Framework For Portfolio Selection Research Articles

Multivariate range Value-at-Risk and covariance risk measures for elliptical and log-elliptical distributions

In this article, we propose the multivariate range Value-at-Risk (MRVaR) and the multivariate range covariance (MRCov) as two risk measures and explore their desirable properties in risk management. To facilitate analytical analyses, we derive explicit expressions of the MRVaR and the MRCov in the context of the multivariate (log-)elliptical distribution family. Frequently-used cases in industry, such as normal, student-t, logistic, Laplace, and Pearson type VII distributions, are presented with numerical examples. As an application, we propose a range-based mean-variance framework for optimal portfolio selection.

SAOFTRL: A Novel Adaptive Algorithmic Framework for Enhancing Online Portfolio Selection

SAOFTRL: A Novel Adaptive Algorithmic Framework for Enhancing Online Portfolio Selection

A hybrid framework for mean-CVaR portfolio selection under jump-diffusion processes: Combining cross-entropy method with beluga whale optimization

<abstract><p>In this paper, a new hybrid meta-heuristic algorithm called CEBWO (cross-entropy method and beluga whale optimization) is presented to solve the mean-CVaR portfolio optimization problem based on jump-diffusion processes. The proposed CEBWO algorithm combines the advantages of the cross-entropy method and beluga whale optimization algorithm with the help of co-evolution technology to enhance the performance of portfolio selection. The method is evaluated on 29 unconstrained benchmark functions from CEC 2017, where its performance is compared against several state-of-the-art algorithms. The results demonstrate the superiority of the hybrid method in terms of solution quality and convergence speed. Finally, Monte Carlo simulation is employed to generate scenario paths based on the jump-diffusion model. Empirical results further confirm the effectiveness of the hybrid meta-heuristic algorithm for mean-CVaR portfolio selection, highlighting its potential for real-world applications.</p></abstract>

Optimal chonsei to monthly rent conversion choice given borrowing constraints

Chonsei, an exceptional but prevalent housing rental system in the Republic of Korea, functions as private loans between individuals, and its implicit interest rate is called the “chonsei to monthly rent conversion rate”. Although the chonsei to monthly rent conversion rate is much higher than the interest rate of savings accounts or mortgage loans, making chonsei a more expensive channel of financing, chonsei remains a dominant type of rent in Korea. This study provides a novel theoretical model in a continuous-time portfolio selection framework that allows a lessor, given borrowing constraints, to optimally choose between chonsei and monthly rent contracts to show a possible solution to this situation. We investigate the optimal choice between chonsei and the monthly rent of lessors using a rigorous theoretical model and find a closed-form solution to the model. We analyze how credit availability in the banking sector affects the choice problem, showing that the lessor may prefer the chonsei contract over the monthly rent contract, given the borrowing limit from a bank. In addition, we analyze the impact of other major factors, such as the market price of risk, the amount of chonsei deposit, the monthly rental rate, and the income rate, on the lessor’s optimal decision between chonsei and monthly rent.

A framework of robust project portfolio selection problem under strategic objectives considering the risk propagation

PurposeThe consideration of the substitution phenomenon in the project portfolio selection problem can improve the robustness of project portfolio selection and help enterprises better achieve their strategic objectives. However, the existence of inter-project risk propagation will have a negative impact on project substitution. This paper proposes a new framework for project portfolio selection and constructs a risk propagation model based on strategic objectives to study the impact of risk propagation on substitution in the project portfolio.Design/methodology/approachThe authors first construct a risk propagation model based on strategic objectives to describe the risk propagation between projects. Then the project substitution phenomenon based on risk propagation is put forward, and the calculation method of substitution loss is given. Finally, a robust project portfolio selection framework based on strategic objectives considering risk propagation is constructed.FindingsThe analysis of a case study demonstrates that (1) With the increase of risk intensity, the strategic loss of the same project portfolio increases linearly, and under the same risk intensity, the more projects in the portfolio, the stronger the robustness. (2) Considering risk propagation, the effect of project substitution is significantly weakened, and the strategic loss rate of the project portfolio is significantly increased compared with that of a direct attack.Originality/valueThis study is the first to take the project substitution into account in the project portfolio selection process. Moreover, the authors describe inter-project risk propagation and analyze the impact of risk propagation on the project substitution phenomenon. Finally, the authors extend the evaluation index of robustness. This paper puts forward a new way to solve the problem of project portfolio selection.

Bayesian Estimation and Optimization for Learning Sequential Regularized Portfolios

This paper incorporates Bayesian estimation and optimization into a portfolio selection framework, particularly for high-dimensional portfolios in which the number of assets is larger than the number of observations. We leverage a constrained minimization approach, called the linear programming optimal (LPO) portfolio, to directly estimate effective parameters appearing in the optimal portfolio. We propose two refinements for the LPO strategy. First, we explore improved Bayesian estimates, instead of sample estimates, of the covariance matrix of asset returns. Second, we introduce Bayesian optimization (BO) to replace traditional grid-search cross-validation (CV) in tuning hyperparameters of the LPO strategy. We further propose modifications in the BO algorithm by (1) taking into account the time-dependent nature of financial problems and (2) extending the commonly used expected improvement acquisition function to include a tunable trade-off with the improvement’s variance. Allowing a general case of noisy observations, we theoretically derive the sublinear convergence rate of BO under the newly proposed EIVar and thus our algorithm has no regret. Our empirical studies confirm that the adjusted BO results in portfolios with higher out-of-sample Sharpe ratio, certainty equivalent, and lower turnover compared to those tuned with CV. This superior performance is achieved with a significant reduction in time elapsed, thus also addressing time-consuming issues of CV. Furthermore, LPO with Bayesian estimates outperforms the original proposal of LPO, as well as the benchmark equally weighted and plugin strategies.

An integrated framework for diversified project portfolio selection

An integrated framework for diversified project portfolio selection

An integrated framework for diversified project portfolio selection

An integrated framework for diversified project portfolio selection

Portfolio selection of power generation projects considering the synergy of project and uncertainty of decision information

Specifying which projects to be invested in under constraints can improve the investment efficiency of enterprise. The article studies on portfolio selection of power generation projects, considering the synergy between a pair of projects and the uncertainty of decision information. Firstly, objectives and constraints for generation power project portfolio are defined by economy, environment and society-three aspects. Synergies between each pair of projects are calculated in resource, management, technology and operation, and adopted into objective in a form of comprehensive synergy. In the process of model solution, fuzzy number is employed to describe the information of alternative projects to enhance the accuracy of decision. Linear reconstruction technique is introduced to equally transform the non-linear planning model into a linear 0–1 problem, reducing the difficulty of solving the model. Through a case study, the feasibility and operability of project portfolio selection (PPS) framework constructed in article are verified. The results show that comprehensive benefit of project portfolio considering the synergy is 8.999, which is significantly higher than the model without synergy, providing convincing support to decision makers. Further, the structure of solution indicates that the current investments in coal-fired power should not be stopped arbitrarily, and different types of power generation projects need to be developed in balance. The research can provide a reference in ideas and models to the investor in multi-project investment decision-making.

Capturing Cross-Section of Return on Nifty 100 Companies: CAPM vs Fama-French Three-Factor Model

In order to explain the relationship between risk and expected return, different researchers use different asset pricing models. In this research paper, we identify whether Sharpe's single factor model termed Capital Asset Pricing Model or Fama-French three-factor model better explains the expected excess return on portfolio. We used NIFTY INDEX as the proxy market portfolio. For the purpose of FF Model, we used Market Capitalization (MC) as a variable of firm size and Book to Price (B/P) as a variable of firm value. Data are taken from 1^st July 2012 to 30^th June 2021 for 85 companies forming part of NSE 100. We have used the same portfolio selection framework used by Fama-French (1993 and 1996). 91 days treasury bills (T-bills) have been used as risk-free proxy and taken from RBI website for the given period. We found that Fama-French three-factor model captured better results than the single factor CAPM in explaining the expected returns for all the double-sorted portfolios constructed based on MC (size factor) and B/P (value factor).

Portfolio Selection Using Multiple Factors: A Machine Learning Approach

In this paper we present a novel framework for portfolio selection using deep neural networks and elastic net regularization: At the beginning of each month T, we follow a three-step methodology. First, for each stock, we use the previous seven years of data in order to compute over 36 firm-specific factors. Second, we perform features selection using elastic net regularization. Finally, we train a deep neural network in order to learn portfolio weights and hold this portfolio until the end of the month T. Compared with momentum, long-term reversal, and short-term reversal strategies, our approach demonstrates a superior performance in terms of the monthly rate of return (2% versus 1.22% for long-term reversal, 1.15% for momentum, and only 0.68% for short-term reversal), Sharpe ratio (21.67% versus 19.31% for momentum, 15.51% for long-term reversal, and 8.69% for short-term reversal), and the monthly risk-adjusted return (1.85% versus 0.74% for momentum, 0.72% for long-term reversal, and 0.31% for shortterm reversal). The results of our approach are all statistically significant at 1% level.

Convexity, two-fund separation and asset ranking in a mean-LPM portfolio selection framework

Convexity, two-fund separation and asset ranking in a mean-LPM portfolio selection framework

A novel project portfolio selection framework towards organizational resilience: Robust Ordinal Priority Approach

The COVID-19 pandemic has affected the world's economic condition significantly, and construction projects have faced many challenges and disruptions as well. This should be an alarm bell for project-oriented organizations to be prepared for such events and take necessary actions at the earliest time. In this regard, project-oriented organizations should establish their business based on the resilience concept, making them flexible in dealing with risks and decreasing the recovery time after disruptions. The current study proposes a practical conceptual framework for project-oriented organizations to select the most appropriate portfolio based on organizational resilience strategy. First, portfolios are identified, and the projects are clustered based on organizational resilience strategy using the Elbow and Fuzzy C-Means methods. The projects' scores are then determined employing the stakeholders' opinions and Robust Ordinal Priority Approach (OPA-R), which can handle the uncertainty of the input data. After that, each portfolio's score is determined using the obtained scores of the projects, and the best portfolio linked to the organizational resilience strategy is selected. The application of the proposed method to a project-oriented organization is examined, and its usage for the managers of project-oriented organizations is discussed in detail.

A safety-first portfolio selection framework: Estimating returns of exchange traded funds through regression analysis

This research provides a possible variant of the basic framework of portfolio selection. First, for the return estimation, considering Asian exchange-traded funds as the selection pool for the investment, regression analysis is applied to generate future return scenarios. Second, for the assignment of weights, the likelihood of these scenarios are assumed to be equally likely. Third, for the portfolio selection model, the safety-first model is utilized with consideration of 5 different diversification factors. The proposed procedure is modeled as a linear programming model into AIMMS optimization software where the resulting portfolios show that safety-first portfolios can outperform the benchmark. These observations can pave the way to a new generic portfolio selection framework that can possibly help investors and may lead to alternative investment instruments.

Bayesian Estimation and Optimization for Learning Sequential Regularized Portfolios

This paper incorporates Bayesian estimation and optimization into portfolio selection framework, particularly for high-dimensional portfolio in which the number of assets is larger than the number of observations. We leverage a constrained 𝓁1 minimization approach, called linear programming optimal (LPO) portfolio, to directly estimate effective parameters appearing in the optimal portfolio. We propose two refinements for the LPO strategy. First, we explore improved Bayesian estimates, instead of sample estimates, of the covariance matrix of asset returns. Second, we introduce Bayesian optimization (BO) to replace traditional grid-search cross-validation (CV) in tuning hyperparameters of the LPO strategy. We further propose modifications in the BO algorithm by (1) taking into account time-dependent nature of financial problems and (2) extending commonly used expected improvement (EI) acquisition function to include a tunable trade-off with the improvement's variance (EIVar). Allowing a general case of noisy observations, we theoretically derive the sub-linear convergence rate of BO under the newly proposed EIVar and thus our algorithm has no regret. Our empirical studies confirm that the adjusted BO result in portfolios with higher out-of-sample Sharpe ratio, certainty equivalent, and lower turnover compared to those tuned with CV. This superior performance is achieved with significant reduction in time elapsed, thus also addressing time-consuming issues of CV. Furthermore, LPO with Bayesian estimates outperform original proposal of LPO, as well as the benchmark equally weighted and plug-in strategies.

A Decision Making Framework for Portfolio Selection in Private Equity Investments

A Decision Making Framework for Portfolio Selection in Private Equity Investments

A framework for screening and portfolio selection in corporate venture capital

PurposeThe purpose of this paper is to outline and demonstrate a method for screening and selection of potential portfolio companies (PCs) during the screening phase in corporate venture capital.Design/methodology/approachThe use of the data envelopment analysis (DEA) enables the consideration of individual, heterogeneous and multidimensional decision criteria in portfolio selection and the preceding screening process by the investor.FindingsThe result of this method is a relative ranking of the PCs, with all the PCs considered serving as peer group. A weighting of individual criteria is not necessary because it is part of the functionality of DEA. The authors validate the proposed approach in a case study and show that it can be well combined with other models and theoretical frameworks.Practical implicationsThe method is particularly useful in two cases. First, if a highly specialized investor wishes to use a variety of individual selection criteria for portfolio selection. Second, if an investor only has insufficient (financial) data on potential PCs, but still wants to make a (pre-) selection based on observable (qualitative) characteristics. This model helps to make consistent, intersubjectively comprehensible decisions based on valid decision criteria and helps to optimize the decision-making process in the context of portfolio selection in CVC.Originality/valueThis method allows the systematic selection of an attractive group from a large number of potential PCs, based on observable characteristics and taking into account individual strategic investment objectives, without having to make assumptions about underlying distributions or weights of decision criteria.

Forward-looking portfolio selection with multivariate non-Gaussian models

In this study, we suggest a portfolio selection framework based on time series of stock log-returns, option-implied information, and multivariate non-Gaussian processes. We empirically assess a multivariate extension of the normal tempered stable (NTS) model and of the generalized hyperbolic (GH) one by implementing an estimation method that simultaneously calibrates the multivariate time series of log-returns and, for each margin, the univariate observed one-month implied volatility smile. To extract option-implied information, the connection between the historical measure P and the risk-neutral measure Q, needed to price options, is provided by the multivariate Esscher transform. The method is applied to fit a 50-dimensional series of stock returns, to evaluate widely known portfolio risk measures and to perform a forward-looking portfolio selection analysis. The proposed models are able to produce asymmetries, heavy tails, both linear and non-linear dependence and, to calibrate them, there is no need for liquid multivariate derivative quotes.

A project portfolio selection framework for transforming Iranian universities into entrepreneurial institutions

PurposeThis research proposes a framework by which universities can define and implement projects that transform them into entrepreneurial universities. The framework helps decision-makers identify suitable goals and strategies, gather a list of projects to fulfill the goals and strategies and prioritize the projects and form a portfolio.Design/methodology/approachIn the proposed framework, importance–performance matrix, hierarchical strategic planning, Delphi technique, DEMATEL-based ANP and a multi-objective model are used. The mathematical model consists of four objective functions including efficiency, quality and balance maximization and also cost and risk minimization. The proposed framework is applied to Amirkabir University of Technology, Tehran, Iran, and the results are brought in this paper.FindingsThe output of the proposed framework is a portfolio of projects that aims to transform a traditional university into a third-generation one. Although the final portfolio must be customized for different universities, the proposed steps of the framework can be helpful for almost all cases.Originality/valueThe suggested framework is unique and uses both qualitative and quantitative techniques for project portfolio selection.

Ranking framework for portfolio selection

Ranking framework for portfolio selection