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.
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