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It is crucial to find a better portfolio optimization strategy, considering the cryptocurrencies' asymmetric volatilities. Hence, this research aimed to present dynamic optimization on minimum variance (MVP), equal risk contribution (ERC) and most diversified portfolio (MDP).

This study applied dynamic covariances from multivariate GARCH(1,1) with Student’s-t-distribution. This research also constructed static optimization from the conventional MVP, ERC and MDP as comparison. Moreover, the optimization involved transaction cost and out-of-sample analysis from the rolling windows method. The sample consisted of ten significant cryptocurrencies.

Dynamic optimization enhanced risk-adjusted return. Moreover, dynamic MDP and ERC could win the naïve strategy (1/N) under various estimation windows, and forecast lengths when the transaction cost ranging from 10 bps to 50 bps. The researcher also used another researcher's sample as a robustness test. Findings showed that dynamic optimization (MDP and ERC) outperformed the benchmark.

Sophisticated investors may use the dynamic ERC and MDP to optimize cryptocurrencies portfolio.

To the best of the author’s knowledge, this is the first paper that studies the dynamic optimization on MVP, ERC and MDP using DCC and ADCC-GARCH with multivariate-t-distribution and rolling windows method.

This paper aims to identify a possible mismatch between the theory found in academic research and the practices of investment managers in Brazil.

The chosen approach is a field survey. This paper considers 78 survey responses from 274 asset management companies. Data obtained are analyzed using independence tests between two variables and multiple regressions.

The results show that most Brazilian investment managers have not adopted current best practices recommended by the financial academic literature and that there is a significant gap between academic recommendations and asset management practices. The modern portfolio theory is still more widely used than the post-modern portfolio theory, and quantitative portfolio optimization is less often used than the simple rule of defining a maximum concentration limit for any single asset. Moreover, the results show that the normal distribution is used more than parametrical distributions with asymmetry and kurtosis to estimate value at risk, among other findings.

This study may be considered a pioneering work in portfolio construction, risk management and performance evaluation in Brazil. Although academia in Brazil and abroad has thoroughly researched portfolio construction, risk management and performance evaluation, little is known about the actual implementation and utilization of this research by Brazilian practitioners.

This paper aims to analyze the impact of transaction costs in portfolio optimization in Peru. The study aims to compare the transaction costs structure applied in Peru with respect to the ones applied in the USA, and over a few dimensions.

The paper opted for an empirical study analyzing the cost of rebalancing portfolios over a set period and dimensions. Stocks have been carefully selected using Bloomberg terminals, and portfolio designed then rebalanced using VBA programming. Over a few dimensions as type and number of stocks, holding period and trading strategy, the behavior of these different transaction costs has been compared. The analysis has been done for four different portfolios.

The paper provides empirical insights about how a retail investor actively trading in Peru can pay up to 14 times more in transaction costs than trading the same portfolio in the USA. These comparatively high transaction costs prevent retail investors to trade in the Peruvian stock market while fueling illiquidity to this market.

The paper deals with a limited amount of Peruvian stocks. Researchers are encouraged to test the proposition further, including other dimensions.

The paper includes implications for any retail investor that wants to invest in Peruvian stocks, giving an insight about how expensive it is to actively rebalance a portfolio in Peru.

This paper fulfils an identified need to study how much it costs to actively invest on the stock market in Peru.

This study analyzes sustainable practices adopted by Italian firms to enhance the circularity of packaging and related results in terms of environmental improvements.

The authors developed an empirical analysis using publicly available data from the National Consortium of Packaging (CONAI) in Italy, which consists of 603 circular packaging projects. The authors ran both descriptive and prescriptive analyses to determine individual sustainable practices and portfolios adopted to enhance packaging circularity and to verify related reductions in terms of CO2 emissions as well as energy usage and water consumption.

The findings reveal that firms are more accustomed to focusing on single sustainable practices than on portfolios of practices to achieve packaging circularity. Raw material saving and logistics optimization are the most frequent sustainable practices adopted by firms to improve circularity of packaging. The reuse of packaging allows firms to simultaneously reduce CO2 emissions, energy usage and water consumption. Preferences in terms of portfolio of sustainable practices are strictly linked to the types of materials used for packaging and environmental targets.

The authors investigate environmental practices that firms adopt to support packaging circularity, and the authors detect portfolios of sustainable practices that positively impact environmental performance indicators. This research extends a significant glimpse into the portfolio of sustainable practices for packaging in the circular economy implemented by firms, filling academic gaps and indicating business opportunities and avenues for economic development.

The purpose of this paper is to implement a genetic algorithmic geared toward building an optimized investment portfolio exploring data set from stocks of firms listed on the Nigerian exchange market. To provide a research-driven guide toward portfolio business assessment and implementation for optimal risk-return.

The approach was to formulate the portfolio selection problem as a mathematical programming problem to optimize returns of portfolio; calculated by a Sharpe ratio. A genetic algorithm (GA) is then applied to solve the formulated model. The GA lead to an optimized portfolio, suggesting an effective asset allocation to achieve the optimized returns.

The approach enables an investor to take a calculated risk in selecting and investing in an investment portfolio best minimizes the risks and maximizes returns. The investor can make a sound investment decision based on expected returns suggested from the optimal portfolio.

The data used for the GA model building and implementation GA was limited to stock market prices. Thus, portfolio investment that which to combines another capital market instrument was used.

Investment managers can implement this GA method to solve the usual bottleneck in selecting or determining which stock to advise potential investors to invest in, and also advise on which capital sharing ratio to reduce risk and attain optimal portfolio-mix targeted at achieving an optimal return on investment.

The value proposition of this paper is due to its exhaustiveness in considering the very important measures in the selection of an optimal portfolio such as risk, liquidity ratio, returns, diversification and asset allocation.

The paper focuses on how knowledge visualization supports the development of a particular multiobjective decision-making problem as a portfolio optimization problem in the context of interorganizational collaboration between universities and a large automotive company. This paper fits with the emergent knowledge visualization literature because it helps to explain decision-making related to the development of a multiobjective optimization model in Lean Product Development settings. We investigate how using ad hoc visual tools supports knowledge translation and knowledge sharing, enhancing managerial judgment and decision-making.

The empirical case in this study concerns the setting up of a multiobjective decision-making model as a portfolio optimization problem to analyze and select alternatives for upgrading the lean production process quality at an FCA plant.

The study shows how knowledge visualization and the associated tools work to enable knowledge translation and knowledge sharing, supporting decision-making. The empirical findings show why and how knowledge visualization can be used to foster knowledge translation and sharing among individuals and from individuals to groups. Knowledge visualization is understood as both a collective and interactional process and a systematic approach where different players translate their expertise, share a framework and develop common ground to support decision-making.

From a theoretical perspective, the paper expands the understanding of knowledge visualization as a system of practices that support the development of a multiobjective decision-making method. From an empirical point of view, our results may be useful to other firms in the automotive industry and for academics wishing to develop applied research on portfolio optimization.

This study investigates the role of cryptocurrencies in enhancing the performance of portfolios constructed from traditional asset classes. Using a long sample period covering not only the large value increases but also the dramatic declines during the beginning of 2018, the purpose of this paper is to provide a more complete analysis of the dynamic nature of cryptocurrencies as individual investment opportunities, and as components of optimal portfolios.

The mean-variance optimization technique of Merton (1990) is applied to develop the risk and return characteristics of the efficient portfolios, along with the optimal weights of the asset class components in the portfolios.

The authors provide evidence that as a single investment, the best cryptocurrency is Ripple, followed by Bitcoin and Litecoin. Furthermore, cryptocurrencies have a useful role in the optimal portfolio construction and in investments, in addition to their original purposes for which they were created. Bitcoin is the best cryptocurrency enhancing the characteristics of the optimal portfolio. Ripple and Litecoin follow in terms of their usefulness in an optimal portfolio as single cryptocurrencies. Including all these cryptocurrencies in a portfolio generates the best (most optimal) results. Contributions of the cryptocurrencies to the optimal portfolio evolve over time. Therefore, the results and conclusions of this study have no guarantee for continuation in an exact manner in the future. However, the increasing popularity and the unique characteristics of cryptocurrencies will assist their future presence in investment portfolios.

This is one of the first studies that examine the role of popular cryptocurrencies in enhancing a portfolio composed of traditional asset classes. The sample period is the largest that has been used in this strand of the literature, and allows to compare optimal portfolios in early/recent subsamples, and during the pre-/post-cryptocurrency crisis periods.

AI-based robo-advisory (RA) represents a FinTech application that is already replacing retail investment advisors. In private banking (PB), clients also increasingly expect service provision across different digital channels, but with a higher degree of personalization. Hence, the present study investigates the impact of intelligent RA on the PB investment advisory process to derive both process (re)design knowledge and strategic guidance for artificial intelligence (AI) usage for PB investment advisory.

The present study applies an AI process impact analysis approach by decomposing AI-based RA into three AI application types: conversational agent, customer segmentation and predictive analytics. The analysis results along a reference PB investment advisory process reveal sub-process transformations which are applied for process redesign integrating AI.

The study results imply that AI systems (1) enable seamless client journeys, (2) increase advisor flexibility, (3) support the client–advisor relationship by applying an omnichannel approach and (4) demand advisor skills to be augmented with technical and statistical knowledge.

The research study contributes (1) an AI process impact analysis approach, (2) derives process (re)design knowledge for AI deployment and (3) develops strategic guidance for AI usage in PB investment advisory.

The purpose of this paper is to provide a method to formalize information security control descriptions and a decision support system increasing the automation level and, therefore, the cost efficiency of the information security compliance checking process. The authors advanced the state-of-the-art by developing and applying the method to ISO 27002 information security controls and by developing a semantic decision support system.

The research has been conducted under design science principles. The formalized information security controls were used in a compliance/risk management decision support system which has been evaluated with experts and end-users in real-world environments.

There are different ways of obtaining compliance to information security standards. For example, by implementing countermeasures of different quality depending on the protection needs of the organization. The authors developed decision support mechanisms which use the formal control descriptions as input to support the decision-maker at identifying the most appropriate countermeasure strategy based on cost and risk reduction potential.

Formalizing and mapping the ISO 27002 controls to the security ontology enabled the authors to automatically determine the compliance status and organization-wide risk-level based on the formal control descriptions and the modelled environment, including organizational structures, IT infrastructure, available countermeasures, etc. Furthermore, it allowed them to automatically determine which countermeasures are missing to ensure compliance and to decrease the risk to an acceptable level.

Using a portfolio comprising liquid global stocks and bonds, this study aims to limit absolute risk to that of a standardised benchmark and determine whether this has a significant impact on expected return in both high volatility period (HV) and low volatility period (LV).

Using a traditional benchmark comprising 40% equity and 60% bonds, a constant tracking error (TE) frontier was constructed and implemented. Portfolio performance for different TE constraints and different economic periods (expansion and contraction) was explored.

Results indicate that during HV, replicating benchmark portfolio risk produces portfolios that outperform both the maximum return (MR) portfolio and the benchmark. MR portfolios outperform those with the same risk as that of the benchmark in LV. The MR portfolio weights assets to obtain the highest return on the TE frontier. During HV, the benchmark replicated risk portfolio obtained a higher absolute risk value than that of the MR portfolio because of an inefficient benchmark. In HV, the benchmark replicated risk portfolio favoured intermediate maturity treasury bills.

There is a dearth of literature exploring the performance of active portfolios subject to TE constraints. This work addresses this gap and demonstrates, for the first time, the relative portfolio performance of several standard portfolio choices on the frontier.