The performance of the S&P Global Luxury Index compared to the
MSCI World Index, during the financial crisis.
“An analysis of 80 luxury goods companies around the world”
Name: Laura van Ballegooijen Student number: 10476008
Programme: BSc Economie en Bedrijfskunde Track: Finance and Organization
Supervisor: Philippe Versijp Date: February 2 2015
Abstract
This study considers the outperformance of the stocks that form the S&P Luxury Goods Index, which consists of 80 companies worldwide, versus the MSCI World index. The outperformance is measured by using the Carhart (1997) four-factor model to adjust for firm characteristics and to obtain risk adjusted abnormal returns. During the heights of the financial crisis from July 2007 till July 2009 using daily datasets there are no significant abnormal returns. The Fama and French (1993) three-factor model and the CAPM are also used to measure the outperformance during this time period, also resulting in non-significant abnormal returns. The time period of July 2007 till July 2011 and monthly data sets are also used later in this study. Resulting in only a few statistically significant abnormal returns. From the results it can be concluded that there is not enough empirical evidence for an outperformance of the luxury industry during the financial crisis, thereby making arguments regarding the source of any outperformance based on the specific nature of this industry, moot.
Statement of originality
I hereby declare that this thesis is my own work. Laura van Ballegooijen
Table of Contents
1. Introduction 3
2. Literature Review 4
3. Hypotheses and Empirical Test Design 7
3.1 Hypotheses 7 3.2 Sample Data 8 3.3 Methodology 10 4. Empirical Results 11 5. Conclusion 24 References 26 Appendix 28
1. Introduction
The global luxury industry is an industry not often discussed in academic literature, hence there is little empirical research available for this industry. There are several reasons to believe that the performance of the luxury industry is different from that of the market. Therefore, it is interesting to fill this gap in the literature. Particularly interesting is the performance of the luxury industry during the financial crisis. Several theories suggest that the luxury industry would perform better than other industries during the financial crisis, because the luxury industry was less affected by the impacts of the financial crisis.
According to Zhan and He (2012), the luxury industry reported strong sales in China, during the financial crisis. Resulting from a report by Goldman Sachs, China’s consumers account for 25% of the global luxury goods consumption (Zhan & He 2012). Zhan and He (2012) study the motivation behind the consumption of the growing Chinese upper middle class. The upper middle class in China is growing rapidly, even during the financial crisis, so therefore it can be explained that Chinese consumers are still interested in luxury products and services, despite the global recession. This strongly suggests, that during the financial crisis, the luxury goods industry could have a better performance compared with other industries due to the interests of Chinese consumers. However, from a financial perspective there are no existing studies regarding the performance of the luxury industry and providing evidence for an outperformance, due to amongst others, the high sales in China.
Furthermore, consumers of the luxury industry pertain largely to the top richest percent of the world. Despite the economic downturns during the financial recession, the top richest percent of the world still is still wealthy enough to keep consuming these luxury goods. Therefore, it is expected the luxury industry is less severely affected by the financial crisis. Following these expectations the research question studied in this paper is:
Did the S&P Global Luxury Index outperform the MSCI World Index during the financial crisis, if so can this be explained?
The outperformance from the above research question could be explained due to a low beta of the luxury industry. When the beta is one, it means that the stock returns of the company move according to the market. If this beta is lower than one, the stock returns are less sensitive than the returns of the market (Fama & French, 2004). Therefore, it is also tested whether the average beta of the S&P Global Luxury Index differs from one. According to the theory studied, a low beta would be expected, as the luxury industry would then be less
sensitive to the effects of the financial crisis and therefore would have shown less negative returns during the downturn in the market.
Regarding the possible motivations for an outperformance of the luxury industry it is relevant to test this empirically and to give empirical evidence for these motivations. The outperformance can be tested using different financial models. The first model used for the regressions is the Carhart (1997) four-factor model, as this model takes into account most different firm characteristics and can measure reliable risk adjusted returns.
Afterwards, when the risk-adjusted returns are measured, the next step is to empirically explain how abnormal returns can exist. Initially, during this study the next step was to use the resulted abnormal returns and to use these as dependent variables to regress against independent factors that might explain these out performances. Control variables, used in the second regression, would have been the sales in China, the exchange rate effect of the yen compared to the dollar and to the euro and the concentration of wealth. All these variables can explain a possible outperformance of the luxury industry.
However, not enough significant abnormal returns followed from the regressions and the second part of the research question could not be answered in this way. Therefore, another method is used. To ascertain whether the sales and consumption of the luxury industry in China and the constant growth of wealth have influence on the overall luxury industry, it is tested whether there is a correlation between these two occurrences and the luxury industry during the financial crisis.
2. Literature Review
According to a report by Goldman Sachs, next to Japan, China has the highest luxury consumption worldwide. China accounts for 25% of the global luxury industry consumption. Even though, during the financial crisis, the rest of the world faced large economic downturns, the luxury industry reported strong sales in China (Zhan & He, 2012). This could be one of the explanations for the luxury goods industry to outperform the MSCI World index during the financial crisis. Zhan and He (2012) study the motivation of the growing Chinese upper middle class for luxury consumption. Their findings suggest that Chinese consumers have a strong tendency to buy luxury goods, as they experience social pressure from important standards established within particular groups. Therefore, they treat luxury products as very valuable. Next to that, Chinese middle class consumers value good quality products and therefore Chinese consumers attach more value to luxury products (Zhan & He 2012).
This paper studies the outperformance of the luxury industry. It uses the motivation of the Chinese upper middle class for luxury consumption, as one of the potential explanations for a possible outperformance.
On the contrary, a study considering the entertainment tourism industry in Macao shows that the entertainment tourism industry correlates highly with the performance of the global market during the financial crisis (Chan, 2011). According to Chan (2011), the entertainment tourism industry is usually seen as part of the luxury industry. He believes the luxury industry can be highly affected by economic recession. The S&P Global luxury index includes fourteen companies that are active in the casinos and gaming industry, as Chan (2011) describes to be the entertainment tourism industry. Therefore the study of Chan could be a relevant explanation for the performance of the luxury industry. Chan (2011) concludes, that the GDP earned from Macao’s entertainment tourism industry, closely follows the patterns of the global financial crisis. Chan (2011) studies the correlation of the entertainment tourism industry with the following financial markets; DAX index, S&P500, Hang Seng Index and Shanghai Stock Exchange Composite Index. This paper will study the performance of the luxury industry compared to the MSCI World Index, which is a better proxy than the one used by Chan (2011), as it covers the entire worldwide market and is therefore more representable for the course of the world market during the global financial crisis.
The outperformance of an index can be measured using several models. In the literature the accuracy of different models are discussed. The most basic model is the capital asset pricing model (CAPM) constructed by Sharpe (1964) and Lintner (1965). The CAPM can be used to predict the relationships between risk and the expected returns of, amongst others, the stock price of an asset, which will be used during this study. The expected return of an asset is composed of the risk free interest rate, plus the asset’s market beta times the expected value of the excess market return, known as the risk premium (Fama & French, 2004). To obtain the abnormal performance of an asset, the intercept, Jensen’s alpha (1968), is measured using the CAPM time-series regression. The CAPM forecasts that the asset’s expected excess return is fully explained by the expected risk premium. This would suggest that the intercept term, Jensen’s alpha, is zero for all assets (Jensen, 1968). However, there exists evidence that the relationship between the beta and the average return is too weak (Fama & French, 2004). Friend and Blume (1970), Black, Jensen and Scholes (1972) and Stambaugh (1982) all carried out time-series tests supporting this evidence. The alphas in time-series regressions, in which the excess returns of the assets are regressed on the excess market returns, are positive for assets with low betas and negative for assets with high betas
(Fama & French, 2004). Therefore, during this study the companies are expected to have low betas, as positive alphas are predicted. It can be concluded that market betas do not fully explain expected returns. Following from this evidence, assets can outperform the market and therefore in this study it can be tested whether this is the case for luxury assets and the global market as well.
However, evidence challenging the CAPM has grown. The market betas do not have enough information regarding stock prices, to completely explain expected returns. The following empirical studies explain some of the missing information. First, stocks with high earnings-price ratios have higher future returns than predicted by the CAPM (Basu, 1977). Second, there exists a size effect, when market capitalization is taken into consideration, smaller stocks have higher returns than predicted by the CAPM (Banz, 1981). Finally, stocks with high book-to-market equity ratios have higher average returns than captured by the CAPM (Statman, 1980 & Rosenberg, Reid & Lanstein, 1985). The last one arises because, investors overreact to good and bad states (Fama and French, 2004). Fama and French (1993) construct a three-factor model for expected returns including the missing challenges of the CAPM. In their paper they study the cross-section of average returns on NYSE, Amex and NASDAQ stocks from 1963 to 1990. They conclude, that if the two variables size and book-to-market equity are added to the CAPM, which results in the three-factor model, it explains average returns more accurately (Fama & French 1993).
The premium factors used to obtain stock returns in the three-factor model explain a large part of the predictability of an asset’s return more accurately than only using the market beta (Carhart, 1997). However, another effect missing is that of the momentum constructed by Jegadeesh and Titman (1993). The momentum effect denotes that stocks that have performed relatively well compared to the market during the last three to twelve months usually continue to do so in the coming months, and stocks that perform bad continue to do so as well (Fama & French, 2004). Carhart (1997) therefore constructs a new model in which it adds the momentum factor to the three-factor model of Fama and French. Concluding from the literature and the evidence that follows from it, the Carhart four-factor model is the most accurate model to use for the regressions, during this study, to measure the performance of the luxury industry, as it takes the most important risk factors into account.
3. Hypotheses and Empirical Test Design 3.1 Hypotheses
Following from the literature review there seem to be plausible theoretical explanations that the luxury industry may have outperformed the rest of the market during the financial crisis. The reverse evidence of Chan (2011) does not necessarily imply underperformance. He gives evidence to the fact that the performance of the casinos and gaming industry within the luxury industry correlates highly with the bad performances of the market during the financial crisis. However, this can also be due to the fact that the companies used in Chan’s study have high positive betas, which could lead to more negative returns in a downward market. The following hypotheses will therefore be tested:
During the financial crisis, the luxury goods industry outperforms the MSCI World market. To test this empirically, two hypotheses are set-up:
H0: The luxury goods industry did not outperform or underperform the global market. α = 0 H1: The luxury goods industry outperformed or underperformed the global market. α ≠ 0 These hypotheses are used to answer the research question. Therefore, the risk adjusted abnormal returns that are earned by each individual stock are tested. If the null hypothesis is not rejected, the risk adjusted abnormal returns are equal to zero. Meaning, the luxury goods industry did not outperform or underperform the global market. On the contrary if the null hypothesis is rejected and the alternative hypothesis is true, the risk adjusted abnormal returns will either be lower or higher than zero. Which implies the luxury goods industry either outperforms or underperforms the global market. For investors this can provide valuable insight if investments in the luxury industry would have resulted in abnormal returns. This both holds for potential outperformance and underperformance. Underperformance would only provide insight in investment decisions if there is the possibility to take a short position in the portfolio of luxury companies. This might only be achieved on the individual stock level which makes it more costly for investors to exploit the potential underperformance.
Furthermore, using the results of these hypotheses tested, the betas will be tested using a student t-test to measure whether the average beta of the luxury industry is different from one.
The following hypotheses will be tested: H0: Beta = 1 (β=1) and H1: Beta ≠ 1 (β≠1) 3.2 Sample Data
The proxy used for the luxury goods industry is the S&P Global Luxury Index. The S&P Global Luxury Index is composed by the S&P Dow Jones Indices. The S&P Global Luxury Index consists of the eighty largest publically traded luxury companies. They are active in either the production or distribution of luxury goods or in the luxury services industries. The index comprises companies active around the world. Therefore, the MSCI World index is a good proxy to measure the outperformance. The stock prices of each individual company are collected from the CapitalIQ database. According to Curran and Zignago (2010), who analyze the impact of the financial crisis on key clothing markets and suppliers, the largest impact of the financial crisis can be seen in the period from July 2007 to July 2009. The index is largely composed of companies engaged in the apparel, accessories and luxury goods industry. Therefore, the data for this period will be used. As this is a relatively short time period, the daily share prices will be used, to obtain enough observations. In this way the results will be less biased and therefore more accurate. Not every company included in the S&P Global Luxury Index was trading publicly during this time period, therefore these companies are left out of the regression as there are no stock returns for this time period. There are however several companies that became public during this time period, these companies are taken into the regression from the moment they are trading publicly. Ultimately, there are 67 companies either listed before or during the time period of July 2007 till July 2009, so therefore these 67 companies are used in the regressions. The largest industry in the S&P Global Luxury Index, consisting of 24% of all the companies, is the Apparel, Accessories and Luxury Goods industry. The second and third largest industries, consisting of 18% and 13% are Casinos and Gaming industry and the Hotels, Resorts and Cruise Lines industry. Furthermore, the index consists of smaller industries accounting for one to eight percent of all companies. By far the most companies have their headquarters in the United States and are listed on either the New York Stock Exchange or the Nasdaq, together 44% of all companies are listed on US stock markets. Next to that, the Stock Exchange of Hong Kong has the largest percentage of companies listed (14%). The rest of the companies are spread quite equally across the world ranging from France to Australia and Japan. For further details see table 1.
Furthermore, the proxy used for the market is the MSCI World Index. Usually, in the literature, the S&P500 is used as a proxy. This is an index constituting 500 of the largest companies in the United States. However, the industry tested is active globally and not only in the United States, therefore, the MSCI World Index is a better proxy. The MSCI World Index consists of 1636 large and mid cap companies in twenty-three developed market countries (MSCI). The MSCI World Index accounts for almost 85% of the free float-adjusted market capitalization in all of the twenty-three countries (MSCI). The daily performance of the Index is downloaded from the CapitalIQ database.
To analyze the risk adjusted abnormal returns of every company the Carhart (1997) four-factor model will be used. The premiums for the daily factors and the risk free rates are obtained from the Applied Quantitative Research website. These premiums are based on global factors and not only on US factors. Which will give a more meaningful outcome of the regressions as it matches the global nature of the luxury industry and the benchmark used. The premiums included are the size risk factor Small Minus Big (SMB), the value risk factor High Minus Low (HML) and the momentum risk factor (MOM).
Finally, to give evidence for the motivations of the luxury industry to outperform the market during the financial crisis, data for these motivations will be downloaded. To give evidence to the motivation of the growing Chinese upper middle class who spend more and more on luxury goods, even during the financial crisis, as strong sales in China reported, data for the consumer confidence will be used. This data is obtained from Bloomberg. The
consumer confidence index is an index comprised by the bank of China, which measures the spending and saving behaviour of consumers, which can be seen by how these consumers pertain their personal finances and how they think of general economical conditions, during a specific time period. For this study monthly indices will be used to calculate the correlation between Chinese consumer confidence and the S&P Global Luxury Industry. Furthermore, the same consumer confidence indices are obtained from Bloomberg, for American and European consumers. The three consumer indices are compared, and from this it can be seen whether Chinese consumer confidence is indeed higher during the financial crisis.
3.3 Methodology
To test the hypotheses following from the research question, the Carhart four-factor model will be used. According to the literature the Carhart four-factor model will give the most accurate results, adjusting for the risks in the returns (Fama & French, 2004).
Carhart four-factor model:
EXRt = α + βmktEXMKTt + βHMLHMLt + βSMBSMBt + βMOMMOMt + εt
To be able to use the datasets explained in the sample data chapter of this paper, the data needs to be adjusted before the regressions can be done. The trading days of each of the companies listed in the S&P Global Luxury Index will be checked using the daily trade volume, and the stock prices of these days will be used for the regressions. This includes, adjusting for weekends when there is no trading and other non-trading days, which is different for every market. First the daily stock returns will be calculated for each firm. All stock prices will be left in local currencies. In this way currency fluctuation do not influence the stock returns calculated. Furthermore, the stock returns need to be calculated in excess of the risk-free rate to obtain excess returns used for the regressions as the dependent variable that needs to be explained. The same method is applied to the market price of the MSCI World Index. The regressions are performed separately for each company using a daily time-series regression. The dependent variable is the excess return of a company’s stock price (EXR). The independent variables are the factors adjusting for risk. These factors are: the risk premium of the market, in this study the daily MSCI World Index return minus the risk free rate (EXMKT), small minus big (SMB) which is the difference between the returns on diversified portfolios of small and big stocks, high minus low (HML) which is the difference between the returns on diversified portfolios of high and low book-to-market stocks (Fama &
French, 1993) and finally the Momentum factor (MOM) which is the difference between the returns on diversified portfolios of short-term winners and losers (Carhart, 1997). Furthermore, the β’s are the exposures of a firm to the premiums and ε is the error term. The regressions are performed using Statplus, an excel plug-in with which linear regressions can be performed. It performs linear regression analysis by using the least squares method, to fit a line through a set of observations. With this it can be analysed how the dependent variable is affected by the values of the independent variables. The resulting intercept is the alpha (α), which shows the abnormal performance of a particular company over the tested time period. This method is applied to all 67 companies for the daily time period of July 1 2007 till June 30 2009. In the next section the results will be presented and discussed.
4. Empirical Results
The most important results of the regressions are the intercepts, otherwise known as the alphas. To see if these are significantly different from zero the results need to be interpreted using a significance level. The significance is calculated by dividing the coefficient of the intercept by its standard deviation, which results in the statistic. It can be seen from the t-statistic whether the intercept is significant or not. First using the degrees of freedom, in this study more than thirty observations are used, therefore the degrees of freedom can be seen as infinitely many. Second a two-sided test, which is used in this study, as the alphas are tested on outperformance and underperformance. For instance, with an infinite degrees of freedom and a two-sided test the t-statistic should be larger than 1.96 for the alpha to be significantly different from zero using five percent as the significance level (Stock & Watson, 2012).
Using the Carhart four-factor model and data on a daily basis, none of the companies in the S&P Global Luxury Index have significant alphas, not even using a five or ten percent significance level. Meaning there is no company that outperformed or underperformed the market during the financial crisis on a risk-adjusted basis. It can be concluded that during the financial crisis the movement in the stock prices can be explained by the four risk factors, used in the model.
The momentum factor might not have an effect in these regressions, as the number of companies used is very low and possibly not very diversified regarding differences in good and bad performance. Therefore, next the three-factor model (Fama & French, 2004) is used. Using only the excess market return, SMB and HML as independent variables for the regression. Again the results show no significant alphas. Lastly, the basic capital asset pricing model is used to do the regressions using daily returns and risk factors. Here only the excess market return is used as the independent variable, allowing only the market beta to explain the expected excess returns. Again resulting in zero significant alphas. Concluding, on a daily basis there are no significant results. See table 2 for all the results regarding these regressions.
The time period of the financial crisis differs across theories in the literature. For instance, Zhan and He (2012) use the period of 2007 to 2010 in their studies. Other researchers believe that the effects of the crisis can be seen even at a later time. Therefore, the next step, to obtain results is to use a wider time span, to be sure to capture all the effects of the financial crisis. The same regressions will be performed, using daily share price data and risk factors, using the period of July 2007 till July 2011 instead of July 2009. There were four companies listed during the time period of July 2009 till July 2011, so these companies are now also taken into the empirical tests, resulting in 71 companies that are now regressed. The regressions are all performed in the same way described as above, first using the Carhart four-factor model, then the Fama and French three-factor model and lastly the CAPM.
For all three models Luk Fook Holdings International Limited, a company listed on the Hong Kong Stock Exchange, showed a significant positive abnormal return at a one percent significance level. Furthermore, at a five percent significance level using the CAPM and the Fama and French (1993) three-factor model, SJM Holdings Limited, a company also listed on the Hong Kong Stock Exchange and YOOX, an Internet Retail Company listed on the Borsa Italiana stock exchange, show a positive abnormal return. Finally, there are a few companies with positive abnormal returns at a ten percent significance level, which can be seen from table 3. However, given the amount of companies tested, resulting in a few false positives would not be unrealistic. Concluding, there is not enough empirical evidence for an outperformance or underperformance of the luxury industry.
Due to the low number of significant alphas found under the methods used above, monthly data is used instead, to reduce the noise that might be present using data on a daily basis. The returns of stock prices are converted to monthly returns using the first day of every month. New risk factors were downloaded from Kenneth French’s library. These included the global monthly risk factors, SMB, HML, MOM and the risk free rate. Monthly excess returns were calculated, for the asset’s stocks and the market index, using French’s risk free rate, based on monthly t-bill rates. The regressions were performed in the same way, first using the Carhart (1997) four-factor model, next the Fama and French (1993) three-factor model and lastly the basic CAPM using Jensen’s (1967) alpha with a monthly time period from July 2007 till July 2011. Extra two years were added to obtain enough observations.
For all three models, using a significance level of one percent, Luk Fook Holdings International Limited showed a positive significant return. Which shows the same results using daily data from 2007-2011. Only using the CAPM there is one more company with a significant alpha at a one percent level. Furthermore, at a five percent significance level there are two companies that show positive abnormal returns using all three models. It can be seen from table 4, that at a ten percent significance level there are fourteen companies with positive significant abnormal returns using the CAPM, sixteen companies using the three-factor model and 13 companies using the four-factor model.
Overall, the results of the empirical analyses are clear. We cannot observe sufficient statistically significant positive abnormal returns so the hypothesis, that during the financial crisis, the luxury goods industry outperforms the MSCI World market, should be rejected. Statistically the null hypothesis, that the risk adjusted abnormal returns are equal to zero, is not rejected. To depict the course of the S&P Global Luxury Index compared to the MSCI World Index the daily returns of both indices are indexed starting one day before the time period used in the regressions, June 30 2006, as the base 100. From figure 1 it can be seen that during the first time period used they moved together closely, as accordance to the regressions where there is seen to be no under or outperformance. However from mid 2009 onwards it can be seen that the S&P Global Luxury Index grows much faster and higher than the MSCI World Index. This is also in accordance with the daily and monthly regressions done from the time period of July 2007 till July 2011. There are more significant results during this time period. Also most abnormal returns are positive, however they are mostly not significant enough to give empirical evidence of an outperformance.
Furthermore, to be able to interpret the betas, a t-test is conducted. From the betas it can be seen whether the stock returns of the companies listed on the S&P Global Luxury Index are sensitive to the market or not. Following from theory, since there are almost no significant abnormal returns, it would be expected that the betas be close to one. The t-test is conducted by testing the null hypothesis H0: β=1 and the alternative hypothesis H1: β≠1. To see whether the S&P Global Luxury Index is sensitive as a whole, the average beta of all the companies is calculated and tested. This is done by using the following t-test.
t = (x-u) / (s/√n)
The x stands for the average beta calculated and the u stands for the beta expected from the null hypothesis, so one here. When subtracted, it is divided by the standard deviation, which is divided by the square root of the number of observations used, which is 67 from 2007 till 2009 and 71 from 2007 till 2011. From table 5 it can be seen that with using daily data from 2007 till 2009 and daily data from 2007 till 2011 all average betas using all three models are not significantly different from one except for the average beta of the daily data from 2007 till 2009 using the Fama and French three-factor model. These results are expected as according to theory. The S&P Global Luxury Index is indeed sensitive to the changes of the market. However, the average betas from the monthly data used do show significant betas that are higher than one, as you can see from table 5. Which is also in line with the theory, as there are more significant abnormal returns present in this time period on a monthly basis.
The results of the second part of the research question, on whether the outperformance can be explained show the same results. The correlation between China’s consumer confidence index and the S&P Global Luxury Index is 0.07. Meaning there is almost no correlation between the consumer spending and saving behavior with regard to the consumption of the luxury industry. However, comparing the consumer confidence index of China with those of the United States and the European union, it can be seen from figure 2, that during the financial crisis China’s consumer confidence was much higher and more stable. It can be interpreted from these results that the consumption was much higher in China during the financial crisis. However, the very small correlation with the luxury industry could be due to the fact that there are billions of consumers in China, on which this consumer confidence index is based. The upper middle class whom allegedly buys luxury products and services is only a very small percentage of all consumers in China.
5. Conclusion
Concluding, the empirical results obtained are not significant enough to give a evidence for the outperformance of the S&P Global Luxury Index compared to the MSCI World market. Therefore, it can be concluded that the luxury industry did not outperform the world market during the financial crisis on a risk-adjusted basis. The theories suggesting the luxury industry did perform better than other industries during this time period are evidently not significant enough. The high sales of the luxury industry in China were not high enough to prevent the luxury industry to fall during the financial crisis. However, the results of the monthly regressions done using the three different financial models, did result in more companies who showed to have risk-adjusted abnormal returns during the financial crisis. Two of the companies with a positive significant alpha, Luk Fook Holdings International Limited and SJM Holdings Limited are listed on the Hong Kong Stock Exchange. These results together with the higher consumer confidence of China compared to the United States and the European Union, during the financial crisis, still give a limited direction to the strong economy of China while the rest of the world faced financial distress. Another interesting observation is that both companies retail or service Chinese products. Luk Fook is a jewellery retailer specialized in Chinese traditional jewellery and gemstones. SJM is a company active in the casino and gaming industry of Macao. One of the motivations of Zhan and He (2012) for Chinese consumers to consume luxury goods is the social pressure of carrying these products. These are merely based on western brands, which give the consumers a better status in social groups. Therefore, it is interesting to see that it is not the western brands that outperformed the market but the traditional Chinese brands. The study of Chan (2011) in which he considers the entertainment tourism industry in Macao, shows that the entertainment tourism industry correlates highly with the performance of the global market during the financial crisis. However, in this study it can be seen that there still is a company active in the entertainment tourism industry of Macao that performed better during the financial crisis. However, this could also be due to a higher beta. On the other hand, these significant alphas could be false positives, as the index used exists out of a limited number of companies. For further research it can be suggested to study the performance of the luxury fashion industry alone. As from most research it is usually the apparel, accessories and luxury goods industry, in the luxury industry, that is said to perform better than the market during the financial crisis. Furthermore, it can be seen that there is a difference in the two time periods used, adding two extra years resulted in more significant results. It can therefore be suggested to test the outperformance using only those extra years, 2009-2011 or to use a longer time period.
Lastly, it can be suggested to study the luxury industry using the sales of this industry with regard to different regions instead of using a global index. Especially, the Asian region could be interesting to study separately.
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