Showing papers on "Efficient frontier published in 2019"
TL;DR: In this article, the authors compared the efficiency of Islamic banks with their conventional counterparts using a common efficiency frontier and ignoring risks, in spite of the two bank groups operating under different technological, market and institutional conditions.
69 citations
TL;DR: In this article, a robust continuous-time Markowitz portfolio selection problem is formulated into a min-max mean-variance problem over a set of non-dominated probability measures that is solved by a McKean-Vlasov dynamic programming approach, which allows the solution in terms of a Bellman-Isaacs equation in the Wasserstein space of probability measures.
Abstract: This paper studies a robust continuous-time Markowitz portfolio selection pro\-blem where the model uncertainty carries on the
covariance matrix of multiple risky assets.
This problem is formulated into a min-max mean-variance problem over a set of non-dominated probability measures that is solved by a McKean-Vlasov dynamic programming approach, which allows us to characterize the solution in terms of a Bellman-Isaacs equation in the Wasserstein space of probability measures. We provide explicit solutions for the optimal robust portfolio strategies and illustrate our results in the case of uncertain volatilities and ambiguous correlation between two risky assets. We then derive the robust efficient frontier in closed-form, and obtain a lower bound for the Sharpe ratio of any robust efficient portfolio strategy. Finally, we compare the performance of Sharpe ratios for a robust investor and for an investor with a misspecified model. MSC Classification: 91G10, 91G80, 60H30
53 citations
TL;DR: Comparisons between CPLEX, IWO and genetic algorithm (GA) shows that the performance of the IwO algorithm is much better than the older algorithms and can be considered as an alternative to algorithms, such as GA, in product portfolio optimization problems.
Abstract: Product portfolio optimization (PPO) is a strategic decision for many organizations. There are several technical methods for facilitating this decision. According to the reviewed studies, the implementation of the robust optimization approach and the invasive weed optimization (IWO) algorithm is the research gap in this field. The contribution of this paper is the development of the PPO problem with the help of the robust optimization approach and the multi-objective IWO algorithm. Considering the profit margin uncertainty in real-world investment decisions, the robust optimization approach is used to address this issue. To illustrate the real-world applicability of the model, it is implemented for dairy products of Pegah Golpayegan Company in Iran. The numerical results obtained from the IWO algorithm demonstrate the effectiveness of the proposed algorithm in tracing out the efficiency frontier of the product portfolio. The average risk of efficient frontier solutions in the deterministic model is about 0.4 and for the robust counterpart formulation is at least 0.5 per product. The efficient frontier solutions obtained from robust counterpart formulation demonstrate a more realistic risk level than the deterministic model. The comparisons between CPLEX, IWO and genetic algorithm (GA) shows that the performance of the IWO algorithm is much better than the older algorithms and can be considered as an alternative to algorithms, such as GA in product portfolio optimization problems.
52 citations
TL;DR: The authors proposed a theory in which each stock's environmental, social, and governance (ESG) score plays two roles: 1) providing information about firm fundamentals and 2) affecting investor preferences.
Abstract: We propose a theory in which each stock’s environmental, social, and governance (ESG) score plays two roles: 1) providing information about firm fundamentals and 2) affecting investor preferences. The solution to the investor’s portfolio problem is characterized by an ESG-efficient frontier, showing the highest attainable Sharpe ratio for each ESG level. The corresponding portfolios satisfy four-fund separation. Equilibrium asset prices are determined by an ESG-adjusted capital asset pricing model, showing when ESG increases or lowers the required return. Combining several large data sets, we compute the empirical ESG-efficient frontier and show the costs and benefits of responsible investing. Finally, we test our theory’s predictions using commercial ESG measures, governance, sin stocks, and carbon emissions.
49 citations
TL;DR: An adaptive RRL-PSO portfolio rebalancing decision system with a market condition stop-loss retraining mechanism is developed, and it is shown that the proposed portfolio trading system outperforms the benchmarks consistently especially under high transaction cost conditions.
Abstract: This study extends a recurrent reinforcement portfolio allocation and rebalancing management system with complex portfolio constraints using particle swarm algorithms. In particular, we propose to use a combination of recurrent reinforcement learning (RRL) and particle swarm algorithm (PSO) with Calmar ratio for both asset allocation and constraint optimization. Using S&P100 index stocks, we show such a system with a Calmar ratio based objective function yields a better efficient frontier than the Sharpe ratio and mean-variance based portfolios. By comparing with multiple PSO based long only constrained portfolios, we propose an optimal portfolio trading system that is capable of generating both long and short signals and handling the common portfolio constraints. We further develop an adaptive RRL-PSO portfolio rebalancing decision system with a market condition stop-loss retraining mechanism, and we show that the proposed portfolio trading system outperforms the benchmarks consistently especially under high transaction cost conditions.
43 citations
TL;DR: A novel portfolio theory-based approach is proposed for optimally managing ES in various markets to maximize benefits and reduce the risk for ES owners.
Abstract: Energy storage (ES) is playing a vital role in providing multiple services in several electricity markets. However, the benefits and risks vary across markets and time, which justifies the importance to optimize ES capacity share in different markets. In this paper, a novel portfolio theory-based approach is proposed for optimally managing ES in various markets to maximize benefits and reduce the risk for ES owners. Three markets are considered, which are energy arbitrage, ancillary services, and distributed network operator's market. They are modeled based on energy cost, frequency response cost, and system congestion cost. Portfolio theory is utilized to quantify ES capacity allocated to each market over time for various levels of risk aversions. The relation between risks and expected return of different markets is efficiently reflected by the portfolio theory, providing implications to storage operation. The extensive demonstration illustrates that the markets that storage can participate in are fundamentally different regarding to its risk aversion. In addition, the optimum portfolio of the markets for storage is on the efficient frontier, providing the maximum return at a certain risk aversion level. This study is particularly useful for guiding market participation and operation of ES to gain maximum economic return at minimum risk.
38 citations
TL;DR: The results show complementarity between wind and solar in China, reflected in more optimal return-volatility performance of wind & solar portfolios, as compared to wind-only and solar-only portfolios, and that portfolios with unconstrained technology shares perform much better in return-Volatility performance than portfolios with constrained technology shares.
37 citations
TL;DR: The proposed multi-objective approach for portfolio selection, which allows investors to consider not only return and downside risk criteria but also to include environmental, social and governance scores in the investment decision-making process, offers promising results for socially responsible investors.
Abstract: We propose a multi-objective approach for portfolio selection, which allows investors to consider not only return and downside risk criteria but also to include environmental, social and governance (ESG) scores in the investment decision-making process. Owing to the uncertain environment of portfolio selection, the return and ESG score of each asset are considered as independent L-R power fuzzy variables. To make the model more realistic, we take budget, floor ceiling and cardinality constraints into account. In order to select the optimal portfolio along the efficient frontier, we apply the Sortino ratio in a credibilistic environment. The subsequent empirical application uses a data set from Bloomberg’s ESG Data in combination with US Dow Jones Industrial Average data. The experimental results show that the proposed model offers promising results for socially responsible investors seeking ethical and sustainability goals beyond the return-risk trade-off and its ability to beat the benchmark.
32 citations
TL;DR: The original algorithm for rich portfolio optimization (ARPO) is developed, using a matheuristic framework that combines an iterated local search metaheuristic with quadratic programming, and efficiently deals with complex variants of the mean-variance portfolio optimization problem, including the well-known cardinality and quantity constraints.
Abstract: This research develops an original algorithm for rich portfolio optimization (ARPO), considering more realistic constraints than those usually analyzed in the literature. Using a matheuristic framework that combines an iterated local search metaheuristic with quadratic programming, ARPO efficiently deals with complex variants of the mean-variance portfolio optimization problem, including the well-known cardinality and quantity constraints. ARPO proceeds in two steps. First, a feasible initial solution is constructed by allocating portfolio weights according to the individual return rate. Secondly, an iterated local search framework, which makes use of quadratic programming, gradually improves the initial solution throughout an iterative combination of a perturbation stage and a local search stage. According to the experimental results obtained, ARPO is very competitive when compared against existing state-of-the-art approaches, both in terms of the quality of the best solution generated as well as in terms of the computational times required to obtain it. Furthermore, we also show that our algorithm can be used to solve variants of the portfolio optimization problem, in which inputs (individual asset returns, variances and covariances) feature a random component. Notably, the results are similar to the benchmark constrained efficient frontier with deterministic inputs, if variances and covariances of individual asset returns comprise a random component. Finally, a sensitivity analysis has been carried out to test the stability of our algorithm against small variations in the input data.
29 citations
TL;DR: The present study aims to assess Iranian road safety performance by proposing a novel double-frontier Cross-Efficiency Method taking into account both optimistic and pessimistic points of view, simultaneously, which results in a more realistic and comprehensive assessment.
24 citations
TL;DR: In this article, the authors analyzed the relationship between gold quoted on the Shanghai Gold Exchange and Chinese sectorial stocks from 2009 to 2015 and showed that there is a weak but significant tail dependence between gold and Chinese stock returns.
Abstract: This article analyzes the relationship between gold quoted on the Shanghai Gold Exchange and Chinese sectorial stocks from 2009 to 2015. Using different copulas, our results show that there is weak but significant tail dependence between gold and Chinese sectorial stock returns. This means that the dependence between extreme movements of the two assets is not pronounced and confirms the role of gold as a safe haven asset. Based on analyzing the efficient frontier, CCC-GARCH optimal weights, hedge ratios and hedging effectiveness, we further show that adding gold into Chinese stock portfolios can help to reduce their risk. Gold appears to be the most efficient diversifier for stocks of the materials sector and the less efficient for the utilities sector. As a robustness check, we also compare gold to oil and indicate that gold is more efficient than oil in the diversification of Chinese stock portfolios.
TL;DR: In this paper, the authors examine the returns of more than 4500 stocks from 22 frontier countries for the years 1997-2018 and find no consistent evidence regarding size, investment, and profitability premia.
TL;DR: In this paper, the authors use vine copula approaches to model the co-dependence and portfolio value-at-risk of six cryptocurrencies using data of daily periodicity from September 2015 to June 2018.
TL;DR: In this paper, the authors consider how to optimally allocate investments in a portfolio of competing technologies using the standard mean-variance framework of portfolio theory, and characterize the optimal diversification in terms of progress rates, variability, initial costs, initial experience, risk aversion, discount rate and total demand.
TL;DR: In this paper, the authors investigate a discrete-time mean-risk portfolio selection problem, where risk is measured by the conditional value-at-risk (CVaR), and the optimal investment strategy is a fully adaptive feedback policy, and the cumulative amount invested in the risky assets is of a characteristic V-shaped pattern as a function of the current wealth.
TL;DR: In this article, the authors consider an insurer who manages its underlying risk by purchasing proportional reinsurance and investing in a financial market consisting of a risk-free bond and a risky asset.
Abstract: In this study, we consider an insurer who manages her underlying risk by purchasing proportional reinsurance and investing in a financial market consisting of a risk-free bond and a risky asset. The objective of the insurer is to identify an investment–reinsurance strategy that minimizes the mean–variance cost function. We obtain a time-consistent open-loop equilibrium strategy and the corresponding efficient frontier in explicit form using two systems of backward stochastic differential equations. Furthermore, we apply our results to Vasicek’s stochastic interest rate model and Heston’s stochastic volatility model. In both cases, we obtain a closed-form solution.
TL;DR: The authors developed an Epsilou-based measure meta-DEA approach for measuring the efficiencies and technology gaps of different bank types and found that the average efficiency and technology gap of nonfinancial holding banks are better than those of financial holding banks.
Abstract: Previous bank efficiency studies assumed that the same efficient frontier system exists in different types of banks, but the conventional radial data envelopment analysis (DEA) model slacks are not counted in efficiency scores, and nonradial DEA models do not consider radial features. This paper develops an Epsilou‐based measure meta‐DEA approach for measuring the efficiencies and technology gaps of different bank types. The results are as the follows: The average efficiency and technology gaps of nonfinancial holding banks are better than those of financial holding banks. The nonfinancial holding banks are more efficient than financial holding banks in investment and other income.
TL;DR: In this paper, the estimation of the three determining parameters of the efficient frontier, the expected return, and the variance of the global minimum variance portfolio and the slope slope is considered.
Abstract: In this paper, we consider the estimation of the three determining parameters of the efficient frontier, the expected return, and the variance of the global minimum variance portfolio and the slope ...
TL;DR: By introducing and discussing a new system of mean-field backward stochastic differential equations driven by a Markov chain, this paper revisits the Markovian regime-switching model in which the coefficients are deterministic functions of theMarkov chain.
TL;DR: This paper introduces a dual multiobjective linear programming formulation of data envelopment analysis in terms of input and output prices and proposes a procedure based on objective space algorithms for multiobjectives linear programmes to compute the efficient frontier.
Abstract: Data envelopment analysis is a linear programming-based operations research technique for performance measurement of decision-making units. In this paper, we investigate data envelopment analysis from a multiobjective point of view to compute both the efficient extreme points and the efficient facets of the technology set simultaneously. We introduce a dual multiobjective linear programming formulation of data envelopment analysis in terms of input and output prices and propose a procedure based on objective space algorithms for multiobjective linear programmes to compute the efficient frontier. We show that using our algorithm, the efficient extreme points and facets of the technology set can be computed without solving any optimization problems. We conduct computational experiments to demonstrate that the algorithm can compute the efficient frontier within seconds to a few minutes of computation time for real-world data envelopment analysis instances. For large-scale artificial data sets, our algorithm is faster than computing the efficiency scores of all decision-making units via linear programming.
TL;DR: The proposed kinetic model has the aim to pave the way to many different research perspectives and applications discussed eventually in the paper, and the case of efficient frontier obtained by minimizing the Conditional Value-at-Risk (CVaR) is introduced and a preliminary result is proposed.
Abstract: We introduce and discuss a dynamics of interaction of risky assets in a portfolio by resorting to methods of statistical mechanics developed to model the evolution of systems whose microscopic state may be augmented by variables which are not mechanical. Statistical methods are applied in the present paper in order to forecast the dynamics of risk/return efficient frontier for equity risk. Specifically, we adopt the methodologies of the kinetic theory for active particles (KTAP) with stochastic game-type interactions and apply the proposed model to a case study analyzing a subset of stocks traded in Milan Stock Exchange. In particular, we evaluate the efficient risk/return frontier within the mean/variance portfolio optimization theory for 13 principal components of the Milan Stock Exchange and apply the proposed kinetic model to forecast its short-term evolution (within one year). The model has the aim to pave the way to many different research perspectives and applications discussed eventually in the paper. In particular, the case of efficient frontier obtained by minimizing the Conditional Value-at-Risk (CVaR) is introduced and a preliminary result is proposed.
TL;DR: An integrated portfolio selection and adjustment framework applying data envelopment analysis (DEA) is presented that enables adjustment by evaluating the impact of control variables via two-stage DEA and increases the number of suitable portfolios.
Abstract: Project portfolio frameworks usually present the selection and adjustment phases sequentially, lacking the perspective that these phases can be performed iteratively, allowing feedback looping This paper aims to narrow this gap by investigating the interactions between selection and adjustment phases, and the effects on project portfolio efficiency Besides, the effect of the control variables—time, effort, and requirements–is investigated along the project design phase The research approach was based on simulation with five anecdotal projects and real portfolio data gathered in a case-based approach with ten projects As a result, an integrated portfolio selection and adjustment framework applying data envelopment analysis (DEA) is presented The dominant portfolios are determined based on mean-Gini selection to construct an efficient frontier comparing return and risk of each one The proposed framework enables adjustment by evaluating the impact of control variables via two-stage DEA The results show that the proposed framework indeed increases the number of suitable portfolios Furthermore, the framework demonstrates how efficiency is impacted by the control variables of a project
TL;DR: In this paper, the authors argue that investors are better off using the implied cost of capital based on analysts' earnings forecasts as a forward-looking return estimate, and demonstrate that mean-variance optimized portfolios based on these estimates outperform on both an absolute and a risk-adjusted basis the minimum volatility portfolio as well as naive benchmarks, such as the value-weighted and equally weighted market portfolio.
Abstract: Despite its theoretical appeal, Markowitz mean-variance portfolio optimization is plagued by practical issues. It is especially difficult to obtain reliable estimates of a stock’s expected return. Recent research has therefore focused on minimum volatility portfolio optimization, which implicitly assumes that expected returns for all assets are equal. We argue that investors are better off using the implied cost of capital based on analysts’ earnings forecasts as a forward-looking return estimate. Correcting for predictable analyst forecast errors, we demonstrate that mean-variance optimized portfolios based on these estimates outperform on both an absolute and a risk-adjusted basis the minimum volatility portfolio as well as naive benchmarks, such as the value-weighted and equally-weighted market portfolio. The results continue to hold when extending the sample to international markets, using different methods for estimating the forward-looking return, including transaction costs, and using different optimization constraints.
TL;DR: In this article, the authors developed an algorithm for the reconstruction of the frontier of the free disposal hull (FDH) technology that corresponds to its sections by different two-dimensional planes.
Abstract: The free disposal hull (FDH) model of production technology assumes free disposability of all inputs and outputs, but does not make any convexity assumptions. In this paper, we develop an algorithm for the reconstruction of the frontier of the FDH technology that corresponds to its sections by different two-dimensional planes. From a practical perspective, the suggested algorithm is useful for the visualization and exploration of the efficient frontier of the FDH technology. Furthermore, based on the suggested algorithm, we develop a new procedure for the evaluation of returns to scale in the FDH technology. Compared to the existing methods, our approach does not require assessing the efficiency of productive units in the reference technologies, which is known to be a computationally intensive task. Our theoretical results are confirmed by computational experiments in applications with real-life data sets from different industries.
TL;DR: Using the techniques of stochastic linear-quadratic control, under the mean–variance criterion, analytic expressions are obtained for the optimal investment and reinsurance strategies, and closed-form expressions for the efficient strategies and the efficient frontiers which are based on the solutions to some systems of linear ordinary differential equations.
Abstract: In this paper, we consider the problem of optimal investment-reinsurance with two dependent classes of insurance risks in a regime-switching financial market. In our model, the two claim-number processes are correlated through a common shock component, and the market mode is classified into a finite number of regimes. We also assume that the insurer can purchase proportional reinsurance and invest its surplus in a financial market, and that the values of the model parameters depend on the market mode. Using the techniques of stochastic linear-quadratic control, under the mean–variance criterion, we obtain analytic expressions for the optimal investment and reinsurance strategies, and derive closed-form expressions for the efficient strategies and the efficient frontiers which are based on the solutions to some systems of linear ordinary differential equations. Finally, we carry out a numerical study for illustration purpose.
24 Dec 2019
TL;DR: In this article, a discrete choice analysis and hierarchical Bayes method was used to measure individual investors' preferences for portfolio selection criteria, as well as the problem of optimal portfolio determination from the investors' point of view.
Abstract: Behavioral finance literature shows that in addition to Markowitz’s rate of return and risk, private investors consider various other stock features. This paper discusses the problem of determining investors’ preferences for portfolio selection criteria, as well as the problem of optimal portfolio determination from the investors’ point of view. The study primarily focuses on private investors who are interested in one-time investments rather than stock trading. We use a discrete choice analysis and hierarchical Bayes method to measure individual investors’ preferences, and a logit model to determine individual shares of preferences. We treat the share of preferences as the share of certain stocks in an optimal portfolio. The proposed methodology is illustrated by the example of companies whose stocks are traded on the Belgrade Stock Exchange. We measure respondents’ preferences for companies, preferences for return rates, riskiness of stocks, and dividend rates. The results of comparing the performance of the resulting portfolio with the efficient frontier obtained using Markowitz’s portfolio theory indicate its high efficiency, thus validating the proposed approach.
TL;DR: In this paper, the authors examined bank portfolio management under banking regulation and asymmetric information about borrower types and screening by banks and imperfect competition in the credit market and showed that the cooperative efficient portfolio diversification strategy will dominate any non-cooperative strategy whenever portfolio returns are negatively correlated between any pair of interacting banks as it reduces portfolio variance for a given package of interest and loans.
Abstract: This paper examines bank portfolio management under banking regulation and asymmetric information about borrower types and screening by banks and imperfect competition in the credit market. A bank tries to maximize expected profit subject to a portfolio variance constraint. The analysis yields the following results: For a monopoly bank, the incentive constraint of the efficient type of borrowers will be binding and the participation constraint of the inefficient type of borrowers will be binding. Further, given the variance constraint being binding, the optimal portfolio will be on the efficiency frontier. The paper also examines duopoly competition between aggressive (predator) and defensive (prey) banks and the scope for potential cooperation and reveals that among the alternatives of natural monopoly, entry deterrence, takeovers and efficient portfolio diversification through mergers or interest swaps, the cooperative efficient portfolio diversification strategy will dominate any non-cooperative strategy whenever portfolio returns are negatively correlated between any pair of interacting banks as it reduces portfolio variance for a given package of interest and loans.
TL;DR: In this article, the authors investigated continuous-time mean-variance (MV) portfolio selection under the Volterra Heston model, and they obtained the optimal investment strategy, which depends on the solution to a Riccati-Volterra equation.
Abstract: Motivated by empirical evidence for rough volatility models, this paper investigates continuous-time mean-variance (MV) portfolio selection under the Volterra Heston model. Due to the non-Markovian and non-semimartingale nature of the model, classic stochastic optimal control frameworks are not directly applicable to the associated optimization problem. By constructing an auxiliary stochastic process, we obtain the optimal investment strategy, which depends on the solution to a Riccati-Volterra equation. The MV efficient frontier is shown to maintain a quadratic curve. Numerical studies show that both roughness and volatility of volatility materially affect the optimal strategy.
TL;DR: The experimental results strongly support the use of extended factors for portfolio selection problems and the assumption of meeting decision maker's preferences and utilities better than the portfolios based entirely on risk and return.
TL;DR: Two initialization approaches are proposed for multi/many-objective optimization algorithms to obtain a better convergence and distribution solution set for the portfolio optimization problem and are integrated with eight different optimization algorithms.
Abstract: Selecting the number of assents to obtain the maximized expected return under the possible lowest risk is the main concern of portfolio optimization problems. Optimization algorithms -multi/many-objective- are evaluated to find the desired/possible level of investment. Converging to the best possible asset set and -if possible- distribution of the many possible solution sets for an efficient frontier is expected as the result of the multi/many-objective optimization algorithms. Obtaining an accurate and well-distributed set of solutions is the main motivation. Hence, in this paper, two initialization approaches are proposed for multi/many-objective optimization algorithms to obtain a better convergence and distribution solution set for the portfolio optimization problem. The initial population set is composed of the assets with the largest income and binary combinations of the assets where their sum returns the maximum income. These proposed approaches are integrated with eight different optimization algorithms and the performance of the algorithms is compared with respect to the convergence and diversity metrics.