Portfolio Construction Process Research Articles

Perception of Multimodal Portfolios as a Pedagogical Strategy for Learning Writing in a Digital Context

Active learning is increasingly recognized as a cornerstone of higher education's teaching-learning paradigm. In this context, educators serve as facilitators, empowering students to take the lead and assume co-responsibility for their learning. Among the array of active learning strategies available, the portfolio stands out as an instrument that not only aids in knowledge construction but also fosters the development of crucial skills like autonomy and creativity. Furthermore, it serves as an interactive and participatory learning approach that encourages self-assessment and reflection on the teaching-learning journey. Portfolios, through the continual (re)construction of knowledge and ongoing collaborative engagement between students and educators, promote self-regulated learning by progressively fostering students' awareness of their learning process. This study reflects on the pedagogical application of individual portfolios, aiming to explore their role in enhancing writing skills within a digital context and nurturing cross-disciplinary skills highly valued by employers. Specifically, it delves into students' perceptions of the portfolio construction process within the Internet Writing course, a segment of the Master's degree program in Marketing and Digital Communication at the University of Aveiro (Portugal). After a brief examination of active learning strategies, with a specific emphasis on portfolios as tools for self-reflection and learning, the study analyzes questionnaire survey results utilizing SPSS software. The survey was administered to students enrolled in the course to assess the effectiveness of the applied teaching strategy. Lastly, based on the findings, this work identifies potential limitations and suggests strategies for improvement in future implementations.

Decision Support Systems in Stock Investment Problems

This study compiles decision support systems that aim to optimize financial decision processes by examining the literature studies targeting stock investments. The review encompasses a range of methodologies and applications, from traditional approaches such as Markowitz’s Modern Portfolio Theory, Black-Litterman, and Single Index models to artificial intelligence-based techniques. In detail, the contributions of Decision Support Systems to stock portfolio construction and portfolio optimization processes along with comparative analyses between these systems are scrutinized. The review also aims to enable researchers and practitioners to be engaged in portfolio optimization with a framework for future investigations in areas such as historical data analysis, future price movement prediction, assessment of risk factors, and determination of optimal portfolio distribution. Furthermore, it seeks to enhance the understanding of decision support systems employed in portfolio optimization, facilitating a more comprehensive grasp of their utility within stock investments.

Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach

We introduce a new dimension in constructing relative investor views for the Black-Litterman model by incorporating fear/greed technical indicator predictions as a proxy for investor sentiment in the portfolio construction process. We apply a hybrid CEEMDAN-GRU deep learning model to forecast this indicator and the XGBoost ensemble learning algorithm to forecast returns for ten country ETFs and create relative views for the Black-Litterman model. These models beat several benchmark forecasting models. Our empirical results show that the proposed approach outperforms the Markowitz, minimum-variance, equally-weighted and risk-parity strategies along with four other Black-Litterman approaches from the literature for six investment periods.

Portfolio Optimization Using Markowitz Model on Sri-Kehati Index

This thesis investigates the portfolio optimization process using the Markowitz model on the SRI-KEHATI index, an esteemed sustainable investment index. The study aims to explore the potential advantages of incorporating environmental, social, and governance (ESG) factors into portfolio construction. By leveraging historical financial data and reliable ESG metrics, this research develops optimized portfolios that strike a balance between risk and return while adhering to the sustainability criteria of the SRIKEHATI index. The methodology encompasses the collection of credible ESG data and financial information for the constituents of the SRIKEHATI index. The Markowitz model is subsequently employed to analyze the risk and return characteristics of each asset within the portfolio. Through the application of optimization algorithms, the study seeks to identify the optimal asset allocation that maximizes risk-adjusted returns, taking into account the ESG criteria outlined by the SRI-KEHATI index. The outcomes of this research provide valuable insights into the effectiveness of portfolio optimization techniques within the realm of sustainable investing. By considering both financial metrics and ESG factors, investors can construct portfolios that align with their sustainability objectives while optimizing risk and return. The findings shed light on the performance of the optimized portfolios and compare them with conventional approaches, thereby demonstrating the potential benefits of integrating ESG considerations into portfolio decision-making. Additionally, this study examines the practical implications associated with implementing sustainable portfolio strategies based on the SRI-KEHATI index. Overall, this thesis contributes to the expanding body of knowledge on sustainable investing and portfolio optimization, specifically focusing on the SRI-KEHATI index. It provides valuable insights for investors, asset managers, and policymakers interested in sustainable investment strategies. Furthermore, it offers a framework for incorporating ESG considerations into the portfolio construction process using the Markowitz model, thereby aiding in the development of more robust and sustainable investment portfolios.

Non-contour efficient fronts for identifying most preferred portfolios in sustainability investing

The paper focuses on investors whose strength of interest in sustainability issues (such as environmental, social, and governance) causes ESG to become a third criterion alongside risk and return in portfolio selection. This causes the efficient frontier to become an efficient surface. This means that an investor’s optimal portfolio is no longer the point of most preferred risk/return tradeoff on the mean-variance (M-V) efficient frontier, but is the point of most preferred risk/return/ESG tradeoff on the investor’s M-V-ESG efficient surface. However, to find such a point requires non-trivial ESG integration which is the name given to the process of integrating ESG into the portfolio construction process after screening. With the third objective transporting the problem into 3D-space, it is difficult to search the efficient surface in any kind of comprehensive fashion using M-V based or other bi-criterion techniques as this is akin to a 2-dimensional being trying to view a 3-dimensional object. To remedy the situation, the paper proposes a tri-criterion approach that computes efficient surfaces and special non-contour curves (called NC-efficient fronts in the paper) that are stretched across the efficient surface so as to dragnet it for the points of best ESG integration within it. Using the methodology and data from the S&P500, the paper conducts computational tests on problems with up to 500 securities and under different constraint conditions so as to know what to expect from the new approach over a range of situations.

Sharpe Parity Redux

In this article, the authors investigate the performance of the Sharpe parity asset allocation strategy relative to the more well-known 60/40, mean–variance, risk parity, and min-variance strategies. Each portfolio selection strategy was tested under a number of different constraints and asset class configurations. In addition to historical data, bootstrapped simulated time series were used to test the robustness of the analysis. The primary conclusion was that the performance of each of the strategies considered was highly dependent on the constraints applied during the portfolio construction process. As such, portfolio managers choosing to implement a given portfolio construction methodology must be careful in choosing their asset universe and calibrating their asset class level constraints to give their portfolios the maximum performance advantage versus rival portfolio selection methodologies and manager benchmarks.

Assessment of portfolio investment effectiveness during the covid-19 pandemic – multivariate fundamental approach1

Making long-term investment decisions is possible using the portfolio approach. In this respect, there are classical (e.g., Markowitz model) and non-classical (e.g., fundamental model) approaches. The key element in the construction of models is not only the measurement of risk and rate of return, but also the method of selecting assets (companies) to the portfolio. The method of selecting assets often determines the direction of the investor's investment strategy. The portfolio construction and asset selection processes remain an open question. The aim of the study is to assess the effectiveness of portfolio investment using the concept of fundamental strength. The results of the study indicate that the fundamental approach, depending on the portfolio construction procedure used, gives different effects, but allows for risk diversification in times of crisis

Practical Applications of The Sustainability Conundrum

In <b><i>The Sustainability Conundrum</i></b>, from the March 2020 issue of <b><i>The Journal of Portfolio Management</i></b>, authors <b>Mark Anson, Deborah Spalding, Kristofer Kwait</b>, and <b>John Delano</b> (all of <b>Commonfund</b>) examine the portfolio construction process in environmental, social, and governance (ESG) investing. Inconsistent findings from studies attempting to empirically evaluate the value of ESG portfolios likely result in part from the lack of consistent definitions and standards associated with ESG. The authors therefore create an empirical model to calculate sustainable investing’s value, which indicates that sustainable investing produces a negative alpha relative to portfolios unconstrained by sustainable mandates. Then, they derive an “E” factor that is effective for screening both companies and asset managers as green, or environmentally sensitive. This provides an important component of an eventual factor model for sustainable investing. <b>TOPICS:</b>ESG investing, factor-based models

Stratégies d'investissement bas-carbone

Les investisseurs du monde entier sont aujourd'hui de plus en plus nombreux à prendre en considération le risque climatique dans leurs décisions d'investissement. Plusieurs approches ont été développées pour intégrer cette problématique dans l'analyse des investissements et la constitution d'un portefeuille. Dans cet article, nous analysons deux stratégies standards dédiées à l'optimisation de l'empreinte carbone d'un portefeuille : l'exclusion et l'optimisation. Nos simulations révèlent que l'optimisation permet une importante réduction de CO 2 en portefeuille, sans accroître son risque et ce avec un faible biais sectoriel. L'optimisation améliore le profil de risque ESG global du portefeuille et répond aux enjeux climatiques soulevés par les investisseurs. Cette approche devrait selon nous devenir la norme dans un avenir proche. Classification JEL : G11, Q54, Q56.

The Devil Is in the Details: The Risks Often Ignored in Low-Volatility Investing

With increasing investor interest in low-volatility equity strategies comes a need for greater scrutiny of different methodologies used to achieve low-volatility exposure. In an earlier article, the authors investigated the analytical differences between a variety of approaches to constructing low-volatility portfolios. In this article, the authors turn their attention to the empirical differences between common approaches to low volatility. They find that a traditional optimizer-based approach to building low-volatility portfolios has large sensitivities to the risk inputs used in the process. In fact, using the same portfolio construction methodology but changing the risk inputs even slightly can lead to large differences. The magnitude of this sensitivity should give investors pause; even across risk inputs in which differences are valid, variations persist and can be harmful to portfolio performance. The authors show that there are other, more robust, ways of achieving low-volatility portfolios without this input sensitivity (e.g., risk balancing) and suggest that investors should consider this lack of input sensitivity as a valuable characteristic in low-volatility investing. TOPICS:Volatility measures, exchanges/markets/clearinghouses, risk management Key Findings • Using an optimizer to gain exposure to the low-volatility anomaly/premiums subjects the portfolio construction process to input sensitivity, which can lead to wide variation in results. • Input sensitivity affects not only the portfolio’s sector and stock positioning but also its risk exposures. This variation in risk exposures can lead to very different outcomes for investors. • There are more robust ways to gain exposure to the low-volatility anomaly/premiums, and the authors show that a risk-balanced approach not only captures the anomaly/premiums well but does so under different risk model assumptions.

Using Active Share to Evaluate Single- and Multi-Manager Portfolios

Active share is a statistic that describes the degree to which the holdings of an investment fund differ from the holdings of the fund’s benchmark. Practitioners and academics have produced numerous papers on active share; some portray it as a powerful metric that can be used to isolate effective active managers, whereas others highlight its imperfections and warn of its limited utility. The authors of this article evaluate how active share can be used to guide individual manager selection decisions and to assist in ongoing manager monitoring. After considering multiple perspectives, the authors assert that active share is indeed a useful statistic for evaluating investment strategies, but investors must take into account several limitations to use it appropriately. They also present a new application of active share by employing it at the asset class level. Specifically, they use active share to evaluate multimanager, US large-cap equity portfolios. The authors chose US large-cap equity because this subasset class is widely viewed as highly efficient, and it provides a particularly robust dataset for analysis. With this exercise, the authors demonstrate how investors can apply active share to help determine the optimal number of managers to include in a multimanager portfolio. The intent of the article is to help investors improve both the quality of individual manager selection decisions and the portfolio construction process for multimanager portfolios. TOPICS:Portfolio management/multi-asset allocation, manager selection, performance measurement Key Findings • In an effort to diversify the risk of adverse manager selection, institutional investors often hire multiple investment managers to fill their allocations to various asset classes. However, many investors are subsequently disappointed with the results because the benefits of diversification are often exceeded by the costs in terms of active share dilution, higher investment management fees, and greater oversight requirements. • Using large-cap US equity as a test case, this article demonstrates how active share can help quantify the impact of manager diversification on active management efficiency in multimanager portfolios. In addition, the article quantifies the incremental fee impact of adding investment managers to the portfolio. The results demonstrate how active share can decline rapidly after a relatively small number of managers is added to a large-cap equity portfolio. Simultaneously, both indirect and direct costs of portfolio oversight steadily and meaningfully increase with each new manager. • The results suggest that investors should conduct a thorough cost–benefit analysis before determining the number of investment managers to include in a multimanager portfolio. To support such endeavors, we encourage investors to quantify the incremental impact of additional managers on active share, investment management fees, and general oversight requirements. After quantifying these costs, investors can weigh them against the potential diversification benefits and identify the number of managers that provides the optimal balance.

Performance Dispersion Risk Assessment in Alternatives and Active Strategies

Alternatives have gained significant investor attention recently. Unfortunately, the lack of a holistic investment framework to incorporate alternatives in a traditional portfolio poses a challenge. Traditional risk–return based approaches, alone, over allocate to alternatives—a result of underestimation of risks resulting from (a) significant manager dispersion and (b) smoothness in the pooled return indexes used for alternatives. Much research corrects for smoothness of returns; however, the risk from performance dispersion remains mostly unaddressed. In this study, the author discusses a methodology to quantify performance dispersion risk and incorporate it in the portfolio construction process. TOPICS:Portfolio management/multi-asset allocation, real assets/alternative investments/private equity, private equity, performance measurement Key Findings • Alternative investments exhibit a much higher degree of performance dispersion among managers than traditional asset classes. • Traditional risk measurement approaches on pooled return indices utilized for alternatives underestimate the actual risk of an investment in alternative asset classes. • Performance dispersion risk of investing in an alternative strategy can be measured similar to the idiosyncratic risk of a stock and should be included in any risk-based portfolio construction process.

The Sustainability Conundrum

Sustainable or environmental, social, and governance investing remains a topic of keen debate. Thousands of research papers have been dedicated to answering the question of whether sustainable investing adds to, subtracts from, or has no impact on portfolio performance. Unfortunately, the databases that accumulate sustainable metrics are both inconsistent and incomplete, leading to a large dispersion of results and conclusions. In this article, the authors devise an empirical test of the value of sustainable investing that does not depend upon the choice of sustainable database or metrics used. They find that sustainable investing produced a negative alpha relative to a portfolio not constrained by a sustainable mandate. They also identify a sustainable beta factor that is successful in screening both companies and asset managers as green or nongreen. This is an important step in building a factor model for sustainable investing. TOPICS:Portfolio management/multi-asset allocation, ESG investing, factor-based models Key Findings • An empirical analysis of sustainable investment funds versus unconstrained investment funds found a negative alpha associated with the sustainable funds. • A sustainable factor can be identified and applied to both companies and fund managers. • Sustainable funds have consistent factor and sector tilts that must be accounted for as part of the portfolio construction process.

Analyzing precious metals returns using a Kalman smoother approach

PurposeThis paper aims to analyze the sensitivity of different external factors to the returns of the precious metals of gold, silver, platinum and palladium. The goal is to find similarities and differences between the dependencies of every factor to each metal in a time-varying framework.Design/methodology/approachA brief co-integration test for the precious metals is conducted followed by a Kalman smoother approach to study the different sensitivities to the price changes of precious metals. A dynamic time warping (DTW) approach finally compares sensitivities for pairs of precious metals to a specific factor.FindingsResults point to strong time-dependencies of the sensitivities, such as a declining relationship of gold to equity volatility. Consistent strong relationships are rare and can be identified for the consumer price index and the dollar. the DTW approach finds higher similarities between platinum and palladium compared to other pairs.Practical implicationsThe similarities and differences of the precious metals can be used by investors and risk managers in portfolio construction processes and risk analyses.Originality/valueThe focus of the research is put on a broader context of precious metals with different external factors instead of focusing on a single factor, enabling a comparison of differences and similarities of the sensitivities. The analysis via a Kalman Rauch–Tung–Striebel smoother together with a DTW approach has not been conducted before in this way and is able to characterize the dependencies by a single number.

How Much Beta Is Just Right? Linking Investment Objective and Portfolio Choice

Alpha forecasting for hedge funds is much less reliable than beta forecasting, which explains the proliferation of mostly risk-based portfolio construction processes for alternatives. When implementing those processes, investors are faced with certain strategic choices, including choosing the long-term level of market exposure, or beta, on the portfolio level. There is no universal guidance that drives that choice. That said, the authors have found that for a particular—and quite popular—form of the investment objective, one can develop such guidance, derive analytical expressions for the correct level of market exposure, and create a practical portfolio construction framework that directly links the investment objectives and portfolio choice. TOPICS:Real assets/alternative investments/private equity, portfolio construction, performance measurement

A New Empirical Perspective on the Minimum Variance Portfolio

This study examines the market level determinants of the portfolio construction process behind minimum variance investing. I show that the minimum variance portfolio (MVP) weight composition will be similar to that of equally weighted portfolio (1/N portfolio) if the ratio of idiosyncratic volatility relative to total return volatility is high and the number of securities in the portfolio is large.

O portfólio e a construção de saberes docentes na pós-graduação em saúde

Resumo O atual cenário de mudanças e inovações na formação dos profissionais de saúde desencadeou esta investigação, que teve como objetivo analisar o papel do portfólio reflexivo na construção de saberes docentes no contexto da disciplina Formação Didático-Pedagógica em Saúde, com 68 pós-graduandos dessa área. A pesquisa documental, de natureza qualitativa, contou com análise de conteúdo na modalidade temática, da qual emergiram seis categorias: experiência prévia e construção do portfólio; construção de subjetividade; estímulo à autonomia/autoconhecimento; integração teoria-prática e atuação docente; estímulo à reflexão e à síntese; e mudanças nas concepções de avaliação. Concluiu-se que o portfólio propicia a construção de saberes para a docência numa perspectiva emancipatória, com metarreflexão sobre o processo formativo, implicando o sujeito com sua aprendizagem e com as necessidades de saúde da população.

Is high active share always good?

More and more asset managers are committing to an explicit active share target, which takes the form of a lower bound (e.g. at least 75%) or a range of values (e.g. between 75 and 85%). The active share target is used either in conjunction with, or as a substitute for a tracking error limit. We analyse the implications of active share targets on the portfolio construction process using a simple optimisation framework. Our results suggest, counter to conventional wisdom, that in certain situations the portfolio construction process benefits from imposing a limit on active share rather than from boosting active share. In particular, if the signal used in a strategy is weak, then a constraint on active share can help mitigate the effects of model uncertainty.

Portfolio management with tail dependence

ABSTRACTMany publications, that treated with Portfolio Management, were devastating for all asset allocation models in the context of portfolios. The elimination of extreme events (asymmetric or tail dependence) during the portfolio construction process can reduce the skills of asset managers to reduce risk through diversification. The copula theory allows us to calculate an alternative to measure the dependence of extreme events in assets through the index lower tail dependence. We check that the strategies with tail dependence overcame Talmud rule, the Markowitz model and the model of Tu and Zhou by simulating 1,000 portfolios with 3, 5, 10 and 20 randomly selected assets from DJIA for the period 03/1990 until 12/2016. We conclude that models of tail dependence and Markowitz had more performance ex-ante than Talmud and the Tu and Zhou model for portfolios with 3, 5, 10 and 20 assets. Tail dependence models overcome Markowitz, in terms of cumulative return, in over 60% of months considered in the analysis. The results indicate that the Talmud rule should be discarded in a context of constructing portfolios with individual stocks ahead strategies with tail dependence.

Robust dependence modeling for high-dimensional covariance matrices with financial applications

A very important problem in finance is the construction of portfolios of assets that balance risk and reward in an optimal way. A critical issue in portfolio development is how to address data outliers that reflect very unusual, generally non-recurring, market conditions. Should we allow these to have a significant impact on our estimation and portfolio construction process or should they be considered separately as evidence of a regime shift and/or be used to adjust baseline results? In financial asset allocation, a fundamental step is often a mean-variance optimization problem that makes use of the location vector and dispersion matrix of the financial assets. In this paper, we introduce a new high- dimensional covariance estimator that is much less sensitive to outliers compared to its classical counterparts. We then apply this estimator to the active asset allocation application, and show that our proposed new estimator delivers better results compared to many existing asset allocation methods. An important bonus is that on our examples, the method has a smaller proportion of stock weights greater than 10% and, in many cases, a higher alpha. Covariance estimation is more challenging than mean estimation and only locally and not globally optimal solutions are available. Our proposed new robust covariance estimator uses a regular vine dependence structure and only pairwise robust partial correlation estimators. The resulting robust covariance estimator delivers high performance for identifying outliers for large high dimensional datasets, has a high breakdown point, and is positive definite. When the full vine structure is not available, we propose using a minimal spanning tree algorithm to replace missing vine structure.