市場法之台灣產業價值乘數模擬彙總與差異性調整

Abstract

本研究首先藉由蒙地卡羅馬可夫鏈之Gibbs sampling模擬流程，估計指標公司間產業價值乘數之平均代表，以獲得最有效之估計量。同時建議實務上欲採取簡易平均作法時，應使用調和平均估計以避免其他平均方法較大之偏誤。於挑選各產業之最佳價值乘數時，企業價值乘數因考慮資本結構而相對股權價值乘數較適用於多數產業；針對各種價值指標之適用性，則以淨值類乘數應用最為廣泛、現金流量類乘數與盈餘類乘數居次，營收類乘數因未考慮費用結構之經營特性而評價表現最差。 最後本研究針對個別公司之價值乘數與蒙地卡羅馬可夫鏈所模擬出之產業平均進行差異化調整，將個別公司價值與產業平均之差異歸納為三個層面之因素所影響。四大層面茲分類為成長性：研發費用率、成長穩定性、成立年齡、資本支出率；獲利性：資產報酬率；風險程度：資產規模。藉由納入差異化調整，可量化標的企業與產業間之差異，以反映個別公司特有之經營特性與評價考量。

This thesis primarily estimates the minimum variance industry valuation multiples by the Markov Chain Monte Carlo (MCMC) Gibbs sampling method and suggests that the harmonic mean is a better estimator compared to simple average, which incorporates much more valuation desperation. Furthermore, the thesis discovers that entity valuation multiples are more appropriate than equity valuation multiples, indicating that we should consider and measure the whole enterprise value to include the different capital structure effects on valuation. And the book value multiples are the best multiples for most industries, but the sales multiples are the worst in the mature industries. Finally, the thesis quantifies the deviation between the individual firm’s valuation multiple and the industry average MCMC simulated valuation multiple based on the regression model that contains three dimensions factors: growth opportunity, profitability, and risk degree. The regression model helps capture and explain the financial and structural differences between the target firm and the industry average and provides a benchmark of adjustment.