Asset Allocation

From Theory to Practice and Beyond
 
 
Standards Information Network (Verlag)
  • 1. Auflage
  • |
  • erschienen am 26. Juli 2021
  • |
  • 368 Seiten
 
E-Book | ePUB mit Adobe-DRM | Systemvoraussetzungen
978-1-119-81772-7 (ISBN)
 
Discover a masterful exploration of the fallacies and challenges of asset allocation

In Asset Allocation: From Theory to Practice and Beyond-the newly and substantially revised Second Edition of A Practitioner's Guide to Asset Allocation-accomplished finance professionals William Kinlaw, Mark P. Kritzman, and David Turkington deliver a robust and insightful exploration of the core tenets of asset allocation.

Drawing on their experience working with hundreds of the world's largest and most sophisticated investors, the authors review foundational concepts, debunk fallacies, and address cutting-edge themes like factor investing and scenario analysis. The new edition also includes references to related topics at the end of each chapter and a summary of key takeaways to help readers rapidly locate material of interest.

The book also incorporates discussions of:



The characteristics that define an asset class, including stability, investability, and similarity
The fundamentals of asset allocation, including definitions of expected return, portfolio risk, and diversification
Advanced topics like factor investing, asymmetric diversification, fat tails, long-term investing, and enhanced scenario analysis as well as tools to address challenges such as liquidity, rebalancing, constraints, and within-horizon risk.

Perfect for client-facing practitioners as well as scholars who seek to understand practical techniques, Asset Allocation: From Theory to Practice and Beyond is a must-read resource from an author team of distinguished finance experts and a forward by Nobel prize winner Harry Markowitz.
1. Auflage
  • Englisch
  • Newark
  • |
  • USA
John Wiley & Sons Inc
  • Für Beruf und Forschung
  • Reflowable
  • 4,66 MB
978-1-119-81772-7 (9781119817727)

weitere Ausgaben werden ermittelt
WILLIAM KINLAW, CFA, is a Senior Managing Director and Global Head of State Street's academic affiliate, State Street Associates, a unique partnership that bridges the worlds of financial theory and practice.

MARK KRITZMAN, CFA, is a Founding Partner and Chief Executive Officer of Windham Capital Management, LLC and the Chairman of Windham's investment committee. He is responsible for managing research activities and investment advisory services. He is also a Founding Partner of State Street Associates and teaches a graduate course at the Massachusetts Institute of Technology.

DAVID TURKINGTON, CFA, is a Senior Managing Director and Head of Portfolio and Risk Research at State Street Associates.
Foreword to the First Edition

Preface

Key Takeaways

Chapter 1: What is an asset class

Chapter 2: Fundamentals of asset allocation

Chapter 3: The importance of asset allocation

Chapter 4: Time diversification

Chapter 5: Divergence

Chapter 6: Correlation asymmetry

Chapter 7: Error maximization

Chapter 8: Factors

Chapter 9: 1/N

Chapter 10: Policy portfolios

Chapter 11: The private equity leverage myth

Chapter 12: Necessary conditions for mean-variance analysis

Chapter 13: Forecasting

Chapter 14: The stock-bond correlation

Chapter 15: Constraints

Chapter 16: Asset allocation versus factor investing

Chapter 17: Illiquidity

Chapter 18: Currency risk

Chapter 19: Estimation error

Chapter 20: Leverage versus concentration

Chapter 21: Rebalancing

Chapter 22: Regime shifts

Chapter 23: Scenario analysis

Chapter 24: Stress testing

Chapter 25: Statistical and theoretical concepts

Glossary

Index

Key Takeaways


Chapter 1: What Is an Asset Class?


  • The composition of an asset class should be stable.
  • The components of an asset class should be directly investable.
  • The components of an asset class should be similar to each other.
  • An asset class should be dissimilar from other asset classes in the port- folio as well as combinations of other asset classes.
  • The addition of an asset class to a portfolio should raise its expected utility.
  • An asset class should not require selection skill to identify managers within the asset class.
  • An asset class should have the capacity to absorb a meaningful fraction of a portfolio cost-effectively.

Chapter 2: Fundamentals of Asset Allocation


  • A portfolio's expected return is the weighted average of the expected returns of the asset classes within it.
  • Expected return is measured as the arithmetic average, not the geometric average.
  • A portfolio's risk is measured as the variance of returns or its square root, the standard deviation.
  • Portfolio risk must account for how asset classes co-vary with one another.
  • Portfolio risk is less than the weighted average of the variances or stan- dard deviations of the asset classes within it.
  • Diversification cannot eliminate portfolio variance entirely. It can only reduce it to the average covariance of the asset classes within it.
  • The efficient frontier comprises portfolios that offer the highest expected return for a given level of risk.
  • The optimal portfolio balances an investor's goal to increase wealth with the investor's aversion to risk.
  • Mean-variance analysis is an optimization process that identifies effi- cient portfolios. It is remarkably robust. For a given time horizon or assuming returns are expressed in continuous units, it delivers the correct result if returns are approximately elliptically distributed, which holds for return distributions that are not skewed, have stable correlations, and comprise asset classes with relatively uniform kurtosis, or if investor preferences are well described by mean and variance.

Chapter 3: The Importance of Asset Allocation


  • It is commonly assumed that asset allocation explains more than 90% of investment performance.
  • This belief is based on flawed analysis by Brinson, Hood, and Beebower.
  • The analysis is flawed because it implicitly assumes that the default portfolio is not invested; it thereby fails to distinguish between the risk driven by asset allocation decisions and the risk driven by the fundamental decision to invest in the first place.
  • Also, this study, as well as many others, analyzes actual investment choices rather than investment opportunity. By analyzing actual investment choices, these analyses confound the natural importance of an investment activity with an investor's choice to emphasize that activity.
  • Bootstrap simulation of the potential range of outcomes associated with asset allocation and security selection reveals that security selection has as much or more potential to affect investment performance as asset allocation does.
  • It does not necessarily follow, though, that investors should devote more resources to security selection than asset allocation, because, as argued by Paul Samuelson, it is easier to be successful at asset allocation than security selection.
  • Asset allocation is very important, but not for the reasons put forth by Brinson, Hood, and Beebower.

Chapter 4: Time Diversification


  • It is widely assumed that investing over long horizons is less risky than investing over short horizons, because the likelihood of loss is lower over long horizons.
  • Paul A. Samuelson showed that time does not diversify risk because, though the probability of loss decreases with time, the magnitude of potential losses increases with time.
  • It is also true that the probability of loss within an investment horizon never decreases with time.
  • Finally, the cost of a protective put option increases with time to expira-tion. Therefore, because it costs more to insure against losses over longer periods than shorter periods, it follows that risk does not diminish with time.

Chapter 5: Divergence


  • Investors commonly assume that standard deviations of asset returns scale with the square root of time and that correlations of returns between assets are invariant to the return interval from which they are estimated.
  • Both beliefs rest on the same underlying assumption that returns are serially independent from one period to the next.
  • However, this assumption is empirically false; standard deviations and correlations of longer-interval returns diverge substantially from the standard deviations and correlations estimated from shorter-interval returns.
  • Investors usually attribute this divergence to non-normality of the returns, but instead it is usually driven by nonzero lagged autocorrelations and cross-correlations.
  • The divergence of high- and low-frequency estimates of standard deviations and correlations has important implications for portfolio construction, performance measurement, and risk management.

Chapter 6: Correlation Asymmetry


  • Investors mistakenly believe that diversification is unconditionally beneficial because they implicitly assume that correlations are symmetric.
  • The evidence shows, however, that correlations differ significantly depending on the size and direction of returns.
  • Investors should seek diversification when their portfolios' main growth component is performing poorly and unification when their portfolios' main growth component is performing well.
  • Unfortunately, most asset class pairs exhibit unfavorable correlation profiles characterized by unification on the downside, when it is not wanted, and diversification on the upside, when it is not needed.
  • Investors can employ full-scale optimization, which is introduced in Chapter 12, to construct portfolios that account implicitly for asymmetric correlations.

Chapter 7: Error Maximization


  • Some investors believe that optimization is hypersensitive to estimation error because, by construction, optimization overweights asset classes for which expected return is overestimated and risk is underestimated, and it underweights asset classes for which the opposite is true.
  • We argue that optimization is not hypersensitive to estimation error for reasonably constrained portfolios.
  • If asset classes are close substitutes for each other, it is true that their weights are likely to change substantially given small input errors, but because they are close substitutes, the correct and incorrect portfolios will have similar expected returns and risk.
  • If asset classes are dissimilar from each other, small input errors will not cause significant changes to the correct allocations; thus, again the correct and incorrect portfolios will have similar expected returns and risk.
  • Nevertheless, estimation error is an important challenge to optimization, and investors would be well served to explore ameliorative measures such as Bayesian shrinkage, resampling, and the use of stability-adjusted return distributions.

Chapter 8: Factors


  • Some investors believe that factors offer greater potential for diversification than asset classes because they appear less correlated than asset classes.
  • Factors appear less correlated only because the portfolio of assets designed to mimic them includes short positions.
  • Given the same constraints and the same investable universe, it is mathematically impossible to regroup assets into factors and produce a better efficient frontier.
  • Some investors also believe that consolidating a large group of securities into a few factors reduces noise more effectively than consolidating them into a few asset classes.
  • Consolidation reduces noise around means, but no more so by using factors than by using asset classes.
  • Consolidation does not reduce noise around covariances.
  • Our results challenge the notion that investors should use factors as portfolio building blocks.
  • Nevertheless, factors can be useful for other reasons. Factor analysis can help investors understand and manage risk, harvest risk premiums, and enhance returns for investors who are skilled at predicting factor behavior. But we should weigh these potential benefits of factor investing against the incremental noise and trading costs associated with factor replication.

Chapter 9: 1/N


  • It has been argued that equally weighted portfolios perform better out of sample than optimized portfolios.
  • The evidence for this result is misleading because it relies on extrapolation of historical means from short samples to estimate expected return. In some samples, the historical means for riskier assets are lower than the historical means for less risky assets, implying, contrary to reason, that investors are occasionally risk seeking.
  • Optimization with plausible estimates of expected return reliably per- forms better than equal weighting.
  • Also, equal weighting limits the investor to a single portfolio, regardless of the investor's risk tolerance, whereas optimization offers a wide array of investment choices.

Chapter 10: Policy...


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