Beta值變動因素之探討與資金成本之估計：以航空公司為例

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研究生：	劉永欽 Yong-Chin Liu
論文名稱：	Beta值變動因素之探討與資金成本之估計：以航空公司為例 The Investigation of Factors of Change in Beta Risks and the Estimation of the Cost of Capital: A Study of Airlines
指導教授：	洪榮華 Jung-Hua Hung
口試委員:
學位類別：	博士 Doctor
系所名稱：	管理學院 - 企業管理學系 Department of Business Administration
畢業學年度：	93
語文別：	英文
論文頁數：	55
中文關鍵詞：	資本資產定價模式、貝他（beta）值、內部報酬率、資金成本、三因子定價模式
外文關鍵詞：	three-factor pricing model, capital asset pricing model, beta value, the cost of capital, internal rate of return
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中文提要：
系統風險—beta 值是權益成本估計與股票評價時極重要的資料，尤其是航空公司，因其投資資本資產金額頗大，且其營運對系統風險極敏感。由於了解 beta 變動的因素有助 beta 的估計，本文目的之一即檢視在應用資本資產定價模式 (CAPM) 或三因子模式 (TFM) 時，航空公司 beta 波動情形及其波動之影響因素。
本文以兩家上市航空公司—中華與長榮航空—為樣本，資料取自財務報表與股市，以 t 檢定檢驗 beta 穩定性，及以 Tobit 迴歸分析影響 beta 變動之因素。
研究發現樣本公司 beta 會隨時間而變動，變動之區間大於一，而影響因素有：空難事件、股市多空、景氣循環、營業與財務槓桿、資本結構、流動比率 (以上為正相關) 以及權益報酬率 (負相關)。
另一部分，前述結果隱喻 CAPM 和 TFM 可能不可靠，為正確估計資金成本，本文擴充 Fama 與 French (1999) 所用以估計全體公司資金成本的內部報酬率法 (IRR)，用於估計個別公司過去資金成本，即以公司市值為基礎計算 IRR。
Fama 與 French 指出，簡單年報酬之幾何平均可估計未來資金成本，但因後者可能為負且不穩定，本文改以逐年移動的年 IRR 之正值的均數預估資金成本，使成本變動幅度極小且必為正值，符合必要報酬應為正值之前提。結果發現：華航與長榮在2000-2003，平均資金成本分別為4.02%與6.68%；預估未來資金成本分別為3.03%與6.43%。

ABSTRACT
Beta value, an indicator of systematic risk, is critical for estimating the cost of equity and evaluating stock value; particularly for airlines because the amount of their investments in capital assets is large and operations are relatively sensitive to systematic risks. To obtain a better beta estimate, one should identify the factors for betas’ wandering. This research, in Part I, aims to examine the volatility of airline betas in CAPM and three-factor pricing model (TFM), and explore the potential factors affecting betas.
The sample is two airlines—the China Airlines and EVA Airways—where their stocks are listed in the Taiwan Stock Exchange. The empirical data include information in both financial statements and stock market. Research approaches consist of stability test of betas for CAPM and TFM in up/down market conditions and crashes by t test and Tobit regression with the dependent variable being moving airline betas resulted from rolling regression (CAPM and TFM).
In Part I, the results suggest that airline betas are volatile over time, the range almost exceeding 1.00, and that crashes and transitions in stock market trends may also affect betas. Moreover, the business cycle, operating and financial leverage, and capital structure all positively influence the sample airlines’ betas. The effects of ROE on betas are negative, and liquidity might also impact betas. The findings are helpful for estimating or forecasting airline betas.
In Part II, based on the findings in Part I that airline betas are unsteady, the CAPM and TFM tend to be unreliable. To estimate the history cost of capital for individual airlines, this research applies and extends Fama-French (1999) internal rate of return (IRR) technique and estimates IRR on value (rv) (i.e. investors’ required returns and being also the overall cost of capital). The rv equates the initial market value of a firm with the present value of its net cash flows of each period and its terminal market value. The merit of the IRR method is that it has less measurement errors than other approaches such as the CAPM.
Furthermore, Fama and French (1999) proposed that the geometric mean of simple annual returns could be used to estimate the future cost of capital. However, the simple returns are not necessarily positive and may highly fluctuate over time; accordingly, their mean may also be negative, not consistent with the logic that required returns should be positive. Thus, this research, alternatively, adopts the arithmetic mean of positive multi-year moving rv to estimate the future cost of capital.
The results in Part II are that as the estimation period is 2000-2003, the average costs of capital are around 4.02% and 6.68% for the China Airlines and EVA Airways, respectively. For sample airlines, the range of the positive values of the multi-year moving rvs is smaller than the results of other methods such as simple annual returns, CAPM and TFM. The estimation result is that the future costs of capital equal around 3.03% and 6.43% for the China Airlines and EVA Airways, respectively.

TABLE OF CONTENTS
Page
ABSTRACT                                                                 I
ACKNOWLEDGMENTS                                                         IV
TABLE OF CONTENTS	                                                       V
LISTS OF FIGURES                                                       VII
LIST OF TABLES                                                        VIII
PART I  AN EXAMINATION OF THE FACTORS OF CHANGE IN AIRLINE BETAS	1
CHAPTER 1  INTRODUCTION	                                              1
1.1. MOTIVATION	1
1.2. RESEARCH PURPOSE	                                              3
1.3. THESIS STRUCTURE	                                              4
CHAPTER 2 METHODS FOR CALCULATING BETAS	                            6
2.1. THE CAPITAL ASSET PRICING MODEL	                            6
2.2. THE THREE-FACTOR PRICING MODEL	                            7
2.3. THE PROBLEMS OF THE CAPM AND TFM	                            8
CHAPTER 3 EMPIRICAL METHODS AND APPLICATION	                           10
3.1. SAMPLE AIRLINES	                                             10
3.2. THE DEFINITIONS OF VARIABLES IN CAPM AND TFM	                  10
3.3. THE STABILITY TESTS FOR AIRLINE BETAS	                           13
3.3.1. The t test for betas in models	                           13
3.3.2. The rolling CAPM and TFM	                           18
3.4. THE FACTORS CAUSING BETAS TO VARY	                           20
3.4.1. Description of variables	                           20
3.4.2. Tobit regression analysis	                           21
3.4.3. Further discussion of results	                           24
CHAPTER 4  DISCUSSIONS	                                             27
PART II  THE COST OF CAPITAL AND MULTI-PERIOD FINANCIAL PERFORMANCE: A STUDY FOR AIRLINES	                                                      29
CHAPTER 5  INTRODUCTION	                                             29
5.1. MOTIVATION	                                                      29
5.2. RESEARCH PURPOSE	                                             30
5.3. THESIS STRUCTURE	                                             33
CHAPTER 6  METHODOLOGY—IRR APPROACH	                           34
6.1. THE COST OF CAPITAL AND PROFITABILITY	                           34
6.2. ESTIMATING THE FUTURE COST OF CAPITAL	                           37
CHAPTER 7  RESEARCH DATA AND EMPIRICAL RESULTS	                  40
7.1. DATA	                                                      40
7.2. RESEARCH RESULTS	                                             41
7.2.1. The IRR on cost and on value	                           41
7.2.2. The Future Cost of Capital	                           45
CHAPTER 8  DISCUSSIONS	                                             49
PART III  CONCLUSIONS	                                             50
REFERENCES	                                                      54
LIST OF FIGURES
Page
FIGURE 1  Moving Betas in Rolling CAPM	                          19
LIST OF TABLES
Page
TABLE 1 Descriptive Statistics of Return Variables: Period 2/4th/93－2/4th/2004	                                                              12
TABLE 2 Weekly Local Highest/Lowest Indexes in Bull/Bear Markets
and the Time of Crash for China Airlines Ltd.	                         14
TABLE 3 The Stability Tests of Betas for CAPM and TFM in Bull/Bear Markets and Crashes by T Test	                                                    17
TABLE 4 Descriptive Statistics of the Moving Betas of Sample Airlines 18
TABLE 5 The Estimation Results of Tobit Regression Analysis with the Dependent Variable Being Moving Airline Betas. The Probability Values Are in Parentheses.	                                                             23
TABLE 6 The IRRs on Cost and on Value and Their Differences (in %).   44
TABLE 7 Forecast of Annual Cost of Capital (in %).	               47

                                

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