From Black Box to Explainable Portfolio Optimization: Tracing Allocations to Views and Constraints

Independent Auditor’s Report

A Volatility Driven Asset Allocation

The subject of the study is to evaluate the contribution of strategic asset allocation to the variability of Moroccan pension funds performance. The aim of the paper is to identify the role of active management factors, namely tactical allocation and security selection, in generating a performance surplus compared to strategic allocation. The relevance of the study is justified by the need to identify the sources of performance creation in order to face the commitments of Moroccan pension funds and to compensate for the decline and volatility of asset returns. The article addresses, through the use of simple linear regression methods, the relative importance of strategic asset allocation in explaining the variability of the performance of Moroccan pension funds. It introduces a scientific novelty through the use of the “performance attribution” method. The conclusions of the paper confirm the main role of strategic asset allocation, which varies according to the size of the fund, the asset classes, and the risk aversion of the manager.

Strategic Asset Allocation and Markov Regime Switch with GARCH Model

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.

Asset Allocation by Investment Professionals: Integration or Segmentation?

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.

Strategic Asset Allocation in Stochastic Environment and Incomplete Markets: Evidence on Horizon and Hedging Effects

In spite of theoretical benefits, Markowitz mean-variance (MV) optimized portfolios often fail to meet practical investment goals of marketability, usability, and performance, prompting many investors to seek simpler alternatives. Financial experts Richard and Robert Michaud demonstrate that the limitations of MV optimization are not the result of conceptual flaws in Markowitz theory but unrealistic representation of investment information. What is missing is a realistic treatment of estimation error in the optimization and rebalancing process. The text provides a non-technical review of classical Markowitz optimization and traditional objections. The authors demonstrate that in practice the single most important limitation of MV optimization is oversensitivity to estimation error. Portfolio optimization requires a modern statistical perspective. Efficient Asset Management, Second Edition uses Monte Carlo resampling to address information uncertainty and define Resampled Efficiency(TM) (RE) technology. RE optimized portfolios represent a new definition of portfolio optimality that is more investment intuitive, robust, and provably investment effective. RE rebalancing provides the first rigorous portfolio trading, monitoring, and asset importance rules, avoiding widespread ad hoc methods in current practice. The Second Edition resolves several open issues and misunderstandings that have emerged since the original edition. The new edition includes new proofs of effectiveness, substantial revisions of statistical estimation, extensive discussion of long-short optimization, and new tools for dealing with estimation error in applications and enhancing computational efficiency. RE optimization is shown to be a Bayesian-based generalization and enhancement of Markowitz’s solution. RE technology corrects many current practices that may adversely impact the investment value of trillions of dollars under current asset management. RE optimization technology may also be useful in other financial optimizations and more generally in multivariate estimation contexts of information uncertainty with Bayesian linear constraints. Michaud and Michaud’s new book includes numerous additional proposals to enhance investment value including Stein and Bayesian methods for improved input estimation, the use of portfolio priors, and an economic perspective for asset-liability optimization. Applications include investment policy, asset allocation, and equity portfolio optimization. A final chapter includes practical advice for avoiding simple portfolio design errors. A simple global asset allocation problem illustrates portfolio optimization techniques. The presentation is intuitive, rigorous and informed with institutional management experience to appeal to investment management executives, consultants, fund trustees, brokers, academics, and anyone seeking to stay abreast of the future of investment technology. With its important implications for investment practice, Efficient Asset Management’s highly intuitive yet rigorous approach to defining optimal portfolios will appeal to investment management executives, consultants, brokers, and anyone seeking to stay abreast of current investment technology. Through practical examples and illustrations, Michaud and Michaud update the practice of optimization for modern investment management.

Asset allocation, changes in risk premia and benchmarks Asset allocation typically involves a two-step process with, first, a rigid strategic allocation, second, a time-varying tactical allocation whose performances are properly controlled through the use of benchmarks. This widely-accepted process, however, led most investors to be overly exposed to equity markets when they reached a peak in 1999-2000. It would be a mistake to consider that this overexposure was simply an unfortunate accident. In fact, it is an indication that this asset allocation process does not perform well when markets valuations are exposed to cyclical fluctuations. Since, these valuation cycles are unlikely to wane, a change in the asset-allocation process is in order. In particular, an improved process would render the strategic allocation more responsive to changes in assets’ relative valuations. Various solutions to the current crisis could be considered, differing in particular by the extent of the delegation granted to asset managers. JEL classification : G11, G12, G14

Tactical asset allocation (TAA) is a dynamic investment strategy which seeks actively to adjust fund allocation to a variety of asset classes by systematically exploiting inefficiencies and temporary imbalances in equilibrium values. This approach contrasts with strategic asset allocation (SAA) in which a long-term investment view target allocation is established using a combination of target return and risk tolerance. Asset returns are forecasted using the Capital Asset Pricing Model (CAPM), complemented with results obtained from the Kalman filter. Performance of TAA and SAA approaches are compared using several diagnostic metrics. The TAA approach outperforms its SAA counterpart for most of these metrics for the period under consideration, showing some potential benefits of using this approach.

With the global economy experiencing a prolonged period of low interest rates, investors face new challenges in asset allocation. Asset allocation in a low-interest-rate environment has increasingly become a focus of academic research, leading to a growing body of related literature. This paper provides a comprehensive review of the literature on asset allocation in a low-interest-rate environment from four perspectives. First, the paper reviews literature on the impact of low interest rates on major asset classes, forming the basis for understanding the effects of low interest rates on asset allocation. Second, it examines studies on asset allocation strategies for single asset types and multi-asset portfolios under low-interest-rate conditions. Furthermore, considering that some studies distinguish between types of investors or analyze asset allocation in investment markets across different countries, this paper also summarizes the literature from these two perspectives. In addition to synthesizing the main findings of existing research, this paper identifies gaps in the current literature and provides recommendations for future academic studies.

As a result of the worldwide pandemic, many industries are in need of growth stimuli, and the US equity markets, as the main growth engine, have intrigued investor interest. This article renders a performance analysis using the weights portfolio optimization given as a focal process that is skillfully implemented by financial practitioners. Each portfolio which includes Apple, IBM, Microsoft, Home Depot, Starbucks, NIKE, P&G, QUALCOMM, and JPM, gathers weekly return data from January 2017 to December 2020 and performs a Monte Carlo simulation. The amount of portfolio alternatives will be defined by the strategies being utilized, and it will choose ten stocks from a pool of 100 stocks to form a portfolio. Asset allocation strategies with Mean-Variance and Minimum Variance were constructed using the efficient frontier. Comparing a portfolio's asset allocation performances to that of the benchmark, the S&P 500 index, and an equally weighted strategy portfolio, with respect to asset allocation, the portfolio with the highest Sharpe ratio is the most optimal. The results in this study benefit investors and industry stakeholders in the post-covid era.

We study nearly 7,000 retirement accounts during the April 1994–August 1998 period. Several interesting patterns emerge. Most asset allocations are extreme (either 100 percent or zero percent in equities) and there is inertia in asset allocations. Equity allocations are higher for males, married investors, and for investors with higher earnings and more seniority on the job; equity allocations are lower for older investors. There is very limited portfolio reshuffling, in sharp contrast to discount brokerage accounts. Daily changes in equity allocations correlate only weakly with same-day equity returns and do not correlate with future equity returns.