Microsoft Word - 11077-40874-1-SM-writer2-new Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 274 The Foreign Exchange Exposures and Enterprise Risk Management: Evidence from Hospitality Industry in Taiwan Hsiao, Chiu-Ming Department of Finance, National Chung Cheng University, Taiwan & National Yunlin University of Science and Technology, Taiwan No.168, Sec. 1, University Rd., Min-Hsiung Township, Chia-yi County 621, Taiwan (R.O.C.) E-mail: shiaucm@yahoo.com.tw. Received: April 2, 2017 Accepted: May 17, 2017 Published: June 1, 2017 doi:10.5296/ajfa.v9i1.11077 URL: http://dx.doi.org/10.5296/ajfa.v9i1.11077 Abstract This paper adopts ARIMA model to explore the relationship between business performance and the fluctuation of exchange rate. The empirical results show that the impacts of the fluctuation of foreign exchange rate on the business performance of hotels are significant and different across currencies and the size of a hotel. Furthermore, based on the framework of Kim (2013), a modern portfolio theory proposed by Markowitz (1952) gives an optimal allocation of foreign exchange for a hotel’s decision-makers, who would avoid exchange rate risk exposure and complete the construction of enterprise risk management system (ERM) to reduce losses. Keywords: Foreign exchange exposures, modern portfolio theory, enterprise risk management, financial performances, ARIMA Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 275 1. Introduction Tourism can be thought as an integral industry that combines public and private sectors. Anderson and Getz (2009) discussed that the public sectors should make the tourism policies to stimulate the development of tourism and promote some non-profit-festivals to attract tourists. Likewise, the private sectors, such as in the field of transportation, communication, leisure, department, etc., should be integrated under the same goals to generate the profit from tourism-related businesses. The more the tourists and the longer they stay, the more revenue generates from the hospitality industry. In Candela and Figini (2011), they developed the tourism economics which is said to be a no-smokestack industry. From the economic point of view, tourism will create value from catering, hotels, aviation, transportation and many other related industries. In 2013, Japanese Prime Minister Shinzo Abe implemented a policy combining fiscal expansion (i.e. quantitative easing) and structural reform in the hope of revitalizing Japan’s domestic economy. Indeed, this so-called “Abenomics” has resulted in a significant growth in Japan’s domestic economy. Nieh and Cho (2017) adopted the panel data analysis to investigate the relationship between foreign exchange rate and financial ratios of Taiwanese automobile and integrated circuit industries. They found when the Abenomics applied a policy of QE to instigate a sharp depreciation of the yen, the effects of the policies absolutely benefit to Japanese export industries. Accordingly, the impact of exchange rates on some industries becomes even more obvious and important, especially in the tourism industry while Japanese yen is depreciated in order to stimulate the economy. A depreciation of the exchange rate against other currencies will increase a country’s international competitive advantage or exports. Implementing quantitative easing policy that caused the depreciation of Japanese yen increases Japan’s foreign trade and also successfully leads the economy back to situation. Surprisingly, the tourism industry has gained the most benefits of all. This paper, therefore, wants to study the case and examine if the situation could as well apply to the tourism industry in Taiwan. Oh (2005) addressed the causal relations between tourism growth and economic expansion for the Korean economy. He employed the Granger causality test and found that the Korean tourism industry is economic-driven. Kim, Chen and Jang (2006) examined the relationship between tourism expansion and economic development in Taiwan. They found a bi-directional causality between them. In other words, in Taiwan, tourism expansion and economic development reinforce each other. Min (2013) used panel data approach to test the tourism-led economic growth hypothesis. He found that the tourism-led growth hypothesis is more strongly supported when the time-specific effects are eliminated, which will cause a biased estimate in the Granger causality test. According to the data of the World Tourism Organization, the number of international tourist visited in Taiwan in 2012 was estimated 9.91 million, ranked the world’s 31 and createdrevenues$14.7 billion. In 2014, Taiwan inbound tourists grew 23.6%, ranked the 2nd place of the world’s top 50 tourist destinations, only less than of Japan's growth rate 29.4%.Tourism revenue has growth 18.9%, ranked the 4th place in the world’s top 50 tourism Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 276 revenue areas. Gradually, Taiwan’s tourism has been recognized considerable potential. Po and Huang (2008) used 88 cross-sectional countries’ data to investigate the nexus between tourism development and economic growth. They found a significantly positive relationship of them when the proportion of tourism receipts in GDP is either less than 4.05% or more than 4.73%, but not when it lies between these two ratios. Moreover, Chen and Song Zan (2009) showed that the tourism industry is greatly contributed to Taiwan’s economy. In other words, Taiwan is a tourism-led economy. Taiwan authority has opened to Chinese tourists since the summer of 2008. In order to increase the number of tourists, Taiwanese government has undertaken a number of initiatives to promote the tourism industry, such as Doubling Tourist Arrivals Plan (DTAP) introduced in 2002,Challenge 2008, Taiwan’s 2015-2018 Tourism Action Plan, Mid-term Plan for Construction of Major Scenic Sites(2012-2015),Project Vanguard for Excellence in Tourism, and Tour Taiwan and Experience the Centennial. According to Taiwan Tourism Bureau, these plans are proposed to deepen the “Time for Taiwan” core promotional programs, implement “quality, uniqueness, intelligence, and sustainability” as strategies toward the goals of “development of international tourism, enhancement of domestic travel quality, and increased foreign-exchange revenues” to bring Taiwan’s new tourism allure to the attention of the world1. Portnov and Li (2013) suggested that in order to achieve a greater stability in the number of inbound tourist arrivals, Taiwan should diversify sources of their inbound tourism, by giving priority to neighboring countries with relatively larger, more productive, and more steadily growing economies, such as China, Malaysia, or the other emerging countries. According to Taiwan Tourism Bureau, the inbound number of tourists was 2,624,037 in 2000, 9,910,204in 2014 and over 10 million in the end of 2015.This tendency shows the visibility and attractiveness of international tourists traveling to Taiwan. Moreover, Taiwan’s foreign exchange earnings generated by tourism leaped from $3,738 million in 2000 to $14,615 million in 2014, which its share in total GDP reached 2.76% from 1.13%.It shows that Taiwan tourism industry earns a large part of foreign exchange earnings. The fluctuation in exchange rates for Taiwan’s tourism industry is an important factor for Taiwan’s overall economic development. The recent ten-year annual revenues generated from tourism, foreign exchange and domestic tourism are shown in Figure 1. The highest line is the tourism revenue (in red), which grows rapidly in 2009 due to the effect of opening of Chinese tourists to visit Taiwan. The lowest line is the domestic tourism revenue (in purple), which attains the maximum (331 billion of NT dollars) in 2011 and declines in the following years. The foreign exchange earnings (in green) smoothly increases in years. 1http://admin.taiwan.net.tw/public/public_en.aspx?no=6. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 277 Figure 1. Revenues generate from tourism and foreign exchange Source: Tourism Bureau, M.O.T.C., Republic of China (Taiwan). Taiwan’s tourism revenues have increased in recent years. The hotel industry plays an important role in the tourism industry, resulting in a huge source of foreign exchange earnings. Among the tourists, the number of Chinese tourists accounted for the largest part of all, followed by Japanese, European and the United States. Bilateral trade between Taiwan and China, Japan, Europe and the United States, respectively, is not only very close, but also represents the effect of the changes in exchange rates. The number of tourists traveling to Taiwan contributes the foreign exchange earnings. Pritamani, Shome and Singal (2005) divided the U.S. companies into five categories and found that neither exporters nor multinational firms were the most affected by changes in exchange rates. The firms that suffered most from exchange rate fluctuations were wholly domestic U.S. companies facing foreign competition. Taiwan’s hotel industry has the same situation. Based on the above point of view, we mainly discuss Taiwan’s hotel industry for exposure to foreign exchange fluctuations and corporate risk management. Through our study, it suggests the hedging strategies to the decision-makers of firms and then to enhance Taiwan’s hotel industry’s risk management. The structure of this study is as follows: Section 2 is literature review and methodologies will be discussed in Section 3; data collection and its statistical descriptions are in Section 4. The empirical results and analysis are shown in Section 5. The last section is the conclusion. 2. Literature Review From the 1980s, there are numerous studies to discuss the foreign exchange exposures. The landmark papers, Dumas (1978), Adler and Dumas (1980) and then Hodder (1982) implemented the change of foreign exchange rates into the regression models to study the U.S. multinational firm’s values. And Jorion (1990, 1991) followed their studies and found that the stock returns of U.S. multinational firm are significantly positively correlated to the volatility of the U.S. dollar. Moreover, Bodnar and Gentry (1993) studied the different effects of the fluctuations of foreign exchange rates on the different industries in U.S., Canada and Japan. Schnabel (1989, 1994) extended the Adler-Dumas model to a multi-factor model by including several currencies exchange rate movement. 351.1 389.4 390.5 371.3 408.1 514.0 636.3 618.4 638.9 753.0 158.5 165.1 171.2 187.1 225.3 275.9 326.0 348.5 366.8 443.8193 224 219 184 183 238 313 270 272 309 0 500 1,000 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Toursim Revenue (Billions of NT$) Foreign Exchange Earnings (Billions of NT$) Year Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 278 Moreover, He and Ng (1998) studied Japan 171 multinational firms there are about 25% firm’s stock returns significantly positively correlated to the foreign exchange exposures, themselves. And the effects are increasing as firm’s size increases. Morelli (2007) found the same effects of firm’s size on the UK listed firms’ stock returns. Dominguez and Tesar (2006) examined the relationship between exchange rate movement and firm value. No matter in the firm- or industry-level, they found that the foreign exchange exposure do affect the value of firm. Salifu, Osei, and Adjasi, (2007) examined the foreign exchange exposure of Ghanaian listed companies. Their results showed that about 55% companies are exposed to the fluctuation of US dollar and 35% companies are exposed to that of UK pound. Moreover, Chen and Kuo (2016) found that the exchange rate variability was significantly related to foreign reserves foreign reserves growth rate and the stock index return. Jahan (2016) studied the efficiency of using currency derivatives to reduce the effect of currency fluctuations on the performances of the Advanced Chemical Industries in Bangladesh. On the other hand, Maloney (1990) and Koo (1994) paid attention on the Australian mining industry. They indicated that the fluctuations of the exchange rates between Australia dollars against to the major currencies will affect the firm’s profit. So they suggested that firm should find some strategies to manage the positions of foreign currencies in order to avoid the losses caused by the fluctuations of exchange rates and then reduce the firm’s performance. Aggarwal and Harper, (2010) investigated the foreign exchange exposures that U.S. domestic companies faced to. Their results showed that the domestic company suffers more foreign exchange exposures than MNEs. They concluded that the MNEs may use more currency derivatives and more trading skills to reduce the foreign exchange exposures than the pure domestic companies. In Addae, Nyark-Baasi, and Tetteh (2014), they found that the foreign exchange rate movement will affect the quality of assets of Ghanaian banks. In fact, the foreign exchange market in Taiwan is a small, regional market, so some foreign currencies transaction is not warm and the trading volume is small. The liquidities of the other currencies are lower than that of the international major currencies. As Harris (2003) has said that liquidity is the ability to trade large size quickly, at low cost, when you want to trade. Such that a lower liquidity will result to the company when buying foreign currency in order to circumvent the exposure of foreign exchange transactions, it cannot immediately trade at a predetermined price to the required foreign currency position. Hence, the transaction costs and carrying costs of the currencies will increase the cost of risk reduction and then increase the liquidity risk of assets. Here, according to Campbell, Medeiros, and Viceira (2010), regression models can be adopt to help the company to find out the effect on its performance causing by the currency’s fluctuation. After finding the magnitudes of currency fluctuations which affect to the company’s performance, then the framework of the Modern Portfolio Theory, proposed by Bailey, Ng, and Stulz, (1992) and Kim (2013), can be used to form their own foreign exchange risk management strategies and then to reduce the effect of foreign exchange exposures. This study applies the framework of Kim (2013) to investigate the effects of foreign exchange exposures on the performance of Taiwan hospitality industry and try to propose some hedging strategies and strengthen their corporate risk management. Therefore, as the shown in Jorion (1991), I will impose the changes of Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 279 exchange rates of several currencies to study the effects of the fluctuations of exchange rates on the performance of Taiwan hotel companies. 3. Methodologies 3-1. Modern Portfolio Theory, MPT Modern portfolio theory is proposed by Markowitz in 1952.In the paper, Probability Theory and Linear Algebra method are applied to investigate the correlation between the securities. It puts forward the possibility to diversify the main investment risks for this theory that regardless of the dispersion of individual investment targets the risks associated with some other securities can reduce the risk. In this way, individual company information becomes less important. The theory is mainly to solve an investor’s risk-reward problem and to form a rational combination of his/her own funds in order to maximize the proceeds. According to the Markowitz’s framework, there is a certain special relationship between investment risk and return of a portfolio of financial assets. His assumptions are based on four conditions: 1. Assume the market is efficient, investors can learn more of the benefits and risks of financial market changes and their causes. 2. Suppose investors are risk averse and are willing to get a higher rate of return if they must bear a greater risk to get a higher expected return as compensation. Risk is the variability of yields as measured by standard deviation. 3. Investors’ choices are based on the expected returns and standard deviations of selected financial assets portfolio. They select portfolios with higher yields or lower risk. 4. The incomes between various financial assets are correlated with the correlation coefficient between each financial asset, it is possible to choose the lowest risk of the portfolio. An efficient portfolio should be subject to the following conditions: under certain risk (standard deviation).This combination of securities has the highest average reward; and in certain average reward, it has the lowest degree of risk (standard deviation).Therefore, the portfolio should be on the curve of efficient frontier. According to Huang and Litzenberger (1988)and Elton, Gruber, Brown, and Goetzmann (2007), suppose an economy which there are n risky assets with its return and standard deviation iR and iσ , ni , , , 21= , respectively. Moreover, the covariance between any two assets is ( )jiji RRCov , ≡,σ ， nji , , , 21=≠ . If we denote the portfolio weight on each assets in the portfolio to be iw , ni , , , 21= , then the expected return of the portfolio is RwP ⋅′≡μ , where ( )′≡ nRRRR , , , 21 and ( )′≡ nwwww , , , 21 。And the variance of Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 280 the portfolio is wwP ⋅Σ⋅′≡2σ , where ( ) ( ) nnjiRVar × =≡Σ , σ the variance-covariance matrix is. Hence, in the framework of Markowitz (1952) and Kim (2013), we have to minimize the degree of risk of the portfolio under a pre-specified return, 0μ ,and budget constrain. Namely, wwP wi ⋅Σ⋅′= 2 1 2 2 min σ (1)       =≤≤ ==⋅′ ≥⋅′=  = niw wwJ Rw ts i n i in P , , , , 2110 1.. 1 0μμ (2) where, ( ) n nJ ℜ∈′= 111 , , ,  . Using the Lagrange Multipliers method, the above problem can be transformed as follows: ( ) ( )      ⋅′−⋅+⋅′−⋅+⋅Σ⋅′= wJRwwww n wi 1 2 1 20121min 21 λμλλλ λλ , , , ,  . (3) Hence, the F.O.C.is          =     ⋅′−= ∂ ∂ =     ⋅′−= ∂ ∂ =⋅−⋅−⋅Σ= ∂ ∂ 01 0 0 2 0 1 21 Pn P nP wJ Rw JRw w λ μ λ λλ     nP JRw ⋅Σ⋅+⋅Σ⋅= −− 1 2 1 1 * λλ . And then we have,     =⋅Σ⋅′⋅+⋅Σ⋅′⋅ =⋅Σ⋅′⋅+⋅Σ⋅′⋅ −− −− 11 2 1 1 0 1 2 1 1 nnn n JJRJ RJRR λλ μλλ        ⋅−= − ⋅− = −⋅= − −⋅ = 02 0 2 02 0 1 μμλ μμλ D B D A BAC BA D B D C BAC BC , where, RRA ⋅Σ⋅′≡ −1 ， nn JRRJB ⋅Σ⋅′=⋅Σ⋅′≡ −− 11 , nn JJC ⋅Σ⋅′≡ −1 , and 2BACD −≡ .Such that, the optimal wealth allocation portfolio is nP JRw ⋅Σ⋅+⋅Σ⋅= −− 1 2 1 1 * λλ nJ D B D AR D B D C ⋅Σ⋅      ⋅−+⋅Σ⋅      −⋅= −− 1 0 1 0 μμ . (4) The properties of this portfolio are Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 281 1. RJ D B D ARR D B D CRw nPP ⋅Σ⋅′⋅      ⋅−+⋅Σ⋅′⋅      −⋅=⋅′= −− 1 0 1 0 * μμμ .000 2 00 μμμμμ ==⋅−=⋅      ⋅−+⋅      −⋅= D D D BACB D B D AA D B D C 2. **2 PPP ww ⋅Σ⋅′=σ Σ⋅ ′       Σ⋅      −+Σ⋅      −= −− nJ D B D AR D B D C 1 0 1 0 μμ       Σ⋅      −+Σ⋅      −⋅ −− nJ D B D AR D B D C 1 0 1 0 μμ nn JJ D B D ARR D B D C ⋅Σ⋅′⋅      ⋅−+⋅Σ⋅′⋅      −⋅= −− 1 2 0 1 2 0 μμ nJR D B D A D B D C ⋅Σ⋅′⋅      ⋅−⋅      −⋅+ −1 002 μμ 2 0 2 0       ⋅−⋅+      −⋅⋅= μμ D B D AC D B D CA       ⋅−⋅      −⋅⋅⋅+ 002 μμ D B D A D B D CB ( ) CC B D CABC D 121 2 00 2 0 +      −⋅=+⋅−⋅= μμμ (5) Such that, CP 12 ≥σ , and the equality holds when C B=0μ . Next, considering a riskless asset can be invested, and then the pre-described model will be rewritten as follows: wwP wi ⋅Σ⋅′= 2 1 2 2 min σ (6) ( ) 01.. μμ ≥⋅′+⋅⋅′−= RwrJwts fnP , (7) where， fr is the return of the riskless asset. Again, by using the Lagrange Multipliers method, we have to solve the following problem: ( ) ( )[ ]fn w rJwRwwww i ⋅⋅′−−⋅′−⋅+⋅Σ⋅′= 1 2 1 021min μλλλ λ , , ,  . (8) Thus, the F.O.C. is Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 282 ( ) ( )   =⋅⋅′−−⋅′−= =⋅−⋅−⋅Σ= 01 0 0 fn fnw rJwRw rJRw μ λ λ   ( ) H r JrRw f nfP − ⋅⋅−⋅Σ= − 01* μ , where, ( ) ( ) 21 2 ffnfnf rCrBAJrRJrRH ⋅+⋅−=⋅−⋅Σ⋅′⋅−≡ − . The properties of this portfolio are: 1. fnPPP rJwRw ⋅     ⋅′−+⋅′= 1*μ ( ) fnP f nf rJwR H r JrR ⋅     ⋅′−+⋅ − Σ⋅′⋅−= − 101 μ ( ) ( )nfnf f JrRJrR H r ⋅−⋅Σ⋅′⋅−⋅ − = −10μ ( ) ( ) fnPnnf ff rJwJJrR H rr ⋅      ⋅ ′ −+⋅Σ⋅′⋅−⋅ −⋅ + − *10 1 μ . 2. **2 PPP ww ⋅Σ⋅′=σ ( ) ( )       − ⋅⋅−⋅Σ⋅Σ⋅      − Σ⋅′⋅−= −− H r JrR H r JrR f nf f nf 0101 μμ ( ) ( )nfnf f JrRJrR H r ⋅−⋅Σ⋅′⋅−⋅      − = −1 2 0μ     Σ′⋅+Σ′⋅−Σ′⋅      − = −−− nnfnf f JJrJRrRR H r 121 2 0 2 μ ( ) H rf 2 0 − = μ . (9) Hence, its standard deviation H rf P − = 0μ σ , that is, Pf Hr σμ ⋅±= 0 (10) 3.2 Autoregression Integrated Moving Average Models, ( )qdpARIMA ,, In Witt and Witt (1992，1995), they use many econometric models to investigate the topics of tourism industries. Empirically, they suggested that the autoregression and moving average models can be implemented to forecast the performance of tourism industries. This study will focus on the effects of the fluctuations of foreign exchange on the performance of hotel industry. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 283 According to Bodie, Kane and Marcus (2002) and Muriithi, Muturi, and Waweru (2016), we can use the ROA or ROE, reported in the annual financial statements, to be the measures of the corporate’s performance. There are at least two reasons for applying ROA/ROE to proxy the firm’s performance. First, since ROA is the return of corporate’s total assets, which is defined by the product of profit margin and total asset turnover, so it tells us how effectively a firm uses its assets to generate profits. Therefore, a well-performed firm will have a higher ROA. Second, the definition of ROE is the net profit over the average equity, so that by the DuPont equation, we have Ratio Equity AssetTurnoverAsset MarginProfit Net ××=ROE . (11) As a result, it tells us how efficiently a company is operated. It also provides insights into the firm’s use of assets via turnover.That is, a well-performed firm also has a higher ROE. As a result, in our study, we will apply these two measures to be the proxies of the firm’s performance and investigate the magnitude of the effects of foreign exchange rate’s fluctuations. Hence, by the multi-factor model in Jorion (1991) and Bartram (2008), the autoregression moving average model is given as follows:  = −⋅+⋅+= p k ktikitmiiti ePerformancRMRFePerformanc 1 , , , , φβα  = − = ⋅+⋅+Δ⋅+ q s stistii n j tjji aSizeFX 01 , , , , θδγ , (12) ( ) ( ) iTqpqpt , , , , , 2max1max ++= , Ni , , , 21= . Here, tiePerformanc , represents the i-th firm’s performance in the t-th quarter, and ktiePerformanc − , is its k-th lagged variable. In Sharpe (1964), he defined that tRMRF is the market portfolio’s excess return in the t-th quarter, i.e., ftt rRmRMRF −≡ ,and tRm is the market portfolio’s return and fr is the rate of return of riskless asset. Furthermore, as indicated in Smithson andSimkins (2005), although themanagement of interest rate and foreign exchange rate risksdoes indeed add value, the effect is larger than would beexpected.And by the definition ofMuller and Verschoor (2006),let tjFX , Δ be the percentage change of exchange rate of the j-th currency in the t-th quarter, that is 100 1 1 × − ≡Δ − − tj tjtj tj E EE FX , , , , , (13) where tjE , is the closed price in the end of the quarter in terms of direct quotation. Moreover, Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 284 in Fama and French (1993, 1995), they formed six portfolios of the stocks listed on NYSE, AMX, and NASDAQ Stock Market by the firm’s size and found that firm’s size and BE/ME proxy for sensitivity to risk factors that capture strong common variation in stock returns and will help to explain the average returns and then firm’s profitability. Such that, let tiSize , denote the size of the i-th firm in the t-th quarter which is defined as ( )titi CapSize , , ln≡ , and tiCap , is the capitalization of the firm in the t-th quarter. tia , is the white noises. 4. Data This paper selected twelve hotel companies listed on Taiwan Stock Exchange (TWSE), and downloaded their quarterly ROA, ROE and capitalization from Taiwan Economic Journal (TEJ). They are Hotel Holiday Garden(2702), The Ambassador Hotel Ltd.(2704), The Leofoo Development Co., Ltd.(2705), First Hotel Company Ltd.(2706), Formosa International Hotels Corporation(2707), Farglory Hotel Co., Ltd.(2712), Pleasant Hotels International Inc.(2718), Chateau International Development Co., Ltd.(2722), FX Hotels Group Inc.(2724-F), Janfusun Fancyworld Corp.(5701), The Landis Taipei Hotel Co., Ltd.(5703), and Hotel Royal Chihpen(5704). Period is from 2000Q1 to 2015Q3 and sum to 489 firm-quarters. Table 1 shows the descriptive statistics of the firm’s ROA and ROE, respectively. Table 1(A). Descriptive statistics of ROA. ROA (%) Obs. Mean Std. dev. Max Min Median 2702HG 63 0.661 0.820 2.94 -1.43 0.740 2704AMBH 63 0.641 0.565 1.47 -1.29 0.740 2705Leofoo 32 -0.136 1.298 5.40 -5.03 -0.225 2706First Hotel 32 1.398 1.105 7.28 0.54 1.160 2707GFRT 63 4.392 1.195 7.55 1.27 4.360 2712FGH 11 1.383 1.244 3.68 -0.12 0.870 2718PH 25 0.944 0.873 2.40 -1.20 0.840 2722Chateau 21 2.732 2.632 8.38 -0.60 2.380 2724FX Hotels 21 1.179 1.770 5.74 -2.45 1.550 5701JFS 32 -1.462 1.341 1.73 -5.76 -1.470 5703Landis Taipei 63 0.419 1.724 3.20 -8.97 0.740 5704Chihpen Royal 63 1.040 1.381 3.62 -3.49 1.210 Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 285 Table 1(B). Descriptive statistics of ROE ROE (%) Obs. Mean Std. dev. Max Min Median 2702 HG 63 0.804 1.276 3.77 -2.97 0.940 2704AMBH 63 0.704 0.956 2.18 -2.96 0.880 2705Leofoo 32 -0.415 2.565 11.04 -9.90 -0.695 2706First Hotel 32 1.668 1.339 8.71 0.67 1.390 2707GFRT 63 6.645 2.205 11.13 1.50 6.700 2712FGH 11 1.794 1.782 4.81 -0.44 1.100 2718PH 25 1.220 1.109 2.99 -1.62 1.160 2722Chateau 21 3.313 3.335 11.24 -0.68 3.060 2724FX Hotels 21 1.418 3.958 6.96 -8.83 2.210 5701JFS 32 -3.217 2.462 2.47 -11.24 -3.300 5703Landis Taipei 63 0.612 2.338 4.48 -11.76 1.040 5704Chihpen Royal 63 1.192 1.581 4.19 -3.83 1.300 Source: Taiwan Economic Journal (TEJ). In Table 1, we may find that the Formosa International Hotels Corporation(2707) has the highest ROA and ROE, however, Janfusun Fancyworld Corp.(5701) has the lowest ROA and ROE. And except of Janfusun Fancyworld Corp. and the Leofoo Development Co., Ltd. (2705), the others are well-performed since they all have a positive average ROA or ROE. Moreover, the Ambassador Hotel Ltd. (2704) has the lowest volatility of ROA and ROE. On the other hand, Chateau International Development Co., Ltd. (2722) and the FX Hotels Group Inc. (2724-F)have the highest volatility of ROA and ROE, respectively. It may result from the shortest listing data of these two companies. Next, the foreign exchange rates were collected from 2000 to 2015 through the website2 of the Central Bank of Taiwan. The equation (13) calculates the quarterly and monthly percentage change of exchange rates for the currencies against to the NT dollars (NTD). Table 2 shows the descriptive statistics of the monthly change of foreign exchange rates. 2http://www.cbc.gov.tw/content.asp?mp=1&CuItem=36599. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 286 Table 2. Descriptive statistics of the monthly change of exchange rates Monthly Change (%) Mean Std. dev. Max Min Median CV USD 0.0280 1.1865 3.3313 -3.5798 0.0232 42.3750 JPY -0.0353 2.3233 8.8309 -6.0498 -0.2280 -65.8159 GBP 0.0135 1.9269 5.7608 -8.0517 0.0901 142.7333 CNY 0.1664 1.1449 3.0715 -3.4686 0.1749 6.8804 EUR 0.1016 2.2251 7.1016 -5.2660 0.2209 21.9006 HKD 0.0294 1.1841 3.3179 -3.5798 0.0205 40.2755 KRW 0.0170 1.8737 6.7195 -12.3726 0.2496 110.2176 CAD 0.0940 1.8282 5.0170 -8.6264 0.0351 19.4489 SGD 0.1155 0.9621 3.0253 -3.4506 0.1384 8.3299 AUD 0.1074 2.6040 6.9746 -13.5568 0.2725 24.2458 IDR -0.3005 3.1096 20.7023 -13.5534 -0.2083 -10.3481 THB 0.0602 1.2869 3.6022 -4.5300 0.0806 21.3771 MYR -0.0366 1.2266 3.0689 -4.0603 -0.0825 -33.5137 PHP -0.0412 1.5530 4.3555 -4.5580 -0.1633 -37.6942 Source: Central Bank of Taiwan. In Table 2, the lowest percentage change (0.96%) of the exchange rate is the Singapore dollar against to NT dollar, and the highest percentage change (3.11%)of the exchange rate is the Indonesian rupiah against to NT dollar. Indonesian rupiah has a maximum appreciation (20.70%) and minimum depreciation (13.56%) against to NT dollar. Moreover, the coefficient of variation is also reported in Table 2.The standard deviation of data describes the dispersion of the data away from the mean. In contrast, the coefficient of variation is the multiple of the standard deviation to the mean, i.e. μ σ≡CV . For comparison between data sets with different units or widely different means, this paper uses the coefficient of variation instead of the standard deviation. Scheel (1978) proposed that the coefficient of variation can also be a measure of relative risk in the elementary risk and insurance. An asset with lower value of coefficient of variation means either a lower-risk asset among that of the same return or a higher-return asset among that of same level of risk. As shown in Table 2, China yuan (CNY) and Singapore dollar (SGD) have lower coefficient of variation, 6.8804and 8.3299, respectively, and Great British pound and Korean won have higher coefficient of variation. Both Great British pound and Korean won are either high-risk or low-return. 5. Empirical Results and Analysis First, we have to test whether the series of performance is stationary or not. That is, we should test the null hypothesis that it has a unit root. In Tsay (2005), he indicated that the fundamental time series analysis is stationarity. A time series ty is said to be strictly Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 287 stationary if the joint distribution of ( )′ kttt yyy , , ,  21 is identical to that of ( )′+++ ststst k yyy , , ,  21 for all k, where s is an arbitrary positive integer. In other words, strict stationarity requires that the joint distribution of ( )′ kttt yyy , , ,  21 is invariant under time shift. And a time series ty is weakly stationary if both the mean of ty and ( )stt yyCov −, are time-invariant, where sis an arbitrary integer. In the Table 3, we show the Augmented Dicky-Fuller test results. As shown in Table 3, we can find that almost all the ROA/ROE series are non-stationary except the Ambassador’s ROA/ROE. On the other hand, according to Hurvich and Tsai (1989), there will be biased estimates resulting from a non-stationary series. Such that, applying Wei (2006), we take the first-ordered difference on the series, i.e., 11 −−≡ ttt ROAROAROAD and 11 −−≡ ttt ROEROEROED . (14) And then, we test the unit-root-test again to verify its stationarity. The Augmented Dicky-Fuller test results are also shown in Table 3. After differencing the series, all of them are stationary. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 288 Table 3. The stationarity test results of company’s performances SEC Series Obs. ADF test statistic p-value Stationarity 2702 ROA 56 -1.539 0.5140 Non-stationary △ROA 61 -14.607 0.0000 Stationary 2704 ROA 56 -2.884 0.0472 Stationary 2705 ROA 56 -2.158 0.2217 Non-stationary △ROA 61 -12.592 0.0000 Stationary 2706 ROA 56 -2.312 0.1683 Non-stationary △ROA 61 -17.520 0.0000 Stationary 2707 ROA 56 -2.640 0.0849 Non-stationary △ROA 61 -12.541 0.0000 Stationary 2712 ROA 9 -3.466 0.0089 Stationary 2718 ROA 18 -1.651 0.4567 Non-stationary △ROA 23 -9.001 0.0000 Stationary 2722 ROA 14 -1.810 0.3755 Non-stationary △ROA 19 -5.904 0.0000 Stationary 2724 ROA 14 0.025 0.9606 Non-stationary △ROA 19 -5.816 0.0000 Stationary 5701 ROA 56 -1.476 0.5452 Non-stationary △ROA 61 -11.789 0.0000 Stationary 5703 ROA 56 -1.977 0.2967 Non-stationary △ROA 61 -10.758 0.0000 Stationary 5704 ROA 56 -1.421 0.5722 Non-stationary △ROA 61 -17.149 0.0000 Stationary Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 289 Table 3(B). The stationarity test results of company’s ROE SEC Series Obs. ADF test statistic p-value Stationarity 2702 ROE 56 -1.604 0.4814 Non-stationary △ROE 61 -15.323 0.0000 Stationary 2704 ROE 56 -2.993 0.0356 Stationary 2705 ROE 25 -2.061 0.2604 Non-stationary △ROE 30 -8.296 0.0000 Stationary 2706 ROE 25 -2.441 0.1306 Non-stationary △ROE 30 -13.890 0.0000 Stationary 2707 ROE 56 -1.808 0.3764 Non-stationary △ROE 61 -11.738 0.0000 Stationary 2712 ROE 9 -3.501 0.0080 Stationary 2718 ROE 18 -1.689 0.4365 Non-stationary △ROE 23 -8.826 0.0000 Stationary 2722 ROE 14 -2.132 0.2320 Non-stationary △ROE 19 -6.034 0.0000 Stationary 2724 ROE 14 0.394 0.9813 Non-stationary △ROE 19 -7.304 0.0000 Stationary 5701 ROE 25 -2.505 0.1143 Non-stationary △ROE 30 -8.123 0.0000 Stationary 5703 ROE 56 -1.942 0.3124 Non-stationary △ROE 61 -10.652 0.0000 Stationary 5704 ROE 56 -1.332 0.6146 Non-stationary △ROE 61 -17.278 0.000 Stationary Next, Patro, Wald, and Wu (2002) found the significant currency risk exposures in country equity index returns by using the GARCH model. And, Polodoo, Seetanah, and Sannassee (2016) discussed the nexus between exchange rate volatility and manufacturing trade. They found that exchange rate volatility has an adverse effect on the real manufacturing trade of the Africa countries. As shown in Kelilume (2016), he applied the dynamic panel regression approach to investigate the effects of exchange rate volatility on firm performance by examining 20 companies listing in Nigerian Stock Exchange. It revealed that exchange rate volatility has significant negative impacts on the ROAs, ATRs. Here, that the effects of the fluctuations of exchange rates on the firm’s performance is the main purpose of this study. Therefore, like the work in Kim (2012), the following autoregression moving average (ARIMA) model:  = −⋅+⋅+= p k ktikitmiiti ePerformancDRMRFePerformancD 1 11 , , , , φβα Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 290  = − = ⋅+⋅+Δ⋅+ q s stistii n j tjji aSizeFX 01 , , , , θδγ ,(15) ( ) ( ) iTqpqpt , , , , , 2max1max ++= , Ni , , , 21= . Where, tiePerformancD , 1 represents the first-ordered difference of the i-th firm’s performance in the t-th quarter, and ktiePerformancD − , 1 is its k-th lagged variable. Use the STATA13 to find the regression results and shown in the Table 4. Model I regresses ROAD1 on all exchange fluctuations, lagged variables and the control variables. Model II regresses ROAD1 on all variables but selected by eliminating higher p-value explanatory variables. Table 4. Regression on ROA The regression model is given as follows: . , , , , , , , , titii n j tjji p k ktikitmiiti aSizeFXROADRMRFROAD +⋅+Δ⋅+⋅+⋅+=  == − δγφβα 11 11 Model I regresses 1,,,1 −⋅⋅⋅ −≡ ttt ROAROAROAD on all exchange fluctuations, lagged variables and the control variables. Model II regresses ROAD1 on all variables but selected by eliminating higher p-value explanatory variables. The values in the parentheses are standard error of the estimates. And *, ** and *** stand for 10%, 5% and 1% level of significance, respectively. Company Hotel Holiday Garden (2702) The Leofoo Development Co., Ltd. (2705) Formosa International Hotels Corporation (2707) Variables Model I Model II Model I Model II Model I Model II Const. 10.31 (7.59) 0.10 (0.09) 106.98 (81.09) 101.70** (47.14) 23.71 (16.21) -0.06 (0.14) RMRF 0.01 (0.01) -0.05 (0.07) -0.01 (0.02) USD 0.14 (0.14) 0.15 (0.33) 0.10 (0.20) JPY 0.03 (0.03) -0.01 (0.09) -0.05 (0.05) Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 291 CNY -0.09 (0.14) -0.38 (0.36) -0.21 (0.21) EUR -0.05 (0.05) 0.09 (0.13) -0.00 (0.08) KRW 0.02 (0.04) 0.28* (0.14) 0.20** (0.08) 0.04 (0.07) GBP 0.03 (0.05) -0.12 (0.17) -0.03 (0.08) SGD 0.05 (0.14) 0.51 (0.62) 0.41* (0.21) 0.06 (0.21) AUD -0.04 (0.04) -0.04* (0.02) -0.21 (0.19) -0.20** (0.07) 0.06 (0.06) 0.09*** (0.03) IDR 0.03 (0.03) 0.05** (0.02) 0.01 (0.13) -0.09* (0.05) -0.07** (0.03) THB 0.05 (0.06) 0.22 (0.20) -0.00 (010) MYR -0.03 (0.07) -0.17 (0.21) -0.01 (0.11) PHP -0.04 (0.06) -0.07 (0.18) 0.09 (0.09) Lag1 -0.83*** (0.18) 0.67*** (0.13) -0.91** (0.33) -0.76*** (0.18) -0.69*** (0.15) -0.57*** (0.12) Lag2 -0.63*** (0.20) -0.49*** (0.14) -0.34 (0.34) -0.35** (0.17) -0.80*** (0.16) -0.67*** (0.12) Lag 3 -0.45** (0.19) -0.45*** (0.11) 0.01 (0.33) -0.51*** (0.15) -0.35*** (0.11) Lag4 0.01 (0.15) -0.04 (0.25) -0.13 (0.15) SIZE -0.53 (0.36) -4.70 (3.57) -4.48** (2.08) -1.06 (0.73) Adj. R2 0.46 0.51 0.03 0.32 0.38 0.44 Obs. 59 58 32 32 58 58 Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 292 Table 4. Regression on ROA (Continued) Company First Hotel Company Ltd. (2706) Pleasant Hotels International Inc.(2718) Chateau International Development Co., Ltd. (2722) Variables Model I Model II Model I Model II Model I Model II Const. -8.17 (15.86) -14.17* 37.34 (58.01) 0.71*** (0.12) -1957** (542.5) 0.29 (0.85) RMRF 0.01 (0.01) -0.12 (0.10) -0.06** (0.03) 3.33*** (0.79) USD 0.06 (0.05) 1.54 (1.07) 0.66*** (0.15) -13.15** (4.20) JPY 0.01 (0.01) -0.30 (0.12) 3.14*** (0.76) CNY -0.05 (0.06) -1.36 (0.72) -0.78*** (0.14) 12.03** (3.78) EUR 0.00 (0.02) -0.29 (0.20) -0.16*** (0.04) 7.60** (1.96) KRW -0.00 (0.02) 0.23 (0.23) -5.31** (1.49) GBP -0.02 (0.03) -0.26 (0.38) -6.44*** (1.50) SGD -0.05 (0.07) -0.66 (0.43) -0.66** (0.11) 1.41 (0.74) 1.89** (0.85) AUD -0.01 (0.02) 0.21 (0.13) 0.13** (0.05) -4.36*** (0.91) -0.58* (0.29) IDR 0.03 (0.02) 0.20 (0.08) 0.22*** (0.03) 0.59* (0.24) THB 0.00 (0.04) 0.58 (0.32) 0.55*** (0.12) -13.42*** (3.22) MYR -0.02 (0.03) -0.06 (0.16) 3.75** (0.96) PHP -0.04 (0.03) -0.04*** (0.01) -1.06 (0.43) 18.45** (4.82) Lag1 -1.05*** (0.22) -1.21*** (0.14) -1.90 (0.46) -1.46*** (0.10) Lag2 -0.28 (0.24) -1.46 (0.44) -1.13*** (0.12) Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 293 Lag 3 -0.47 (0.27) -0.98 (0.63) -0.44*** (0.10) Lag4 0.34*** (0.10) -0.30*** (0.07) -0.22 (0.25) SIZE 0.41 (0.72) 0.67* (0.34) -1.80 (2.85) 92.79** (25.69) Adj. R2 0.81 0.82 0.84 0.93 0.78 0.16 Obs. 32 32 20 20 20 20 Table 4. Regression on ROA (Continued) Company Janfusun Fancyworld Corp. (5701) The Landis Taipei Hotel Co., Ltd. (5703) Hotel Royal Chihpen(5704) Variables Model I Model II Model I Model II Model I Model II Const. 29.91 (30.30) -0.12 (0.18) 55.80 (50.46) 0.13 (0.17) 32.16* (19.05) 0.00 (0.12) RMRF 0.04 (0.05) 0.06 (0.03) 0.07*** (0.02) 0.05** (0.02) 0.03** (0.01) USD -0.24 (0.31) 0.10 (0.24) 0.07 (0.17) JPY -0.01 (0.08) -0.11 (0.08) 0.07 (0.05) CNY 0.38 (0.36) 0.10 (0.27) 0.22 (0.19) 0.22*** (0.07) EUR 0.03 (0.12) -0.11 (0.10) -0.12* (0.07) 0.01 (0.07) KRW 0.08 (0.12) 0.10* (0.05) 0.07 (0.09) 0.27*** (0.07) 0.17*** (0.04) GBP -0.24 (0.15) -0.16** (0.06) 0.00 (0.10) -0.09 (0.07) SGD 0.29 (0.37) -0.05 (0.24) -0.31* (0.18) -0.29*** (0.09) AUD -0.10 (0.12) 0.22** (0.09) 0.20*** (0.06) 0.02 (0.06) IDR 0.08 (0.11) -0.02 (0.06) -0.01 (0.04) Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 294 THB -0.21 (0.18) -0.12 (0.12) -0.20** (0.08) 0.00 (0.09) MYR 0.06 (0.17) -0.17 (0.13) 0.04 (0.10) PHP 0.06 (0.15) -0.02 (0.10) -0.10 (0.07) Lag1 -0.65** (0.28) -0.64*** (0.16) -0.77*** (0.12) -0.77*** (0.09) -1.08*** (0.15) -0.99*** (0.09) Lag2 -0.31 (0.36) -0.31* (0.16) -0.32** (0.14) -0.35*** (0.09) -0.83*** (0.18) -0.66*** (0.12) Lag 3 0.09 (0.37) 0.01 (0.13) -0.71*** (0.19) -0.56*** (0.09) Lag4 0.14 (0.26) -0.07 (0.11) -0.11 (0.16) SIZE -1.35 (1.36) -2.68 (2.41) -1.58* (0.93) Adj. R2 0.07 0.39 0.59 0.63 0.75 0.76 Obs. 32 32 58 58 58 59 In Table 4, almost all estimates of the lagged variables are significant and negative, such as, Leofoo Development Co., Ltd. (2705), Formosa International Hotels Corporation (2707), Janfusun Fancyworld Corp. (5701), The Landis Taipei Hotel Co., Ltd. (5703), and Hotel Royal Chihpen(5704).It implies that those ROAD1 are mean-reverting. As the estimates of third-lagged variables are also significant, then we can conclude that there is a seasonal effect on the company’s ROA. Moreover, some estimates of SIZE are significant in Table 4. If it is positive, such as Chateau International Development Co., Ltd. (2722), then the company may increase its own assets to increase its ROAD1 , so to its ROA. Hence, it can operate efficiently by its assets to generate profit and then to be a well-performed company. If the estimate of SIZE is negative, such as those in Leofoo Development Co., Ltd. (2705) and FX Hotels Group Inc. (2724-F), then the company may dispose some of its idle assets or non-performed assets to reduce the inefficient effect of these assets. As a result, the company’s ROA will be improved. Next, Table 4 shows significant effects on the performances of Taiwan hotel industry due to the fluctuations of foreign exchange rates. The changes of foreign exchange rates have significant impacts on the ROAD1 s. Some are positive and some are negative. The same currency has different impact on different companies. For example, Singapore dollar has Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 295 positive effect on the ROAD 1 of Leofoo Development Co., Ltd. (2705), Chateau International Development Co., Ltd. (2722), and on the ROA of Ambassador Hotel Ltd. (2704), but negative effect on that of Pleasant Hotels International Inc.(2718) and Hotel Royal Chihpen (5704). Moreover, the Australian dollar has positive effect on the ROAD1 of Formosa International Hotels Corporation (2707), Pleasant Hotels International Inc. (2718), and Landis Taipei Hotel Co., Ltd. (5703), and on the ROA of Ambassador Hotel Ltd. (2704), but negative effect on that of Chateau International Development Co., Ltd. (2722). And the Korean won has a positive effect on the ROAD1 of Leofoo Development Co., Ltd. (2705), Janfusun Fancyworld Corp. (5701), Hotel Royal Chihpen (5704), and then on those company’s ROA. Furthermore, the number of significant variables and the component of significant variables are different to each company. For example, the significant variables of the Pleasant’s ROAD1 are the change of USD, CNY, EUR, SGD, AUD, IDR, THB, however, that of the Chateau’s ROAD1 are only the changes of Singapore dollar and Australia dollar. As a result, the portfolio of currencies should be different for each company. Table 5. Regression on ROE The regression model is given as follows: . , , , , , , , , titii n j tjji p k ktikitmiiti aSizeFXROEDRMRFROED +⋅+Δ⋅+⋅+⋅+=  == − δγφβα 11 11 Model I regresses 1,,,1 −⋅⋅⋅ −≡ ttt ROEROEROED on all exchange fluctuations, lagged variables and the control variables. Model II regresses ROED 1 on all variables but selected by eliminating higher p-value explanatory variables. The values in the parentheses are standard error of the estimates. And *, ** and *** stand for 10%, 5% and 1% level of significance, respectively. Company Hotel Holiday Garden (2702) The Leofoo Development Co., Ltd. (2705) Formosa International HotelsCorporation (2707) Variables Model I Model II Model I Model II Model I Model II Const. 8.39 (10.97) 0.14 (0.13) 550.62 (305.52) 232.88** (110.24) 28.18 (15.24) 0.10 (0.21) Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 296 RMRF 0.01 (0.02) -0.07 (0.21) -0.03 (0.04) USD 0.24 (0.19) -1.77 (1.44) 0.05 (0.31) JPY 0.05 (0.05) 0.08** (0.03) -0.64* (0.33) -0.39** (0.14) -0.12 (0.08) -0.09* (0.05) CNY -0.16 (0.20) 1.37 (1.40) -0.27 (0.33) -0.25** (0.11) EUR -0.11 (0.08) -0.07* (0.04) -0.00 (0.34) -0.10 (0.13) KRW 0.05 (0.06) 0.08* (0.04) 0.47 (0.46) 0.08 (0.11) GBP 0.00 (0.07) -0.19 (0.42) -0.04 (0.12) SGD 0.22 (0.20) 4.02** (1.70) 1.74** (0.62) 0.34 (0.34) 0.39** (0.19) AUD -0.00 (0.20) -0.63 (0.50) 0.06 (0.10) IDR 0.02 (0.05) 0.12 (0.28) -0.13* (0.07) -0.09* (0.05) THB 0.11 (0.09) 0.10* (0.06) -0.52 (0.77) 0.02 (0.16) MYR -0.06 (0.11) -1.36** (0.56) -0.68** (0.29) -0.01 (0.18) PHP -0.11 (0.08) 0.92 (1.24) 0.05 (0.13) Lag1 -0.90*** (0.15) -0.88*** (0.12) -1.27*** (0.37) -0.64*** (0.18) -0.61*** (0.15) -0.61*** (0.12) Lag2 -0.67*** (0.19) -0.58** (0.15) -0.15 (0.43) -0.66*** (0.16) -0.63*** (0.12) Lag 3 0.41** (0.19) -0.42*** (0.18) 0.20 (0.33) -0.49*** (0.16) -0.46*** (0.12) Lag4 -0.04 (0.15) 0.33 (0.28) -0.01 (0.16) SIZE -0.39 (0.57) -24.32 (13.46) -10.28** (4.86) -1.26 (1.13) Adj. R2 0.51 0.54 0.16 0.32 0.30 0.39 Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 297 Obs. 58 58 27 27 58 58 Table 5. Regression on ROE (Continued) Company First Hotel Company Ltd. (2706) Pleasant Hotels International Inc.(2718) Chateau International Development Co., Ltd. (2722) Variables Model I Model II Model I Model II Model I Model II Const. -21.59 (37.72) 0.84** (0.38) 31.69 (89.17) 0.98*** (0.18) -2289** (737.7) -0.44 (1.05) RMRF 0.05* (0.02) 0.05*** (0.01) -0.15 (0.15) -0.08* (0.04) 3.87** (1.08) USD 0.39** (1.74) 0.12*** (0.04) 1.77 (1.66) 0.85*** (0.21) -15.42** (5.71) JPY 0.04 (0.03) -0.02 (0.18) 3.60** (1.03) CNY -0.31* (0.16) -1.61 (1.12) -1.04*** (0.21) 14.08** (5.14) EUR -0.05 (0.05) -0.37 (0.31) -0.24*** (0.06) 8.86** (2.67) KRW -0.04 (0.05) 0.24 (0.35) -6.07** (2.03) GBP 0.01 (0.06) -0.35 (0.63) -7.56** (2.03) SGD 0.03 (0.14) -0.76 (0.72) -0.86*** (0.15) 1.93 (1.00) 2.46** (1.05) AUD 0.03 (0.05) 0.25 (0.21) 0.16** (0.07) -5.22*** (1.24) -0.77** (0.36) IDR -0.04 (0.05) 0.27 (0.13) 0.29*** (0.05) 0.77* (0.32) THB 0.05 (0.07) 0.77 (0.50) 0.74*** (0.17) -15.90** (4.38) MYR -0.04 (0.06) -0.13 (0.25) 4.41** (1.30) PHP -0.08 (0.11) -1.32 (0.67) -0.93*** (0.23) 21.70** (6.56) Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 298 Lag1 -1.37*** (0.18) -2.88*** (0.37) -1.88 (0.55) -1.48*** (0.12) Lag2 -0.31 (0.21) -0.82** (0.40) -1.50 (0.55) -1.15*** (0.13) Lag 3 -0.19 (0.24) -0.99 (0.80) -0.44*** (0.11) Lag4 0.50** (0.21) 1.86*** (0.36) -0.21 (0.29) SIZE 1.06 (1.74) -1.51 (4.37) 108.51** (34.93) Adj. R2 0.87 0.85 0.77 0.92 0.74 0.18 Obs. 28 31 20 20 20 20 Table 5. Regression on ROE (Continued) Company Janfusun Fancyworld Corp. (5701) The Landis Taipei Hotel Co., Ltd. (5703) Hotel Royal Chihpen(5704) Variables Model I Model II Model I Model II Model I Model II Const. 55.72 (141.90) 0.04 (0.42) 70.20 (69.75) -0.28 (0.23) 39.26* (21.32) 0.02 (0.14) RMRF 0.09 (0.33) 0.07 (0.04) 0.06** (0.02) 0.04** (0.02) USD 0.11 (1.45) 0.17 (0.33) 0.04 (0.19) JPY 0.12 (0.35) -0.15 (0.10) -0.15** (0.06) 0.08 (0.05) CNY 0.01 (1.23) 0.11 (0.37) 0.28 (0.21) 0.25*** (0.08) EUR 0.12 (0.45) -0.15 (0.14) -0.25*** (0.09) 0.02 (0.08) KRW 0.37 (0.72) 0.08 (0.12) 0.30*** (0.07) 0.18*** (0.05) GBP -0.59 (0.43) -0.31** (0.14) -0.02 (0.14) -0.09 (0.08) SGD 0.16 (1.00) -0.09 (0.33) -0.36* (0.20) -0.32*** (0.10) Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 299 AUD -0.51 (0.37) -0.22* (0.11) 0.30** (0.12) 0.39*** (0.07) 0.01 (0.06) IDR 0.23 (0.33) -0.03 (0.08) -0.01 (0.05) THB -0.42 (0.66) -0.11 (0.17) -0.00 (0.10) MYR 0.51 (0.48) 0.36* (0.20) -0.23 (0.18) -0.24** (0.11) 0.05 (0.11) PHP 0.20 (1.13) -0.03 (0.14) -0.11 (0.08) Lag1 -0.60* (0.31) -0.75*** (0.17) -0.76*** (0.13) -0.75*** (0.09) -1.09*** (0.14) -0.99*** (0.09) Lag2 -0.29 (0.43) -0.52*** (0.17) -0.35** (0.15) -0.42*** (0.09) -0.83*** (0.19) -0.66*** (0.12) Lag 3 0.30 (0.73) -0.01 (0.13) -0.73*** (0.19) -0.56*** (0.09) Lag4 0.26 (0.48) -0.08 (0.11) -0.12 (0.16) SIZE -2.50 (6.39) -3.37 (3.33) -1.93* (1.05) Adj. R2 0.04 0.48 0.57 0.61 0.76 0.77 Obs. 27 27 58 58 58 58 In Table 5, Model I regresses ROED 1 on all exchange fluctuations, lagged variables and the control variables. Model II regresses ROED 1 on all variables but selected by eliminating higher p-value explanatory variables. We may find that the results in Table 5 are almost the same as in Table 4. There is seasonal effect for Taiwan hotel industry’s ROE, too. And, ROED1 of First Hotel Company Ltd. (2706) and Pleasant Hotels International Inc. (2718) are mean-reverting. Moreover, the number of significant variables and the component of significant variables are different to each company. For example, the significant variables of the ROE of Landis Taipei Hotel Co., Ltd. (5703) are the changes of euro, Japan yen, Australia dollar and Malaysian Ringgit, but that of the Chateau International Development Co., Ltd. (2722) are the changes of euro, pound, Chinese yuan, Japan yen, Korean won, Singapore dollar, Australia dollar, Thailand Baht, Malaysian Ringgit, and Philippine peso. Therefore, it supports the results in Table 4, which the portfolio of currencies should be different for each company. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 300 Table 6. Regression on Financial Performances of the Ambassador Hotel The regression model is given as follows: . , , , , , , , , tti n j tjj p k ktktmt aSizeFXyRMRFy 27042704 1 2704 1 27042704270427042704 +⋅+Δ⋅+⋅+⋅+=  == − δγφβα 2704y represents the performance of the Ambassador Hotel, that is, 2704ROA or 2704ROE . Model I regresses 2704y on all exchange fluctuations, lagged variables and the control variables. Model II regresses 2704y on all variablesbut selected by eliminating higher p-value explanatory variables. The values in the parentheses are standard error of the estimates. And *, ** and *** stand for 10%, 5% and 1% level of significance, respectively. Performance 2704ROA 2704ROE Variables Model I Model II Model I Model II Const. -25.80 (34.26) 0.28** (0.11) -34.80 (58.29) 0.25* (0.14) RMRF 0.02 (0.01) 0.15** (0.01) 0.03 (0.02) 0.03*** (0.01) USD -0.21** (0.09) -0.32** (0.15) JPY 0.01 (0.02) 0.02 (0.03) CNY 0.22** (0.09) 0.34** (0.16) EUR -0.05 (0.03) -0.05** (0.02) -0.08 (0.06) -0.08** (0.03) KRW 0.01 (0.03) 0.03 (0.05) GBP 0.04 (0.03) 0.06 (0.06) SGD 0.10 (0.10) 0.14** (0.06) 0.17 (0.17) 0.26** (0.10) AUD -0.02 (0.03) -0.05 (0.05) IDR 0.01 (0.02) 0.02 (0.04) THB 0.02 (0.04) 0.04 (0.07) MYR -0.11** (0.05) -0.10*** (0.04) -0.18** (0.08) -0.17*** (0.06) Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 301 PHP 0.02 (0.04) 0.01 (0.07) Lag1 0.20 (0.14) 0.26** (0.12) 0.30** (0.14) 0.32*** (0.11) Lag2 -0.06 (0.16) -0.06 (0.15) Lag 3 -0.12 (0.14) -0.10 (0.14) Lag4 0.25* (0.14) 0.28** (0.11) 0.24* (0.14) 0.24** (0.11) SIZE 1.13 (1.48) 1.52 (2.52) Adj. R2 0.27 0.40 0.30 0.36 Obs. 59 59 59 59 Next, because Taiwan’s foreign trade is mainly denominated in US dollars, Taiwan foreign exchange market for a long time pegged to the dollar exchange rate. Therefore, enterprises may have a greater proportion of dollar holdings. Furthermore, due to the opening of Chinese tourists to Taiwan, it results in the hotel industry to increase its Chinese yuan transaction needs, and thus ROA/ROE reflect the effect from the change of Chinese yuan. In additional, Taiwan is also the first choice for Japanese and Korean tourists traveling abroad, so accommodation of the Korean won and the Japanese yen in trading volume should not be underestimated. As shown in Table 7, major hotels aggregated by Taiwan’s Tourism Bureau in 2012 showed that the Japanese and Korean inbounds were over 1/5 of guests in the half of the hotels. For example, Pleasant Hotel located closed to the Taoyuan International Airport, and most Chinese tourists stay at the hotel in order to conveniently entry and exit. Both Jang and Chen (2008) and Chen, Jang and Peng (2011) employed the modern portfolio theory to investigate the mixes of Taiwan inbounds. They suggested that the government should take the high-reward/high-volatility option and shift more available resources to attract the Japanese tourists. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 302 Table 7. Distribution of guests’ sources in 2012 Hotel Region Royal Hotel Pleasant Hotels (Taoyuan) Ambassa dor Hotel Landis Taipei Hotel Formosa Internatio nal Hotels Leofoo Westin Hotel Holiday Garden Hotel Farglory Hotel Domestic 55.2 18.0 34.0 24.2 21.3 9.7 61.2 94.9 Oversea Chinese 0.0 7.7 1.9 6.4 0.0 0.0 1.0 0.0 Mainland 6.35 56.35 13.29 11.08 11.5 19.4 22.45 3.9 North American 4.7 0.3 6.9 10.3 8.4 20.8 0.8 0.2 Japan 21.9 2.1 29.9 29.9 36.7 17.3 7.0 0.1 Asian (exclusive Japanese) 5.0 9.2 8.2 8.4 15.3 25.9 6.5 0.5 European 2.5 0.3 3.8 7.5 4.7 4.3 0.5 0.1 Australia 0.3 0.0 0.4 1.8 0.9 1.4 0.5 0.0 Others 4.1 6.1 1.6 0.4 1.2 1.2 0.0 0.3 Total (%) 100 100 100 100 100 100 100 100 Source: Tourism Bureau, M.O.T.C., Republic of China (Taiwan). Kim (2013) discussed of foreign exchange position and make recommendations in Table 8.In Table 4,5, and 6, a portfolio of currencies that has significant impacts on the company’s ROA/ ROE can be formed. Markowitz (1952) proposed the Modern portfolio theory that based on the weighted each company the average cost of capital (WACC), and along with the calculation of Matlab programs for foreign exchange positions, an optimum allocation of currencies can reach the lowest degree of risk under a pre-specified rate of return constraint. Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 303 Table 8. Optimal Portfolio of Foreign Currencies for each company. Company 2702 2704 2705 2706 2707 2718 USD 0.00 6.35 GBP 62.18 EUR 20.47 13.79 JPY 2.32 1.30 2.09 0.00 KRW 3.84 1.56 30.56 CNY 0.00 19.47 0.00 AUD 52.35 83.81 74.37 SGD 84.35 3.84 0.00 IDR 1.73 6.16 7.14 MYR 1.85 9.94 5.17 5.53 THB 0.00 PHP 0.00 7.19 WACC (%) 10.00 12.00 13.50 12.80 9.50 12.60 Portfolio risk (%) 0.431 1.850 0.513 2.501 0.356 0.458 Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 304 Table 8. Optimal Portfolio of Foreign Currencies for each company. (Continued) Currency 2722 2724 5701 5703 5704 Full USD 3.27 2.26 0.00 GBP 2.24 34.33 0.29 EUR 6.04 44.14 JPY 0.00 0.00 0.11 0.00 KRW 6.07 0.31 0.76 14.90 0.07 CNY 5.17 4.47 13.80 1.49 AUD 73.79 82.84 85.53 79.93 9.41 SGD 0.00 0.00 0.00 0.00 IDR 5.47 36.97 0.52 MYR 4.66 11.47 13.03 0.64 THB 0.90 0.00 PHP 43.44 WACC (%) 10.60 9.80 13.20 11.50 9.80 12.20 Portfolio risk (%) 0.515 0.481 0.520 0.476 2.062 0.055 The results in Table 8 show the optimal allocation of currencies for each company. Japanese yen, Korean won, Chinese Yuan, Australian dollar and Malaysian Ringgit configuration still play significant roles among those target companies, including Leofoo Development Co., Ltd. (2705), Formosa International Hotels Corporation (2707),Pleasant Hotels International Inc.(2718), Chateau International Development Co., Ltd. (2722), FX Hotels Group Inc. (2724-F), Janfusun Fancyworld Corp. (5701), The Landis Taipei Hotel Co., Ltd. (5703).The configuration of the Australian dollar reached 52.35%, 83.81%, 44.37%, 73.79%,85.48%, 82.84%, 85.53% and 79.93 %, respectively, more than 50% have switched. Hotel Holiday Garden(2702), Leofoo Development Co., Ltd. (2705), First Hotel Company Ltd. (2706), Chateau International Development Co., Ltd. (2722),FX Hotels Group Inc. (2724-F), Janfusun Fancyworld Corp. (5701), and Landis Taipei Hotel Co., Ltd.(5703) for the Korean Asian Journal of Finance & Accounting ISSN 1946-052X 2017, Vol. 9, No. 1 ajfa.macrothink.org 305 won configuration, respectively, 3.84%, 1.56%, 30.56%, 6.07%,0.31%, 0.76%, and 14.90. As to Chinese yuan, Formosa International Hotels Corporation (2707), Chateau International Development Co., Ltd. (2722), FX Hotels Group Inc. (2724-F), and Hotel Royal Chihpen(5704) should put the weight ranging from 4.47% to 19.47%. 6. Conclusions In recent years, the changes in exchange rates significantly affect a company’s performances, such as, ROE, ROA, etc. Faced with the dramatic changes in the international economic environment, many central banks continue to adopt a more aggressive monetary policy, such as, negative interest rates by Bank of Japan, the monetary easing by ECB, and monetary easing by People's Bank of China. Likewise, the gradual recovery of the economy of the United States have taken actions to raise interest rates. The auspices of monetary policy in these countries shows that the currencies flow across countries and international hot money have allowed changes in exchange rates. Under these actions of monetary policies, enterprises in Taiwan need to actively adopt configuration to reduce the negative impact. Changes in the foreign exchange market in the past is not as dramatic as in today. In addition to monetary policies that attract more investors to the market, the investment of foreign exchange market as well significantly affect the change in exchange rates among countries. Therefore, a positive formal foreign exchange risk management will better help for future operation, which can significantly reduce the risk of foreign exchange movements. This study found that hotels in Taiwan, accounting for the largest part of the tourism industry, are subject to have the impacts on their performance and profitability due to the exchange rate fluctuations. Enterprises may apply the results to manage their foreign exchange risk exposure, and then increase the overall capabilities and range of enterprise risk management (ERM).By doing so, companies can increase their profits and reduce the negative impacts of exchange rate changes on corporate ROE/ROA through foreign exchange operations. 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