Efficient Asset Management

Markowitz’s portfolio theory is one of the most important theoretical developments in finance. Its elegance and theoretical appeal are illustrated by the fact that it is taught in virtually every undergraduate and MBA level course on portfolio management. Yet the Markowitz mean-variance (MV) optimization is not widely used by the investment community. In this book, Richard Michaud, a leading researcher in this area, explains why this is so. Based on his diagnosis of the problem, he advocates a “statistical view” of the MV optimization that leads to new procedures that can reduce or eliminate many practical deficiencies of the MV optimization. Using a simple global asset allocation example throughout the book, he illustrates the problems associated with a naïve implementation of the MV optimization procedure. He then describes how the new procedures can be applied, and shows how they improve the MV optimization results. The bottom line: the “statistical view” and the new procedures offer the potential of substantial improvements in the investment performance of the MV optimization. I agree with this assessment. I find this book a must-read for industry professionals and researchers who are interested in this area.

Against the backdrop of the accelerating penetration of global ESG (Environmental, Social, Governance) investment concepts, traditional financial investment models face the need for adaptability upgrades. Taking the US stock market as the research scenario, this paper combines Markowitz's mean-variance theory with the index model (extended from the single-factor CAPM) and integrates the ESG rating dimension to construct an investment analysis framework. By selecting targets with different ESG ratings from the S&P 500 Index components, it empirically tests the impact of ESG factors on the risk-return characteristics of investment portfolios and explores the optimal asset allocation scheme and venture capital decision-making logic under ESG integration. The study finds that high ESG-rated assets can reduce portfolio volatility in the medium and long term and form an effective complement to the systematic risk measurement of the index model; the diversification principle of Markowitz's theory remains valid under ESG constraints, but the efficiency of covariance matrix estimation needs to be optimized through the index model. Ultimately, it provides investment decision-making references for investors in the US stock market to balance ESG responsibilities and financial returns.

It has been shown in the literature that the oil and gas industry deals with a substantial number of biases that impact project evaluation and portfolio performance. Previous studies concluded that properly estimating uncertainties will significantly impact the success of risk takers and their profits. Although a considerable number of publications investigated the impact of cognitive biases, few of these publications tackled the problem from a quantitative point of view. The objective of this study is to demonstrate the value of quantifying uncertainty and evaluate its impact on the optimization of oil and gas portfolios, taking into consideration the risk of each project. A model has been developed to perform portfolio optimization using Markowitz's theory. In this study, portfolio optimization has been performed in the presence of different levels of overconfidence and directional bias to determine the impact of these biases on portfolio performance. The results show that disappointment in performance occurs not only because the net present value (NPV) of the realized portfolio is lower than estimated, but also because the risk of the realized portfolio is higher than estimated. This disappointment is due to both incorrect estimation of value and risk (estimation error) and incorrect project selection (decision error). The results of the cases analyzed show that, for investors with high-risk-tolerance, moderate overconfidence and moderate optimism result in an expected disappointment of about 50% of the estimated portfolio. For investors with low-risk-tolerance, the same amounts of moderate overconfidence and optimism result in an expected disappointment up to 78% of the estimated portfolio. The value of reliably quantifying uncertainty is reducing the expected disappointment and the expected decision error. This can be achieved by eliminating overconfidence in project evaluation and portfolio optimization; optimism is reduced in the process. Consequently, overall industry performance can be improved because reliable uncertainty assessment enables identification of superior portfolios, with optimum reward and risk levels, and increases the probability of meeting expectations.

I use the simulation approach of Jobson and Korkie (J Portfolio Manag 7:70–74, 1981), combined with Michaud optimization (Michaud and Michaud, Efficient asset management: a practical guide to stock portfolio optimization and asset allocation, Oxford University Press, Oxford, 2008), to evaluate whether US international equity closed-end funds (CEF) provide out-of-sample diversification benefits. My study finds that international CEF do not provide diversification benefits across the whole sample period. However, the out-of-sample diversification benefits of international CEF do vary across economic states. I find that there are significant diversification benefits when the lagged one-month US Treasury Bill return is lower than normal, and when higher than normal, regardless of the benchmark investment universe used.

Using the concepts from information theory, it is possible to improve the traditional frameworks for long-only asset allocation. In modern portfolio theory, the investor has two basic procedures: the choice of a portfolio that maximizes its risk-adjusted excess return or the mixed allocation between the maximum Sharpe portfolio and the risk-free asset. In the literature, the first procedure was already addressed using information theory. One contribution of this paper is the consideration of the second procedure in the information theory context. The performance of these approaches was compared with three traditional asset allocation methodologies: the Markowitz's mean-variance, the resampled mean-variance and the equally weighted portfolio. Using simulated and real data, the information theory-based methodologies were verified to be more robust when dealing with the estimation errors.

PurposeThis study aims to investigate the association between Muslim individuals’ portfolio allocation choice and Islamic religiosity (levels and dimensions), controlling for risk tolerance and sociodemographic factors.Design/methodology/approachThe study uses primary data collected via survey questionnaires from a sample of 751 Muslim working individuals in Kuala Lumpur, Malaysia. Owing to the ordinal nature of the dependent variable, which reflects the levels of proportions of risky assets in portfolios, the data were analyzed using an ordered probit regression model.FindingsThe findings reveal that Islamic religiosity levels in general were insignificantly related to portfolio allocation, but that two dimensions of religiosity (virtue and obligation) significantly impact the allocations of risky assets in the portfolio. The higher the level of virtue, the lower the propensity to allocate risky assets into the portfolio. On the contrary, the higher the level of obligation, the higher the propensity to allocate risky assets in the portfolio. Meanwhile, individuals with higher risk tolerance, income and education levels show greater propensity to allocate risky assets in the portfolio.Research limitations/implicationsThe sample is restricted to Muslims in Kuala Lumpur; hence, the findings are not easily generalized to Muslim investors in general. Findings may differ between Muslims across the world, so future research needs to expand from a country specific to an international analysis. In addition, future studies could include other determinants of portfolio allocation, such as financial literacy.Practical implicationsThe findings of this study may assist financial planners and policymakers to better understand the drivers of portfolio allocation among their Muslim clients.Originality/valueWhile other studies have tended to focus on the impact of religiosity on the holdings of specific financial assets, such as Islamic bank accounts or Takaful, the present study explores the effect of Islamic religiosity dimensions on the allocations of risky assets in the portfolio. The study also develops an ordinal measure of portfolio allocation and makes a methodological contribution by using an ordered probit regression analysis.

The covariance matrix, which should be estimated from the data, plays an important role in many multivariate procedures, and its positive definiteness (PDness) is essential for the validity of the procedures. Recently, many regularized estimators have been proposed and shown to be consistent in estimating the true matrix and its support under various structural assumptions on the true covariance matrix. However, they are often not PD. In this paper, we propose a simple modification to make a regularized covariance matrix be PD while preserving its support and the convergence rate. We focus on the matrix $$\ell_{\infty }$$ norm error in covariance matrix estimation because it could allow us to bound the error in the downstream multivariate procedure relying on it. Our proposal in this paper is an extension of the fixed support positive-definite (FSPD) modification by Choi et al. (2019) from spectral and Frobenius norms to the matrix $$\ell_{\infty }$$ norm. Like the original FSPD, we consider a convex combination between the initial estimator (the regularized covariance matrix without PDness) and a given form of the diagonal matrix minimize the $$\ell_{\infty }$$ distance between the initial estimator and the convex combination, and find a closed-form expression for the modification. We apply the procedure to the minimum variance portfolio (MVP) optimization problem and show that the vector $$\ell_{\infty }$$ error in the estimation of the optimal portfolio weight is bounded by the matrix $$\ell _{\infty }$$ error of the plug-in covariance matrix estimator. We illustrate the MVP results with S&P 500 daily returns data from January 1978 to December 2014.

A portfolio optimization problem involves optimal allocation of finite capital to a series of assets to achieve an acceptable trade-off between profit and risk in a given investment period. In the paper, the extended Markowitz's mean-variance portfolio optimization model is studied with some practical constraints. We introduce a new operator and an adaptive strategy for improving the performance of the multi-dimensional mapping algorithm (MDM) proposed specially for the portfolio optimization. Experimental results show that the modification is efficient on tackling large-scale portfolio problems.

This paper presents a detailed analysis of portfolio construction strategies aimed at maximizing risk-adjusted returns for investors, utilizing Python-based methodologies. By leveraging historical data on stock returns and risks, a meticulous selection process identified 5 stocks with superior risk-return profiles, outperforming the dataset's average measures with higher returns and lower risks. The selected stocks from the defense industry formed the foundation of an optimal portfolio designed to minimize sector-specific risks while maximizing growth potential. Through rigorous portfolio optimization techniques, including Markowitz's mean-variance optimization, an optimal portfolio allocation was identified with a return of 62.40% and volatility of 27.74%. Insights from the study provide valuable perspectives for investors seeking realistic and sustainable wealth accumulation strategies. The research also highlights future avenues for exploration, such as integrating alternative asset classes, incorporating behavioral finance insights, and leveraging advancements in risk management technologies. This study contributes to advancing portfolio theory and offers practical guidance for investment decision-making.This study demonstrates the effectiveness of Python in financial analysis and portfolio optimization while furthering the theory of portfolios and providing valuable guidance for making investment decisions. Keywords: Returns, Risk, Volatility, Optimal Portfolio Construction, Defense Sector, Markowitz Portfolio Theory,

Summary This study demonstrates how portfolio insights can be created by combining well-known optimization methods that are generally used individually—genetic algorithm (GA), linear programming (LP), and portfolio filtering (PF). An integrated optimization approach combines the advantages of individual methods while mitigating their shortcomings. Effective portfolio management requires a comprehensive understanding of the tradeoffs between different portfolio choices. Given a set of constraints, portfolio-optimization techniques based on LP and GAs can be applied to identify an optimal portfolio. However, this optimal portfolio might not be the preferred portfolio. Decision makers have to understand the tradeoffs between generally conflicting objectives and constraints before one portfolio can be identified as the preferred option. Such assessment of the overall search space is not made with LP and GAs when used to identify a single best solution, and many portfolio options will have been eliminated before an understanding of these alternatives has been developed. Markowitz's mean-variance (M-V) approach and the traditional “rank and cut” approach are used typically to establish a relationship between a portfolio's value and its variance or associated development cost. Although these methods enable decision makers to compare and contrast different options, the optimization is limited to the portfolio value measure and a single other metric. This latter limitation is overcome by the more recently developed PF approach. This method is practical and transparent and allows for a quick development of strategic portfolio alternatives while considering a large number of portfolio attributes. Its main drawback is that the analyzed set of portfolios generally represents a subset of the total search space. Thus, as the number of feasible portfolio options increases, so does the chance that the optimal portfolio is not present in the population of sampled portfolios.

Sir, We thank Heong et al (2010) for their interest in our study on chemotherapy treatment pathways of advanced colorectal cancer (CRC) patients in the United Kingdom (Shabaruddin et al, 2010a). The purpose of our national survey of UK consultant oncologists was to identify predominant treatment pathways within NHS CRC specialties and to determine the extent of variation within current practice. Economic models need to establish current practice as a basis from which to evaluate the incremental cost and benefits of introducing a new intervention, such as the UGT1A1 pharmacogenetic test. In the United Kingdom, available national data sets do not provide the level of detail needed to describe current clinical practice and the respective outcomes. Some cancer-related registries do exist, such as the UK Association of Cancer Registries and the National Bowel Cancer Audit Programme for CRC surgeries, but do not contain data relevant to current clinical practice in oncology specialties. The National Cancer Intelligence Network (http://www.ncin.org.uk) is currently conducting a chemotherapy data set review that aims to capture data on all chemotherapy carried out in the country. The level of detail for the data collected is yet to be determined. It is necessary to consider that comprehensive UK NHS patient registries are not an easy option. The size of the NHS, funding constraints, issues with data management and quality assurance challenges preclude the establishment and maintenance of such registries for all conditions across the United Kingdom. In an ideal world, information technology in the NHS should allow extraction of data on current practice. This ideal situation does not exist in the current NHS (Anderson, 2010). In the absence of real-world data on routine care, other means of generating parameters to inform economic analyses are necessary, which may be sufficient depending on the purpose of the evaluation (Cooper et al, 2007). Expert estimates are useful starting points to describe current practice and patients' clinical pathways. It is necessary to elicit expert estimates from across the United Kingdom rather than from one locality in order to understand the variation in routine NHS practice. Economic models allow the systematic compilation of data from various sources, such as epidemiological studies and clinical trials, within a single framework. Expert estimates are primarily used to inform model parameters in the absence of other data sources. The degrees of uncertainty in the parameters are then explored using the ranges and measures of variation from the expert estimates. An economic model will allow for exploration of the key areas of uncertainties, generate the expected value of further research to minimise these uncertainties and help inform what research should be conducted to address them. Value of information analysis can indicate whether funding is better directed to more clinical trials or to setting up robust observational studies. We believe that the usefulness of expert estimates will only be as good as the design, construct and framing of the questions asked. Our final questionnaire was informed by a pilot study of eight consultant oncologists. To address the concern of Heong et al regarding the agreement between expert estimates and standard practice, we present (Table 1) preliminary results from an observational study of 48 advanced CRC patients on IrMdG chemotherapy in a UK NHS tertiary care oncology centre (Shabaruddin et al, 2010b), in which there were similar parameters with the experts' estimates (Shabaruddin et al, 2010a). Table 1 Expert estimates from 44 NHS consultant oncologists compared with data from an observational study at a UK tertiary care oncology centre Currently, on the basis of our data, there is no evidence to suggest that pooled UK experts' estimates varied widely from routine NHS practice. In the absence of other data sources, we believe that expert opinion is useful to inform economic evaluations and to help focus research resources by identifying key areas of uncertainties that offer the best value for future research investments. Finally, we do agree with Heong et al on the need to collect data on current clinical practice and acknowledge the value of access to such data. The key question from the UK perspective is: who will pay and be responsible for collecting, storing and maintaining data on routine NHS clinical practice?

This paper explores the use of a combined approach of Markowitz portfolio theory and Monte Carlo simulations for optimizing asset allocation and risk management in financial markets. First, we introduce the portfolio optimization framework of the Markowitz theory, focusing on how to construct an efficient portfolio by minimizing risk and maximizing expected return. Next, by integrating the Monte Carlo simulation method, the paper simulates asset returns and risk fluctuations under different market scenarios, aiming to assess the potential risks of various asset portfolios. To make the model more realistic, we assume an efficient market, rational investors, and neglect transaction costs and market frictions. Additionally, we consider the covariance matrix between assets, assuming its stability during the analysis period. Finally, based on a single-period investment model, the paper proposes an optimized asset allocation strategy, offering a more scientifically grounded decision-making approach for investors. The results show that combining these two methods can effectively improve the accuracy of asset allocation and reduce the overall risk of the portfolio. This study provides a new perspective and practical solution for financial risk assessment and asset management.

We use data from the PSID to investigate how households' portfolio allocations change in response to wealth fluctuations. Persistent habits, consumption commitments, and subsistence levels can generate time-varying risk aversion with the consequence that when the level of liquid wealth changes, the proportion a household invests in risky assets should also change in the same direction. In contrast, our analysis shows that the share of liquid assets that households invest in risky assets is not affected by wealth changes. Instead, one of the major drivers of households' portfolio allocation seems to be inertia: households rebalance only very slowly following inflows and outflows or capital gains and losses.

Canner, Mankiw and Weil (1997) point out that the popular financial advisors on portfolio allocation among cash, bonds and stocks appear not to follow the mutual-fund separation theorem and call the inconsistency between separation theorem and popular financial advice ”an asset allocation puzzle.” For solving the asset allocation puzzle, we provide an analysis of the optimal dynamic asset allocation strategy for a long-horizon investor who has nontraded assets under an economic environment with stochastic investment opportunities and incomplete financial markets. We propose another distinguishing hedging component of the dynamic asset allocation for the stock index fund: the human capital hedging component, the hedging demand which characterizes the demand arising from the desire to hedge against changes in the labor income in contrast to Merton (1973). When we incorporate nontraded assets with intertemporal hedging, we can solve the asset allocation puzzle successfully.

The investment value of optimized portfolios depends on proper input estimation as well as effective portfolio optimization. For many analysts and investors, input estimation methods may seem a largely settled and uncontroversial issue. Managers usually base asset allocation risk-return estimates on sample means, standard deviations, and correlations computed from historic returns, with adjustments for current information. Institutional equity portfolio optimization generally employs commercial risk measurement services for estimating the components of portfolio risk and advanced statistical methods for computing return. Contemporary professional statisticians use a variety of modern statistical techniques that are designed to improve the forecast value of estimates of risk and return from historic data.1 Many of these new procedures are based on results that indicate current investment practice may be suboptimal. These results are particularly important because investment intuition is unreliable. Portfolio optimization requires multivariate statistical estimation; that is, the simultaneous estimation of means, standard deviations, and correlations for many securities. Since financial assets are generally related to each other, there is likely to be information in the group of returns that can improve parameter estimates for each asset.