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Infrastructure is the backbone of economies and societies, and it is not only governments that invest in digital, energy, transport, and utilities companies. Increasingly, institutional investors have increased their allocations to infrastructure as an asset class. In this article, the authors empirically evaluate four main motives to include infrastructure in the investment mix. Their results show that portfolio diversification, downside risk protection, and stable income yields are all valid reasons for investing in infrastructure. However, the evidence on infrastructure's inflation-hedging capabilities is less compelling, despite its significant role in asset-liability management frameworks. This article offers valuable empirical insights into infrastructure as an investable asset class that has important applications for portfolio construction. [ABSTRACT FROM AUTHOR]

This article explores diversification within the infrastructure investment asset class. The focus is on constructing well-diversified portfolios of unlisted infrastructure equity and the strategic asset allocation benefits of including infrastructure in a multi-asset class portfolio. The authors show that, despite common perceptions, simply increasing the number of assets, sectors, and geographies does not efficiently enhance diversification. Instead, achieving a well-diversified portfolio of infrastructure investments is possible with a limited number of bets, as long as the key risk factors found in these investments are used to build the portfolio accordingly. In a multi-asset class portfolio, they demonstrate that, whatever the investor profile and portfolio optimization objective, unlisted infrastructure equity can play a key role in the total portfolio, especially if it can be invested as an asset class, on a well-diversified basis, for example using the Smart Infra approach described in this article. [ABSTRACT FROM AUTHOR]

Since the Global Financial Crisis, central banks have created an environment in which risk taking and leveraging were the predominant strategies in the markets. Unprecedented central bank stimulus has led to market dynamics in which the availability of market liquidity has replaced the fundamentals of the economy as a driver of investment returns. The COVID crisis ended a 40-year period of low inflation, declining interest rates, and decreasing macro volatility in developed markets. The rise of inflation changed the market dynamics, and the diversification effect between liquid bonds, credit, and equity markets weakened significantly. As the investment market has become less diversified, it has become necessary to find new sources of diversification in portfolios. The author proposes a functional framework for asset allocation under uncertainty of correlation regimes. The functional categories for the asset classes in a portfolio are return-seeking assets, diversifiers, and tail risk hedges. The framework exploits the characteristics of defensive and diversifying alternative risk premium strategies, as well as the artificial diversification effect of illiquid asset classes. [ABSTRACT FROM AUTHOR]

Ian Toner, from Verus, discusses the firm's investment strategy and how it adapts to changing market conditions. The firm uses the same investment team for all clients, but the application of the strategy varies based on the client's decisions. The process includes setting the strategic asset allocation, introducing short-term tilts, and active manager selection. The firm focuses on measuring twice and cutting once, with a long-term and risk-focused approach. They prioritize meeting investment goals while staying within the client's risk tolerance. The firm also emphasizes the importance of understanding and implementing ESG factors, although they have chosen not to sign any industry commitments. Toner's leadership style emphasizes discipline, skepticism, and humility. The firm considers global economic trends in their investment decisions and prepares for potential international crises through ongoing research. Toner advises aspiring CIOs to learn from others and embrace continuous learning. The biggest opportunities and threats in the asset management industry lie in technological changes, but the firm remains focused on meeting client needs. [Extracted from the article]

The Journal of Portfolio Management has published a special issue that includes in-depth interviews with four chief investment officers (CIOs) and five theme-oriented articles on critical investment topics. The interviews provide insights into the decision-making processes and strategies of influential asset management companies. The CIOs emphasize the importance of long-term strategies, technology, risk management, client expectations, and leadership styles. The theme-oriented articles cover topics such as fixed-income investing, portfolio diversification in the post-COVID era, leadership in investment management, and the complexities of integrating environmental, social, and governance (ESG) factors into investment portfolios. The articles offer valuable insights into the evolving investment landscape and provide practical knowledge for market professionals. [Extracted from the article]

A goals-based methodology built on an intertemporal CAPM (ICAPM) asset allocation framework provides a goal-relative definition of risk for portfolio selection and risk management, efficient capital allocation to fund goals, and efficient goal-aligned subportfolios that accrue to an efficient total portfolio. It offers an elegant goals-based solution for both high-net-worth investors with significant financial assets and retirement accumulators with significant human capital. [ABSTRACT FROM AUTHOR]

As another US presidential election approaches, investor interest in the impact of these elections on asset returns and the existence of a presidential puzzle reemerges. To help investors decide on whether or not they should consider presidential elections and related criteria in making tactical asset allocation decisions, we update prior literature to include more recent elections and we directly test for differences in real returns on various asset classes. Considered independently, there is evidence that the party of the President, political gridlock, and monetary policy continue to have a relationship to stock, bond, and T-Bill returns that continues to diminish over time. Examining these relationships in a simultaneous, multivariate setting yields similar results. In an efficient frontier portfolio setting, Democrats provide investors with better risk-return tradeoffs than Republicans for stock, government bond, and T-Bill allocations with higher allocation toward equities, but Republicans have provided better tradeoffs for conservative investors that are more weighted toward bonds and T-Bills. As the election approaches, active investors should remain cognizant of a presidential puzzle and related risk-return tradeoffs. [ABSTRACT FROM AUTHOR]

Despite the long history of calculating inflation betas, estimates of investment returns' response to the temporary and persistent components of inflation are strikingly absent. This article attempts to fill that void. Using officially reported inflation as well as real economic, survey, and time-series measures, the author decomposes inflation and estimates the reaction of stocks, bonds, and Treasury bills to its components. Persistent inflation is destabilizing, can harm economic growth, and, if this article's findings are reliable, is negatively associated with equity returns. But it is rare. Large relative-price changes are common, but equities may react indifferently to them. Portfolio inflation protection isn't free: It comes at the expense of allocations to higher-return asset classes. The author's findings might affect the amount of costly inflation insurance practitioners require. [ABSTRACT FROM AUTHOR]

This study aims to improve asset allocation by combining traditional finance theory and behavioral finance. The authors construct efficient portfolios using modern portfolio theory (MPT) and identify an appropriate portfolio for a particular investor through a risk assessment score (RAS). The identified portfolio is not only optimal but also suitable based on the investor's risk profile. An RAS questionnaire is designed to produce an additive score in the range of 0 to 100 (i.e., a percentage) that maps linearly to the range of the tangency slope. Their framework is highly flexible and cost effective in terms of both capital market assumptions and the delineation of investor preferences. To emphasize the flexibility of the process, they provide an example questionnaire for retail investors in India, as well as an illustration of a three-asset-class efficient frontier. They also briefly discuss the ability of mapping responses to qualitative inquiries to the tangency slope in the allocation decisions of institutional investors (e.g., defined-benefit pension funds). [ABSTRACT FROM AUTHOR]

Our article contains three critical elements. First, by way of motivation, we consider active optimal mean-variance portfolios within the confines of a three-risky-asset model (based on common equity, corporate bonds, and long-dated Treasury securities) and a short-term, riskless security. Within that context, our article models and numerically demonstrates the impact of the magnitude and sign of the interest-rate term premium in optimal asset allocation. Then, based on interest-rate survey data, we demonstrate the occurrence of a negative term premium in the Treasury market, and proceed to permit such an occurrence within a model whereby asset returns covary with a market portfolio. Finally, turning to empirics, we document the relationship between the sign of the term premium and the sign of the covariance between Treasury and equity returns. Recognizing the deficiency in using historical data as inputs to active optimal mean-variance portfolios, we seek to obtain forward-looking inputs to render our model prospective (rather than merely retrospective): we design and implement tests for obtaining forward-looking measures of covariation. [ABSTRACT FROM AUTHOR]

Allocation of investment assets is key in attaining a sustainable retirement portfolio. In this research article, the authors analyzed the most recent research publications in the area related to asset allocation for retirement and identified those which have the highest impact. The authors' research was conducted using the bibliometric analysis technique of research articles collected from the Scopus database. Most of the research articles were published in reputed journals in the United States, United Kingdom, Australia, and Germany. It was also observed that most of the highly cited research articles in the research area of asset allocation for retirement are focused on financial literacy, increase in retirement age, aging, and pension reforms. The authors' findings identified six research themes in asset allocation for retirement such as 1) asset allocation for retirement planning, 2) methods to increase efficiency, 3) investment preferences for retirement savings 4) financial literacy and retirement planning, 5) reforms on retirement savings, and 6) annuities for retirement income. Furthermore, nineteen future research directions are also provided. In conclusion, the authors aim to assist future researchers in identifying highly cited articles, key authors, contributing countries and research themes in asset allocation for retirement. Overall, the analysis provides comprehensive information in addressing research questions in the field of asset allocation for retirement. [ABSTRACT FROM AUTHOR]

The aggregate portfolios used by end-clients (e.g., pension, endowment, or retired couple) are most often broadly diversified across stocks, bonds, and alternatives. Such portfolios are constructed and maintained using a widely accepted portfolio construction approach. This approach is frequently shorthanded as "modern portfolio theory" or MPT. A simplified version of this approach assumes random variable distributions (to represent the future returns for each asset category) and associated defining summary statistics (means, standard deviations, and correlations). This is the approach upon which all retail investment software tools are based and underlies most institutional asset allocation and asset/liability modeling efforts. For this approach to be successful, the statistical behavior of the underlying asset categories must satisfy certain behavioral properties. Specifically, (a) means must be sufficiently stable, (b) return distributions must not suffer from regime change, (c) distributions must not deliver risk without return over excessively long time periods, (d) the implementor cannot ex-post cherry pick the best geography and most attractive time period and then base the results on that after-the-fact handpicked-selection, (e) return distributions must not be defined by extreme episodic eras, and (f) excessive levels of trending and momentum must not be present. The objective of this article is to provide meaningful statistical evidence that each of these six requirements is violated. If true, then our industry's generally accepted portfolio construction process is suspect. The article ends with three suggestions for portfolio construction techniques that are generally immune to the issues raised and suggests why forward-looking capital market assumptions are unable to overcome the challenge. [ABSTRACT FROM AUTHOR]

The authors estimate the alpha and beta of private equity investments using their desmoothed quarterly returns. Although computing the alpha and beta using a lagged linear regression between private and public market returns is straightforward, reconstructing desmoothed private market returns is trickier. A time series of desmoothed private capital returns has application in asset allocation, risk modeling, and return attribution. The authors use a state space modeling framework in which the desmoothing and the parameter estimation are done simultaneously using Bayesian inference. This is likely the first attempt to solve a full Bayesian model that can simultaneously estimate a desmoothed time series of returns as well as perform a lagged linear regression to estimate capital asset pricing model (CAPM) style parameters for private capital. The authors perform extensive model checking, look out for the model's convergence, and resolve any issues leading to overfitting. The estimates are precise with very low Monte Carlo simulation errors and are also in alignment with those in the literature, which validates this model and desmoothing technique. [ABSTRACT FROM AUTHOR]

The growing trend of sovereign wealth and pension funds to allocate more toward private investments has made the management of asset allocation more complex. Traditional rebalancing methods, such as fixed-weights rebalancing, encounter problems when applied to private assets, as their illiquidity and lags in appraisal valuations pose challenges. During financial crises, the delayed and smoothed valuations of private assets lead them to be overweight in portfolios as public assets decline in values. Rebalancing the underweight public assets can increase leverage usage and, more importantly, deteriorate the fund's liquidity position. To address these challenges, this article proposes a holistic rebalancing strategy: rebalance a portfolio to the desired factor allocation by complementing the factor exposures of existing private assets with an allocation to public assets that overall delivers the required factor allocation. This approach safeguards the liquidity position of a fund during market downturns by maintaining a stable risk and leverage profile. It presents a dynamic and risk-aware approach for rebalancing portfolios with private assets. [ABSTRACT FROM AUTHOR]

This article begins with a discussion of private fund performance metrics and turns to the closely related topic of performance benchmarking, a crucial issue in light of the widely held belief that private equity and venture capital investments outperform publicly traded assets. The authors then discuss how to estimate the alpha and beta of private investment funds and move onto a second set of topics concerned with portfolio construction and risk management. They examine the distorting effects that serially correlated return streams can have on risk analysis and asset allocation decisions and finally conclude with a discussion of practical remedies that allocators can employ to mitigate the risks of owning long-dated illiquid assets. [ABSTRACT FROM AUTHOR]

This article introduces a practical method to maximize expected after-tax wealth and to fine-tune risk management utilizing linear programming instead of traditional Markowitz mean-variance optimization (MVO). Our simpler approach simultaneously optimizes asset allocation and asset location while considering capital gain impacts at the tax lot level. It is designed to be applied to portfolios individually. The approach's primary benefits include: explicit tax consideration across multiple types of accounts and tax-lot analysis within the optimization model, the ability to control risk under a variety of scenarios, clearer client communication via explicit quantification of the change in expected future after-tax wealth and the performance of the portfolio under selected scenarios, elimination of model-based and rule-of-thumb allocations, and better portfolios through customization. The technique can both identify a client's risk budget and rebalance portfolios. [ABSTRACT FROM AUTHOR]

Smoothed returns that can occur as a result of an appraisal-based valuation process distort asset characteristics and, in particular, volatility and correlations. Using a simple but often-used model for smoothed return series, this article analyzes what unsmoothing techniques can achieve with respect to uncovering the true time-series properties. The properties of interest are volatility, autocorrelation, self-correlation, and cross-asset correlation. It is shown that, specifically for heavily smoothed return series, volatility estimates might overstate the true volatility, and cross-asset correlations cannot fully be recovered. [ABSTRACT FROM AUTHOR]

The future path of interest rates is highly uncertain, but we can at least be fairly confident that the level of rates will be substantially higher in the medium term than it has been in recent decades—in the United States and many other major economies. What, if any, are the implications for asset allocation? Does a higher-cash-rate tide lift all asset return boats? This article presents a simple empirical analysis covering stocks, bonds, and alternative investments and compares those historical patterns to yield-based expected returns as of early 2024. One result is clear: Equity markets have earned slimmer excess returns on average when the cash baseline is higher. The author discusses the role of so-called cash-plus liquid alternative strategies—overlooked beneficiaries of higher cash rates—and concludes with a simple allocation case study. In a higher-rate world that investors haven't seen for many years, diversification away from equities may prove to be especially valuable. [ABSTRACT FROM AUTHOR]

The supervised portfolios approach is an effective asset allocation strategy that engineers optimal weights before feeding them to a supervised learning algorithm. Yet, supervised learning algorithms are often seen as opaque, which undermines trust in those models, thereby limiting their adoption. To alleviate this issue, the authors apply an enhanced version of RuleFit, an intrinsic interpretable algorithm, that transforms a black box nonlinear predictive algorithm into a simple combination of rules. It fits a sparse linear model that includes feature interactions derived from decision tree ensembles. The first empirical analysis illustrates that the interpretable approach is statistically as accurate as gradient boosting on three different investment universes. The second analysis highlights which characteristics and interactions matter for an equity portfolio manager. [ABSTRACT FROM AUTHOR]

Research and product development efforts aimed at addressing retirement income needs have generally been focused on the accumulation side of financial planning. However, innovation and product development on the decumulation side of the equation has been limited. In this article, we utilize an asset allocation model for retirement that can include non-traditional investments to demonstrate how annuities are an integral component of a multi-asset portfolio, aiding in the management of risks unique to retirees and near-retirees. For optimal decumulation allocation, we propose a methodology that maximizes a measure of household financial well-being that considers the two most important, yet often conflicting, objectives in the decumulation phase of the retirement: a) minimizing shortfall risk, and b) maximizing potential bequest. We use this model to demonstrate how annuities could be a prudent addition to a portfolio of traditional stocks and bond investments to better manage sequence of returns or sequence of cash flows risk. [ABSTRACT FROM AUTHOR]

The authors propose a framework for long-term cross-asset portfolio choice in which the estimation of the covariance matrix is subject to climate risk. They model the future volatility and correlation of assets as a linear function of three types of forward-looking variables: the long-term future average, climate-aware projections of economic indicators, and scenarios for the temperature anomaly. They analyze the shifts from a baseline 60/40 equity/bond allocation when taking climate risk into account. The takeaways are the following: 1) these changes are small and mostly favorable to bonds if the focus is on the estimation of risk components; 2) including climate-driven expected returns in the optimization substantially alters the compositions but to the benefit of equities; 3) in all cases, the risk-adjusted returns decrease, often significantly, when taking climate impact into account. [ABSTRACT FROM AUTHOR]

High inflation and aggressive monetary policy tightening in 2022 triggered one of the largest return drawdowns for a US 60/40 portfolio in the last 100 years. In this article, the authors develop a dynamic asset allocation framework based on macro regimes using machine learning to improve the risk/reward versus static balanced portfolios with higher macro volatility. Using both macro and market data, they construct indicators for growth, inflation, and policy to track the business cycle and for risk appetite since 1950. They then use a random forest algorithm on those indicators to identify macro regimes that drive tail risks that matter for portfolio construction around a US 60/40 equity/bond portfolio. Based on real-time regime probabilities, they implement one of three dynamic asset allocation overlays: 1) switch between a 60/40 portfolio and cash, 2) rotate between equities and bonds, and 3) allocate to commodities/gold. The overlays materially enhanced risk-adjusted returns compared with a static 60/40 portfolio since 1950, although results were mixed over time. [ABSTRACT FROM AUTHOR]

Risk parity methods focused on volatility have gained traction in the last decade. A few extensions have been proposed, including tail risk parity. The authors show that, at its limits, tail risk parity converges toward the risk parity portfolio or the tangency portfolio. The authors also introduce a new risk parity measure called "outcome risk parity," which allows for asset return predictability. They find that outcome risk parity allocates more weight to assets with mean reversion and less weight to assets with momentum. The outcome risk parity portfolio reduces to volatility risk parity when returns do not exhibit autocorrelation. Finally, to mitigate some of the challenges associated with utility-based asset allocation approaches, the authors suggest a method to integrate outcome risk parity into a multiperiod expected utility model by using a penalty function. [ABSTRACT FROM AUTHOR]

This article is an introduction to a special issue on multi-asset strategies and asset allocation. It discusses the importance of portfolio rebalancing in maintaining a consistent risk level and avoiding impulsive decisions during market volatility. The article also covers topics such as quantifying portfolio diversification benefits, diversifying multi-asset multi-factor portfolios, personalized target-date funds, risk parity strategies, dynamic asset allocation using machine learning, time-varying factor allocation, alternative risk premia strategies, and the impact of climate change risk on long-term asset allocation. The authors provide insights, frameworks, and empirical findings to help investors make informed decisions in these areas. [Extracted from the article]

This article aims to reconcile theory and practice by proposing a dynamic model of asset allocation that allows, with the help of genetic algorithms, the creation of reasonable portfolios for investors chasing a variety of objectives over multiple time horizons. Our results are interesting and confirm the efficiency of the model, due to its good convergence toward the space of best solutions in a limited computation time. The methodology may be applied in the fields of: 1) wealth management, to define approaches that are useful for investors whose wealth must be invested to offset future liabilities; and 2) pension funds, to define--based on an individual's pension needs--personalized investment dynamics that are useful for meeting retirement needs. [ABSTRACT FROM AUTHOR]

Sustainable investing refers to a range of practices in which investors work toward financial goals while incorporating environmental, social, and governance (ESG) considerations into the investment process. There is growing suspicion that many firms and investment companies may have only a nominal commitment to sustainability. We investigate mutual funds and exchange-traded fund (ETFs) that report a sustainability mandate in their prospectus, as identified by Morningstar, and find that 85% of these mutual funds and 60% of these ETFs possess a Morningstar Sustainability rating in the top one-third of all funds. Morningstar ratings indicate that while mandated mutual funds have done well in the past, their short-term financial outlook, according to analysts, is below their non-mandated peers. Additionally, mandated mutual funds tend to invest in stocks with better longer-term economic moats and typically have lower expense ratios, but are more likely to carry a load charge. Mandated ETFs offer investors lower expenses than mandated mutual funds but do not have as strong of a commitment to sustainability. Although Morningstar's Analyst ratings predict similar performance, mandated ETFs with Quantitative ratings are projected to outperform not only their non-mandated peers but also mandated mutual funds. Overall, mandated funds appear to be walking the sustainability walk and not charging more for doing so, allowing investors to include ESG considerations in the selection process without being financially impaired. [ABSTRACT FROM AUTHOR]

This article explores the factors that drive risk and active risk in asset manager model portfolios. The study reveals that model portfolios with different allocations to index and active funds have similar levels of total and active risk. It also finds that a significant portion of the active risk in model portfolios is due to style factors, although the average model has limited exposure to commonly rewarded style factors. The article suggests that there is room for improvement in model performance by adjusting factor exposures. The data used in the analysis comes from Morningstar and BlackRock's Aladdin Risk system. The article discusses the impact of asset allocation on active risk calculations, including the potential for double counting certain exposures in fixed income indexes. It also notes that the Aladdin Risk Model used in the study does not capture the impact of asset class timing. The results show that there is little variation in total risk between models using active funds and index funds. The article further examines active risk relative to peer and custom benchmarks, as well as asset class risk decompositions. It concludes that style factors contribute the most to risk. This document focuses on the factor and risk exposures of model portfolios, which are increasingly popular among individual investors. The analysis primarily looks at equity and fixed income exposures and finds that most model portfolios have minimal exposure to rewarded style factors such as size, value, momentum, profitability, and volatility. The study suggests that model builders could potentially enhance returns by adjusting the magnitude of their style factor exposures. Additionally, the document highlights the potential for future research on performance [Extracted from the article]

This article examines the relationship between asset classes and risk factors in portfolio management. It explains that asset class returns are influenced by common risk factors associated with equities and fixed income. The article suggests that portfolios diversified across risk factors are more resilient during market turbulence than those allocated across traditional asset classes. It also discusses the importance of monitoring changes in risk factor exposures over time. The author argues that risk factor models can be used in portfolio optimization without the need for a new optimizer. The article explores various risk premiums, such as equity market beta, value, size, and momentum, and discusses their potential as compensation for risk or behavioral anomalies. It highlights the success of the "betting against beta" strategy, which leverages low-risk assets and shorts high-risk assets, but also questions its practicality due to the overweighting of micro- and nano-cap stocks. The article also examines the volatility risk premium and the use of macro factors in investment decision-making. It emphasizes the importance of considering live track records, theoretical foundations, and potential crowding when evaluating risk premiums. The author suggests using the factor approach in risk models and considering small allocations to robust strategies. [Extracted from the article]

Missing from institutional investing today is excellence. Complexity and supposed sophistication abound. But these are not the same as excellence. Most institutional investors today underperform passive management by 1 to 2 percentage points a year. They rely on dubious asset allocation architecture predicated on mythically "uncorrelated" asset classes to justify large commitments to labor-intensive alternative investments, where exceptional profit opportunities were exhausted years ago. They invest to their detriment via countless active management strategies. They claim success while running against slow rabbits of their own devising. Inefficient diversification is the crux of the problem. Institutional investors have two paths to excellence. One is to engineer consummately efficient passive investment portfolios that operate at next to no cost. The other path is to get serious about active investing. This means using vastly fewer investment managers along with enough indexing for efficient diversification. [ABSTRACT FROM AUTHOR]

Changes in how private venture-backed companies raise capital to fund their growth have altered the nature of capital markets. The implication of these changes is enormous, as private equity allocations occupy a considerable share of institutional portfolios and are gaining popularity in retail portfolios, too. However, accurately measuring the returns and risks of late-stage venture investments without the right tools can be challenging. We introduce a new mark-to-model approach to value venture-backed companies. The model supports the creation of benchmarks that provide more timely insights into the late-stage venture market and improve the understanding of the behavior of this market. Ultimately, it will aid in making better decisions regarding asset allocation and portfolio construction. [ABSTRACT FROM AUTHOR]

Credit spreads behavior displays a range of features that are challenging to model--strongly fat-tailed distribution of changes, periods of relative stability interrupted by prolonged violent shifts, lack of symmetry in spread rises vs. falls, etc. This article proposes a new model for spread behavior that incorporates these peculiarities without bringing excessive mathematical complexity. At the core of our approach is a Hidden Markov Model (HMM) that assumes that spreads follow a 2-state stochastic process. In a key departure from traditional HMM, the authors introduce explicit auto-regression into their formulation. The assumption behind that innovation is that while regime switches may be instantaneous and regimes may be characterized by different spread "fair" levels, the transition between such levels is gradual as opposed to instantaneous. As they illustrate, this assumption is critical for proper description of the spreads dynamic. The model lends itself well to tactical asset allocation involving high-yield credit assets and in a broad, multi-asset class setting. [ABSTRACT FROM AUTHOR]

Risk budgeting (RB) portfolio optimization is one of the popular methods in asset allocation. The key benefit of this method is to control the risk contribution of each asset individually and reduce the unnecessary fluctuation in the allocation by not relying on the expected return of assets. The RB portfolio optimization requires one important parameter, a risk budget vector, and the portfolio performance is strongly influenced by the delicate choice of the values in this vector. Moreover, if the risk strategy allows deviation from a predefined risk budget, then it introduces the problem of finding the optimal time-dependent risk budget deviations. In this article, the author presents a reinforcement learning framework that can select this critical parameter optimally by learning how to control time-dynamic risk budgets in an automated and efficient manner. The experiment result shows that our agent can improve the target performance metric with statistical significance in the different asset universes, indicating that our agent can pick close to optimal risk budget deviations based on the learned policy. [ABSTRACT FROM AUTHOR]

The authors first characterize a variety of systematic strategies in terms of timing and sizing skills based on their frequencies and magnitudes of gains and losses over time and then move on to analyze how these characteristics differ over macroeconomic regimes, such as inflationary and recessionary periods. The results are based on new methodologies for significance testing of gain- and loss-based performance measures and complement previous results based on analysis of the Sharpe ratio of these strategies. The empirical results have implications for outcome-orientated portfolio construction as well as strategic and tactical asset allocation, because strategies that have desirable properties--as well as strategies with problematic performance--are identified under each regime. This approach also allows for identification and attribution of the inherent sources of these differences. [ABSTRACT FROM AUTHOR]

Well-being is more than financial well-being. The domains of well-being also include family and friends, health, both physical and mental, education, work and activities, values, religion and spirituality, and more. Whereas many financial advisers see themselves as wealth managers, focusing on maximizing clients' wealth, well-being advisers strive to maximize clients' well-being. Financial advisers must evolve into well-being advisers if they are to compete for today's clients and future ones, because many of the traditional services of financial advisers, whether portfolio asset allocation or portfolio rebalancing, are now generic, provided by robo-advisers at lower costs. Well-being advisers enhance not only the well-being of their clients but also their own, turning a job and career into a vocation whereby they do good for themselves as they do good for their clients. [ABSTRACT FROM AUTHOR]

We characterize the wealth management literature that The Journal of Wealth Management helped define over the last quarter century and then assess its impact. Eight key areas of emphasis include goals-based asset allocation, tax barriers, the extended portfolio, asset location, after-tax asset allocation, tax-loss harvesting, concentrated portfolios, and qualitative matters. The Journal of Wealth Management has contributed valuably in each of these areas and is increasingly well regarded among academic researchers. Some gaps in the literature, however, remain. [ABSTRACT FROM AUTHOR]

Asset allocation should rely on a sound theoretical foundation that is empirically valid and robust in practice. Intertemporal CAPM (ICAPM) portfolio theory resembles the hedging/return-seeking portfolios approach sometimes used in practice, but with a sound theoretical foundation, empirical support, and attractive features for functional implementation. ICAPM portfolio theory largely resolves key issues with modern portfolio theory and standard CAPM portfolio theory, while providing a unified framework for liability-relative, goals-based, and asset-only asset allocation. The author documents the application of ICAPM portfolio theory to practice, addressing key implementation and technical issues related to the liability hedge, risky-asset portfolio optimization and constraints, portfolio selection and Monte Carlo simulation, and extensions to goals-based and asset-only asset allocation. [ABSTRACT FROM AUTHOR]

The article focuses on a webinar held in October 2022, discussing multi-asset strategies. Topics covered include the roles of risk allocation and factors in multi-asset portfolios, the roles of alternative investments in portfolios, and diversification in today's markets amidst shocks to macro factors like economic growth, inflation and real interest rates.

Inflation risk poses a significant challenge to strategic asset allocators and is forcing many to reevaluate the suite of quantitative tools they use. In particular, standard simulation methods are inadequate for modeling inflation dynamics and do not generate uncertainty in long-term average inflation. Furthermore, despite low-frequency regime switching between negative and positive stock-bond correlation regimes, linked to inflation dynamics, standard methods do not incorporate this either. Finally, when markets are undergoing long-term structural changes, modeling choices should be able to integrate the forward-looking expectations of subject matter experts on joint economic and market dynamics. This article describes a simple way of retrofitting these features to an existing simulation engine. [ABSTRACT FROM AUTHOR]

Although regret can impact the ex post perceived quality of investment decisions, it is not something that is typically explicitly considered when building portfolios. Even so, both retail investors (i.e., households), who tend to be less sophisticated and more likely to exhibit trend chasing, and institutional investors, who tend to have either implicit or explicit performance benchmarks, are subject to regret. This article introduces an objective function to incorporate regret aversion into portfolio optimizations as a parameter distinct from risk aversion and explores the implications of regret on an individual stock portfolio. Considering regret can result in notable changes in optimal portfolio weights, leading to higher allocations to relatively inefficient and potentially risky assets, although the portfolio impact varies depending on investor preferences and modeling assumptions. [ABSTRACT FROM AUTHOR]

The article focuses on multi-asset portfolios in context of a new order characterized by heightened risks in various economic and market environments. Topics include the importance of resilience in multi-asset portfolios, risk mitigation strategies, and the role of asset and factor allocations in managing these portfolios. .

Market conditions change over the course of the business cycle. When are investors compensated to take risk? And what type of risk? This article proposes a practical regime-based framework for tactical asset allocation (TAA), combining leading economic indicators and global risk appetite to identify four macro regimes: recovery, expansion, slowdown, and contraction. The authors document distinct performance characteristics across regimes for traditional asset classes and their underlying risk factors, focusing on the term premium, credit premium, and equity premium. They provide simple and practical examples of TAA strategies for long-only multi-asset and fixed-income portfolios with the potential to generate attractive excess returns. Results are statistically significant and economically relevant after transaction costs, with information ratios between 0.70 and 0.80. [ABSTRACT FROM AUTHOR]

Long-term investors tilt their portfolios given their views on the evolving investment landscape. In the literature, portfolio tilting is often implemented with methodologies that use investors' views on point estimates of conditional assets' expected returns. These conditional return expectations are notoriously difficult to estimate, and using them often results in unstable portfolio weights when existing methodologies are applied. The authors avoid such shortcomings by providing a methodology that incorporates views on the likelihood of economic regimes (e.g., growth and inflation surprises) instead. Using data on equities, bonds, and commodities, the authors show--both in simulation and empirically--that this approach generates stable portfolio weights and outperformance that is minimally affected by forecast errors. [ABSTRACT FROM AUTHOR]

Implementing established corporate bond factors in real-world portfolios poses many challenges for investors. First, the investment universe is reduced by non-tradable assets. In this article, the authors avoid investing into these bonds by constructing a tradability measure based on past transaction data. A realistic strategy to implement should target bonds that are being traded in the market. Second, factor performance is typically measured against a benchmark. To allow for a fair comparison, the authors limit the deviation between the portfolio and the benchmark in key dimensions. Consequently, not only bonds with the best signals can be included in the portfolio. Overall, this reduced universe, along with other restrictions and higher transaction costs than on the equity side, leads to a substantial performance decrease. Nevertheless, the authors show that factor investing in credit is still a successful strategy if it is approached with realistic expectations and common pitfalls are avoided. Their enhanced approach translates signals into usable trading strategies, outperforming the benchmark with an information ratio of 1.1 (1.4) for investment grade (high yield). [ABSTRACT FROM AUTHOR]

The authors develop a methodology to perform a Brinson-style attribution over a set of factors that may have continuous exposures to the assets. They show that for a set of factor returns, the Brinson-style allocation and selection effects are synonymous with the factor and specific contributions respectively. This puts an end to an open question of how to make sense of two different kind of attributions, namely, Brinson attribution over sectors and factor attribution over factors. The authors show that they are identical if the factor returns used in the attribution completely explain the underlying benchmark portfolio. Their method is simple, intuitive, and reduces to the standard Brinson attribution for the case of binary factors, such as industry, country, and currency factors. [ABSTRACT FROM AUTHOR]

Factor investing has been around for several decades, backed by an enormous body of literature, and yet it is still surrounded by much confusion and debate. Some of the rhetoric and myths have existed for a long time, while others have arisen in response to the difficult performance from 2018 to 2020 and the subsequent turnaround. This article examines many claims about factor investing; some are timeless, while others are focused on specific concerns that have emerged recently. The authors reference an extensive academic literature and perform simple, yet powerful, analysis to address these claims. [ABSTRACT FROM AUTHOR]

Using filtering techniques, spectral analysis, and Markov chain models, the author documents trends and cycles of factors that have significantly changed over the period to December 2000 compared with the period post-January 2001. The recent weaker performance of the value factor in the 21st century, including the value drawdown over 2017 to 2022, which is the worst value drawdown ever experienced, can be attributed to both a decreasing trend component and downturns in cyclical components. Momentum performance has also declined in the post-2001 period due to decreasing trends, while the trends of the quality and size factors have increased. Low-volatility portfolios still significantly reduce equity market risk in the 21st century, but the factor spends slightly longer durations in a low return regime. [ABSTRACT FROM AUTHOR]

The article focuses on the concept of "factor purity" in investment portfolios, emphasizing the importance of managing and measuring all systematic exposures that can affect returns. Topics discussed include the benefits of factor investing, distinguishing active factor management from naive approaches, and incorporating new factors into the risk management framework.

The article focuses on the practical challenges in designing and managing factor strategies in the context of factor investing. Topics discussed include the emergence of factor investing, the impact of the Global Financial Crisis (GFC) on factor strategies, the importance of defining factors, the complexities of portfolio construction in factor investing, and the challenges of attribution in explaining factor strategy performance.

The article focuses on factor investing and its relevance in the current investment landscape. Topics discussed include identifying relevant factors such as size, value, momentum, quality, and low risk, the difficulties in rationalizing momentum as a risk factor, and the various approaches to combining factors in a portfolio.

The article discusses about various research topics in financial data science. topic introduces a novel asset allocation approach using a classification and regression tree (CART) model trained on efficient frontier coefficients for market forecasting. it delves into the impact of technology, big data, and analytics on the evolving data-driven model of innovation in the finance industry.