Factor Returns and Geography

2020 ◽  
pp. 97-109
Author(s):  
Leslie Curry
Keyword(s):  
Factor Returns

Size, book/market ratio and risk factor returns: evidence from China A‐share market

2007 ◽  
Vol 33 (8) ◽  
pp. 574-594 ◽  
Author(s):  
Jianguo Chen ◽  
Kwong Leong Kan ◽  
Hamish Anderson
Keyword(s):  
Risk Factor ◽  
Book Market ◽  
Factor Returns ◽  
A Share

On portfolio optimization

2016 ◽  
Vol 17 (3) ◽  
pp. 295-309 ◽  
Author(s):  
Theo Berger ◽  
Christian Fieberg
Keyword(s):  
Minimum Variance ◽  
Factor Models ◽  
Scale Factor ◽  
Covariance Matrices ◽  
Portfolio Performance ◽  
Content Type ◽  
Portfolio Decisions ◽  
Multi Scale ◽  
Out Of Sample ◽  
Factor Returns

Purpose The purpose of this paper is to show how investors can incorporate the multi-scale nature of asset and factor returns into their portfolio decisions and to evaluate the out-of-sample performance of such strategies. Design/methodology/approach The authors decompose daily return series of common risk factors and of all stocks listed in the Dow Jones Industrial Index (DJI) from 2000 to 2015 into different time scales to separate short-term noise from long-run trends. Then, the authors apply various (multi-scale) factor models to determine variance-covariance matrices which are used for minimum variance portfolio selection. Finally, the portfolios are evaluated by their out-of-sample performance. Findings The authors find that portfolios which are constructed on variance-covariance matrices stemming from multi-scale factor models outperform portfolio allocations which do not take the multi-scale nature of asset and factor returns into account. Practical implications The results of this paper provide evidence that accounting for the multi-scale nature of return distributions in portfolio decisions might be a promising approach from a portfolio performance perspective. Originality/value The authors demonstrate how investors can incorporate the multi-scale nature of returns into their portfolio decisions by applying wavelet filter techniques.

scholarly journals Economic Implications of the Regional Comprehensive Economic Partnership for Asia and the Pacific

2021 ◽  
Author(s):  
Cyn-Young Park ◽  
Peter A. Petri ◽  
Michael G. Plummer
Keyword(s):  
Computable General Equilibrium ◽  
Computable General Equilibrium Model ◽  
Regional Trade ◽  
Economic Implications ◽  
The Pacific ◽  
Economic Partnership ◽  
Regional Comprehensive Economic Partnership ◽  
Factor Returns ◽  
Investment Patterns ◽  
Trade And Investment

The Regional Comprehensive Economic Partnership (RCEP) presents strong potential to mold regional trade and investment patterns well into the future and to influence the direction of global economic cooperation at a challenging time. This paper evaluates the RCEP’s impact on global and regional incomes, trade, economic structure, factor returns, and employment using a computable general equilibrium model. The results suggest that the RCEP agreement could generate sizable global income gains. Together with the Comprehensive and Progressive Agreement on Trans-Pacific Partnership, the RCEP will also strengthen the region’s manufacturing supply chains, raising productivity and increasing wages and employment.

scholarly journals Uncertain Portfolio Selection with Borrowing Constraint and Background Risk

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Linjing Lv ◽  
Bo Zhang ◽  
Jin Peng ◽  
Dan A. Ralescu
Keyword(s):  
Portfolio Selection ◽  
Programming Model ◽  
Optimal Portfolio ◽  
Uncertain Variables ◽  
Subjective Beliefs ◽  
Borrowing Constraint ◽  
Optimal Portfolio Selection ◽  
Selection Decision ◽  
Factor Returns ◽  
Portfolio Return

Due to the complexity of financial markets, there exist situations where security returns and background factor returns are available mainly based on experts’ subjective beliefs, such as in the case of lack of historical data. To deal with such indeterminate quantities, uncertain variables are introduced. Based on uncertainty theory, this paper discusses the distribution function of the optimal portfolio return. Two types of new uncertain programming models, namely, the chance-mean model and the measure-mean model, are proposed to make an optimal portfolio selection decision in an uncertain environment. It is proved that there exists an equivalent relation between the chance-mean model and a deterministic linear programming model, which leads to an approach to obtain the optimal solutions of the proposed models. Finally, some numerical examples are illustrated to show the modelling ideas and the effectiveness of the models.

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