Portfolio Selection Techniques Research Articles

Industrial R&D project portfolio selection method using a multi-objective optimization program: A conceptual quantitative framework

Design/methodology/approach: Research and development (R&D) activities are crucial if companies are to adapt to technology changes, but budget constraints and limited resources often force companies to select a subset of candidate projects through portfolio selection techniques. However, existing models for R&D portfolio selection do not adequately consider interdependencies and types of projects, and this can lead to suboptimal selection and misalignment with corporate objectives.Findings: A Multi-Objective Optimisation Program (MOOP) is suggested transcending from classic manpower, time and financial planning into addition of strategic, skills and commercial objectives. A Pareto front is used as validation mechanism.Research limitations/implications: Project selection processes are widened with select and critical quantitative positions. Potentials remain in areas of team capability, corporate capabilities, deeper skill understanding, and stakeholder engagement.Practical implications: A quantitative validation is often overlooked in PPM project selection over more qualitative or idiosyncratic selection methods.Originality/value: A quantitative validation is often overlooked in PPM project selection over more qualitative or idiosyncratic selection methods.

Construction of Optimal Portfolio Using Efficient Portfolio and Zero Opportunity Cost

In this manuscript, we introduce asset allocation and portfolio selection techniques based on efficiency condition, Sharpe ratio error condition and order three zero opportunity condition. Investors expect the same level of risk and return from alternative investment options unless they want the advantage of diversification for risk. There are two fundamental investment portfolios. The first one is risk free fundamental portfolio, and the second one is risky fundamental portfolio. Investors use zero opportunity cost to select portfolio objective. In this research, mathematical derivation of portfolio construction approach is described in advance. Historical data of this research show that there is a positive linear relationship between natural logarithm of standard deviation of securities’ return and square of co-variance between securities and market return. Furthermore, it shows that there is a positive linear relationship between Treynor ratio of securities’ return and Sharpe ratio of securities’ return. We use global search optimization tool in MatLab and R software to solve empirical portfolio selection and asset allocation problem. Moreover, we apply direct and indirect mathematical proof methods to prove mathematical facts of this study.

Does a Skew-Normal Return Find Better Bayesian Optimal Portfolios?

In the presence of asset returns’ non-normal behavior, optimal portfolio selection techniques should consider higher-moment risks. In this paper, we extend the Black-Litterman (BL) asset allocation model (Black & Litterman, 1990) by applying the hidden truncation skew-normal distribution (Arnold & Beaver, 2000). Based on Simaan’s approach (Simaan, 1993), we also develop a three-moment BL model considering skewness. Using the skew-normal returns, the skew-normal BL model provides optimal portfolios with the same expected return but less risk compared to an optimal portfolio of the classical BL model. We find that the portfolios become more negatively skewed as the expected returns of portfolios increase at any level of N (non-spherical variance), which suggests that the investors trade a negative skewness for a higher expected return. We also find the negative relation between portfolio volatility and portfolio skewness. In other words, the investors trade a lower volatility for a higher skewness or vice versa reflecting that stocks with big price declines are more volatile.

Robust conditional expectation reward–risk performance measures

In this paper, we develop robust portfolio optimization models for conditional expectation type reward–risk performance measures that significantly improve upon conventional portfolio selection techniques. In particular, we directly address estimation error in the portfolio optimization process by adopting a robust optimization method that is typically used with conventional robust statistical estimation techniques. Alongside this robust optimization, we propose the use of an early-warning system based on moving averages to predict market crises. Empirical analyses based on the US stock market validate the proposed robust approaches and highlight the implications of financial crises for portfolio selection problems. The results confirm that the proposed robust portfolio optimization models substantially improve upon their conventional counterparts for out-of-sample portfolios, providing valuable managerial insights.

Volatility forecasting for low-volatility portfolio selection in the US and the Korean equity markets

We consider the problem of low-volatility portfolio selection which has been the subject of extensive research in the field of portfolio selection. To improve the currently existing techniques that rely purely on past information to select low-volatility portfolios, this paper investigates the use of time series regression techniques that make forecasts of future volatility to select the portfolios. In particular, for the first time, the utility of support vector regression and its enhancements as portfolio selection techniques is provided. It is shown that our regression-based portfolio selection provides attractive outperformances compared to the benchmark index and the portfolio defined by a well-known strategy on the data-sets of the S&P 500 and the KOSPI 200.

Scaling up a Project Portfolio Selection Technique by using Multiobjective Genetic Optimization

This paper proposes a multiobjective heuristic search approach to support a project portfolio selection technique on scenarios with a large number of candidate projects. The original formulation for the technique requires analyzing all combinations of the candidate projects, which turns to be unfeasible when more than a few alternatives are available. We have used a multiobjective genetic algorithm to partially explore the search space of project combinations and select the most effective ones. We present an experimental study based on four real-world project selection problems that compares the results found by the genetic algorithm to those yielded by a non-systematic search procedure (random search). A second experimental study evaluates the best parameter settings to perform the heuristic search. Experimental results show evidence that the project selection technique can be used in large-scale scenarios and that the genetic algorithm presents better results than simpler search strategies.

Online portfolio selection

Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining. This article aims to provide a comprehensive survey and a structural understanding of online portfolio selection techniques published in the literature. From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then we survey a variety of state-of-the-art approaches, which are grouped into several major categories, including benchmarks, Follow-the-Winner approaches, Follow-the-Loser approaches, Pattern-Matching--based approaches, and Meta-Learning Algorithms. In addition to the problem formulation and related algorithms, we also discuss the relationship of these algorithms with the capital growth theory so as to better understand the similarities and differences of their underlying trading ideas. This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in the financial industry to help them understand the state of the art and facilitate their research and practical applications. We also discuss some open issues and evaluate some emerging new trends for future research.

Selecting balanced portfolios of R&D projects with interdependencies: A Cross-Entropy based methodology

The intensive growth of technology makes firms rely on research and development (R&D) activities in order to adapt to technology changes in an ever-changing and uncertain environment. Due to R&D budget constraints and limited resources, firms are often forced to select a subset of all candidate projects by means of project portfolio selection techniques mitigating the corresponding risks and enhancing the overall value of portfolio. Projects' interdependencies and types were rarely considered in existing models of R&D portfolio selection that may result in selecting wrong projects. This flaw hinders the projects alignment with corporate objectives and strategy and leads to excessive risk and missing the promised values. In this paper, a balanced set of R&D project evaluation criteria was proposed. Next, to construct R&D project portfolio, a 0–1 nonlinear mathematical programming method for balancing portfolio values and risks was proposed, in which research projects' interdependencies, types and other constraints were all considered. Finally, a Cross-Entropy algorithm was developed to solve the proposed model and results were reported. The algorithm proved to be very effective in terms of solution quality and computational time. The proposed algorithm especially suits large scale instances while exact approaches are doomed to fail.

A network-based data mining approach to portfolio selection via weighted clique relaxations

We introduce a new network-based data mining approach to selecting diversified portfolios by modeling the stock market as a network and utilizing combinatorial optimization techniques to find maximum-weight s-plexes in the obtained networks. The considered approach is based on the weighted market graph model, which is used for identifying clusters of stocks according to a correlation-based criterion. The proposed techniques provide a new framework for selecting profitable diversified portfolios, which is verified by computational experiments on historical data over the past decade. In addition, the proposed approach can be used as a complementary tool for narrowing down a set of “candidate” stocks for a diversified portfolio, which can potentially be analyzed using other known portfolio selection techniques.

Risk-Factor Diversification and Portfolio Selection

Traditionally, investment portfolios have been constructed with a focus on what asset classes to invest in and how much to invest in each. Recent research, however, has shown that focusing on risk-factor allocations, rather than asset class allocations, can result in better risk-adjusted portfolio performance. The existing literature has focused on simple allocation strategies such as equal-weighted and equal-risk-weighted portfolios. In addition to these simple allocation techniques, this paper compares the performance using mean-variance analysis, and presents evidence that the outperformance of risk-factor diversification may not be as conclusive as has been previously presented in the literature. While confirming some of the prior findings on risk-factor diversification, the research shows that previous findings may be subject to strong caveats. Specifically, the evidence suggests that the selection of risk-factors, portfolio selection techniques and time-period have a large impact on performance outcomes.

Desempenho Fora-da-Amostra da Otimização Robusta de Carteiras

A otimização robusta de carteiras tem recebido grande interesse nos últimos anos devido a possibilidade de considerar o erro de estimação dentro do problema da seleção de carteiras. Uma questão abordada por poucos estudos é se esta nova abordagem é capaz de gerar uma performance fora-da-amostra superior aos métodos tradicionais. Além disso, é importante saber esta abordagem é capaz de gerar carteiras mais estáveis ao longo do tempo, contribuindo assim para reduzir os custos de administração e facilitando a implementação na prática. Este estudo analisa a performance fora-da-amostra e a estabilidade das composições ótimas das carteiras obtidas com otimização robusta e com métodos tradicionais. Os resultados indicam que, para dados simulados, a otimização robusta obtém uma performance superior aos métodos tradicionais tanto em termos de índice de Sharpe como em termos de portfolio turnover. Os resultados para dados reais de indicam que a performance ajustada ao risco foi estatisticamente equivalente, entretanto as composições ótimas foram mais estáveis do que as obtidas através de métodos tradicionais de otimização.

Improved Estimates of Higher-Order Comoments and Implications for Portfolio Selection

In the presence of nonnormally distributed asset returns, optimal portfolio selection techniques require estimates for variance-covariance parameters, along with estimates for higher-order moments and comoments of the return distribution. This is a formidable challenge that severely exacerbates the dimensionality problem already present with mean-variance analysis. This article extends the existing literature, which has mostly focused on the covariance matrix, by introducing improved estimators for the coskewness and cokurtosis parameters. We find that the use of these enhanced estimates generates a significant improvement in investors’ welfare. We also find that it is only when improved estimators are used that portfolio selection with higher-order moments dominates mean-variance analysis from an out-of-sample perspective.

Growth Optimization with Downside Protection: A New Paradigm for Portfolio Selection

This study introduces growth optimization with downside protection as a portfolio selection technique based on power-log utility functions that combine long-term portfolio growth maximization with the behavioral tenets of prospect theory. This simple but powerful technique can be used with all types of assets, including those with highly skewed and fat-tailed return distributions. We use three assets with very different types of return distributions to show how effective this technique is in constructing portfolios with positively skewed returns that combine high upside potential with downside protection.

Selecting Portfolios with Fixed Costs and Minimum Transaction Lots

The original Markowitz model of portfolio selection has received a widespread theoretical acceptance and it has been the basis for various portfolio selection techniques. Nevertheless, this normative model has found relatively little application in practice when some additional features, such as fixed costs and minimum transaction lots, are relevant in the portfolio selection problem. In this paper different mixed-integer linear programming models dealing with fixed costs and possibly minimum lots are introduced. Due to the high computational complexity of the models, heuristic procedures, based on the construction and optimal solution of mixed integer subproblems, are proposed. Computational results obtained using data from the Milan Stock Exchange show how the proposed heuristics yield very good solutions in a short computational time and make possible some interesting financial conclusions on the impact of fixed costs and minimum lots on portfolio composition.

A Stochastic Convergence Model for Portfolio Selection

Portfolio selection techniques must provide decision makers with a dynamic model framework that incorporates realistic assumptions regarding financial markets, risk preferences, and required portfolio characteristics. Unfortunately, multistage stochastic programming (SP) models for portfolio selection very quickly become intractable as assumptions are relaxed and uncertainty is introduced. In this paper, I present an alternative model framework for portfolio selection, stochastic convergence (SC), that systematically incorporates uncertainty under a realistic assumption set. The optimal portfolio is derived through an iterative procedure, where portfolio plans are evaluated under many possible future scenarios then revised until the model converges to the optimal plan. This approach allows for scenario analysis over all stochastic components, requires no limitation on the structural form of the objective or constraints, and permits evaluation over any length planning horizon while maintaining model tractability by aggregating the scenario tree at each stage in the solution process. Through focused aggregation schemes, the SC approach allows for the implementation of a lower partial variance riskmetric, which is preferred in investment selection. In simulated tests, the SC model, with scenario aggregation, generated portfolios exhibiting performance similar to those generated using the SP model form with no aggregation. Empirical tests using historical fund returns show that a multiperiod SC decision strategy outperforms various benchmarkstrate gies over a long-term test horizon under both asset-liability matching and return maximization frameworks.

A Stochastic Convergence Model for Portfolio Selection

Portfolio selection techniques must provide decision-makers with a dynamic model framework that incorporates realistic assumptions regarding financial markets, risk preferences and required portfolio characteristics. Unfortunately, multi-stage stochastic programming (SP) models for portfolio selection very quickly become intractable as assumptions are relaxed and uncertainty is introduced. In this paper I present an alternative model framework for portfolio selection, stochastic convergence (SC), that systematically incorporates uncertainty under a realistic assumption set. The optimal portfolio is derived through an iterative procedure where portfolio plans are evaluated under many possible future scenarios then revised until the model converges to the optimal plan. This approach allows for scenario analysis over all stochastic components, requires no limitation on the structural form of the objective or constraints, and permits evaluation over any length planning horizon while maintaining model tractability by aggregating the scenario tree at each stage in the solution process. In simulated tests, the SC model, with scenario aggregation, generated portfolios exhibiting performance similar to those generated using the SP model form with no aggregation. Empirical tests using historical fund returns show that a multi-period SC decision strategy outperforms various benchmark strategies over a long-term test horizon.

Estimation Methods in Portfolio Selection and the Effectiveness of Short Sales Restrictions: UK Evidence

Forecasting the mean returns vector and the covariance matrix is a key feature in implementing portfolio theory. The performance of the Bayes-Stein method for forecasting these parameters for use in the Markowitz model (with and without short sales) was compared with that of seven other estimation methods, and three alternative portfolio selection techniques. This paper represents the first large scale empirical investigation of the usefulness of the Bayes-Stein approach using historical data. This data was drawn from the London Stock Exchange. In contrast to earlier studies, the relative performance of Bayes-Stein was mixed. While it produced reasonable estimates of the mean returns vector, there were superior methods, e.g., overall mean, for estimating the covariance matrix when short sales were permitted. When short sales were prohibited, actual portfolio performance was clearly improved, although there was little to choose between the various estimation methods.

A Note on Optimal Portfolio Selection and Diversification Benefits with a Short Sale Restriction on Real Estate Assets

This paper develops an optimal portfolio selection technique when short sales on real estate assets are restricted. Using the well-known mean-variance efficient concept, we are able to derive the o...

The choice of a multicurrency portfolio for a central bank: Bonds, eurodeposits, and forward contracts

This study describes a Mean-Vkiunce framework to analyze the risk/reward tradeoffs involved in the selection of a multicurrency benchmark portfolio for a Central Bank. Standard portfolio selection techniques a la Markowitz [9] can be, to some extent, applied to the management of Central Bank reserves. There are, however, important additional considerations that have to be incorporated in view of the special role played by Central Bank reserves. The framework presented in this paper is a particular application of the traditional Mean-Variance analysis.1 Based on estimates of risk and return for each of the assets considered individually and in a portfolio context, this method allows to explicitly quantify the tradeoff between risk and return. This approach has also the flexibility to incorporate policy constraints as stated by a Central Bank. The efficient portfolios are the ones which provide the maximum return available at a given level of risk consistent with policy constraints on currency composition, limits on maturity, liquidity requirements created by the potential need for immediate action in the foreign exchange market when intervention is required, and other explicitly stated criteria. This study introduces the concept of a trade-based numeruire in which returns are defined and measured, and uses as an illustration a real-life situation where the numeraire used to deflate all returns is a basket of three currencies with the following weights: 40% U.S. dollar, 51% Japanese Yen, and 9% Deutsche Mark. The currency weights capture the importance of each currency in the structure of import flows. Within each local market, the menu of assets includes Govemment bonds of various maturities as well as short-term Eurodeposits to conform to traditional Central Bank portfolio management practices.2 When expressed in the numeraire, the returns on Government bonds are the result of the combined impact of both local interest-rate risk and exchange-rate