Asset Weights Research Articles

쌀 브랜드 자산 평가모형의 개발에 관한 연구

마케팅 활동에서 브랜드의 영향력이 증가됨에 따라 국내의 농식품 시장에서 다양한 쌀 브랜드가 개발되고 있으나 지속적인 관리 부재로 인하여 경쟁력이 상실되고 있다. 본 연구는 지방정부, 개별경영체들이 개발한 쌀 브랜드의 인지도를 향상하기 위한 브랜드 자산평가 모형을 개발하기 위하여 수행하였다. 이를 위해 먼저 브랜드 전문가를 통한 전략캔버스(ERRC) 분석을 실시하여 쌀 브랜드 자산 요소를 도출하고, 설문조사를 실시하여 브랜드에 대한 자산 속성의 가중치를 도출하였다. 이는 브랜드 개발에 대한 기존연구에서 더 확장된 분석방법으로써 기 개발된 브랜드의 자산평가를 통해 마케팅 전략을 수립하는데 있어 중요한 산출지표로 활용할 수 있다는데 의의가 있다고 하겠다. 연구결과 브랜드 자산에 대한 가치를 향상시키기 위해서는 고객과의 유대감 형성해야하고, 유대감을 측정하는 세부항목은 만족도, 만족우위성, 재구매 의향, 가격 프리미엄, 구매의도, 주위권유 등으로 나타났다. 브랜드 자산의 가치향상을 위한 유대감 형성방안 전략은 다음과 같다. 첫째, 일정수준의 품질을 확보한 쌀에 대해서 지역의 대표 쌀 브랜드명을 포장재에 표시하도록 하여 명품화를 추구한다. 품질수준이 확보된 브랜드 쌀은 지역 브랜드로 통합하여 규모화하고, 지속적인 품질관리와 브랜드 관리를 해야 한다. 둘째, 품질과 브랜드가 뒷받침되는 상품은 프리미엄 상품으로서 고가전략으로 접근하되 포장단위를 다양하게 구성하여 고객 만족도를 높여야 한다. 셋째, 지인추천이 이루어질 수 있는 프로모션, 이벤트 등을 실시하여 홍보력을 높여야 한다. As brand has increasingly influenced on marketing activities, local government, agricultural enterprises, and individual farmhouses autonomously develop and promote their own brands in the field of agrifood. Among developed brands, however, it could be possible for products with having no competitiveness and continuous control to lapse in the marketing. This study aimed to try develop to brand equity model in order for agri-food brands to evaluate and complement in the agricultural company. For doing this, ERRC were administered to interview the brands experts to discuss rice. The results of the analyses indicated that the areas with attribute weight of assets may consider bonding with customers for enhancing the value in the brand equity.

Optimal Diversification in the Presence of Parameter Uncertainty for a Risk Averse Investor

We consider an investor who faces parameter uncertainty in a continuous-time financial market. We model the investor's preference by a power utility function leading to constant relative risk aversion. We show that the loss in expected utility is large when using a simple plug-in strategy for unknown parameters. We also provide theoretical results that show the trade-off between holding a well-diversified portfolio and a portfolio that is robust against estimation errors. To reduce the effect of estimation, we constrain the weights of the risky assets with an L1-norm leading to a sparse portfolio. We provide analytical results that show how the sparsity of the constrained portfolio depends on the coefficient of relative risk aversion. Based on a simulation study, we demonstrate the existence and the uniqueness of an optimal bound on the L1-norm for each level of relative risk aversion.

A mean-CVaR-skewness portfolio optimization model based on asymmetric Laplace distribution

In the presence of uncertainty of asset returns, choosing an appropriate risk measure and determining the optimal weights of assets in a portfolio remain formidable and challenging problems. In this paper, we propose and study a mean-conditional value at risk-skewness portfolio optimization model based on the asymmetric Laplace distribution, which is suitable for describing the leptokurtosis, fat-tail, and skewness characteristics of financial assets. In addition, skewness is added into the portfolio optimization model to meet the diverse needs of investors. To solve this multi-objective problem, we suggest a simplified model with exactly the same solution. This modified model greatly reduces the complexity of the problem. Therefore, the mean-conditional value at risk-skewness model can be correspondingly solved. In order to illustrate the method, we provide an application concerning the portfolio allocation of 19 constituent stocks of S&P 500 index using our model. We show that this model could make important contributions to research on investment decision making.

Robust minimum variance portfolio with L-infinity constraints

Portfolios selected based on the sample covariance estimates may not be stable or robust, particularly so in situations with a large number of assets. The l1 or l2 norm constrained portfolio optimization method has been used as a robust method to control the sparsity or to shrink the estimated weights of assets. In this paper, we propose to add an additional l∞ norm constraint or to add a pairwise l∞ norm constraint in the l1 norm constrained minimum-variance portfolio (MVP) problem. The l∞ constraint controls the largest absolute component of the weight vector and the pairwise l∞ constraint encourages retaining the cluster structure of highly correlated assets in MVP optimization. By simulation study and analysis of empirical data, we find that the proposed portfolios often have better out-of-sample performance in terms of Sharpe ratios, variances and turn-overs than existing popular portfolio strategies including the l1 norm constrained MVP, l2 norm constrained MVP and the 1/N portfolio. In addition, we provide moment shrinkage interpretations of the new strategies and an upper bound of errors in the approximation of the empirical optimal portfolio risk based on the theoretical optimal portfolio risk.

Practical Applications of Risk Parity, Maximum Diversification, and Minimum Variance: An Analytic Perspective

This study provides portfolio managers with a number of insights into why particular weights of assets exist in the different risk-based portfolios. It also investigates the optimal relationships between low- and high-volatility stocks for each portfolio. Risk Parity, Maximum Diversification, and Minimum Variance: An Analytic Perspective, in the Spring 2013 issue of The Journal of Portfolio Management, presents the first analytic study of a long-only, large-set risk-parity portfolio. Co-authors Roger Clarke, Chairman of Analytic Investors, Harindra de Silva, President of Analytic Investors, and Steven Thorley, H. Taylor Peery Professor of Finance at Brigham Young University Marriott School of Management compare the findings with the previous research on long-only maximum-diversification and minimum-variance portfolios. In this Practical Applications report, co-authors Roger Clarke explains how their approach yield new insights for investors. Clarke points out that that although institutional investors and their advisors are considering alternative ways of approaching risk-based portfolios, their views may be constrained by empirical or historical limitations. Using an analytic approach rather than an empirical one, “we wanted to understand the core characteristics and tilts of three of the most popular risk-based portfolio construction approaches,” says Clarke.

Practical Applications of Risk Disparity

Most institutional investors today maintain a formal investment policy portfolio, which allocates among asset classes according to their risk and return objectives. But while the policy portfolio provides a yardstick on overall active management, it does not always meet the risk preferences that are implicit in the institution9s policy portfolio. In <b><i>Risk Disparity</i></b>, which appeared in the Fall 2013 issue of <b><i>The Journal of Portfolio Management</i></b>, <b>Mark Kritzman</b>, President and CEO of <b>Windham Capital Management</b>, explores alternative approaches. In this <b><i>Practical Applications</i></b> report,Kritzman suggests a more flexible investment policy. One that does not adhere to fixed asset weights but instead opportunistically changes exposure to assets.The article is a winner of the <b>15^th Annual Bernstein Fabozzi/Jacobs Levy Awards</b> (Outstanding Article).

Socially Responsible Investment Performance: Impacts of Weighting by Capitalization

Risk-based allocation strategies, also known as Smart Beta allocation, define the weights of assets in portfolios as functions of the individual and common asset risk. In this paper we focus on the Minimum Variance (MV), Maximum Diversification (MD), Equal Risk Contribution (ERC) and Equal-Weight (EW) risk-based allocation strategies. The popularity of risk-based strategy is commonly justified by their good record of out-performing the cap-weighted (CW) allocation strategy. Because of the low-volatility profile of risk-based allocation this is especially true when crises occur. From March 15, 2002 to May 1, 2012 we investigate how using a Socially Responsible Investment universe impacts performance of risk-based allocation strategies. We use different measures of performance, included risk-adjusted one (multi-factor models), and we propose to disentangle the effect of using a SRI universe from the effect of using risk-based allocations. SRI universe only contains firms that have good environmental, social and governance performance. This kind of filtering is increasingly popular among institutional investors. On the estimation period, using European stocks, we find that the use of the SRI universe improves performance of risk-based allocations. However this improvement is not uniform among all the risk-based allocation strategies.

Reconsidering Bank Capital Regulation: A New Combination of Rules, Regulators, and Market Discipline

Despite revisions to bank capital standards, fundamental shortcomings remain: the rules for setting capital requirements need to be simpler, and resolution should be an essential part of the capital requirement framework.We propose a new system of capital regulation that addresses these needs by making changes to all three pillars of bank regulation: only common equity should be recognized as capital for regulatory purposes, and risk weighting of assets should be abandoned; capital requirements should be assigned on an institution-by-institution basis according to a regulatory (s,S) approach developed in the paper; a standard for prompt, corrective action is incorporated into the (s,S) approach.

Reconsidering Bank Capital Regulation: A New Combination of Rules, Regulators, and Market Discipline

Despite revisions to bank capital standards, fundamental shortcomings remain: the rules for setting capital requirements need to be simpler, and resolution should be an essential part of the capital requirement framework.We propose a new system of capital regulation that addresses these needs by making changes to all three pillars of bank regulation: only common equity should be recognized as capital for regulatory purposes, and risk weighting of assets should be abandoned; capital requirements should be assigned on an institution-by-institution basis according to a regulatory (s,S) approach developed in the paper; a standard for prompt, corrective action is incorporated into the (s,S) approach.

Sparse and Robust Normal and t-Portfolios by Penalized Lq-Likelihood Minimization

Two important problems arising in traditional asset allocation methods are the sensitivity to estimation error of portfolio weights and the high dimensionality of the set of candidate assets. In this paper, we address both issues by proposing a new minimum description length criterion for portfolio selection. The new criterion is a two-stage description of the available information, where the q-entropy, a generalized measure of information, is used to code the uncertainty of the data given the parametric model and the uncertainty related to the model choice. The information about the model is coded in terms of a prior distribution that promotes asset weights sparsity. Our approach carries out model selection and estimation in a single step, by selecting few assets and estimating their portfolio weights simultaneously. The resulting portfolios are doubly robust, in the sense that they can tolerate deviations from both, assumed data model and prior distribution for model parameters. Empirical results on simulated and real-world data support the validity of our approach in comparison to state-of-art benchmarks.

Predictable ETF Order Flow and Market Quality

Research on certain types of institutional order flow has highlighted potential destabilizing effects on market quality related to the fact that these orders can be anticipated by other market participants. Examples include the rebalancing of rules-based indexes and ETFs, including end-of-day rebalancing of leveraged and inverse ETFs, rebalancing to maintain target asset weights and option hedges, and rolls out of expiring futures contracts. The goal of this paper is to provide a deeper analysis of the infrastructure that exists and the issues that arise from the execution of predictable institutional orders, with emphasis on order flows related to ETF activity. Considering the roles of information and competition, predictable orders should typically have minimal effects on prices because they are not motivated by fundamental information, they attract natural counterparties, and they benefit from additional liquidity supply. Empirical research on the price effects of ETF trading or other forms of predictable order flow should be guided by relevant economic theory and a full appreciation of the infrastructure that exists to facilitate such execution. Estimates of the price effects of ETF trading or other forms of predictable order flow are unlikely to be accurate unless the researcher can also measure other order flow that is correlated with the predictable order flow being studied.

Risk Disparity

Investors seek to grow their wealth over time and avoid large draw-downs along the way, but these goals conflict. A policy portfolio serves as an expression of how investors balance these conflicting goals. A policy portfolio that maintains constant asset weights, however, experiences significant inter-temporal disparity in its risk profile, thereby defeating the purpose for which it is intended. This article offers evidence of inter-temporal risk disparity and shows how investors can use measures of intrinsic portfolio fragility and extrinsic market fragility to stabilize a portfolio’s risk profile. <b>TOPICS:</b>Portfolio theory, risk management, portfolio construction

Solving norm constrained portfolio optimization via coordinate-wise descent algorithms

A fast method based on coordinate-wise descent algorithms is developed to solve portfolio optimization problems in which asset weights are constrained by lq norms for 1≤q≤2. The method is first applied to solve a minimum variance portfolio (mvp) optimization problem in which asset weights are constrained by a weighted l1 norm and a squared l2 norm. Performances of the weighted norm penalized mvp are examined with two benchmark data sets. When the sample size is not large in comparison with the number of assets, the weighted norm penalized mvp tends to have a lower out-of-sample portfolio variance, lower turnover rate, fewer numbers of active constituents and shortsale positions, but higher Sharpe ratio than the one without such penalty. Several extensions of the proposed method are illustrated; in particular, an efficient algorithm for solving a portfolio optimization problem in which assets are allowed to be chosen grouply is derived.

A dynamic portfolio theory model based on minimum semi‐absolute deviations criterion with an application in the Chinese stock markets

PurposeThe purpose of this paper is to prove the effectiveness of minimum semi‐absolute deviations (MSAD) method in dynamic portfolio investment.Design/methodology/approachIn financial investment, the classical static portfolio theory of Markowitz type lacks the dynamic adaptability to the changing market situations. This paper proposes a dynamic portfolio theory which uses MSAD criterion on a moving window to replace the Markowitz mean‐variance analysis.FindingsTwo specific models are developed to test the validity of the MSAD method: the first model constructs a portfolio consisting of Shanghai‐Shenzhen 300 Index and a national debt as two contrarian assets; the second model constructs a portfolio consisting of a complete set of 18 Chinese stock sector indices and a national debt. The empirical results of the test using six‐year monthly data (2005 to 2010) provide significant evidence that the MSAD method is valid, producing superior returns of investment over the stock index during the test period.Research limitations/implicationsThe findings in this study clearly highlight the validity of the MSAD method in determining the weights of assets in Chinese stock markets.Practical implicationsIn order to resolve the problem of portfolio investment in Chinese stock markets, the MSAD method with stop loss control strategy can be used for investors to obtain the weights of assets and control the risk.Originality/valueThis study analyzes and verifies the effectiveness of the MSAD method in dynamic portfolio investment. The stop loss control strategy designed and used in the MSAD method is a pioneering and exploratory experiment.

Accuracy and Rounding in Portfolio Construction

Portfolio theory got it all wrong: asset weights are not decision variables, because security prices a portfolio manager does not have full control over asset prices. In this note, we are trying to create awareness that real-valued allocations which have been calculated from numerical portfolio construction algorithms require further processing in order to become investable quantities. Depending on the size of the portfolio and asset price structure, the differences between paper portfolios and investable allocations can be significant. We also derive a portfolio-based accuracy threshold which can be used as a criteria to assess various numerical accuracy issues in quantitative portfolio construction.

Risk Parity, Maximum Diversification,and Minimum Variance: An Analytic Perspective

Analytic solutions to risk parity, maximum diversification, and minimum variance portfolios provide useful perspectives about their construction and composition. Individual asset weights depend on both systematic and idiosyncratic risk in all three risk-based portfolios, but systematic risk eliminates many investable assets in long-only, constrained, maximum-diversification, and minimum-variance portfolios. On the other hand, risk-parity portfolios include all investable assets, and idiosyncratic risk has little effect on weight magnitude. The algebraic forms for optimal asset weights derived in this article yield generalizable properties of risk-based portfolios, in contrast to empirical simulations that employ a specific set of historical returns, proprietary risk models, and multiple constraints. These analytic solutions reveal precisely how various kinds of predicted risk affect the relative magnitude of security weights in each type of risk-based portfolio construction. <b>TOPICS:</b>Portfolio construction, portfolio theory, volatility measures

Risk-Based Allocation of Principal Portfolios

Risk-based asset allocation strategies are mainly used to diversify nominal asset weights. In this paper, we discuss the diversification of risk factors. The analysis is based on the idea of Partovi and Caputo (2004), who use principal component analysis to transform a portfolio into a set of uncorrelated principal portfolios. Risk-based asset allocation strategies can be applied to these uncorrelated sources of risk. A similar route has been taken by Meucci (2009) with his idea of a maximum entropy portfolio. We discuss the relation of this approach with the concept of principal risk parity. Both strategies are backtested against nominal diversification strategies in a multi-asset portfolio. We find no evidence that risk diversification does outperform nominal diversification and discuss possible reasons for this.

Reconciling Ex Post and Ex Ante Volatility Figures

Ex post volatility is defined as dispersion of ex post portfolio returns over the measurement period. Ex post volatility takes into account the variability in asset returns and changes of asset weights over time due to trading and drift. Ex ante volatility, on the other hand, is defined as forward-looking portfolio volatility calculated from current assets weights and asset covariance estimates. Except in very unrealistic circumstances, the two volatility measurements will typically differ. In this research note, we propose a Brinson-style attribution scheme that can be used to quantify the effects. Being able to attribute differences in ex ante and ex post volatilities to trading and risk surprises provides valuable information in investment process reviews for internal purposes as well as client communications.

Do the Romanian and Spanish Accounting Environments Meet the Challenges Concerning the Acknowledgement of Intangible Elements?

Research motivation – Our aim is to identify the extent to which companies quoted in the Romanian and Spanish stock exchanges, that must apply the International Financial Reporting Standards truly comply with the provisions of these norms in presenting, in the financial statements, the information on intangible assets. We have also studied a series of indicators of intangible capital, in order to identify the extent to which the Romanian and Spanish accounting environments meet the challenges concerning the acknowledgement of intangible elements. Methodology – In order to identify the policies used by companies in the reporting of intangible assets and to determine the extent to which companies forced to apply the IAS/IFRS comply with the IAS 38 provisions, we have analyzed the consolidated financial statements, drawn according to IFRS by 19 Romanian companies quoted in the Bucharest Stock Exchange and 27 Spanish companies quoted in the Madrid Stock Exchange, components of the IBEX 35 index. We have determined the weight of the intangible assets in the fixed assets and have computed the mean degree of dissemination of the information on intangible assets based on the scores obtained by each company to a set of questions, designed in the spirit of the IAS 38 provisions. Main findings – To the 8 questions in the set, created in the spirit of the IAS 38 provisions, the Iberian companies generally give more analytical answers. Also, they present significant weights of the intangible assets into the fixed assets. Besides the financial reporting of intangible assets, we have been concerned with analyzing non-financial information referring to human, relational, and structural capital. It is worth mentioning that, unlike the Romanian companies, the Spanish companies present in their annual reports a large amount of data and information on intangible capital.

Factor attribution that adds up

Existing implementations of factor attribution only explain part of a quantitatively managed portfolio's return, even when factor models are all that is behind the investment strategy. We propose an alternative method that aligns stock-specific risk in how exposure to factors is taken in the portfolio with how their performance is measured, thus making factor attribution ‘add up’. As part of developing this framework, we show how bounds on asset weights and industry exposures in mean-variance optimization implicitly protect against model risk.