Portfolio Optimization Model Research Articles

A technology portfolio optimization model considering staged financing and moratorium period under uncertainty

Technology commercialization needs a large amount of financial resources and governments in developed and developing countries play a critical role in resource allocation to the technology commercialization, especially through “Technology Development Funds (TDFs)”. But, because of resource limitations, determining high priority technologies with higher impact on the country’s innovative performance and the optimal resource allocation to technology development is very important for science and technology policymakers. “Technology portfolio planning” has been developed and applied in this regard. Accordingly, a two-phase decision-making framework has been proposed. At the first phase, the priorities of technology fields are determined by using the best-worst method (BWM) and at the second phase, a two-stage stochastic technology portfolio planning model is developed by considering technological projects’ risks and export market, as one of the important factor in the “Global Innovation Index” (GII) ranking. It also has been considered technology fields’ priorities, staged-financing, moratorium period, reinvestment strategy, and technology readiness levels (TRL) in allocating financial resources to technological projects The main advantages of our proposed model are considering uncertainty and early signaling about under performing technological projects Due to the uncertain nature of the problem, our solution methodology is based on the Sample Average Approximation (SAA). In order to demonstrate the applicability of this model, a real case study and its computational results are presented.

A New Model for Screening Shariah Compliant Firms

This paper develops a new quantitative measure that reflects the extent to which a firm complies with Shariah relative to the other firms located in a certain region at a certain time. This measure can be customized to be consistent with each investor’s objectives, constraints, and beliefs. I argue that the use of this measure is preferable to the existing use of ratio thresholds for the following two reasons. First, it is more Shariah-appropriate because it provides the Shariah-compliant investor with a clear understanding of the relative compliance status of each firm he wishes to invest in. Second, it can be incorporated into any portfolio optimization model to create a balance between improving Shariah compliance and not compromising investment returns. In conclusion, the paper provides illustrative results using a sample of all publicly traded US firms over the period from 2010 to 2016.

Econometric Model Used in the Portfolio Optimization over Several Periods

"The classic problem of optimizing a portfolio can be extended to a multi-stage programming problem. The purpose of the multi-period portfolio optimization problem is to determine the optimal portfolio for a certain finite time horizon. In a multi-period model in which investors are allowed to change the composition of the portfolio, it is essential to take into account trading costs, a solution in this regard being the use of tree-type scenarios. The study undertaken by the authors considered the construction of a portfolio optimization model in case there is a certain constraint on returns. ARMA type processes were used to model the conditional mean equation."

An interactive approach to multi-objective mean semi-variance model with transaction cost for portfolio optimization

Portfolio selection may be considered as a complicated decision making under uncertain conditions. Regardless of an unknown future, a fund manager has to make critical financial choices based on the investors’ preferences towards risk and return and perceptions. Since the pioneer work of Markowitz, a lot of studies have been published considering his Mean-Variance (MV) model as a basis. In our proposed model we had considered Semivariance as the risk measure in our proposed portfolio optimization model. We also extend our model to include the effect of transaction cost in portfolio optimization and then the concept of entropy is incorporated as an objective function in our model to obtain a well diversified portfolio within optimal asset allocation. An interactive approach is used to solve the model. A numerical example is used to illustrate that the model can be efficiently used in practice. Finally the result obtained in interactive method has been compared with the result obtained by Fuzzy Non-Linear Programming (FNLP) and Fuzzy Additive Goal Programming (FAGP) techniques.

Behavioral Portfolio Management with Layered ESG Goals and Ai Estimation of Asset Returns

Portfolio managers and individual investors alike are in quest of efficient asset allocation models that simultaneously express environmental, social, and governance (ESG) considerations along with investor behavioral biases. The current study presents a novel approach to optimize the behavioral portfolio management model in the presence of investor biases for ESG sustainability, loss aversion, and cognitive dissonance. We extend the factor pricing literature by implementing a factor extraction protocol to identify three unique and pervasive ESG factors. Upon examining the interconnectedness of the factors, machine learning methods are applied to a production-theoretic six-factor Fama and French model to predict individual asset returns. Enumeration of efficient asset allocations is obtained by solving a hierarchical multiobjective portfolio optimization model. simulation results from solving alternate specifications of the layered goal ESG-driven model corroborate and extend emergent research on portfolio sustainability, network theory, and the interconnectedness of financial returns. Additionally, we provide results to amplify the existence of a hump-shaped ESG efficiency frontier. The results provide new information about the trade-offs available for resolving cognitive dissonance when investors are conflicted between holding ‘green’ versus ‘brown’ asset diversification plans.

Ortalama-Aşağı Yönlü Varyans Tabanlı Risk Ölçütleri ve Stokastik Getirili Portföy Optimizasyonu

The downside risk measure methods have gained in popularity within the frame of the Post-Modern Portfolio theory. Many studies have suggested different cosemivariance formulas with the downside risk measures. The purpose of this study is to compare the performances of the portfolio optimization models that apply different risk measures such as variance, semi-variance, and lower partial moment (LPM) with the different cosemivariance formulas. In the study, the Estrada (2007) and Nawrocki (1991) approaches were chosen to demonstrate the differences between different cosemivariance statistics used in the semi-variance risk measures. The results of the models are also compared with the mean-variance results. Sharpe and Sortino ratios are used as a comparison measure. In the study, efficient portfolios are created for investors having different risk attitudes with the LPM approach. Additionally, portfolio optimization is conducted by using the LPM approach with the stochastic modeling to avoid the uncertainty of portfolio returns. It was concluded that the Nawrocki model is beneficial to control the downside risk and it is a better choice as it includes the calculation of correlations between the assets and that the stochastic return model produces more conservative results compared to the deterministic model.

A New Data-Driven Distributionally Robust Portfolio Optimization Method Based on Wasserstein Ambiguity Set

Since optimal portfolio strategy depends heavily on the distribution of uncertain returns, this article proposes a new method for the portfolio optimization problem with respect to distribution uncertainty. When the distributional information of the uncertain return rate is only observable through a finite sample dataset, we model the portfolio selection problem with a robust optimization method from the data-driven perspective. We first develop an ambiguous mean-CVaR portfolio optimization model, where the ambiguous distribution set employed in the distributionally robust model is a Wasserstein ball centered within the empirical distribution. In addition, the computationally tractable equivalent model of the worst-case expectation under the uncertainty set of a cone is derived, and some theoretical conclusions of the box, budget and ellipsoid uncertainty set are obtained. Finally, to demonstrate the effectiveness of our mean-CVaR portfolio optimization method, two practical examples concerning the Chinese stock market and United States stock market are considered. Furthermore, some numerical experiments are carried out under different uncertainty sets. The proposed data-driven distributionally robust portfolio optimization method offers some advantages over the ambiguity-free stochastic optimization method. The numerical experiments illustrate that the new method is effective.

A hybrid approach to cardinality constraint portfolio selection problem based on nonlinear neural network and genetic algorithm

Portfolio Optimization (PO) is an extremely popular method that selects the best portfolio for an investor. When the classic methods fail to find an exact solution, heuristic techniques are designed to find an approximate solution. In the literature, such techniques have often been used in the portfolio selection problem. However, hardly any of these methods utilize a neural network to apportion the extent of stocks. The basic objective of portfolio optimization is to minimize the risk of the portfolio while maximizing the expected return of the portfolio. In reality, the Standard Portfolio Optimization Model does not fulfill stock market requirements in a money related world. Indeed, this problem cannot limit the amount of stock in the portfolio. On the other hand, the precise number of stocks are taken into account by the Cardinality Constraint Portfolio Optimization model, which is the Mixed-Integer Quadratic Programming problem. While minimizing the risk of the portfolio, limiting the expected return and the number of stocks is the main subject of this study. In this study, a hybrid approach is proposed, based on the Nonlinear Neural Network and the Genetic Algorithm to solve the Cardinality Constraint Portfolio Optimization Model. To investigate the effectiveness of the proposed hybrid approach, the Istanbul Stock Exchange (ISE-301) data is used. The ISE-30 data1 consists of daily prices, from May 2015 to May 2017. The ISE-30 data1 from May 2017 to July 2018 is used as out-of-sample. To clarify the effectiveness proposed, the method was applied to publicly data sets which are used for numerous different portfolio selection strategies in many articles. The proposed hybrid approach to the cardinality constraint portfolio optimization problem has more viable outcomes than current strategies.

Portfolio Optimization Model with and without Options under Additional Constraints

In this paper, first, we study mean-absolute deviation (MAD) portfolio optimization model with cardinality constraints, short selling, and risk-neutral interest rate. Then, in order to insure the investment against unfavorable outcomes, an extension of MAD model that includes options is considered. Moreover, since the data in financial models usually involve uncertainties, we apply robust optimization to the MAD model with options. Finally, a data set of S&P index is used to compare the effectiveness of options in the models in terms of returns and Sharpe ratios.

Gated Neural Network-Based Mean–EVaR–Skewness Portfolio Optimization under Uncertain Environment

Numerous empirical studies show that portfolio returns are generally asymmetric, and investor would prefer a portfolio return with larger degree of asymmetry along with risk and return. In this paper, a concept of skewness is defined as the third central moment and studied its mathematical properties. To predict the stock prices, a novel recurrent neural network with gated recurrent unit (GRU) cell is preferred. Based on these predictions, stock returns, entropic value at risks and skewness are calculated. A mean–EVaR–skewness multi-objective portfolio optimization model is devised to account for market uncertainty. Cardinality, bounding restrictions, and liquidity are considered in addition to risk and return to make the model more effective. Uncertain goal programming is used to solve the proposed model. Finally, an example portfolio is presented to display the efficacy and the feasibility of the model suggested in this paper.

Data-Driven Optimization of Reward-Risk Ratio Measures

We investigate a class of fractional distributionally robust optimization problems with uncertain probabilities. They consist in the maximization of ambiguous fractional functions representing reward-risk ratios and have a semi-infinite programming epigraphic formulation. We derive a new fully parameterized closed-form to compute a new bound on the size of the Wasserstein ambiguity ball. We design a data-driven reformulation and solution framework. The reformulation phase involves the derivation of the support function of the ambiguity set and the concave conjugate of the ratio function. We design modular bisection algorithms which enjoy the finite convergence property. This class of problems has wide applicability in finance, and we specify new ambiguous portfolio optimization models for the Sharpe and Omega ratios. The computational study shows the applicability and scalability of the framework to solve quickly large, industry-relevant-size problems, which cannot be solved in one day with state-of-the-art mixed-integer nonlinear programming (MINLP) solvers.

Closed-form solutions for short-term sparse portfolio optimization

ABSTRACT The short-term sparse portfolio optimization (SSPO) models are dedicated to constructing sparse portfolio in each short period. In this paper, we discuss some existing SSPO model and propose two realistic sparse optimization models via the -norm. Closed-form solutions are obtained, based on which strategies are proposed with clear mathematical and financial interpretations. Numerical experiments are conducted on five benchmark datasets from real-world stock markets, which illustrate a competitive portfolio efficiency and computation time superiority comparing to the existing SSPO system.

Multi-period portfolio optimization using coherent fuzzy numbers in a credibilistic environment

In this paper, we use an extension of fuzzy numbers, called coherent fuzzy numbers, to model asset returns and an investor’s perception of the stock market (pessimistic, optimistic, or neutral) simultaneously. Two multi-period multi-objective portfolio optimization models are formulated using mean absolute semi-deviation and Conditional Value-at-Risk (CVaR) as risk measures, respectively. We aim to provide more flexibility to the investor in specifying the risk tolerance and devise optimum investment plans for different investment horizons. The proposed models also incorporate bound, cardinality, and skewness constraints for each investment period to capture various stock market scenarios. A real-coded genetic algorithm is employed to solve the resultant models. Two real-life case studies involving 20 assets of the National Stock Exchange (NSE), India, and another involving 50 assets listed in the S&P 500 and NASDAQ-100 indexes have been provided to illustrate the efficacy and advantages of the models. An in-sample and out-of-sample analysis have been done for both the models to analyze the performance in the real-world scenario. The conclusion drawn from the analysis strongly emphasizes on accurately assessing the current stock market prospects, i.e., adopting the right attitude (pessimistic, optimistic, or neutral), is of paramount importance and must be included in the portfolio optimization problem.

Decentralized Transactive Energy for Flexible Resources in Distribution Systems

The increasing penetration of proactive agents in electric power distribution systems calls for mechanisms to coordinate their trading activities. The commonly employed centralized and distributed methods for supply-demand coordination are not only limited by hardware requirements but also restrictive for active participation of the market agents. This paper proposes a novel decentralized transactive coordination framework that allows participants to bid and clear transactions via a bilateral interaction process. The framework is based on 1) power consumption flexibility individually reported by demand agents using model-predictive control, 2) bilateral supply-side bidding individually determined by the supply agents using a Markowitz Portfolio Optimization model, and 3) individual simultaneous clearing of transactions by demand agents based on second-price sealed-bid auctions. The suppliers bid in the market to achieve desired returns while minimizing their risks. The demand agents harness their demand flexibility by competitively clearing the supplier bids. Unlike traditional approaches, the proposed framework does not require master nodes to orchestrate transactions and, therefore, facilitates massive participation of market agents. Simulation cases demonstrate that the proposed framework successfully performs the market function for bilateral transactions among thousands of proactive agents.

A comparison on particle swarm optimization and genetic algorithm performances in deriving the efficient frontier of stocks portfolios based on a mean‐lower partial moment model

AbstractIn this paper, a portfolio optimization model on the basis of the risk measure of lower partial moment of the first order is discussed. Two meta‐heuristic methods of particle swarm optimization and genetic algorithm performances are applied and compared from different aspects to derive the stocks portfolios efficient frontier. The data belongs to the monthly returns of 20 randomly selected and approved stocks in the New York Stock Exchange for the financial period of 2005–2011. The results prove that both algorithms are quite efficient in solving the mean‐lower partial moment of the first order model with the particle swarm optimization being superior.

A novel backtesting methodology for clustering in mean–variance portfolio optimization

The decisions of asset selection and allocation lie at the heart of financial portfolio management. For these challenging tasks, the mathematical programming model of the mean–variance optimization problem proposes to use the concept of diversification. The novel methodology in this article is a representation of the accumulated knowledge of this model from the modern portfolio theory. It is a practical application for portfolio managers to help synthesize the available historical data and to infer rational decisions. The state-of-the-art backtesting methodology integrates the unsupervised machine learning method of clustering analysis into the mean–variance portfolio optimization model. The test results from the proposed novel methodology show that clustering with Euclidean distance measures outperform the results of the benchmark and other specified clustering methods for different datasets, backtesting periods, and temporal scales of major stock indices.

Portfolio optimization with irreversible long-term investments in renewable energy under policy risk: A mixed-integer multistage stochastic model and a moving-horizon approach

Portfolio optimization is an ongoing hot topic of mathematical optimization and management science. Due to the current financial market environment with low interest rates and volatile stock markets, it is getting more and more important to extend portfolio optimization models by other types of investments than classical assets. In this paper, we present a mixed-integer multistage stochastic model that includes investment opportunities in irreversible and long-term infrastructure projects in the context of renewable energies, which are also subject to policy risk. On realistic time scales for investment problems of this type, the resulting instances are by far too large to be solved with today’s most evolved optimization software. Thus, we present a tailored moving-horizon approach together with suitable approximations and simplifications of the model. We evaluate these approximations and simplifications in a computational sensitivity analysis and derive a final model that can be tackled on a realistic instance by our moving-horizon approach.

Portfolio optimization with return prediction using deep learning and machine learning

Integrating return prediction of traditional time series models in portfolio formation can improve the performance of original portfolio optimization model. Since machine learning and deep learning models have shown overwhelming superiority than time series models, this paper combines return prediction in portfolio formation with two machine learning models, i.e., random forest (RF) and support vector regression (SVR), and three deep learning models, i.e., LSTM neural network, deep multilayer perceptron (DMLP) and convolutional neural network. To be specific, this paper first applies these prediction models for stock preselection before portfolio formation. Then, this paper incorporates their predictive results in advancing mean–variance (MV) and omega portfolio optimization models. In order to present the superiority of these models, portfolio models with autoregressive integrated moving average’s return prediction are used as benchmarks. Evaluation is based on historical data of 9 years from 2007 to 2015 of component stocks of China securities 100 index. Experimental results show that MV and omega models with RF return prediction, i.e., RF+MVF and RF+OF, outperform the other models. Further, RF+MVF is superior to RF+OF. Due to the high turnover of these two models, this paper discusses their performance after deducting the transaction fee cased by turnover. Experiments present that RF+MVF still performs the best among MVF models and omega model with SVR prediction (SVR+OF) performs the best among OF models. Moreover, RF+MVF performs better than SVR+OF and high turnover erodes nearly half of their total returns especially for RF+OF and RF+MVF. Therefore, this paper recommends investors to build MVF with RF return prediction for daily trading investment.

The Black-Litterman Model for Portfolio Optimization on Vietnam Stock Market

The Black-Litterman asset allocation model is an extended portfolio management model to construct optimal portfolios by combining the market equilibrium with investor views into asset allocation decisions. In this paper we apply Black-Litterman model for portfolio optimization on Vietnames stock market. We chose ARIMA methodology utilized in financial econonometrics to predict the views of investor which are used as inputs of the Black-Litterman asset allocation process to find optimal portfolio and weights.

Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization.

In the field of asset allocation, how to balance the returns of an investment portfolio and its fluctuations is the core issue. Capital asset pricing model, arbitrage pricing theory, and Fama–French three-factor model were used to quantify the price of individual stocks and portfolios. Based on the second-order stochastic dominance rule, the higher moments of return series, the Shannon entropy, and some other actual investment constraints, we construct a multiconstraint portfolio optimization model, aiming at comprehensively weighting the returns and risk of portfolios rather than blindly maximizing its returns. Furthermore, the whale optimization algorithm based on FTSE100 index data is used to optimize the above multiconstraint portfolio optimization model, which significantly improves the rate of return of the simple diversified buy-and-hold strategy or the FTSE100 index. Furthermore, extensive experiments validate the superiority of the whale optimization algorithm over the other four swarm intelligence optimization algorithms (gray wolf optimizer, fruit fly optimization algorithm, particle swarm optimization, and firefly algorithm) through various indicators of the results, especially under harsh constraints.