JOIM: 2020
Volume 18, No. 1, First Quarter 2020
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Article
The Fully-Anticipated P/E Promise and Its Realization
In this paper, time paths of P/Es are projected, by applying a theoretical model in which the totality of “fully anticipated” future “franchise” investments serve as the source of higher P/Es. At the outset, the P/E path slowly ascends until the first franchise opportunity is reached and funded. The actual act of funding transforms the “anticipated” franchise value potential into “realized” value-equivalent earnings. This equivalence does not change the price in the P/E numerator but, the P/E ratio declines because the new earnings flow into the ratio’s denominator. The P/E decline bottoms out at a level based on new going-forward franchise investment potential and then rises until the next franchise event.
In summary, this saw tooth pattern should not be surprising. If we view a firm’s Franchise Value as a promise of future productive growth, then realization should lead to on-going Franchise Value declines – unless previously unexpected sources of future growth are uncovered. And, in actual markets, it is just such “unexpected” events and prospects – of both a positive as well as a negative nature – that intermittently bubble to the surface. While admittedly based on a highly theoretical model with the limiting assumption of perfect foresight, this specter of a downward gravitational P/E pull should act as a cautionary note for analyses that project an existing high P/E forward onto a higher earnings level assumed to be reached at some future point in time. -
Article
Do High-Frequency Traders Improve Your Implementation Shortfall?
We take advantage of a regulatory change that effectively imposed a “tax” on HFT order activity on Canadian equity venues to study the resulting effect on the execution costs of large institutional trades.We find that bid–ask spreads increase and price impact decreases for these trades following the regulatory change. The price impact effect is strongest for informed institutional traders. Our evidence indicates that this tax on high-frequency trading is associated with higher transaction costs for small, uninformed trades and lower transaction costs for large, informed trades. Hence, the tax increased the subsidy for informed traders from uninformed traders.
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Article
Timing is not Everything—Assessing Manager Skill in Factor Timing
We introduce an innovative framework to assess the contribution and persistence of factor timing within US large-cap equity funds. After decomposing active returns into three components—strategic factor contribution, tactical factor contribution and security selection—we find that they are all significant but security selection is the dominant contributor. We also find that the portfolio managers who rely on factor timing to drive performance do not seem to exhibit persistence in their abilities. Finally, across all funds, strategic and tactical factor tilts do not drive future active returns. Security selection is the key differentiator for future outperformance.
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Article
Trends Everywhere
We provide new out-of-sample evidence on trend-following investing by studying its performance for 82 securities not previously examined and 16 long–short equity factors. Specifically, we study the performance of time series momentum for emerging market equity index futures, fixed income swaps, emerging market currencies, exotic commodity futures, credit default swap indices, volatility futures, and long–short equity factors. We find that time series momentum has worked across these asset classes and across several trend horizons. We examine the co-movement of trends across asset classes and factors, he performance during different market environments, and discuss the implications for investors.
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Article
Time-Series Variation in Factor Premia: The Influence of the Business Cycle
Factor cyclicality can be understood in the context of factor sensitivity to aggregate cash-flow news. Factors exhibit different sensitivities to macroeconomic risk, and this heterogeneity can be exploited to motivate dynamic rotation strategies among established factors: size, value, quality, low volatility and momentum. A timely and realistic identification of business cycle regimes, using leading economic indicators and global risk appetite, can be used to construct long-only factor rotation strategies with information ratios nearly 70% higher than static multifactor strategies. Results are statistically and economically significant across regions and market segments, also after accounting for transaction costs, capacity and turnover.
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Case Study
Fair and Responsible Drug Pricing: A Case Study of Radius Health and Abaloparatide
“Case Studies” presents a case pertinent to contemporary issues and events in investment management. Insightful and provocative questions are posed at the end of each case to challenge the reader. Each case is an invitation to the critical thinking and pragmatic problem solving that are so fundamental to the practice of investment management.
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Book Review
Nonlinear Time Series Analysis
“Book Reviews” identifies important, and often popular, new books from a wide range of investment topics. Beyond providing a summary and review of the content and style of the books, “Book Reviews” seeks to contribute to a conscious, critical, and informed approach to investment literature.
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Practitioner's Digest
Practitioner’s Digest • Vol. 18, No. 1
The “Practitioners Digest” emphasizes the practical significance of manuscripts featured in the “Insights” and “Articles” sections of the journal. Readers who are interested in extracting the practical value of an article, or who are simply looking for a summary, may look to this section.
Volume 18, No. 2, Second Quarter 2020
Machine Learning in Capital Markets
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Article
On the Stability of Machine Learning Models: Measuring Model and Outcome Variance
How do you know how much you should trust a model that is learned from data? We propose that a central criterion in measuring trust is the decision-making variance of a model. We call this “model variance.” Conceptually, it refers to the inherent instability machine learning models experience in their decision-making in response to variations in the training data. We report the results from a controlled study that measures model variance as a function of (1) the inherent predictability of a problem and (2) the frequency of the occurrence of the class of interest. The results provide important guidelines for what we should expect from machine learning methods for the range of problems that vary across different levels of predictability and base rates, thereby making the results of general scientific interest.
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Article
Can Machines “Learn” Finance?
Machine learning for asset management faces a unique set of challenges that differ markedly from other domains where machine learning has excelled. Understanding these differences is critical for developing impactful approaches and realistic expectations for machine learning in asset management. We discuss a variety of beneficial use cases and potential pitfalls, and emphasize the importance of economic theory and human expertise for achieving success through financial machine learning.
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Article
Dynamic Goals-Based Wealth Management Using Reinforcement Learning
We present a reinforcement learning (RL) algorithm to solve for a dynamically optimal goal-based portfolio. The solution converges to that obtained from dynamic programming. Our approach is model-free and generates a solution that is based on forward simulation, whereas dynamic programming depends on backward recursion. This paper presents a brief overview of the various types of RL. Our example application illustrates how RL may be applied to problems with path-dependency and very large state spaces, which are often encountered in finance.
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Article
Using Machine Learning to Predict Realized Variance
Volatility index is a portfolio of options and represents market expectation of the underlying security’s future realized volatility/variance. Traditionally the index weighting is based on a variance swap pricing formula. In this paper we propose a new method for building volatility index by formulating a variance prediction problem using machine learning.We test algorithms including Ridge regression, Feed forward Neural Networks and Random Forest on S&P 500 Index option data. By conducting a time series validation we show that the new weighting method can achieve higher predictability to future return variance and require fewer options. It is also shown that the weighting method combining the traditional and the machine learning approaches performs the best.
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Book Review
Smart(er) Investing – How Academic Insights Propel the Savvy Investor
“Book Reviews” identifies important, and often popular, new books from a wide range of investment topics. Beyond providing a summary and review of the content and style of the books, “Book Reviews” seeks to contribute to a conscious, critical, and informed approach to investment literature.
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Case Study
Collective Defined Contribution Plans
“Case Studies” presents a case pertinent to contemporary issues and events in investment management. Insightful and provocative questions are posed at the end of each case to challenge the reader. Each case is an invitation to the critical thinking and pragmatic problem solving that are so fundamental to the practice of investment management.
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Practitioner's Digest
Practitioner’s Digest • Vol. 18, No. 2
The “Practitioners Digest” emphasizes the practical significance of manuscripts featured in the “Insights” and “Articles” sections of the journal. Readers who are interested in extracting the practical value of an article, or who are simply looking for a summary, may look to this section.
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Insight
Introduction to the Special Issue on Machine Learning
“Insights” features the thoughts and views of the top authorities from academia and the profession. This section offers unique perspectives from the leading minds in investment management.
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Article
Local, Global, and International CAPM: For Which Countries Does Model Choice Matter?
For individual stocks of 46 countries, this study investigates empirical differences in discount rate estimates between three risk–return models of interest to practitioners who perform discounted cash-flow valuation analysis: (1) the traditional (local) CAPM; (2) the global CAPM (GCAPM), where the only risk factor is the global market index; and (3) an international CAPM (ICAPM) with two risk factors, the global market index and a wealth-weighted foreign currency index. The study finds that model choice makes a substantial difference for many, but not all, countries.
Volume 18, No. 3, Third Quarter 2020
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Article
Measuring Risk Preferences and Asset-Allocation Decisions: A Global Survey Analysis
We use a global survey of over 22,400 individual investors, 4,892 financial advisors, and 2,060 institutional investors between 2015 and 2017 to elicit their asset allocation behavior and risk preferences. We find substantially different behaviors among these three groups of market participants. Most institutional investors exhibit highly contrarian reactions to past returns in their equity allocations. Financial advisors are also mostly contrarian; a few of them demonstrate passive behavior. However, individual investors tend to extrapolate past performance. We use a clustering algorithm to partition individuals into five distinct types: passive investors, risk avoiders, extrapolators, contrarians, and optimistic investors. Across demographic categories, older investors tend to be more passive and risk averse.
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Article
Comparing Anomalies Using Liquidity and Earnings
We compare three factor models and their ability to explain a set of portfolio anomalies. Two of these models are based on market capitalization which most of the industry currently uses to characterize stocks. We replace this line of thinking by utilizing both earnings and liquidity to construct a competing model, which is intuitive to practitioners. Partitioning and characterizing stock returns in this way enables us to dispel some of the most challenging asset pricing anomalies. Historically, investors have concerned themselves with our proposed stock descriptors for far longer than they have with value and size characteristics
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Case Study
Is “1 and 10” The New “2 and 20”?*
“Case Studies” presents a case pertinent to contemporary issues and events in investment management. Insightful and provocative questions are posed at the end of each case to challenge the reader. Each case is an invitation to the critical thinking and pragmatic problem solving that are so fundamental to the practice of investment management.
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Article
Attribution of Ex-Post Realized Sharpe Ratio to The Predictability Of The Ex-Ante Forecast Return and Risk.
We propose to use an attribution formula that enables the ex-post realized Sharpe ratio to be decomposed into realized market conditions, ex-ante predictability of the returns, risk magnitude, and risk factors. We compare the predictability of the ex-ante return and ex-ante risk directly, quantitatively identifying the main source of the reduction of the Sharpe ratio using the attribution. Furthermore, we use excess Sharpe ratio attribution analysis to simultaneously evaluate the qualities of the portfolio and benchmark. We additionally provide numerical examples of the attributions using sector indices.
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Article
Is Sell-Side Research More Valuable in Bad Times?
Little is known about whether the value of sell-side research is different in bad times compared to good times. Because uncertainty is high in bad times, investors find it harder to assess firm prospects and hence should value analyst output more. However, higher uncertainty makes analysts’ tasks harder, so it is unclear whether analyst output is more valuable in bad times. We find that in bad times, analyst revisions have a larger stockprice impact, earnings forecast errors per unit of uncertainty fall, and analyst reports are more frequent and longer. These results are consistent with analysts working harder and investors relying more on analysts in bad times.
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Article
Correlation Shrinkage: Implications For Risk Forecasting
In this article, we study the impact of shrinking sample correlations toward zero. We find that while such shrinkage may be beneficial from a portfolio-construction perspective, there is virtually no benefit in terms of the accuracy of risk forecasts. In fact, we show that correlation shrinkage typically increases the errors in risk forecasts, sometimes by a large margin. Hence, we conclude that for purposes of estimating portfolio risk, the estimated correlations should not deviate significantly from the sample correlation.
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Practitioner's Digest
Practitioner’s Digest • Vol. 18, No. 3
The “Practitioners Digest” emphasizes the practical significance of manuscripts featured in the “Insights” and “Articles” sections of the journal. Readers who are interested in extracting the practical value of an article, or who are simply looking for a summary, may look to this section.
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Book Review
The Man Who Solved The Market
“Book Reviews” identifies important, and often popular, new books from a wide range of investment topics. Beyond providing a summary and review of the content and style of the books, “Book Reviews” seeks to contribute to a conscious, critical, and informed approach to investment literature.
Volume 18, No. 4, Fourth Quarter 2020
Retirement Investing
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Article
A Six-Component Integrated Approach to Addressing the Retirement Funding Challenge
This paper offers an integrated approach to addressing the global retirement funding challenge, especially in light of the coronavirus shock that has created an unanticipated and unprecedented impact on lifetime income/consumption. It frames the problem in a six-component approach to the funding challenge with an integrated package presented in a transparent, detailed modular fashion, so that any one module can be replaced with a different version and the rest of the system works. This also means that all six components need not be employed simultaneously, but can be done in a secular fashion. Finally, it develops and proposes in detail a new financial instrument, SeLFIES (Standard-of-Living indexed, Forward-starting, Income-only Securities)—a single financial innovation that provides greatly improved efficiency of implementation to four of the six components. SeLFIES can help complete financial markets and could be a timely innovation given the coronavirus crisis because they are beneficial to governments that seek long-term, local currency debt financing.
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Insight
Towards Replacing the Defined Benefit Plan: Assured Retirement Income Provided by a Liquid Investment Fund
Traditional corporate defined benefit (DB) plans provided retirees with constant retirement income, butDB plans have now all but disappeared. While defined contribution (DC) plans now permit low-fee wealth accumulation, the conversion of wealth to predictable nominal or real income during retirement remains opaque and expensive. Complicated, illiquid, and high-fee products dominate the landscape. The goal of acquiring low-fee, predictable future income in retirement, has remained elusive.We describe a relatively simple, liquid, and low-cost series of funds that can address this challenge. The key features are: (1) A minimum assured annual income (real or nominal) for a significant period of time. (2) Maximal exposure at all times to a higher-expected-return risky asset, while meeting the income assurance per share. (3) Liquidity that allows investors’ flexibility to withdraw funds or change assured levels of income at any time, with minimal cost. (4) A simple but significant “behavior nudge” that gives clarity on the future income levels: each share will provide a minimum income of $1 per year for 20 years with the possibility to be extended for lifetime. An investor will know future assured income simply by knowing the number of shares she/he owns. (5) Scalability through reliance on underlying securities backed by the deepest markets in the world. While a strategy to provide the features above is relatively straightforward for a single investor, a deeper challenge is to create a fund that provides these features to all investors, regardless of when shares are purchased. We consider the nature of asset management that achieves all the previous features in a single fund, and believe that it can be done while qualifying for QDIA status.
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Article
How Much Can Collective Defined Contribution Plans Improve Risk-Sharing?
Collective Defined Contribution (CDC) plans have been suggested as an attractive and sustainable alternative to public sector DB plans. A CDC plan is a hybrid structure, designed to provide more predictable retirement benefits than a traditional DC plan while operating at the lower cost of a DB plan. It does this by sharing investment risk across worker cohorts and centralizing asset management. We develop a model of an unsubsidized CDC plan, and use it to characterize the risk-sharing rules and investment policies that maximize a “scheduled benefit” for retirees that is almost always achieved or exceeded. We compare the outcomes under the CDC system with those from an otherwise similar options-augmented DC model, where participants have access to self-financing strategies that involve trading in one-year put and call options. The ability to effectively trade long-dated options in the CDC framework delivers a somewhat higher scheduled benefit than can be achieved by self-insuring in an options-augmented DC plan. However under current contribution policies, the scheduled benefit in the CDC plan falls short of what most would consider an adequate retirement income.
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Article
A Personal Tribute to Professor Harry Markowitz on the Occasion of the JOIM Special Achievement Award
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Article
Multi-Period Portfolio Selection: A Practical Simulation-Based Framework
The topic of optimal portfolio selection over time has garnered significant attention from investment researchers since the introduction of portfolio theory in 1952. While computational, theoretical, and numerical methods have advanced, solutions introduced to date have yet to effectively address many practical aspects of the multi-period portfolio selection problem.
In this paper, we propose three key requisites for practical multi-period portfolio selection solutions that highlight the central challenges of managing portfolios across a multi- period investment horizon: effective duration management, incorporating real-world asset dynamics, and considering investment frictions and illiquidities. Based on these criteria, we detail an analytical framework for multi-period portfolio selection that provides intuition and yields guiding principles that describe how allocations and duration should evolve across a multi-period investment horizon, given specific investor objectives. We then introduce a practical simulation-based portfolio selection (SBPS) framework and present solutions for common investor objectives that are not only aligned with intuitive principles but also demonstrate the flexibility afforded by SBPS in allowing us to address the three stated requisites for practical multi-period solutions. -
Practitioner's Digest
Practitioner’s Digest • Vol. 18, No. 4
The “Practitioners Digest” emphasizes the practical significance of manuscripts featured in the “Insights” and “Articles” sections of the journal. Readers who are interested in extracting the practical value of an article, or who are simply looking for a summary, may look to this section.
-
Book Review
The Ascent of Market Efficiency: Finance That Cannot Be Proven
“Book Reviews” identifies important, and often popular, new books from a wide range of investment topics. Beyond providing a summary and review of the content and style of the books, “Book Reviews” seeks to contribute to a conscious, critical, and informed approach to investment literature.