菲律宾全能银行市盈率的价值驱动因素:面板回归方法

A. Dayag, Fernando L. Trinidad
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引用次数: 0

摘要

利用市盈率倍数进行估值主要是在发达国家和包括菲律宾在内的一些发展中国家。然而,影响合理市盈率估值的变量尚未确定。本研究的目的是通过使用变量来开发一个使用市盈率倍数的估值模型,这将导致公司的合理估值。该应用将在银行业,特别是全能银行,因为这些机构是菲律宾重要的经济驱动力。利用2010 - 2017年菲律宾十大全能银行[10]的资产和资本化数据,采用面板数据回归分析方法,选取宏观经济变量、股票市场指数和公司特定变量,建立估值模型。这些变量分别是GDP增长率、通货膨胀率、利率、股本回报率(ROE)、ROE增长率、净收入增长率、每股收益(EPS)增长率、派息率、菲律宾证券交易所(PSE)指数和市净率(P/B)价值比。面板回归结果显示,自变量中ROE、EPS增长率、P/B比率、净收入增长率、PSE指数具有统计学意义。在显著变量中,ROE降低了51倍的市盈率。这意味着ROE的增加会降低投资者愿意支付的每股价格。模型的拟合优度[R2]为74.83%,较好
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VALUE DRIVERS OF PRICE-EARNINGS MULTIPLES OF UNIVERSAL BANKS IN THE PHILIPPINES: A PANEL REGRESSION APPROACH
Valuation using P/E multiples has been done mostly in developed countries and some   developing countries including the Philippines. However, variables that impact sound P/E multiples valuation has yet to be determined. The purpose of the study is to develop a valuation model using P/E multiples by using variables that will lead to sound valuation of firms. The application would be in the banking sector, specifically universal banks since these institutions are significant economic drivers in the Philippines. Utilizing data from the top ten [10] universal banks in the Philippines in terms of assets and capitalization from 2010 to 2017, selected macroeconomic variables, stock market index, and firm-specific variables were used in the proposed valuation model using panel data regression analysis. These variables are GDP growth rate, inflation rate, interest rate, return on equity [ROE], ROE growth rate, net income growth rate, earnings per share [EPS] growth rate, dividend payout ratio, Philippine Stock Exchange [PSE] index, and price-to-book [P/B] value ratio. Panel regression results showed that among the independent variables, ROE, EPS growth rate, P/B ratio, net income growth rate, and PSE index are statistically significant. Among the significant variables, ROE reduces P/E ratio by 51 times. This implies that an increasing ROE reduces the price per stock investors are willing to pay. Goodness of fit [R2] of the model is 74.83%, which is relatively good
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