in earnings management tend to remit more capital to subsidiaries in NMC. We evidence that agency cost and earnings management are the rationale for why FDI in NMC suffers from higher downside risk. An interesting finding is that CG is significantly associated with INVTAO. That is, the governance mechanisms work to oppose investment in less transparent areas, such as tax havens. This study shows that shareholder rights are inversely related to ambiguous FDI. Interestingly, we can see that CG has a marginal effect on the overseas investment decision. This result, to some degree, is consistent with the finding of Fan and Yu (2016) that firms with high intuitional investors (better governed) are more likely to deviate from common practice in civil law countries, resulting in reduced efficiency of corporate governance. In addition, the results show a positive relationship between the ROA and the FDI in NMC. They support the literature that FDI is beneficial. However, the negative relationship appears between the firm size and the FDI. It is possible that larger firms are generally governed better (Black et al. 2006), leading to less FDI in NMC. The results also show that investment in NMC is negatively associated with a firm's debt, supporting the argument of Wang (2017) that the presence of FDI restricts the borrowing capacity of FDI firms and their further capital investment. Finally, the RD is found to be positively associated with FDI. This finding is consistent with our expectation that R&D provides a competitive advantage to FDI firms, leading to cross-countries expedition. Kayalvizhi and Thenmozhi (2018) find that the inward FDI increases as technology absorption and innovation capacity increase in emerging markets. This study shows R&D as it relates to outward FDI. 5. Conclusions Literature on foreign direct investment argues that firms can earn higher profits by taking advantage of cheaper operational costs, larger market bases, and additional financial resources from FDI. The greater profit is expected to reduce a firm's downside risk. However, the financial support to the subsidiary in foreign countries and the increased agency cost and/or information asymmetry likely increase the downside risk. With the purpose of shareholder protection, this study uses Taiwanese data to investigate how FDI affects downside risk, the channels to the downside risk, and the role of corporate governance in it. The use of Taiwanese data is interesting since Taiwan authorities request that firms that invest in Mainland China disclose their business activities on an official website, resulting in easier monitoring of the FDI in Mainland China for stakeholders to access. 6

Considering the overfitting issue, we test many probably variables that might fit in the first-step model. The model (4) generates the lowest AIC. 13

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Table 10 Analysis on the Foreign Direct Investment in non-Mainland China areas. This study uses panel regression analysis to analyze the relations between the amount of foreign investment in NMC (INVTAO) and the agency cost (TATURN), the degree of earnings management (SMTH or Corr), and the corporate governance (CG). TATURN is the total asset turnover computed as the sales to total assets adjusted by industry average. SMTH is computed as the standard deviation of firm's operating income scaled by the standard deviation of operation cash flow in recent five quarters multiplied by minus 1. Corr is computed as the Correlation between changes in accruals and changes in cash flow from operation in recent five quarters multiply by minus 1. Nfirm is the number of subsidiaries that FDI firms owned in foreign countries. CG is the composed corporate governance index which is computed as the sum of standardized ownerships of blockholder, management, institutional investors and the standardized percentage of independent directors on the board. The other variables are as same as those in Table 5. The p-value of the Sargan test and the test for zero autocorrelation in first-differenced errors are reported. ROAt LnSizet DEBTt VROAt MarketPt TATURNt FIOt RDt Nfirmt Corrt SMTHt

0.0025 (0.0532) −0.0287 (0.0105) −0.0009 (0.0108) −0.0136 (0.1877) 0.0022 (0.2235) −0.2012 (0.0023) 0.0001 (0.6927) 2.5E-05 (0.0416) 0.0107 (0.0000) 0.0626 (0.0141)

* ** **

***

** *** **

(0.7899) (0.0817) (0.2855)

*

(0.0531) −0.0135 (0.0644) −0.0007 (0.0549) −0.0019 (0.8940) 0.0022 (0.3843) −0.2005 (0.0171) −7.7E-06 (0.9583) 1.2E-05 (0.0622) 0.0108 (0.0000)

* *

**

* ***

0.0240 (0.0342)

CGt P(J-statistic) AR(1) AR(2)

0.0031

*

**

(0.4313) (0.0668) (0.2407)

*

0.0025 (0.0609) −0.0296 (0.0093) −0.0009 (0.0100) −0.0160 (0.1582) 0.0025 (0.1957) −0.2116 (0.0029) 0.0005 (0.0911) 2.6E-05 (0.0205) 0.0107 0.0000 0.0642 (0.0121) −0.0069 (0.0974) (0.6603) (0.0785) (0.2869)

* *** **

*** * ** *** **

* *

0.0034 (0.0300) −0.0128 (0.0776) −0.0006 (0.0753) −0.0073 (0.5907) 0.0023 (0.3742) −0.2123 (0.0120) 0.0007 (0.0703) 1.2E-05 (0.0513) 0.0109 (0.0000) 0.0242 (0.0346) −0.0116 (0.0522) (0.5564) (0.0611) (0.2418)

** * *

** * * ***

** * *

*, ** and *** significance at the 10%, 5%, and 1% level, respectively.

Contrarily, firms who invest in non-Mainland China areas need not report their business activities clearly, and most create subsidiaries in tax havens. Such differences offer us a good opportunity to better understand how FDI affects downside risk. Initially, this study finds the downside risk increasing with the increase in investment in foreign countries. The FDI seemingly gives rise to the downside risk. Since a firm can invest in different countries simultaneously, this study separates the FDI effects from Mainland China and from non-Mainland China areas based on Taiwan's unique regulation; it finds that the downside risk does not change for FDI in Mainland China but increases for FDI in non-Mainland China areas. Such findings reveal that the FDI effect to downside risk is variable. Since most firms that invest in non-Mainland China areas own at least one subsidiary in a tax haven, monitoring by stakeholders is hindered. These firms also need not disclose details about their investment. This study demonstrates that the investment in nonMainland China areas is positively associated with agency cost and earnings management. These two factors explain how the investment scale increases the downside risk in non-China areas. Finally, this study examines whether the governance mechanisms work for shareholders' protection. Using a governance index constructed by four internal governance mechanisms, this study shows that corporate governance can reduce the investment amount to non-Mainland China areas. That is, the internal governance mechanisms oppose investment in opaque markets, consequently reducing the downside risk. Acknowledgments This study was supported by Wenzao Ursuline University of Languages (IBRS 106009) and Ministry of Science and Technology (106-2914-I-160-060-A1). References An, Z., Li, D., Yu, J., 2016. Earnings management, capital structure, and the role of institutional environments. J. Bank. Financ. 68, 131–152. Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econ. Stud. 58, 277–297.

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