摘要：The present study evaluates determinants of price multiples and their prediction accuracy using ordinary least square (OLS) regression and machine learning-based shrinkage methods for the South East Asian markets. Price multiples examined in the research are price to earnings (P/Es),price to book (P/B),and price to sales (P/S). Data has been collected from Thomson Reuters Eikon. The study recommends that the P/B ratio is the best price multiple for developing a price-based valuation model. Beside fundamental determinants of the multiple,various firm-level control variables,namely,firm size,cash holding,strategic holding,stock price volatility,firms’ engagement in Environment,Social, and Governance (ESG) activities,dividend yield,and net profit margin impact firm’s P/B multiple. Positive coefficients of consumer non-cyclical and healthcare dummies indicate a preference for defensive stocks by the investors. Application of machine learning-based shrinkage methods ensures the accuracy of prediction even with out-of-sample forecasting..
关键词：Price multiples;South East Asia;ridge regression;lasso;shrinkage method
