MASTER’S THESIS
Macroeconomic influence on the hotel stock returns - the case in France, Spain and the UK
Nan Sun
Master’s Degree in Economics of Tourism: Monitoring and Evaluation (Specialisation/Pathway Evaluation)
Centre for Postgraduate Studies
Academic Year 2019-20
Macroeconomic influence on the hotel stock returns
- the case in France, Spain and the UK Nan Sun
Master’s Thesis
Centre for Postgraduate Studies University of the Balearic Islands
Academic Year 2019-20
Key words:
MACRO VARIABLES, HOTEL STOCK PRICE, VAR
Thesis Supervisor’s Name Audronė Virbickaitė Tutor’s Name (if applicable)
Tutor’s Name (if applicable)
ABSTRACT
In this paper, we investigate the relationship between macro variables and hospitality stock prices based on the VAR model in three European countries, which are Spain, France and the UK. We are using the data in period from 2000 to 2019 to examine the different impact from the different countries’
macro variables on the changes of the hotel stock prices in one country. The variables we include in this paper are interest rate (IR), money supply (M1), unemployment rate (UNR), consumer price index (CPI), exchange rate (ER), oil price (OP), tourist arrivals (TA), industrial production (IP). Contrary to the existing research, we focus on the three countries simultaneously allowing macro variables of one country to have an effect on the other countries’ hotel stock returns. By using a Vector Autoregressive Model, we found that one country's hotel stock prices are not only influenced by its own macro factors but are also significantly influenced by macro variables in other countries.
1. INTRODUCTION
With the rapid development of the stock market all over the world, forecasting the stock returns has become a very popular research objective between the academics and the practitioners alike. The investor’s expectation of the future earnings about the company will be reflected in the rise or fall of this company's stock price. Many researchers have turned their focus on the factors which influence the returns of stock markets. The performance of the company is highly affected by the external economic and social context, such as the overall business conditions, development status, and prospects of the sector in which the company is operating. Harvey (1991) mentioned that forward-looking external conditions improve the earnings and profits of the company which helps improve the stockholder’s expectation on the corporate’s future earnings. This will lead to an increase in the stock price of the company. On another note, if the external economic situation tends to decline, the corporate’s revenue performance is expected to turn sour. These have identical implication on the movements of the stock markets. In everyday life, we always can observe the following: changes in the stock market are strongly linked to the statements of policy changes and the publication of economic data. Many researchers (e.g.
Asprem, 1989, Barrows and Naka, 1994, Bilson et al., 2001) attempted to select the appropriate macro variables to measure the external economic context in order to identify the factors that exert influence on the stock market. These studies often employ variables that measure real activity (for example industrial production, real gross national product, employment), or the economic variables at the domestic level (such as the exchange rate, consumption, and interest rate).
Tourism, as a rapidly developing sector, has become an important component of the service industry.
As being very representative of the service industry, the development of tourism is heavily influenced by the external economic situation, which makes tourism development very sensitive to the changes in external factors. So, there is no surprise that a lot of studies in the hospitality sector pay attention to this topic. Some researches (Barrows and Naka, 1994) have used data on macroeconomic variables to predict changes in hotel stock returns in different countries and regions, but the empirical evidence showed that in different areas different macroeconomic variables have a significant impact on hospitality stocks. Other researches (Chen, 2007a, Chen, 2015, Demir and Ersan, 2018) introduced the non- macroeconomic factors into the model to estimate their influence. These factors are the political stability, tourist arrivals, terrorist attacks, consumer confidence, and economic policy uncertainty, among others.
In this paper, we are interested in the relationship between hospitality stock returns and macro variables in the hospitality sector in three European countries, which are Spain, France and the UK. We choose these three countries for the following three reasons. Firstly, France, Spain, and the UK all have a developed tourism sector. In 2018, France, Spain, and the UK ranked first, second and fifth in the world respectively for tourist arrivals, while tourism revenue ranked third, second, and fifth respectively (source: UNWTO). The high development in tourism industries has led to a boom in the hospitality sector, making these countries good objects of the study. Also, these countries have stable economic conditions, established and trustworthy legal and banking systems, meaning their equity markets are also stable and well-behaved. Moreover, most studies about the hotel stock performance and the effect of the macroeconomic variables focused on East Asia (Chen, 2007a, Chiang and Kee, 2009, Chen, 2007b, Chen, Jang, and Kim , 2007, Chen, Kim, and Kim, 2005, Chen, Liao, and Huang, 2010) and
US (Wong and Song, 2006, Barrows and Naka, 1994, Chen, 2010). Less research has been done on the determinants of hospitality stocks in European stock markets, although Europe has the largest tourism market volume.
Using the vector autoregressive model (VAR), we analyze whether those essential variables, such as, the exchange rate, inflation, the oil price, industrial production, interest rate, unemployment rate significantly influence the hospitality returns in these stock markets. Especially, this paper answers three critical questions. The first one is whether these macro variables significantly influence the hospitality stock market returns in all three countries. The second one is whether these macro variables have a different impact on hospitality stock in different stock markets. And, finally, do the macro variables of one country have an impact on the hotel stock returns in the other two countries at a significant level.
2. LITERATURE REVIEW
A very important and difficult challenge in financial economics is to predict the change in the stock prices. Many researches focus on the search of the influencing factors of the stock performance. Firth (1979) confirmed the relationship between inflation, which was measured by the monthly Index of Retail Price, and stock returns in the UK. Chen, Roll, and Ross (1986) introduced some macroeconomic factors, such as the oil price, capital consumption, NYSE index, industrial production, expected inflation, unanticipated inflation, unanticipated change in the risk premium and unanticipated change in the term structure into the model to explain and predict the future stock returns. Chen, Roll and Ross (1986) found that the future earnings of the stock are determined by the industrial production, unanticipated risk premium and unexpected changes in the term structure. Wasserfallen (1989) found that macroeconomic variables have little influence on the stock market. He considered the real GNP, industrial production, consumer prices, national nominal wages, real wages, money supply, monetary base, real exports, import prices, nominal interest rate, real interest rate, and exchange rate. The study demonstrated that all these factors have insignificant influence on equity returns. Asprem (1989) examined the connection of the stock indices and macroeconomic variables in ten European countries.
He found that the employment, interest rate, imports, and inflation had a negative and significant effect on the stock prices.
Morelli (2002) analyzed the relationship between the change of the stock market and macroeconomic index based on the monthly data in the UK through the period from 1967 to 1995. The macroeconomic variables used in the model are industrial production, inflation, real retail sale, money supply. The author found that these variables do not influence the stock market. Abugri (2008) moved the focus from the developed markets to the emerging economies, in particular, Latin American market. In the study, Abugri (2008) not only introduced domestic macroeconomic explanatory variables, but also used global indicators to explain the stock market returns. The author employed a VAR model and found that the global index significantly explains the change of returns in all stock markets. Demir, and Ersan, (2018) showed that European and Turkish economic policy uncertainty have a significant negative influence on the tourism-related stock prices. Their findings showed that the returns of the Turkish tourism index are influenced by the domestic and global economic policy uncertainty. Flannery and Protopapadakis (2002) used 17 variables to estimate their impact on the stock earnings. Only 6 variables were found to be
significant from the model validation, which are Consumer Price index, Producer Price Index, balance of trade, unemployment rate, housing starts (the indicator used to measure the number of privately owned new houses on which construction has been started in a given period), and money supply.
Apart from the abundant literature investigating the factors that influence the stock market returns, there has been a considerable amount of research investigating the determinants of the hospitality stock returns. It is reasonable to believe that the macroeconomic factors that affect the hotel price returns would be same as for the stock market price index. Barrows and Naka (1994) link the macroeconomic variables with hospitality stock returns in the US stock market for the first time. They examined that five macroeconomic variables, expected inflation rate, money supply, domestic consumption, term structure of interest rate, and industrial production, have influenced the restaurant stock returns to a greater extent than lodging or industrial sector. Chen, Kim and Kim (2005) and Chen (2007a) studied the relationship between the macroeconomic and non-macroeconomic variables and the movement of hotel stocks in the Taiwan stock market. The result showed that only the unemployment rate and money supply have a significant impact on the hotel stock returns. But the non-macroeconomic forces, like political and sport events, were more influential for the hotel that influences the hotel stocks. Wong and Song (2006) used a VAR model and found that the U.S. Treasury Bond Yields on the 10-year benchmark bond largely explain the movement of the hotel, restaurant, lodging, and casino indices. Chen (2007b) examined whether the relationship between macro variables and hotel stock performance varies due to different monetary policies. In the restricted currency period, only one factor, the change in unemployment rate, significantly influence the hotel equity. Chen (2010) found that the federal funds rate has a significantly influence on the US restaurant stock returns. Chen, Liao and Huang (2010) observed that hotel stock has better performance during the period of expanding monetary policy and hotel stocks were strongly affected by the different monetary policies. Chen et al. (2012) examined the influence on the hotel stock returns by the discount rate, unemployment rate, oil price and found that these variables can significantly explain the hotel stock price in Japan. The study showed that oil price and discount rate exhibit the negative impact on the hotel stock market. The money supply and total trade have the positive impact on the hotel stock returns. Chen (2011) showed that international tourism development which is measured by the inbound tourist arrivals has more significant impact on the profit and hotel sales than on the hospitality stock performance. Chen (2015) examined the relationship between the consumer price index and stock returns in the hotel sector. Consumer confidence index significantly influences the hotel stock returns, hotel sales and risk of hotel returns. An increase in the consumer confidence index leads to a positive impact on the hotel stock returns and hotel sales as well as a decrease in the risk of the hotel returns. Chen, Jang, and Kim, (2007) examined the effect of non-macro variables (the outbreak of SARS) on the hotel stock returns in Taiwan. The influence of the SARS outbreak is significant and negative. Table 1 presents the summary of the most recent literature that investigates the effects of the macroeconomic variables on the hotel price returns.
Table 1. Summary of the literature review.
Paper Geographic area
The impact of the macro variables
Positive (+) Negative （-） Insignificant
Barrows and Naka
(1994) United States Money Supply Expected Inflation
Domestic Consumption, Term Structure of interest
rate, Industrial Production
Chen, Kim, and
Kim (2005) Taiwan Money Supply Unemployment
rate
Industrial Production, Expected Inflation,
Yield Spread
Chen (2007a) China mainland Money supply Unemployment rate
Industrial Production, Expected Inflation,
Yield Spread
Chen (2007b) Taiwan Industrial
production
Discount Rate, Import, Yield
Spread
Inflation, Tourist Arrivals
Chen, Liao, and
Huang (2010) Hong Kong Money Supply Discount Rate
Unemployment Rate, Exchange Rate, Excepted
Inflation
Chen et al. (2012) Japan Money Supply, Total trade
Oil price, Discount Rate
Unemployment Rate, Consumer
Price index, Industrial Production, Exchange Rate
Chen (2012) United States -
surprise component of the federal funds target
rate
expected and actual component of the federal funds
target rate
Source: own elaboration from author
3. METHODOLOGY (1) Variable selection
In this paper we will make use of the VAR model to estimate the effect of the macroeconomic variables on the hotel stock returns. The variable selection was done according to the Barrows and Naka (1994), Chen, Kim, and Kim (2005) and Chen et al. (2012).
We analyzed whether interest rate (IR), money supply (M1), unemployment rate (UNR), consumer price index (CPI), exchange rate (ER), oil price (OP), tourist arrivals (TA), industrial production (IP) are important in explaining the dynamics of the hospitality stock returns. These variables were used to explain the changes in hotel stock returns in Spain, France and the UK.
Firstly, the interest rate is one important factor in measuring the monetary policy. The increase in the interest rate will increase the borrowing cost of funds, as a means of implementing restricted monetary policy, harms the stock market in the short term. Wasserfallen (1989) and Asprem (1989) found that interest rates have a significant and negative affect on stock prices.
Secondly, in the financial economics, the scarcity of money is measured in terms of money supply.
When we increase the money supply, it will cause a drop in the interest rates. Lower interest rates lead to people being more willing to invest their money in the capital markets for higher returns. Those lower interest rates will encourage more spending and increase the purchasing power of the market. Barrows and Naka (1994), Chen, Kim, and Kim (2005) find a significant negative impact on the stock price.
Thirdly, the unemployment rate can be a measure of the country's economic situation to some extent.
Lower unemployment means economic growth and higher unemployment means recession. When the unemployment rate increases, people are pessimistic about the future conditions of their earnings and the whole economy. That will cause a decrease in consumption, which could lead to a chain reaction of lower level of production and the performance of the stock market. Chen, Kim, and Kim (2005) found a significant negative influence of the unemployment rate on the hotel stock returns.
Moreover, the consumer price index is frequently used to measure the cost of living and for calculating the inflation. Asprem (1989) found that CPI has a positive influence on the stock market. But some preceding researches showed a completely different view (Barrows and Naka, 1994; Chen et al. 2012;
Chen, Jang, and Kim, 2007). So, we include the CPI into the model, and we expected CPI to have a negative impact on the stock market.
Many studies mentioned the relationship between exchange rate and stock price. But theoretically, tourism is considered as an export commodity. Because of this property of tourism, the appreciation of the national currency increases the cost of travel for foreign tourists, thereby reducing their willingness to travel or spend and affecting their tourist behavior.
Moreover, petroleum is a very important resource for the service industry, especially the tourism sector. So, an increase in the price of petroleum will cause an increased cost of the service sector, which leads to hurt the cash flow of the hotel and decrease the profit of the hotel.
Finally, TA is a vital index in measuring the development of the tourism sector. More tourist arrivals at the destination will lead to significant expansion in the tourism industry, especially hotel companies.
(2) Data
This part describes the variables and the source of all the data used in this paper. Euro-denominated monthly hospitality stock prices are extracted from the EIKON DataStream database for the period from February 2000 to December 2019. Since three different countries are involved in this study, we first categorize the stocks by country and then using the value-weighed method evaluate the hotel stock
price index (HSPI) of each single country. The weights of the stocks are calculated using the market capitalization. Firstly, we computed the 𝐻𝑆𝑃𝐼𝑡 for one country as the following equation.
H𝑆𝑃𝐼𝑡=∑ 𝑤𝑛𝑖 𝑖𝑋𝑖𝑡 (1) Where 𝐻𝑆𝑃𝐼𝑡 is the hotel stock price index at time t, 𝑤𝑖 is the weight of the stock i, 𝑋𝑖𝑡 is the price of the stock i at time t. Table A.3 in the appendix contains the list of the hotel names and their corresponding market capital value for the three countries.
After we get the value-weighted hotel stock price index for every country, we calculate the return of the HSPI as follows:
∆𝐻𝑆𝑃𝐼𝑡= 𝑙𝑛(𝐻𝑆𝑃𝐼𝑡) − 𝑙𝑛(𝐻𝑆𝑃𝐼𝑡−1) (2) As mentioned before, the monthly macro data includes the exchange rate, the consumer price index, the oil price, industrial production, unemployment rate, interest rate, money supply, and tourist arrivals.
The data is from February 2000 to December 2019, which is the same as for the hotel stock index.
Some of the data is from the Federal Reserve Bank of St. Louis, such as industrial production, unemployment rate, oil price (Brent crude oil) and exchange rate. The remaining data is from the European statistics website (Eurostat.com), which are consumer price index, tourist arrivals, and interest rate. Especially, the tourism arrival is measured by arrivals at tourist accommodation establishments (the classification of tourist accommodation is the hotels and similar accommodation). And M1 is from the EU central bank. In these time-series variables, TA, CPI and M1 have been deseasonalized to remove the seasonal element in these time series.
The factors we introduced into the model are percentage changes in exchange rate (CER), growth rate of oil price (COP), changes in unemployment rate (CUNR), growth rate of consumer price index (GCPI), growth rate of interest rate (GIR), growth rate of industrial production (GIP) and growth rate of tourist arrivals (GTA). The growth rate of exchange rate, oil price, consumer price index, interest rate, industrial production, and tourist arrivals are calculated using the difference in natural logarithmic, for example, 𝐶𝐸𝑅𝑖𝑡= (𝑙𝑛(𝐸𝑅𝑡) − 𝑙𝑛(𝐸𝑅𝑡−1)) × 100 , 𝐶𝑂𝑃𝑡= (𝑙𝑛(𝑂𝑃𝑡) − 𝑙𝑛(𝑂𝑃𝑡−1)) × 100 , 𝐺𝐶𝑃𝐼𝑡= (𝑙𝑛(𝐶𝑃𝐼𝑡) − 𝑙𝑛(𝐸𝑅𝑡−1)) × 100, 𝐶𝐼𝑅𝑡= (𝑙𝑛(𝐼𝑅𝑡) − 𝑙𝑛(𝐼𝑅𝑡−1)) × 100, 𝐺𝐼𝑃𝑡= (𝑙𝑛(𝐼𝑃𝑡) − 𝑙𝑛(𝐼𝑃𝑡−1)) × 100, 𝐶𝑇𝐴𝑡= (𝑙𝑛(𝑇𝐴𝑡) − 𝑙𝑛(𝑇𝐴𝑡−1)) × 100. The unemployment rate is calculated as the first difference, i.e.
𝐶𝑈𝑁𝑅𝑖𝑡 = (𝑙𝑛(𝐶𝑈𝑁𝑅𝑡) − 𝑙𝑛(𝐶𝑈𝑁𝑅𝑡−1)) × 100. The country-specific macro indicators, such as the changes in the unemployment rate, for example, are calculated for each country individually; meanwhile the oil price is the same for all countries.
The summary statistics for the hotel stock index returns and the macro variables in the sample are shown in Table 2. The mean of the hotel stock returns ranges from -0.5 in Spain to 1.14 % in UK. But the standard deviation is relatively high for each country. The standard deviation ranged from 6.31 in France to 10.08 in Spain.
Table 2. Summary statistics for the variables.
Variable Description Mean St.Dev. Min Max
∆ES_HSPI Changes in Hotel stock price index in Spain
-0.50 10.08 -40.58 - 57.56
∆FR_HSPI Changes in Hotel stock price index in France
0.72 6.31 -19.79 69.09
∆UK_HSPI Changes in Hotel stock price index in UK
1.14 8.60 -25.40 84.25
CUNR_ES changes in unemployment rate in Spain
0.01 0.25 -0.4 1.1
CIR_EU Changes in interest rates in Europe
-0.02 0.14 -0.68 0.37
CIR_UK changes in Interest rate in UK -0.02 0.23 -1.76 1.11
GCPI_UK growth rate of CPI in UK 0.17 0.43 -0.67 2.79
GCPI_FR growth rate of CPI in Spain -0.10 0.57 -4.90 2.36 GIP_UK Percentage changes in industrial
production in UK
-0.04 0.91 -4.09 2.79
GIP_ES Percentage changes in industrial production in Spain
-0.07 1.40 -6.12 3.76
GIP_FR Percentage changes in industrial production in France
-0.02 1.41 -5.15 3.94
GTA_UK Changes in United Kingdom’s tourist arrivals
0.01 0.01 -0.24 0.27
GTA_ES Changes in Spanish tourist arrivals -0.00 0.08 -1.09 0.17 CER_EU Changes in exchange rate on Euro 0.04 2.28 -7.80 6.19 CER_GBP Changes in exchange rate on
Great British Pound
-0.10 2.15 -9.54 5.99
COP Growth rate of oil price -0.07 9.72 -51.14 19.53
PMS Changes in money supply 0.00 0.21 -1.79 1.38
Source: own elaboration from author
(3) The Model
In this paper, we examine the dynamic relationship between all these macro factors. Same as Chen et al. (2012), Chatziantoniou et al. (2013), we rely on the VAR modeling framework.
The reduced-form VAR model can be represented in the following general way (Lütkepohl, 2005):
𝑺𝑡= 𝑐0+ ∑𝑘𝑖=1𝑩𝒊𝒀𝒕−𝒊+ 𝜀𝑡 (1) where k is the number of lags,𝜀𝑡 is the white noise, 𝑺𝑡 is the matrix of hotel stock returns in different markets (Spain, UK, France), 𝑩𝒊 is the matrix containing the coefficients to be estimated, and 𝒀𝒕−𝒊 is the matrix containing the exogenous and endogenous variables: [∆𝐸𝑆_𝑆𝑃𝐼𝑡−𝑖, ∆𝐸𝑆_𝑆𝑃𝐼𝑡−𝑖, ∆𝐸𝑆_𝑆𝑃𝐼𝑡−𝑖, 𝐸𝑆_𝐶𝑈𝑁𝑅𝑡−𝑖, 𝑈𝐾_𝐶𝑈𝑁𝑅𝑡−𝑖,𝐹𝑅_𝐶𝑈𝑁𝑅𝑡−𝑖,𝐶𝐸𝑅_𝐸𝑈𝑡−𝑖,𝐶𝐸𝑅_𝐵𝐺𝑃𝑡−𝑖, 𝐺𝐶𝑃𝐼_𝑈𝐾𝑡−𝑖, 𝐺𝐶𝑃𝐼_𝐸𝑆𝑡−𝑖, 𝐺𝐶𝑃𝐼_𝐹𝑅𝑡−𝑖, 𝐺𝑇𝐴_𝑈𝐾𝑡−𝑖, 𝐺𝑇𝐴_𝐸𝑆𝑡−𝑖,𝐺𝑇𝐴_𝐹𝑅𝑡−𝑖, 𝐶𝐼𝑃_𝑈𝐾𝑡−𝑖, 𝐶𝐼𝑃_𝐸𝑆𝑡−𝑖, 𝐶𝐼𝑃_𝐹𝑅𝑡−𝑖, 𝐶𝐼𝑅_𝐸𝑈𝑡−𝑖,𝐶𝐼𝑅_𝑈𝐾𝑡−𝑖, 𝐶𝑂𝑃𝑡−𝑖, 𝑃𝑂𝑀𝑡−𝑖].
First, before estimating the model, the time series data should be tested for stationarity by using the Augmented Dickey-Fuller test. The Table 3 shows that all the time-series data is stationary. We have also calculated the correlations among all macro variables; the results are exhibited in table A2 (in the appendix). The table shows that the variable, which most correlated with ES_HSPI is the hotel stock index in the UK. The variable FR_HSPI most correlated with industrial production in France, while the most correlated with hotel stock index in the UK is the interest rate in Europe.
Table 3. Results of the Augmented Dickey-Fuller test. The null hypothesis is that the series is non- stationary.
Source: own elaboration from author
4. RESULTS
Next, in order to estimate the VAR model, we first need to choose the appropriate lag length. Table A1 in the appendix presents the values of the information criteria for different lag lengths. According to the Akaike information criteria, the most appropriate lag length is 3. Next, we estimate the VAR model, and the result of the coefficients is in the Table 4. After the estimation, we test for the “whiteness” of the residuals (Lütkepohl, 2005) by testing for the absence of autocorrelation, the absence of heteroscedasticity and the Normality. Figures 1a, 1b, 1c draw the model residuals. all residual diagnostics plots are in the Appendix, see Figures A1a-A6c
Variables p-value Variables p-value
ES_HSPI 0.0000 GCPI_UK 0.0000
FR_HSPI 0.0000 GCPI_FR 0.0000
UK_HSPI 0.0000 CIR_EU 0.0000
COP 0.0000 CIR_UK 0.0000
CER_EU 0.0000 GTA_ES 0.0000
CER_GBP 0.0000 GTA_UK 0.0000
PMS 0.0000
ES_CUNR 0.0000
GIP_UK 0.0000
GIP_FR 0.0000
GIP_ES 0.0000
Figure 1a. Residuals for the Spanish series.
Source: own elaboration from author
Figure 1b. Residuals for the French series.
Source: own elaboration from author
Figure 1c. Residuals for the British series.
\
Source: own elaboration from author
Secondly, we test the normality of the residuals. From the Table 4, we can find the residual of equation of UK_HSPI does not follow the normal distribution, meanwhile we do not reject normality for Spain and France.
Table 4. Results of the tests for Normality.
Equation Jarque-Bera Skewness Kurtosis
ES_HSPI 1.685 -0.131 1.015
FR_HSPI 2.675 -0.156 1.729
UK_HSPI 48.27*** -0.109 47.802***
Note: *** is the 1% significance level Source: own elaboration from author
We use the impulse response functions and their graph to exhibit the impulse response of different macro variables to the hotel stock index in different countries.
After we estimated the model, we get the coefficient of each factor which influences the markets. The result is shown in Table 5. From the table we can see that there are no macro variables that significantly influence all the British, Spanish French hospitality stocks. Interest rate in Europe and UK, French stock price index, tourist arrivals in the UK, and Spain and interest rate in all have a significant impact on two of three examined hotel stock markets. And the results show that an increase in tourist arrivals in the UK have a significant negative impact on Spanish hospitality stock returns and French hotel stock returns. It means that there may be some competition between Britain's tourism industry and that of Spain and France. The tourist arrivals in Spain significantly and positively influence the Spanish and British hotel stock returns.
The interest rate in British Pounds has a significant positive influence on the British and Spanish hospitality stock returns. The stock price index in the UK and Spain decreases when the interest rate of Euro increases. Money, unemployment rate, consumer price index, industrial production and exchange rate have a weak influence the hospitality stock index in three countries. Money supply only has a significant impact on the French hospitality stocks index. The unemployment rate in Spain only significantly influences the Spanish hotel stocks. Changes in the consumer price index of the UK have a significant positive impact on the hotel stock prices in Spain. French Consumer price index positively influences the French hotel stock returns. Industrial production in Spain and France both influence the hospitality stock return in France. Comparing three hotel stock returns, the UK is the least influenced by these macro variables.
We use the impulse response function to plot in detail of the influence of macro variables on the stock returns in three countries
Table 5. The coefficients of VAR model.
Variables
Spain SPI France SPI UK Spain
Estimates S.E. Estimates S.E. Estimates S.E.
Constant -1.306 0.836 0.036 0.403 0.290 0.753
∆ES_HSPI (-1) -0.086 0.084 0.021 0.040 -0.075 0.075
∆ ES_HSPI (-2) -0.071 0.082 0.030 0.039 0.011 0.073
∆ES_HSPI (-3) 0.116 0.080 0.059 0.042 0.061 0.072
∆UK_HSPI (-1) 0.183** 0.086 0.030 0.042 0.110 0.078
∆UK_HSPI (-2) -0.006 0.086 -0.045 0.043 -0.078 0.078
∆UK_HSPI (-3) -0.071 0.089 0.045 0.051 -0.030 0.080
∆FR_HSPI (-1) 0.315*** 0.109 0.028 0.053 0.027 0.098
∆FR_HSPI (-2) -0.096 0.106 -0.095* 0.051 -0.087 0.096
∆FR_HSPI (-3) 0.088 0.106 0.045 0.051 0.089 0.095
PMS (-1) -0.857 3.699 3.450* 1.785 -0.337 3.333
PMS (-2) 1.807 3.673 -1.540 1.773 3.482 3.310
PMS (-3) -1.616 4.014 4.498** 1.937 -5.755 3.617
ES_CUNR (-1) -12.693** 5.469 0.033 2.640 -7.457 4.929
ES_CUNR (-2) -0.442 6.057 1.450 2.923 4.895 5.458
ES_CUNR (-3) 7.764 5.338 -1.458 2.577 0.295 4.811
CIR_EU (-1) -5.173 7.816 -1.538 3.772 -6.933 7.044
CIR_EU (-2) -10.015 7.569 0.159 3.653 -12.697* 6.821 CIR_EU (-3) -13.461** 6.814 -0.851 3.289 -9.114 6.140
CIR_UK (-1) 3.255 4.037 2.412 1.949 1.807 3.638
CIR_UK (-2) 0.900 4.639 1.886 2.239 -2.319 4.181
CIR_UK (-3) 10.202*** 3.820 2.930 1.844 8.775** 3.443
GCPI_UK (-1) -1.012 2.026 -0.250 0.978 0.705 1.825 GCPI_UK (-2) 4.136** 1.984 0.722 0.958 -0.042 1.788
GCPI_UK (-3) 2.296 1.953 1.085 0.942 0.439 1.760
GCPI_ FR (-1) 0.586 1.739 2.053** 0.839 2.529 1.567
GCPI_FR (-2) -2.605 1.713 0.381 0.827 1.216 1.544
GCPI_FR (-3) 0.904 1.827 -0.560 0.882 0.235 1.647
GIP_UK (-1) 1.684** 0.828 0.132 0.399 1.100 0.746
GIP_UK (-2) 0.858 0.879 0.319 0.424 -0.049 0.792
GIP_UK (-3) 0.145 0.834 0.307 0.403 -0.526 0.752
GIP_ES (-1) 0.711 0.649 0.203 0.313 0.715 0.585
GIP_ES (-2) 0.670 0.643 -0.351 0.310 0.118 0.580
GIP_ES (-3) 0.324 0.632 -0.612** 0.305 0.062 0.569
GIP_FR (-1) -0.998 0.618 -0.515* 0.298 -0.196 0.557
GIP_FR (-2) -0.532 0.669 0.071 0.323 0.624 0.603
GIP_FR (-3) -0.756 0.618 0.586* 0.298 -0.464 0.557
CER_EU (-1) -0.348 0.419 -0.426** 0.202 0.054 0.378 CER_EU (-2) -0.469 0.424 -0.062 0.205 -0.040 0.382
CER_EU (-3) -0.340 0.421 0.264 0.203 -0.261 0.380
CER_BGP (-1) 0.481 0.445 0.332 0.215 -0.231 0.401
CER_BGP (-2) 0.851* 0.449 0.031 0.217 0.428 0.405
CER_BGP (-3) 0.455 0.462 -0.188 0.223 0.338 0.417
GTA_UK (-1) -20.052** 8.280 -8.663** 3.996 -9.130 7.461
GTA_UK (-2) -12.298 9.346 -8.779* 4.511 -13.388 8.422
GTA_UK (-3) -10.936 8.423 -7.577* 4.065 -3.476 7.590
GTA_ES (-1) -4.048 19.299 -0.137 9.314 0.287 17.391
GTA_ES (-2) 44.487** 21.794 14.988 10.519 48.545** 19.640
GTA_ES (-3) 10.703 19.106 0.982 9.221 4.348 17.218
COP (-1) 0.054 0.089 -0.061 0.043 -0.060 0.080
COP (-2) -0.191** 0.088 -0.010 0.043 -0.004 0.079
COP (-3) 0.033 0.089 -0.014 0.043 0.080 0.080
Source: own elaboration from author
Note: *** represents 1% significance level. ** represents 5% significance level. * represents 10% significance level.
Note: ES_HSPI is the Spanish hotel stock price index. UK_HSPI is the British hospitality stock price index while FR_HSPI is the hotel stock price index. PMS is the percentage change in money supply, while ES_CUNR is the changes in unemployment rate in Spain. CIR_EU is the percentage changes in Euro, while CIR_UK is the growth rate of interest rate in the UK. GCPI_UK is the British growth rate of consumer price index while GCPI_FR is the growth rate of CPI in France. GIP_UK represents the percentage changes in industrial production in Spain. GIP_ES is the growth rate of Spanish industrial production. GIP_FR is the French industrial production. CER_EU is the growth rate of Euro/US dollar exchange rate, while CER_BGP is the percentage change in Great British Pounds/US dollar exchange rate. GTA_UK is the growth rate of the tourist arrivals in the UK, while GTA_ES is the percentage change in Spanish tourist arrivals. COP is the growth rate of the oil price.
(1) Spain
In the case of Spain, the significant variables are unemployment rate in Spain, interest rate of Euro, interest rate of Great British Pounds, consumer price index in the UK, industrial production in the UK, exchange rate of Great British Pounds to US dollars, tourist arrivals of UK and Spain and oil price. Figure
2 exhibits the response of the Spanish hotel stock returns to various macroeconomic shocks. The shock of stock hotel index in the UK negative influenced the hotel stock index in the Spain immediately.The response of the oil prices shock is expected negative and significantly in the second period. This can be explained that the shock of the oil price decreases the industrial production and increases the level of inflation (Basher and Sadorsky, 2006). The changes in oil price also cause economic uncertainty and risk. We also find the response of the hotel stock returns to tourist arrivals in the UK is immediately negative and show the competitive relationship between Spain and the UK. An increase in Spanish tourist arrivals leads to an increase in hotel stock returns. As expected, more tourist arrivals will lead to the expansion of the tourism sector and the increase of the hotel sales which increase the profit of the hotel. So, an increase in this indicator should be considered as positive news to the hotel stocks.
Figure 2. The impulse response functions of the Spanish hotel stock index.
Source: own elaboration from author
In addition, the response to exchange rate in Great British Pounds is positive and significant in the second month. An appreciation in Great British Pounds increases the buying power of British people, and 21.7% of international tourists to Spain come from the UK (source: ine.es). The exchange rate of the Great British Pounds has a positive impact on the tourism sector, which increases the hotel stock prices. The reaction of the unemployment rate in Spain is negative and significant in a short period since high unemployment rate reflects the terrible economic state, even in a recession (Chen et al. 2012).
Industrial production in the UK also has a positive impact on Spanish hotel stock returns and significant in the first month. That also can be explained as the IP measures the economic growth. Since British tourists are the largest group of visitors to Spain, the increase ofthe UK economy has increased their willingness to travel, which has a positive impact on tourism in Spain. A British interest rate shock exerts a positive impact on the hotel sector stock index, but the European interest rate has the opposite influence (negative) on hotel stocks. And both shocks are significant in the third period. The interest rate always has been considered as a monetary policy tool to regulate the economic situation of a country.
Higher interest rates, in turn, increase the cost of capital for the company, which affects the price of its stock (Chen et al. 2012). The increase in the European interest rate will lead to the company to pay more for the borrowed capital, which lower the hotel’s earning (Chen,2007a). And the earning decrease in the UK would increase the competitiveness of hotel in Spain which positively increases their stock prices. The response of the consumer price index in the UK is significant positive in the second period.
The rest of the macro variables do not have a significant influence on the Spanish hotel stocks. These variables are money supply (Spain), consumer price index in France, Spanish industrial production (positive), French industrial production (negative), exchange rate of Euro against US dollar (negative).
(2). France
The impulse response of the French hotel stock returns is shown in the Figure 3. The important variables in explaining the stock returns are lagged French hotel stock price index, money supply, consumer price index in France, industrial production in Spain and France, exchange rate of Euro, tourist arrivals in the UK. The reaction of French hotel stock returns on the shock from itself is significant and negative in the second period, contrary to the economic intuition. Money supply represents the positive shock to the hotel stock index and the response is significant and positive in the first and third months.
The reason is that an increase in money supply represents the accommodative monetary policy, which boosts the economy. That will encourage investment and consumption in France which should be considered as a positive factor to the French stock market.
And the response of French hospitality stock index to the shock in the CPI in France is significant and positive immediately. The inflation will increase the earnings of the tourism-related sector, including hotels. That will increase the hotels’ revenue which causes the hotel stock price increase. But it will have a negative influence in the long run because of the loss of competitiveness on relative prices among all the destinations. The industrial production in Spain has a negative influence and its shock is significant in the third month.
Besides, the effect of changes in the French industrial production is significant and negative in the first month and significant positive in the third month. The changes in industrial production are, to some extent, representative of trends in the country's overall economy. The increase in the industrial production, which measures the real activity and economic growth, leads to the growth of the economy.
Empirical evidence that an increase of the Spanish industrial production has a negative effect shows that the expansion of the Spanish economy has a negative impact on the French hotel stock index. In the Figure 3, we saw that the impact of French industrial production on hotel industry stock earnings was erratic, which varies from a negative impact in the first month to a positive impact in the third month.
In the short term, the increase in the industrial production represents the expansion of the industry.
Chatziantoniou et al. (2013) examined the tourism revenue and industrial production and showed that these two variables have a positive bidirectional causality and industrial production has a positive effect on tourism stock price in the short term. But in the case of France, we can see the positive influence in the third month.
Figure 3. The impulse response functions of the French hotel stock index.
Source: own elaboration from author
Exchange rate of Euro against US dollars exhibits a negative impact on the hotel stock index immediately. Firstly, France is the largest international destination in the world in 2018, which received over 89 worldwide tourists. (source: UNWTO). But the increase in exchange rate will increase the effective price of the French hotel sector. And this increase will affect the competitiveness of the destination. So, the exchange rate increase will lead to a decrease of the tourist arrivals which causes the decrease in the hotel earnings and lower the hotel stock price. Secondly, as we mentioned before, the increase in the interest rate will increase the cost of borrowing capital which affect the cash flow of the hotel, which decreases their earnings. That makes the increase of exchange rate to have a negative shock to the hotel stock returns immediately.
Lastly, the growth rate of Spanish tourist arrivals presents a negative shock to the hotel stock prices and the significant shock ranges from the first month to the third month.
(3). United Kingdom
The significant variables that influence hotel stock market in UK are the interest rate in Europe and in the UK and tourism arrivals in Spain.
Figure 8 exhibits the impulse responses of hotel stock prices to the macro variables shocks. The interest rate in Europe has a negative influence on the British hotel stock returns in the second month, while the graph shows the negative impact will be continued in the long run. The interest rate in the UK has a positive impact on the British hotel stock returns in the third month. This empirical result also was verified by Dinenis and Staikouras (1998). The authors found that the common stock returns in the UK related interest rate with a significant positive coefficient.
In addition, tourist arrivals have significant positive impact in the second month on the hotel stock price. And then it has the negative effect in the long run, contrary to the economic intuition.
Figure 8. The impulse response of the British hotel stock index.
Source: own elaboration from author
5. CONCLUSIONS
This paper examines whether macro variables explain hospitality stock returns in three European countries, Spain, France and the UK by using the VAR model. While the previous research offered some empirical and theoretical evidence about the link between the hotel stock price and the macro variables, this paper used the most recent data in three countries to contribute to the existing literature. Because of the different economic and political structure, the relationship between hotel stock prices and macro variables might vary among three countries. The previous empirical studies, carried out on the US or East Asian countries, have shown that a variety of macro variables might affect the hotel stock prices.
These variables include money supply, unemployment rate, interest rate, consumer price index, industrial production, exchange rate, tourist arrivals and oil price.
The Spanish hotel stocks, like the previous studies, are significantly influenced by the unemployment rate and tourist arrivals. Besides, interest rate of Europe and the UK, consumer price index in UK and industrial production in the UK, exchange rate of Great British Pounds/US dollars, tourist arrivals in the UK and oil price could significantly influence the hotel stock prices. In other words, the British and European variables significantly affect Spanish hospitality industry. The changes in the British interest rate, the British consumer price index, exchange rate of Great British Pounds/US dollars and industrial production in the UK exerts positive influence on the hotel stock markets. The tourist arrivals in the UK and oil price have the negative impact
As for the French hotel stocks, the main determinants are the consumer price index in France and the French industrial production. Not like other papers (Fama, 1988; Barrows and Naka, 1994), consumer price index in France exhibits a positive shock on the hospitality stock price. In addition, money supply, industrial production in Spain, exchange rate of Euro/US dollars, tourist arrivals, which are not the French variables, also can be the vital explanatory factors of the hotel stock return movements. Also, industrial production in Spain and tourist arrivals in the UK exerts the negative influence on the hotel prices. To sum up, the stock price in France is significantly influenced by the British and Spanish macro variables.
As for the British hotel stocks, the British hotel stocks are significantly affected by the interest rate of the EU and the UK and tourist arrivals in Spain. In particular, the interest rate of the EU has a negative impact on the hotel stock returns, meanwhile the interest rate of the UK and tourist arrivals in Spain exhibits a positive impact on the hotel stock prices.
Generally speaking, this paper contributes to the empirical evidence of the developed stock markets of Spain, France, and the UK by extending the research area to include the country-related factors into the explanatory variables. The findings in the article have important implications for investors as well.
While considering the impact of the country's macroeconomic and non-economic factors on hospitality stocks, investors can also take the relevant country's macroeconomic changes in variables to better estimate stock movements. The results of the VAR can also be used to understand the long- or short- term effects of the explanatory variables.At the same time, investors will be able to adjust their portfolios to achieve greater returns through the short- and long-term effects of different economic risk factors.
Moreover, from the government's perspective, the findings provide ideas and insights for the government to formulate and implement macro policies to stabilize the financial market. Since the macro emergency variables at home and abroad are inextricably linked to market performance, this also leads to the fact that these factors bring the negative impact to the stock price. By understanding domestic and international macroeconomic indicators and predicting the impact of stock markets, it is possible to better guide financial market development and promotion of the real economy. At the same time, it is possible to eliminate the negative impact of changes in the macroeconomic environment of other countries on its own stock market through the formulation of sound policies.
Finally, the study might be improved in the following ways. On the one hand, future research can include the modeling of the volatilities of the hotel price returns via GARCH or stochastic volatility models, which might improve the forecasts. Moreover, future research could introduce the non-macroeconomic variables into the model, such as the outbreak of COVID-19, finical crisis in 2008, etc.
6. APPENDIX
Table A1. The choice of the appropriate lag length.
note: * represent the significant choice.
Source: own elaboration from Author
Table A2. Correlation Coefficient Matrix.
(continued)
Lag LL LR df p AIC HQIC SBIC
0 -1586.30 13.712 13.819 13.978*
1 -1006.31 1160 289 0 11.524 13.560* 16.574
2 -662.03 688.56 289 0 11.351 15.316 21.185
3 -327.56 668.94 289 0 11.261* 17.155 25.880
4 -46.78 561.56* 289 0 11.631 19.454 31.034
ES_HS
PI FR_HSPI UK_HSPI COP CER_EU CER_GBP PMS
ES_HSPI 1.000
FR_HSPI 0.234 1.000
UK_HSPI 0.386 0.065 1.000
COP 0.127 -0.007 0.044 1.000
CER_EU 0.163 -0.001 0.037 0.231 1.000
CER_GBP 0.178 -0.004 -0.019 0.335 0.674 1.000
PMS -0.127 -0.053 -0.089 -0.155 -0.133 -0.095 1.000
ES_CUNR -0.300 -0.049 -0.091 -0.111 -0.097 -0.188 0.153
GIP_UK 0.067 -0.011 0.080 0.089 -0.030 0.091 -0.210
GIP_FR 0.156 0.082 0.078 0.037 0.058 0.126 -0.106
GIP_ES 0.116 0.022 0.101 0.189 0.049 0.087 -0.098
GCPI_UK 0.152 -0.065 0.034 0.229 0.017 0.079 -0.064
GCPI_FR 0.045 0.039 -0.013 0.187 0.052 0.062 -0.002
CIR_EU 0.227 0.083 0.112 0.222 0.120 0.151 -0.415
CIR_UK 0.033 -0.057 0.015 0.233 0.033 0.222 -0.167
GTA_ES 0.030 0.011 0.031 0.068 0.002 -0.045 0.056
GTA_UK 0.125 0.015 0.018 0.086 0.024 0.038 -0.054
ES_CU
NR GIP_UK GIP_FR GIP_ES GCPI_UK GCPI_FR CIR_EU
ES_CUNR 1.00
GIP_UK -0.14 1.00
GIP_FR -0.19 0.32 1.00
GIP_ES -0.27 0.27 0.46 1.00
GCPI_UK 0.09 0.00 0.06 0.01 1.00
GCPI_FR 0.04 -0.04 -0.04 0.03 0.35 1.00
(continued)
Source: own elaboration from Author
Table A.3 The list of the hotel stocks in Spain, France and the UK.
Source: own elaboration from Author
CIR_EU -0.45 0.12 0.16 0.17 0.08 -0.03 1.00
CIR_UK -0.33 0.21 0.14 0.12 -0.05 -0.03 0.41
GTA_ES -0.16 -0.04 -0.22 -0.06 -0.10 0.07 0.07
GTA_UK 0.00 -0.04 -0.02 0.01 0.04 -0.04 0.01
ES_CU
NR GIP_UK GIP_FR
ES_CUNR 1.00
GIP_UK -0.14 1.00
GIP_FR -0.19 0.32 1.00
Spanish hotel stock
Market capital value (million
USD)
British hotel stock
Market capital value (million
USD)
French hotel stock
Market capital value (million
USD)
NH Hotel
Group SA 1364.913
InterContinent al Hotels Group PLC
8134.405 Accor SA
7901.277
Melia Hotels International
SA
1049.251 Whitbread
PLC 4540.862
Soc Immobiliere et
Exploit Hotel Majesti SA
264.925
easyHotel PLC 113.218 Les Hotels
Baverez SA 144.497
Safestay PLC 14.147 Les Hotels de
Paris SA 24.102
Minoan Group
PLC 6.566
Societe Hoteliere et Immobiliere de
Nice SA
17.614
Hydro Hotel Eastbourne
PLC
4.875
Figure A1a. Autocorrelation of residuals for the Spanish series.
Source: own elaboration from author
Figure A1b. Autocorrelation of residuals for the British series.
Source: own elaboration from author
Figure A1c. Autocorrelation of residuals for the French series.
Source: own elaboration from author
Figure A2a. Partial autocorrelation of residual for Spanish series
Source: own elaboration from author
Figure A2b. Partial autocorrelation of residual for French series
Source: own elaboration from author
Figure A2c. Partial autocorrelation of residual for France series
Source: own elaboration from author
Figure A3a. Squared residuals for the Spanish series.
Source: own elaboration from author
Figure A3b. Squared residuals for the British series.
Source: own elaboration from author
Figure A3c. Squared residuals for the French series.
Source: own elaboration from author
Figure A4a. Autocorrelation of Squared residuals for the Spanish series.
Source: own elaboration from author
Figure A4b. Autocorrelation of Squared residuals for the French series.
Source: own elaboration from author
Figure A4c. Autocorrelation of Squared residuals for the British series.
Source: own elaboration from author
Figure A5a. Partial autocorrelation of Squared residuals for the Spanish series.
Source: own elaboration from author
Figure A5b. Partial autocorrelation of Squared residuals for the French series.
Source: own elaboration from author
Figure A5c. Squared autocorrelation of Squared residuals for the British series.
Source: own elaboration from author
Figure A6a. Histogram of British residual series.
Source: own elaboration from author
Figure A6b. Histogram of Spanish residual series.
Source: own elaboration from author
Figure A6c. Histogram of French residual series.
Source: own elaboration from author
REFERENCES
Abugri, B. A. (2008). Empirical relationship between macroeconomic volatility and stock returns: Evidence from Latin American markets. International Review of Financial Analysis, 17, 396–410.
Asprem, M. (1989). Stock prices, asset portfolios and macroeconomic variables in ten European countries. Journal Of Banking & Finance, 13(4-5), 589-612. doi: 10.1016/0378-4266(89)90032-0 Barrows, C. W., & Naka, A. (1994). Use of macroeconomic variables to evaluate selected hospitality stock returns in the U.S. International Journal of Hospitality Management, 13, 119–128
Bilson, C. M., Brailsford, T. J., & Hooper, V. J. (2001). Selecting macroeconomic variables as explanatory factors of emerging stock market returns. Pacific-Basin Finance Journal, 9(4), 401- 426.
Chen, N.-f., Roll, R., and Ross, S. A. (1986). “Economic Forces and the Stock Market.” The Journal of Business, 59(3): 383–403.
Chen, M., Kim, W., & Kim, H. (2005). The impact of macroeconomic and non-macroeconomic forces on hotel stock returns. International Journal Of Hospitality Management, 24(2), 243-258. doi:
10.1016/j.ijhm.2004.06.008
Chen, M. H. (2007a). Macro and non-macro explanatory factors of Chinese hotel stock returns. International Journal Of Hospitality Management, 26(4), 991-1004. doi:
10.1016/j.ijhm.2006.04.002
Chen, M. H. (2007b). Hotel stock performance and monetary conditions. International Journal Of Hospitality Management, 26(3), 588-602. doi: 10.1016/j.ijhm.2006.05.003
Chen, M. H., Jang, S. S., & Kim, W. G. (2007). The impact of the SARS outbreak on Taiwanese hotel stock performance: an event-study approach. International Journal of Hospitality Management, 26(1), 200-212.
Chiang, L. C., & Kee, H. T. (2009, June). Macro-Economic and Non-Macroeconomic Variables Link to Singapore Hotel Stock Returns. In Proceedings of the Oxford Business and Economics Conference Program (pp. 1-12).
Chen, M. H. (2010). Federal Reserve monetary policy and US hospitality stock returns. Tourism Economics, 16(4), 833-852.
Chen, M. H., Liao, C. N., & Huang, S. S. (2010). Effects of shifts in monetary policy on hospitality stock performance. The Service Industries Journal, 30(2), 171-184.
Chen, M. H. (2011). The response of hotel performance to international tourism development and crisis events. International Journal of Hospitality Management, 30(1), 200-212.
