Search results

The purpose of this paper is to appraise the risk‐adjusted performance of international mutual funds using measures generated by the optimized variance (OV), and to promote ability of portfolio managers and investors in making logical decisions.

This study appraises the performance of 65 international mutual funds via the optimized risk‐adjusted measures during monthly period of 2001‐2010. Using 65 linear programming models, the OV is calculated to optimize the standard deviation of any funds. Then, another model is run to get the OV of market index. Consequently, seven optimized performance measures namely Treynor, Sharpe, Jensen's alpha, M‐squared, information ratio (IR), MSR, and FPI along with the optimized leverage factor are proposed to evaluate the performance of these mutual funds. Finally, the optimized measures are used to evaluate the funds during pre and post‐crisis periods in order to compare the funds' performance over the crisis periods.

The empirical evidence detects which OV, as measured by the Markowitz's linear programming model, is an important determinant in the performance evaluation measures. Using OV statistic and also its standard deviation, this paper shows that new optimized measures are mostly over‐performed rather than the benchmark index; in addition these optimized measures have close correlation with the conventional performance measures. The evidence shows that the average of the optimized measures during crisis has the lowest performance in comparison with other research periods. The results therefore highlight the importance of using the new optimized measures along with the conventional measures in the evaluation of mutual funds' performance.

It can be worthwhile to compare the optimized measure and also the conventional measures in identifying their superior measures.

The result of this study can be directly used as initial data to make decision by investors and portfolio managers who are seeking the possibility of participating in the global stock market through international mutual funds.

This paper is one of the first studies that optimizes the variance of return for any fund to suggest four optimized measures of Sharpe, IR, MSR, and FPI, and then proposes a new linear programming model to get OV of market index in introducing four optimized new measures of Treynor, M‐square, Jensen's alpha, and leverage factor.

Increasing focus on socially responsible investments (SRIs) and green projects in recent times, coupled with the arrival of COVID pandemic, are the main drivers of this study. The authors conduct a post-factum analysis of investor choice between sin and green investments before and through the COVID outbreak.

A passive investor is introduced who seeks maximum risk-adjusted return and/or investment variance. When presented an opportunity to add sin and/or green investments to her initial one-asset market-only investment position, she views and handles this issue as a portfolio problem (MPT). She estimates value-at-risk (VaR) and conditional-value-at-risk (CVaR) for portfolios to account for downside risk.

Green investments offer better overall risk-return optimization in spite of major inter-period differences in return-risk dynamics and substantial downside risk. Portfolios optimized for minimum variance perform just as well as the ones optimized for minimum downside risk. Return and risk have settled at higher levels since the onset of COVID, resulting in shifting the efficient frontier towards north-east in the return-risk space.

The study contributes to the literature in two ways: One, it examines investor choice between sin and green investments during a global health emergency and views this choice against the one made during normal times. Two, instead of using the principles of modern portfolio theory (MPT) explicitly for diversification, the study uses them to identify investor preference for one over the other investment type. This has not been widely done thus far.

This paper aims to review the development of the Channel Islands exchange and assess the potential diversification benefits arising from the inclusion of this market in investment portfolios containing UK and French equity assets.

First this paper uses a simple stochastic drift, GARCH, and time‐varying parameter CAPM to model total returns indices. Second, it uses the unconditional and conditional means and variances from first stage as inputs into a mean‐variance portfolio quadratic optimisation problem: the solutions of which denote the optimal asset weights.

The evidence suggests that although there are serious difficulties in modelling time series from small illiquid equity markets owing to price‐rigidity, the limited benefits that do exist for the inclusion of Channel Islands assets in portfolios do so preferentially with Paris as opposed to London assets.

This paper extends the literature development policy options for small offshore markets and provides the first analysis of the Channel Islands.

This study investigates the level of variance in the real time demand for bagged cement, induced in response to the climatic sequence of the humid tropics, to support best practice calls for a weather-responsive supply chain strategy.

Data on the consumption of cement and site works for 100 ongoing building construction sites were gathered for a period of 12 months. The variance partitioning capabilities of the Ordinary Least Squares and Hierarchical Linear Modelling forms of regression analysis are comparatively used to evaluate the sensitivity of cement demand to the meteorological profile of wet-humid climate

The study outcome provides statistical evidence demonstrating that the meteorological profile of wet-humid climate induces a significantly high percentage of the variance in the real-time demand for bagged cement on construction sites. However, nested within this variance, are the fixed effects of the cement footprint of the building architecture inherent in the locality. Particularly, positive changes to reduce the wet trade composition of buildings or compensating changes in technological bias, are necessary to combat weather interference in the humid tropics.

The findings are exploratory, and not for the purposes of holistically forecasting cement demand, and can therefore only form part of a more comprehensive decision support system, bespoke to the study area.

The study outcome provides a back-end view to climatic adaptation in wet humid settings, making a compelling case for localized climate-risk adaptive supply chain strategies and policies geared towards sustainability in cement usage.

The study delineates the confounding impact of weather, distinct from local building architecture and technological bias, thus creating a methodological platform for replication and comparative productivity studies in diverse geographical areas.

The primary objective of this study is to propose a robust multiobjective solution search approach for a mean-variance multiple correlated quality characteristics optimisation problem, so-called “multiple response optimisation (MRO) problem”. The solution approach needs to consider response surface (RS) model parameter uncertainties, response uncertainties, process setting sensitivity and response correlation strength to derive the robust solutions iteratively.

This study adopts a new multiobjective solution search approach to determine robust solutions for a typical mean-variance MRO formulation. A fine-tuned, non-dominated sorting genetic algorithm-II (NSGA-II) is used to derive efficient multiobjective solutions for varied mean-variance MRO problems. The iterative search considers RS model uncertainties, process setting uncertainties and response correlation structure to derive efficient fronts. The final solutions are ranked based on two different multi-criteria decision-making (MCDM) techniques.

Five different mean-variance MRO cases are selected from the literature to verify the efficacy of the proposed solution approach. Results derived from the proposed solution approach are compared and contrasted with the best solution(s) derived from other approaches suggested in the literature. Comparative results indicate significant superiorities of the top-ranked predicted robust solutions in nondominated frequency, closeness-to-target and response variabilities.

The solution approach depends on RS modelling and considers continuous search space.

In this study, promising robust solutions are expected to be more suitable for implementation than point estimate-based MOO solutions for a real-life MRO problem.

No evidence of earlier research demonstrates the superiority of a MOO-based iterative solution search approach for mean-variance MRO problems by simultaneously considering model uncertainties, response correlation and process setting sensitivity.

The purpose of this paper is to provide an attempt to evaluate the risk-adjusted performance of international mutual funds using the risk statistic generated by the mean absolute deviation (MAD) and promote the ability of portfolio managers and investors to make the logical decisions for selecting different funds using the new optimized measures.

This study evaluates the performance of 50 international mutual funds using optimized risk-adjusted measures by the MAD over the monthly period 2001-2010. Using 50 linear programming models, the MAD is first computed by the linear programming models, and then seven performance measures of Treynor, Sharpe, Jensen’s α, M2, information ratio (IR), MSR, and FPI are optimized and proposed by the MAD to evaluate the mutual funds.

The empirical evidence detects that the MAD is an important determinant to evaluate the funds’ performance. Using the MAD statistic, this paper shows that new optimized measures are mostly over-performed by the benchmark index; in addition, these optimized measures have close correlation with each other. The results, therefore, detect the importance of using new optimized measures in evaluating the mutual funds’ performance.

The result of this study can be directly used as an initial data for decision of investors and portfolio managers who are seeking the possibility of participating in the global stock market by the international mutual funds.

This paper is the first study which optimizes the variance of returns in the MAD framework for each fund to propose new seven optimized measures of Treynor, Sharpe, Jensen’s α, M2, IR, MSR, and FPI.

The purpose of this paper is to present a new optimisation approach for product variance from the purchasing perspective.

The research is based on a case study of a collaboration with a global acting automotive Tier 1 supplier, who produces steering systems for cars and commercial vehicles. A total of 116 different variants of three components of a car automotive steering system were analysed and evaluated. The data were gathered from 13 sub-suppliers for three different types of a steering system.

Unnecessary time, money, quality and technology can be saved through a greater understanding of such product variances. The results of the case study lead to a general method to optimise existing product variance and present cost improvements and a new key performance indicator to manage product variance out of the purchasing department.

The research is based on a purchasing case study at a Tier 1 supplier of the automotive branch. The approach can be used for other company departments, e.g. logistics and for different industries than automotive.

A company can be successful and competitive when it meets the customer needs with a maximum on satisfaction without generating of waste. Unnecessary existing product variance is a kind of waste. The insights of this paper support the operative user and the strategic company management to reduce and improve unnecessary variance in different sections.

The structured analysis of product variance from the purchasing perspective and the key performance indicator “variance share” are new to company management. The research focuses on the management of existing variance out of the purchasing department which is a segment that has received limited academic attention in research to date.

This paper aims to elaborate on the optimization of two particular cryptocurrency portfolios in a mean-variance framework. In general, cryptocurrencies can be classified to as coins and tokens where the first can be thought of as a medium of exchange and the latter accounts for security or utility tokens depending upon its design.

Against this backdrop, this empirical study distinguishes, in particular, between pure coin and token portfolios. Both portfolios are optimized by maximizing the Sharpe ratio and, subsequently, compared with alternative portfolio strategies.

The empirical findings demonstrate that the maximum utility portfolio of coins, with a risk aversion of λ = 10, outweighs alternative frameworks. The portfolios optimized by maximizing the Sharpe ratio for both coins and tokens indicate a rather poor performance. Testing the maximized utility for different levels of risk aversion confirms the findings of this empirical study and confers them more robustness.

Further investigation is strongly recommended as tokens represent a new phenomenon in the cryptocurrency universe, for which only a limited amount of data are available, which restricts the sampling. Furthermore, future study is to include more sophisticated optimization models using different constraints in portfolio creation.

In light of the persistently substantial volatility in cryptocurrency markets, the empirical findings assert that portfolio managers are advised to construct a global minimum variance portfolio. In the absence of sophisticated optimization models, private investors can invest according to the market values of cryptocurrencies. Despite minor differences in the risk and reward ratios of the portfolios tested, tokens tend to be more speculative, especially, if the Tether token is excluded, which may require enhanced supervision and investor protection by regulating authorities.

As the current literature investigates on diversification effects of blended cryptocurrency portfolios rather than making an explicit distinction, this paper reflects one of the first to explore the investability and role of diversifying coins and tokens using a classic Markowitz approach.

The purpose of this paper is to compare the risk and return characteristics as well as the allocation of mean‐variance (MV) and downside risk (DR) optimized portfolios of international real estate stock markets and to discuss implications for portfolio management.

The analysis focuses on real estate markets only and examines the appropriateness of the Markowitz approach based on MV optimization in comparison to the DR framework suggested by Estrada. Therefore, the two frameworks are presented before the properties of the return distributions are analyzed. Afterwards, the risk and return characteristics as well as the allocation of the efficient portfolios in both frameworks and the divergences are analyzed.

Because of non‐normally distributed returns, negative skewness, and probably non‐quadratic utility functions of investors, MV optimization is not appropriate and the alternative approach by Estrada has its merit compared with other DR frameworks. Furthermore, MV‐efficient and DR‐efficient portfolio allocation differ, as shown by a similarity index. Summarizing, MV optimization is inherent with misleading results and DR optimization shows stronger out‐of‐sample performance – at least during time periods characterized by high market volatility and financial market turmoil.

This study provides some interesting and valuable insights into the DR of international securitized real estate portfolios and the limitations for portfolio management based on MV optimization.

This paper aims to analyze the impact of conjectural “too-big-to-fail” (TBTF) guarantees on big and small US financial institutions’ stock prices during the 2008-2009 banking crisis.

The paper analyzes shocks to stock market investors’ expectations of government aid to banks in distress and respective spillover effects using an event study approach. We focus on three major events in late 2008, namely, the Lehman bankruptcy, the Citigroup bailout and the first announcement of the Capital Purchase Program (CPP) by the US Government.

The authors found significant differences in market reactions to the respective events between small and large banks. For both the Lehman and the CPP event, abnormal returns on big banks’ stocks are negative, while small banks’ stocks tend to generate positive abnormal returns. In contrast, large banks strongly outperform small banks in the case of the Citigroup bailout. Results for a control group of non-financial firms indicate that this behavior may be specific to the banking industry. The authors observed significant spillover effects to both competitors and non-competitors of Lehman and Citigroup, and concluded that while the Lehman event shook the widely held belief in an implicit TBTF subsidy to large banks, the TBTF doctrine was reinstated shortly thereafter.

This paper shows that conjectural TBTF guarantees are priced in by equity investors. While government aid to large banks in distress may prevent negative effects on the stability of the financial system, it may also create negative externalities by putting small banks at a competitive disadvantage. The findings suggest that US and European regulators’ recent policy measures directed at establishing reliable bank resolution schemes should be a step in the right direction to level the playing field for small and large financial institutions.