Modern Asset Allocation for Wealth Management

Modern Asset Allocation for Wealth Management
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An authoritative resource for the wealth management industry that bridges the gap between modern perspectives on asset allocation and practical implementation An advanced yet practical dive into the world of asset allocation , Modern Asset Allocation for Wealth Management provides the knowledge financial advisors and their robo-advisor counterparts need to reclaim ownership of the asset allocation component of their fiduciary responsibility. Wealth management practitioners are commonly taught the traditional mean-variance approach in CFA and similar curricula, a method with increasingly limited applicability given the evolution of investment products and our understanding of real-world client preferences. Additionally, financial advisors and researchers typically receive little to no training on how to implement a robust asset allocation framework, a conceptually simple yet practically very challenging task. This timely book offers professional wealth managers and researchers an up-to-date and implementable toolset for managing client portfolios.  The information presented in this book far exceeds the basic models and heuristics most commonly used today, presenting advances in asset allocation that have been isolated to academic and institutional portfolio management settings until now, while simultaneously providing a clear framework that advisors can immediately deploy. This rigorous manuscript covers all aspects of creating client portfolios: setting client risk preferences, deciding which assets to include in the portfolio mix, forecasting future asset performance, and running an optimization to set a final allocation. An important resource for all wealth management fiduciaries, this book enables readers to: Implement a rigorous yet streamlined asset allocation framework that they can stand behind with conviction Deploy both neo-classical and behavioral elements of client preferences to more accurately establish a client risk profile Incorporate client financial goals into the asset allocation process systematically and precisely with a simple balance sheet model Create a systematic framework for justifying which assets should be included in client portfolios Build capital market assumptions from historical data via a statistically sound and intuitive process Run optimization methods that respect complex client preferences and real-world asset characteristics Modern Asset Allocation for Wealth Management is ideal for practicing financial advisors and researchers in both traditional and robo-advisor settings, as well as advanced undergraduate and graduate courses on asset allocation.

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David M. Berns. Modern Asset Allocation for Wealth Management

Table of Contents

List of Illustrations

Guide

Pages

Modern Asset Allocation for Wealth Management

Preface

Acknowledgments

CHAPTER 1 Preliminaries

EXPECTED UTILITY. Introduction

MPT Is an Approximation

Higher Moment Motivation

Modernized Preference Motivation

A Modern Utility Function

Returns-Based EU Maximization

ESTIMATION ERROR. Introduction

Minimizing Estimation Error

Reducing Sensitivity to Estimation Error

A MODERN DEFINITION OF ASSET ALLOCATION

NOTES

CHAPTER 2 The Client Risk Profile

INTRODUCTION

MEASURING PREFERENCES. Risk Aversion

Loss Aversion

Reflection

Lottery Question Sizing

INCORPORATING GOALS. Preference Moderation via SLR

Discretionary Wealth

Comparison with Monte Carlo

Comparison with Glidepaths

NOTES

CHAPTER 3 Asset Selection

INTRODUCTION

MOMENT CONTRIBUTIONS. Overview

Calculation

Utility Contribution

MIMICKING PORTFOLIOS

A NEW ASSET CLASS PARADIGM. Overview

A Review of Risk Premia

From Assets to Asset Classes

NOTES

CHAPTER 4 Capital Market Assumptions

INTRODUCTION

USING HISTORY AS OUR FORECAST. Background

Estimation Error and Sample Size

Stationarity: Does History Repeat?

ADJUSTING FORECASTS. Pre-Tax Adjustments

Post-Tax Adjustments

NOTES

CHAPTER 5 Portfolio Optimization

INTRODUCTION

OPTIMIZATION RESULTS

TO MPT OR NOT TO MPT?

ASSET ALLOCATION SENSITIVITY

FINAL REMARKS

NOTES

Bibliography

Index

WILEY END USER LICENSE AGREEMENT

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Founded in 1807, John Wiley & Sons is the oldest independent publishing company in the United States. With offices in North America, Europe, Australia, and Asia, Wiley is globally committed to developing and marketing print and electronic products and services for our customers' professional and personal knowledge and understanding.

The Wiley Finance series contains books written specifically for finance and investment professionals, as well as sophisticated individual investors and their financial advisors. Book topics range from portfolio management to e-commerce, risk management, financial engineering, valuation, and financial instrument analysis, as well as much more.

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Chapter 4. Capital Market Assumptions. This chapter justifies the use of historical return distributions as the starting point for asset class forecasts. We review techniques that help diagnose whether history indeed repeats itself and whether our historical data is sufficient to estimate accurately the properties of the markets we want to invest in. A system is then introduced for modifying history-based forecasts by shifting and scaling the distributions, allowing advisors to account for custom forecasts, manager alpha, manager fees, and the effects of taxes in their capital market assumptions.

Chapter 5. Portfolio Optimization. In the fifth and final chapter, we finally maximize our new three-dimensional utility function over the assets selected and capital market assumptions created in the previous chapters. Optimizer results are presented as a function of our three utility function parameters, showcasing an intuitive evolution of portfolios as we navigate through the three-dimensional risk preference space. By comparing these results to other popular optimization frameworks, we will showcase a much more nuanced mapping of client preferences to portfolios. The chapter ends with a review of the sensitivity of our optimal portfolios to estimation error, highlighting generally robust asset allocation results.

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