CHAPTER SEVEN EFFICIENCY OF PUBLICLY LISTED HOTELS IN SINGAPORE

Transcription

1 CHAPTER SEVEN EFFICIENCY OF PUBLICLY LISTED HOTELS IN SINGAPORE

2 CHAPTER SEVEN EFFICIENCY OF PUBLICLY LISTED HOTELS IN SINGAPORE 7.1 Introduction In the previous chapter, the productivity growth of ten publicly listed hotels was tracked in a 3-stage MPI-DEA analysis. There is no evidence that stock market development is positively related to productivity. However, investments can affect efficiency changes although technical changes are limited to a few hotel groups. For firms with market capitalization objectives, productivity may be affected as managers cut their investments in order to keep their jobs. This prediction by the stock market pressure on managers hypothesis is confirmed by the findings of the previous chapter. In view of this hypothesis, the current chapter seeks to address the following issue: whether managers could overcome the stock market pressure on managers by efficiently managing their financial resources so that investment strategies are not negatively affected. If it is possible, then the value of their firms should be evaluated in an outstanding manner and that will be translated to positive performance of their shares in the market. In this way, managers will not be pressurized to succumb to reduce investments just to avoid being fired or taken over. In the economic literature, a country must experience total factor productivity growth (TFPG) in order to attain long-term economic growth. TFPG is a geometric average of two components: efficiency change and technical change (Farell, 1957 and Fare et al., 1994). Efficiency change is the catching up effect of improving the business 215

3 operations through learning-by-doing and the other component is technical (technological) change, the employment of new knowledge in the company, for example, through advancement of technology. This chapter concentrates on studying how firms can improve their financial efficiency in order to achieve better profitability and productivity. This will then contribute to the economic growth of the country in the long run. Successful Strategic Business Units (SBUs) or firms go through a life-cycle according to the Boston Consulting Group s Growth-Share Matrix (Kotler, 2002). They start as question marks, become stars, then cash cows and finally dogs towards the end of their life cycle. A question mark requires a large amount of cash to add capital and labour for production in order to keep up with the fast-growing market. Dogs describe businesses that have weak market shares and generate low profits or suffer losses. The vision of any firm is to be a star or cash cow for as long as they can. A star is a market leader in a high-growth market and a cash cow has annual growth rate falling to less than 10% but produces a large amount of cash for the company. Thus, the ability to manage finances as a firm goes through the life cycle becomes critical for survival especially if the business is in the question mark or dog stages. If the firm is in the star or cow stages, then cash must be managed efficiently to produce the maximum assets turnover. In order for a firm to stay in the global competition in the long run, it must manage its finances efficiently throughout its life cycle. Without proper financial management, a firm may become dog and hence be divested as it is a drag on 216

4 company s profits. This is imperative for companies that have sourced funds from the public to expand their operations and are accountable to all stakeholders. High performing managers of publicly listed firms yearn to have tools that can help them manage their finances efficiently. This includes highlighting both inefficient practices and excellent or best practices. The efficient management of a firm is often evaluated based on generating high profits or maximum asset turnover and hence will lead the company to attain better value of their firms in the stock market. When evaluating the financial performance of Fortune 500 companies (Zhu, 2000), Fortune magazine uses eight multiple measures: revenue, profit, assets, number of employees, stockholders equity, market value, earnings per share and total return to investors. Studying financial data is one of the important tools but not the only tool in signaling any inefficiency that may occur in the company. There are business operations and processes that may need performance evaluation but does not depend on financial data, such as the firm s quality of customer service. Improving quality of service in daily operations is an avenue for improving profits for the company. For this reason, the evaluation of service quality as a performance benchmark of operating efficiency is critical. As pointed out by Sherman (1984), a bank branch may be profitable when profits reflect the interest and revenues earned on the funds generated by the branch less the cost of these funds and less the costs of operating the branch. However, this profit measure does not indicate whether the resources used to provide customer services is being managed efficiently, especially in the service sector such as the hospitality industry. Single output to input financial ratios such as Return on Investment (ROI) and 217

5 Return on Sales (ROS) may be used as indices to characterize the financial performance but they are unsatisfactory discriminants of best-practice and not sufficient to evaluate operating efficiency (Zhu, 2002). This calls for a multiple measurement of both financial and operating efficiency. As the hotel sector is labour intensive, the measurement and determinants of labour productivity and its effect on operational efficiency requires more complex modeling (Hu and Cai, 2004). The modeling of the quality of service provided by employees during their course of their work has been done using SERVQUAL (Parasuraman et al., 1985) and LODGESERV. However, due to the scope of this study, the issue of quality of service as a variable affecting productivity is left to future research. Hence, only financial information from the annual reports of fourteen publicly listed hotels is used in this financial performance benchmarking exercise. At the time of writing, there is no such study on the financial efficiency of publicly listed hotels in Singapore. This chapter seeks to address the following issues in order to overcome the stock market pressure on managers hypothesis: 1) Are these publicly listed hotels in Singapore efficient in managing their financial resources? 2) Which of these hotel management teams are efficient in translating their financial resources into the firm s market capitalization or stock price? Using a mathematical programming methodology, Data Envelopment Analysis (DEA), Abad and Thore (2001) developed a model to benchmark financial performance of Spanish firms using an index or multiple indicators. The novel concept 218

6 developed by the authors is that the efficient frontiers or best practices are estimated in two stages. The DEA efficiency scores obtained during the first stage of the Predictive Information Link Model provide a numerical measure of the aptitude of the hotel management team in using best practices to manage financial resources. In the second stage, the Valuation Link Model is used to evaluate the performance of the hotel management team based on how well they are able to translate a set of financial data (projected revenues, operating expenses and book value) into the greatest values in the stock market. The DEA efficiency scores for the second stage provide a numerical measure of the efficiency of the model in explaining the stock price. The rest of the chapter is organized as follows: Section 2 briefly reviews the literature on using financial ratios and DEA to perform efficiency analysis. Section 3 briefly discusses the rationale for using frontier approach to efficiency analysis and the DEA methodology. Section 4 explains the theoretical background and provides the model specification. Section 5 describes the sources of data and the participating hotel groups. Section 6 reports the empirical results and provides a discussion of the findings of the efficiency of individual hotels. Section 7 concludes with its limitations and future research agenda. Section 8 summarizes the contributions and managerial applications of this study. 7.2 Literature Review Worthington (1998) found that the traditional financial statement analysis using simple accounting ratios may be a useful tool for analyzing financial performance but not sufficient for analyzing corporate performance. Using DEA, he measured the production 219

7 performance of thirty listed Australian gold producers. Model 1 emphasized production efficiency on the finding and exploitation of gold deposits with the minimal use of labour. On the other hard, using financial statement analysis, Model 2 tended to reward those firms which had a balance between costs and assets, and the minimization of all costs (including labour), and the maximization of market value. A relatively young firm with an active exploration profile would perform well under Model 1 whereas Model 2 would place an older and more stable firm in the best light. The author also showed there was a high degree of positive rank correlation between the two models in terms of overall technical efficiency. Chen (2002) demonstrated the use of financial ratios as part of the four categories (capital adequacy, management capability, earnings and liquidity) in measuring the performance of Taiwanese banks. The earnings category was a series of financial ratios obtained from financial statement analysis and provided the highest weight. This revealed that the DEA efficiency score of the earnings category might explain a large portion of the efficiency scores. The author combined the DEA measurements with the manager prior opinion measurement through a qualitative survey and obtained a more discriminatory posterior efficiency score through the Bayesian concept. The study concluded that qualitative measurement of manager s opinions could add value to the measurement of firm s performance using DEA with financial ratios. Pille and Paradi (2002) provided an analysis of how DEA could generate the Failure Prediction Index which was a form of measurement of efficiency. This index could detect weakness or predict potential financial failures in Credit Unions in Canada. The author tested five different DEA models in comparison with financial ratios such as 220

8 equity to asset obtained from financial statements (Model 6). The conclusion was that although all models appeared to be equally competent in predicting failures, Model 1 (DEA with Variable Returns to Scale) offered the indicators of where the problems were and how to address them. Thus, it should be the preferred tool for any Credit Union regulator. Sedzro and Messier (2000) also did a study comparing the use of DEA and financial ratios on assessing the relative efficiency of Credit Union Branches in Quebec. They concluded that certain credit unions had high total and operational profits but were considered weak in terms of efficiency and therefore were candidates for significant improvements. The study of a group of 73 credit unions showed that accounting profits could be an incomplete, or worse, a misleading or shortsighted measure of credit unions operational efficiency. The DEA method provided useful and complementary information to accounting ratio analysis for improving the efficiency of financial institutions. 7.3 Efficiency and DEA Methodology Efficiency using Frontier Analysis Most estimation of production functions done in econometric analysis assumes that the firm is efficient. Such regression analysis is two-sided because observations lie on both sides of the fitted regression relationship. Frontier analysis is a recent development in economics that explicitly recognizes that some decision-makers use suboptimal solutions. The purpose of frontier analysis is to distinguish the optimal or efficient decision making units, which are located at the frontier, from the sub-optimal or the inefficient ones that are located below the frontier. Frontier analysis is also known as 221

9 one-sided since all observations lie either on the frontier or below the frontier. Inefficient units can improve their operations by changing some production or management procedures to move nearer towards the efficient frontier Data Envelopment Analysis Data Envelopment Analysis (DEA) is a non-parametric method of constructing empirically and numerically the efficient frontier, given a set of observations. This is unlike the statistical estimation of coefficients in production functions using some linear regression method. For inefficient decision units or firms, its observed performance is compared with a hypothetical optimal or best performance of a unit or firm at the frontier. Thus, DEA is not only diagnostic in that it identifies those technologies that perform optimally but also prescriptive since it delivers information on how the actual performance of a sub-optimal technology might be improved. DEA is the suitable methodology for the current study since the objectives deal with finding optimality or efficiency in two stages: First, DEA calculates the relative efficiency ratings of each hotel in managing financial resources. The efficient hotel will lie on the frontier and has a score of one. Inefficient hotels will lie below the frontier and have scores of less than one. Secondly, if hotels can improve to its optimal position in managing financial resources and using the projected or target output scores, DEA can calculate the second stage efficiency scores of hotels that are able to manage financial resources to project the firm s value in the stock market. 222

10 7.4 Theoretical Framework of Two-Stage DEA Model Fundamental Analysis of Stock In order to value a firm using financial statement analysis, Abad and Thore (2001) did a survey on various accounting research papers. Nissim and Penman (1999) concluded that identifying ratios in the Residual Income Valuation model is useful for equity valuation. This model postulates that equity value is determined by forecasting residual income (also known as abnormal earnings), and then the task is to identify financial ratios that reflect economic factors that determine future residual income. By forecasting these ratios, the researcher builds a forecast of residual income. Ohlson (1995) provided the theoretical framework for the valuation exercise. He developed an extension of the Residual Income Valuation Model in which the value of the firm can be expressed as a function of: Current book value of equity Current profitability as expressed by abnormal earnings and Other information that modifies the prediction of future profitability. Ou (1990) suggested that in terms of financial statements analysis, the relationship between financial data and firm value can be established by a causal process in two stages: A predictive information link that ties current financial data to projected future earnings, and A valuation link that ties projected future earnings to firm value. Using the above ideas from Ohlson and Ou, we can assume a chain of causation as follows: Financial data (ratios) Projected Revenues Stock price 223

11 In the causal process, the factor projected revenues is an intermediary causal factor. It is the estimated output of the first stage of predictive information link and is the input into the valuation link of the second stage. Thus, the valuation link cannot be established separately without first estimating projected revenues Model Specification Model 1: Predictive Information Link For this model, our objective is to identify the financial ratios that reflect the economic factors that drive future residual income. Well-managed companies are more likely to keep generating a steady stream of revenues in the future as well, and this allows the construction of the predictive information link between current accounting information and future revenues. The past performance is mirrored into its future earnings prospects. Farfield and Yohn (2000) suggested a firm s value is a function of the expected growth and profitability of the firm. Their results showed that only change in assets turnover provided the desired information in calculating change in profitability and not the change in profit margin. The change in assets turnover emerged as a leading indicator of changes in assets utilization and operating performance. With this assumption, the assets turnover ratio defined as revenues divided by total assets is used as an indicator of future profitability. Fundamental Analysis is looking for information in financial statements that gives an indication of future profitability and assets turnover is the indicator of future profitability. 224

12 To assess if a firm is managing its financial resources efficiently, we assume that its objective is to maximize the assets turnover, given its assets and cost of goods sold. Data on full operating expenses is not available from the sources. The following ratios are used as inputs and outputs: Inputs: Accounts Receivables/Total Assets Inventory/Total Assets Fixed Assets/Total Assets Other Assets/Total Assets Cost of Goods Sold/Total Assets Outputs: Revenues/Total Assets Model 2: Valuation Link Using the Residual Income Valuation Model, the value of a firm is a function of both the book value of equity and the present value of future abnormal earnings. Hence, it is assumed that value of the firm is a function of operating income and the book value of equity (reflecting all earnings accumulated during the past). The Market Value of a firm is a function of Book Value, Operating Income (Projected Revenues minus Operating Expenses).To assess if the firm is using its financial resources efficiently in creating value in the market, the following inputs and outputs are used: Inputs: Projected Revenues (from first stage) Cost of Goods Sold Book Value of Equity Outputs: Market Capitalization 225

13 Given this, the first stage uses information contained in financial statements ratios in order to project revenues. During the second stage, the projected revenues are plugged into the Market Value function; together with operating expenses and book value, the model finally projects Market Value The Two-Stage DEA Model The production set of the predictive information link is an extended classical production function, whereby the sales revenue is the output and the inputs are accounts receivables, fixed assets, other assets and operating expenses. The DEA frontier calculates the geometrical locus of all Pareto-optimal points of the production set. Those firms exhibiting best practice in the industry are able to convert the given inputs into the desired outputs more efficiently on the frontier with an efficiency rating of one. Firms that score less than one are inefficient. The DEA efficiency rating in the first stage of predictive information link provides a numerical measure of the aptitude of the management team in allocating financial resources. Inefficient firms from the first stage can find information regarding the optimal targets of their output as their projected revenue whereas efficient firms will have achieved optimal output. This projected revenue from the first stage, together with operating expenses and book value are used as inputs into the second stage in the DEA calculations for the Valuation Link Model. In the second stage, DEA calculates the maximal Market Capitalization as the output. In this way, the explanation of the stock prices is established through a two-stage process where the immediate factors explaining 226

14 stock prices are actually unobserved, but calculated from an earlier DEA optimization process. Note that for the first stage, all inputs and outputs are expressed in ratio form. All variables are scaled in relation to total assets, thus adjusting for the size of the firm. Consequently, there should be present no significant economies of scale. In other words, constant economies of scale are postulated a priori and CCR model of DEA (Charnes, Cooper and Rhodes, 1978) is used to run the first stage. For the second stage, the possibility of variable returns to scale is allowed, hence the BCC model of DEA (Banker, Charnes and Cooper, 1984) is applied. The idea of splitting the DEA procedure into two or more consecutive steps over time is not new. There is extensive discussion of various multi-stage DEA procedures in the last chapter of Charnes, Cooper, Lewin and Seiford (1994). To sum up, the objective of the two-stage DEA model is to rank the performance of each stock relative to each of the two frontiers calculated during two stages: A first-stage frontier for the predictive information link, indicating the maximal assets turnover; A second-stage frontier for the valuation link that indicates the maximal capitalization and stock price. The mathematics of the above two-stage DEA can be found in Abad and Thore (2001) where they applied the same procedure in analyzing the Spanish manufacturing industry from They also have a discussion on using ratios rather than absolute amount as inputs in another paper (Abad, Thore and Laffarga, 2000). 227

15 7.5 Sources of Data and Participating Hotels The secondary data used is obtained from three sources: Stock Exchange of Singapore Corporate Handbook, Osiris, a subscribed website providing financial information and various annual reports from the fourteen hotels for a period of eight years from Although there are available data for more hotels such as Hotel Malaysia and Hotel Negara, the simulation showed uncomparable results. From 1998 onwards, data on annual market capitalization are not available for certain hotel groups. The most complete series of data available for comparable results are the fourteen hotels within the stipulated time. Table 7.1 shows the names of fourteen hotel groups whose data are available for comparable results for the sampled years. Table 7.1: Names of Participating Hotel Groups No. Name of Hotel 1 Apollo Enterprises Limited 2 Goodwood Park Hotel Limited 3 Hind Hotels International Limited 4 Hotel Grand Central 5 Hotel Plaza Limited 6 Hotel Properties Limited 7 Hotel Royal Limited 8 Marco Polo Developments Limited 9 Orchard Parade Holdings Limited 10 Overseas Union Enterprise Limited 11 Republic Hotels and Resorts Limited 12 SC Global Developments Limited (formerly ANA Hotel) 13 Sea View Hotel Limited 14 Shangri-La Hotel Limited 7.6 Empirical Results and Discussion Financial Efficiency Performance Using DEAP computer programme (Coelli, 1996b) to run the two-stage DEA models, the predictive and valuation technical efficiency scores are generated for each of 228

16 the year from (see Table 7.2 and 7.3 for detailed results). Hotel Royal was the most efficient hotel group consistently throughout the eight years at both stages, except for year The findings show that Hotel Royal consistently scored 1.00 for both stages except for year 1996 where the second stage score was Revaluation of properties had affected the book equity value that was an input variable. This could have affected the efficiency of the inputs in reflecting the maximum market capitalization. Otherwise, it can be considered the Best Practice in managing the financial resources at the first stage and outstanding evaluation by investors in stock market at the second stage. Sea View Hotel had been efficient for most years except 1992 and Hotel Grand Central was efficient at both stages for four years (1990, 1991, 1993, 1994). Shangri-La was only efficient for the first two years of study and SC Global (formerly ANA hotel) was efficient for 1992 and The hotel groups that had improved their efficiency ratings in later years were Goodwood Park and Marco Polo. In fact, Goodwood Park has been improving in its management attitude since 1995 and obtained efficiency score of one. All of its 2 nd stage efficiency score was one. The management team was efficient in translating its resources to the stock price and market value. Marco Polo has been efficient during 1996 and The rest of the hotel groups were either efficient in the first stage but not at the second stage and vice versa or not efficient at both stages. Apollo Enterprises, Orchard Parade Holdings and Republic Hotels & Resorts were not efficient at both stages as their scores were always below

17 Table 7.2 : Results of Two-Stage DEA ( ) Predictive CRS Valuation VRS Predictive CRS Valuation VRS No. Company Name 1 APOLLO ENTERPRISES LTD GOODWOOD PARK HOTEL LTD HIND HOTELS INTERNATIONAL LTD HOTEL GRAND CENTRAL HOTEL PLAZA LTD HOTEL PROPERTIES LTD HOTEL ROYAL LTD MARCO POLO DEVELOPMENTS LTD ORCHARD PARADE HOLDINGS LTD OVERSEAS UNION ENTERPRISE LTD REPUBLIC HOTELS & RESORTS LTD SC GLOBAL DEVELOPMENTS LTD SEA VIEW HOTEL LTD SHANGRI-LA HOTEL LTD Mean No. Company Name Predictive CRS Valuation VRS Predictive CRS Valuation VRS 1 APOLLO ENTERPRISES LTD GOODWOOD PARK HOTEL LTD HIND HOTELS INTERNATIONAL LTD HOTEL GRAND CENTRAL HOTEL PLAZA LTD HOTEL PROPERTIES LTD HOTEL ROYAL LTD MARCO POLO DEVELOPMENTS LTD ORCHARD PARADE HOLDINGS LTD OVERSEAS UNION ENTERPRISE LTD REPUBLIC HOTELS & RESORTS LTD SC GLOBAL DEVELOPMENTS LTD SEA VIEW HOTEL LTD SHANGRI-LA HOTEL LTD Mean

18 Table 7.3: Results of Two-Stage DEA ( ) No. Company Name Predictive CRS Valuation VRS Predictive CRS Valuation VRS 1 APOLLO ENTERPRISES LTD GOODWOOD PARK HOTEL LTD HIND HOTELS INTERNATIONAL LTD HOTEL GRAND CENTRAL HOTEL PLAZA LTD HOTEL PROPERTIES LTD HOTEL ROYAL LTD MARCO POLO DEVELOPMENTS LTD ORCHARD PARADE HOLDINGS LTD OVERSEAS UNION ENTERPRISE LTD REPUBLIC HOTELS & RESORTS LTD SC GLOBAL DEVELOPMENTS LTD SEA VIEW HOTEL LTD SHANGRI-LA HOTEL LTD Mean No. Company Name Predictive CRS Valuation VRS Predictive CRS Valuation VRS 1 APOLLO ENTERPRISES LTD GOODWOOD PARK HOTEL LTD HIND HOTELS INTERNATIONAL LTD HOTEL GRAND CENTRAL HOTEL PLAZA LTD HOTEL PROPERTIES LTD HOTEL ROYAL LTD MARCO POLO DEVELOPMENTS LTD ORCHARD PARADE HOLDINGS LTD OVERSEAS UNION ENTERPRISE LTD REPUBLIC HOTELS & RESORTS LTD SC GLOBAL DEVELOPMENTS LTD SEA VIEW HOTEL LTD SHANGRI-LA HOTEL LTD Mean

19 7.6.2 Efficiency at Both Stages Table 7.4 shows the number of efficient firms at each stage and both stages for each year. The highest number of hotels that were efficient at both stages was four. In 1992, only two hotels were efficient at both stages. Hotel Royal was the Best Practice hotel at both stages for almost all the years, except A more detailed study could be conducted on the Best Practices on financial management of the hotel sector in Singapore. In this way, hotels can learn from each other on how improve their financial efficiency and hence achieve greater growth in the long run. If the collection of raw data from the hotels is possible, a more comprehensive study on operational efficiency can be conducted in future which will complement the current financial efficiency analysis. It will add more value to the study if the quality of customer service can be incorporated into the model, as this is an important factor that will affect the operational efficiency of firms. Table 7.4: Efficient Firms for Two-Stage DEA No. of efficient firms No. of efficient firms No. of efficient firms at both Names of hotel groups Year at 1 st stage at 2 nd stage stages Hotel Grand Central, Hotel Royal, Sea View, Shangri-la Hotel Grand Central, Hotel Royal, Sea View, Shangri-la Hotel Royal, SC Global Hotel Grand Central, Hotel Royal, Sea View Hotel Grand Central, Hotel Royal, Sea View Goodwood, Hotel Royal, Sea View Goodwood, Marco Polo, Sea View Goodwood, Hotel Royal, Marco Polo, SG Global 232

20 7.6.3 Returns to Scale From the second stage DEA, Valuation Link using the BCC model allows variable returns to scale. Results generated can reveal if a firm is operating under increasing returns to scale (irs), constant returns to scale (crs) or decreasing returns to scale (drs). Table 7.5 shows the detailed results for each hotel group for every year. Goodwood Park Hotel Ltd was the only hotel group performing on constant returns to scale which means that its size was optimal. Hotel Properties Ltd has been operating at decreasing returns to scale, so if they could scale down their operations, it could improve their returns. Hotel Royal could improve their returns if they increase the size of operations since they were operating on increasing returns to scale. Table 7.5: Returns to Scale No. Company Name APOLLO ENTERPRISES LTD irs irs irs irs drs drs drs irs 2 GOODWOOD PARK HOTEL LTD crs crs crs crs crs crs crs crs 3 HIND HOTELS INTERNATIONAL LTD irs irs irs irs drs drs drs irs 4 HOTEL GRAND CENTRAL irs irs irs crs crs drs drs irs 5 HOTEL PLAZA LTD irs irs drs drs drs drs drs drs 6 HOTEL PROPERTIES LTD drs drs drs drs drs drs drs drs 7 HOTEL ROYAL LTD irs irs irs irs irs irs irs irs 8 MARCO POLO DEVELOPMENTS LTD crs crs drs drs drs drs drs crs 9 ORCHARD PARADE HOLDINGS LTD irs irs irs irs drs drs drs drs 10 OVERSEAS UNION ENTERPRISE LTD drs crs drs drs drs drs drs irs 11 REPUBLIC HOTELS & RESORTS LTD drs irs drs irs drs drs drs drs 12 SC GLOBAL DEVELOPMENTS LTD drs irs crs irs drs drs drs irs 13 SEA VIEW HOTEL LTD crs irs irs irs crs crs crs irs 14 SHANGRI-LA HOTEL LTD drs crs drs drs drs drs drs drs Sea View Hotel has either increasing or constant returns to scale throughout the eight years whereas Marco Polo has either decreasing or constant returns to scale. Orchard Parade was operating at increasing returns to scale during the first four years but at decreasing returns to scale during the last four years. Shangri-La Hotel group has been operating at decreasing returns to scale except for year 1991, which was on constant 233

21 returns to scale. The size of the hotel group seemed to be too big and affected its efficiency. It had efficiency score of one for both stages only during 1990 and Conclusion, Limitations and Research Agenda Conclusion The above exercise provided a simple procedure in analyzing the financial efficiency of hotels in two stages. The first stage shows how efficient each management team is in managing the financial resources of the hotel. The second stage shows how efficient each hotel is in using the projected revenues from the first stage and other financial resources to translate into the firm s value in the stock market. From the empirical finding, Hotel Royal seemed to be consistently efficient for the first stage but for second stage, the efficiency score was only in 1996 when there was a revaluation of the land and buildings. This has affected the book value and decreased the efficiency score at the second stage but did not affect the first stage. It has also operated on increasing returns to scale and hence hotel s returns could have improved by expanding its size. The other high performing hotel was Sea View Hotel that had an efficiency score of one for six years for both stages, except for year 1992 and This group has also operated on increasing or constant returns to scale, and therefore could improve its size of operation to reap more profits. Goodwood Park Hotel has improved its efficiency score tremendously after 1995 and has maintained the efficiency index of one for both stages with constant returns to scale since then. 234

22 Although many other hotel groups had efficiency score less than one at the first or second stage during the eight years, quite many of them are still trading on the Singapore Stock Exchange at the point of writing. Republic Hotel Group was delisted in 2002 and taken over by Millennium & Copthorne Hotel Plc. During 2004, Goodwood was delisted and Sea View Hotel ceased operations as a hotel. This called for investigation into the efficiency of the three financial management teams for more recent years such as from 1998 to This would increase our understanding of how financial performance measurement can be used along with other tools to predict failure or success in the stock market Limitations and Research Agenda Although financial efficiency analysis is a valuable tool, it is not sufficient. For the manufacturing sector, maximization of the revenues or profits from the sale of the product is the goal of the firm. For the service sector, the element of human contact is very important in creating sales. Hence, it is crucial to measure the operational efficiency of the hotels in providing quality customer service. However, the measurement of quality service as an output is another difficult issue that many researchers do not agree, especially when studying productivity (Sherwood, 1994). Another worthwhile investigation would be the environmental factors affecting the efficiency of the hotel sector. From the examples of the Asian Currency crisis during , the September 11 event in 2001 and the Severe Acute Respiratory Syndrome (SARS) outbreak in 2003, the macroeconomic environment had affected the hotel business adversely. It would be of great value to investigate to what extent these factors 235

23 have affected the efficiency levels of the hotel groups. The impact of variables such as manpower training, wages, employee turnover etc. on efficiency of the hotels could also be investigated in future. Lastly but not the least, it would be of interest to perform some statistical tests for more robust results as the non-parametric methodology of the two stage DEA has a disadvantage of being non-statistical. Another methodology such as Stochastic DEA or Window analysis of DEA results may be considered to improve the robustness of the results in future research. Despite the many limitations of the methodologies used in this study, this chapter highlights that financial ratio analysis together with DEA can be used to measure financial performance of a firm. There are not many studies of this nature in the hotel industry in Singapore. This benchmark study of financial efficiency of hotels in Singapore could act as a stepping stone for the investigation of the operational efficiency of the hotel sector whereby the qualitative measurement of customer service is considered. 7.8 Contributions and Managerial Applications Contributions The contributions of this study are two-fold. Firstly, the 2-stage DEA model provides a way to link the financial efficiency of managers at the first stage to the firm s value at the second stage. The ability to manage efficiently is translated into betterprojected revenues, which becomes an input into the second stage. Through this channel, investors will make evaluation on the performance of the company through stock market capitalization. 236

24 Secondly, at the time of writing, there is no such benchmarking exercise done to evaluate the efficiency of publicly listed hotels in Singapore. This study can be replicated for firms in different sectors as only financial data is used. This value of this investigation is that it can be applied widely to many sectors and countries Managerial Applications In view of the stock market pressure on managers hypothesis, this exercise provides an avenue whereby managers need not succumb to stock market pressure if they can manage their financial resources efficiently. Such efficiency can be translated into better investment strategies. In this way, managers need not cut their investments just to keep their job. For the first stage inefficient firms, projected revenue is higher than actual revenue, since the calculation of the projected output is what the firms could have obtained, provided they used inputs as efficiently as the best managed companies in the industry. This implies that it is more difficult for these companies to reach efficiency in the second stage. Hence, the efficiency rating achieved by a company in the second stage is in fact influenced by its relative performance in the first stage. The underlying reasoning is that for valuing a company it is not enough just to look at its earnings, it is also necessary to gauge how the firm is performing relative to the others in generating those earnings. Vice versa, a firm that is efficient in the first stage will find it easier to reach efficiency at the second stage. 237

25 Thus if a manager is efficient in managing financial resources in the first stage, he will be able to translate his efficiency at the second stage to the market value of his firm due to better management of resources and investment strategies at the first stage. This exercise provides an incentive for managers to manage their financial resources efficiently so that they can invest more efficiently and hence increase productivity. In this way, the stock market pressure on managers will not be realized and the managers will be more efficient at maximizing the market value of the firm at the second stage. The efficiency score of one reveals the outstanding evaluation investors give to the well-managed firm through the market capitalization of the firm in the stock market. 238