The Growth Value Model(GVM)proposed theoretical closed form formulas consist-ing of Return on Equity(ROE)and the Price-to-Book value ratio(P/B)for fair stock prices and expected rates of return.Although regression ana...The Growth Value Model(GVM)proposed theoretical closed form formulas consist-ing of Return on Equity(ROE)and the Price-to-Book value ratio(P/B)for fair stock prices and expected rates of return.Although regression analysis can be employed to verify these theoretical closed form formulas,they cannot be explored by classical quintile or decile sorting approaches with intuition due to the essence of multi-factors and dynamical processes.This article uses visualization techniques to help intuitively explore GVM.The discerning findings and contributions of this paper is that we put forward the concept of the smart frontier,which can be regarded as the reasonable lower limit of P/B at a specific ROE by exploring fair P/B with ROE-P/B 2D dynamical process visualization.The coefficients in the formula can be determined by the quantile regression analysis with market data.The moving paths of the ROE and P/B in the cur-rent quarter and the subsequent quarters show that the portfolios at the lower right of the curve approaches this curve and stagnates here after the portfolios are formed.Furthermore,exploring expected rates of return with ROE-P/B-Return 3D dynamical process visualization,the results show that the data outside of the lower right edge of the“smart frontier”has positive quarterly return rates not only in the t+1 quarter but also in the t+2 quarter.The farther away the data in the t quarter is from the“smart frontier”,the larger the return rates in the t+1 and t+2 quarter.展开更多
Certain literature that constructs a multifactor stock selection model adopted a weighted-scoring approach despite its three shortcomings.First,it cannot effectively identify the connection between the weights of stoc...Certain literature that constructs a multifactor stock selection model adopted a weighted-scoring approach despite its three shortcomings.First,it cannot effectively identify the connection between the weights of stock-picking concepts and portfolio performances.Second,it cannot provide stock-picking concepts’optimal combination of weights.Third,it cannot meet various investor preferences.Thus,this study employs a mixture experimental design to determine the weights of stock-picking concepts,collect portfolio performance data,and construct performance prediction models based on the weights of stock-picking concepts.Furthermore,these performance prediction models and optimization techniques are employed to discover stock-picking concepts’optimal combination of weights that meet investor preferences.The samples consist of stocks listed on the Taiwan stock market.The modeling and testing periods were 1997–2008 and 2009–2015,respectively.Empirical evidence showed(1)that our methodology is robust in predicting performance accurately,(2)that it can identify significant interactions between stock-picking concepts’weights,and(3)that which their optimal combination should be.This combination of weights can form stock portfolios with the best performances that can meet investor preferences.Thus,our methodology can fill the three drawbacks of the classical weighted-scoring approach.展开更多
文摘The Growth Value Model(GVM)proposed theoretical closed form formulas consist-ing of Return on Equity(ROE)and the Price-to-Book value ratio(P/B)for fair stock prices and expected rates of return.Although regression analysis can be employed to verify these theoretical closed form formulas,they cannot be explored by classical quintile or decile sorting approaches with intuition due to the essence of multi-factors and dynamical processes.This article uses visualization techniques to help intuitively explore GVM.The discerning findings and contributions of this paper is that we put forward the concept of the smart frontier,which can be regarded as the reasonable lower limit of P/B at a specific ROE by exploring fair P/B with ROE-P/B 2D dynamical process visualization.The coefficients in the formula can be determined by the quantile regression analysis with market data.The moving paths of the ROE and P/B in the cur-rent quarter and the subsequent quarters show that the portfolios at the lower right of the curve approaches this curve and stagnates here after the portfolios are formed.Furthermore,exploring expected rates of return with ROE-P/B-Return 3D dynamical process visualization,the results show that the data outside of the lower right edge of the“smart frontier”has positive quarterly return rates not only in the t+1 quarter but also in the t+2 quarter.The farther away the data in the t quarter is from the“smart frontier”,the larger the return rates in the t+1 and t+2 quarter.
文摘Certain literature that constructs a multifactor stock selection model adopted a weighted-scoring approach despite its three shortcomings.First,it cannot effectively identify the connection between the weights of stock-picking concepts and portfolio performances.Second,it cannot provide stock-picking concepts’optimal combination of weights.Third,it cannot meet various investor preferences.Thus,this study employs a mixture experimental design to determine the weights of stock-picking concepts,collect portfolio performance data,and construct performance prediction models based on the weights of stock-picking concepts.Furthermore,these performance prediction models and optimization techniques are employed to discover stock-picking concepts’optimal combination of weights that meet investor preferences.The samples consist of stocks listed on the Taiwan stock market.The modeling and testing periods were 1997–2008 and 2009–2015,respectively.Empirical evidence showed(1)that our methodology is robust in predicting performance accurately,(2)that it can identify significant interactions between stock-picking concepts’weights,and(3)that which their optimal combination should be.This combination of weights can form stock portfolios with the best performances that can meet investor preferences.Thus,our methodology can fill the three drawbacks of the classical weighted-scoring approach.
