Across 14 developed-economy countries over the past half-century, the authors analyze the behavior of inflation once a country’s inflation rate surges past various thresholds and study how long a burst of inflation typically lingers. If history is a guide, inflation can take far longer to return to normal levels than most people realize. Transitory inflation is certainly possible, but it is hardly a sensible central expectation. Messaging and policy response from the US Federal Reserve Bank should reflect the relatively high empirical risk that inflation may persist.
{"title":"How Transitory Is Inflation?","authors":"R. Arnott, O. Shakernia","doi":"10.3905/jpm.2023.1.468","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.468","url":null,"abstract":"Across 14 developed-economy countries over the past half-century, the authors analyze the behavior of inflation once a country’s inflation rate surges past various thresholds and study how long a burst of inflation typically lingers. If history is a guide, inflation can take far longer to return to normal levels than most people realize. Transitory inflation is certainly possible, but it is hardly a sensible central expectation. Messaging and policy response from the US Federal Reserve Bank should reflect the relatively high empirical risk that inflation may persist.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"21 - 33"},"PeriodicalIF":1.4,"publicationDate":"2023-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45278397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, the authors establish a connection between the effective number of portfolio constituents and the ex ante ratio of specific to total portfolio risk. Portfolios with a higher effective number of constituents have lower specific risk, and the decay follows a power law. An easy rule of thumb is that doubling the effective number of constituents approximately halves the proportion of stock-specific risk. The authors investigate the proportion of specific risk of the S&P 500 Index and find that in the period from 2002–2022 the S&P 500 portfolio had a proportion of specific risk below the expected range, except for the post–COVID-19 period, and the ratio was never abnormally high.
{"title":"Portfolio Concentration and Stock-Specific Risk","authors":"M. Shammaa, Stoyan V. Stoyanov","doi":"10.3905/jpm.2023.1.467","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.467","url":null,"abstract":"In this article, the authors establish a connection between the effective number of portfolio constituents and the ex ante ratio of specific to total portfolio risk. Portfolios with a higher effective number of constituents have lower specific risk, and the decay follows a power law. An easy rule of thumb is that doubling the effective number of constituents approximately halves the proportion of stock-specific risk. The authors investigate the proportion of specific risk of the S&P 500 Index and find that in the period from 2002–2022 the S&P 500 portfolio had a proportion of specific risk below the expected range, except for the post–COVID-19 period, and the ratio was never abnormally high.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"70 - 81"},"PeriodicalIF":1.4,"publicationDate":"2023-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48524624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is a positive relationship between the performance of volatility managed strategies and the accuracy of the volatility estimation—more-accurate forecasts result in higher Sharpe ratios. Industry-standard volatility managed strategies allow a full day between volatility estimation and execution. In other words, we estimate volatility after the close of t − 2, execute the trade market-on-close t − 1, and capture net profits on t. This full-day lag naturally degrades the forecast accuracy, potentially resulting in suboptimal Sharpe ratios. The authors propose a robust framework that shortens the lag, effectively achieving a more accurate forecast by incorporating more-current information in the prediction model. The result is higher Sharpe ratios, higher utility, and lower volatility of volatility.
{"title":"Modernizing Volatility-Managed Strategies","authors":"Junseung Bae, Ryan Poirier","doi":"10.3905/jpm.2023.1.466","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.466","url":null,"abstract":"There is a positive relationship between the performance of volatility managed strategies and the accuracy of the volatility estimation—more-accurate forecasts result in higher Sharpe ratios. Industry-standard volatility managed strategies allow a full day between volatility estimation and execution. In other words, we estimate volatility after the close of t − 2, execute the trade market-on-close t − 1, and capture net profits on t. This full-day lag naturally degrades the forecast accuracy, potentially resulting in suboptimal Sharpe ratios. The authors propose a robust framework that shortens the lag, effectively achieving a more accurate forecast by incorporating more-current information in the prediction model. The result is higher Sharpe ratios, higher utility, and lower volatility of volatility.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"148 - 166"},"PeriodicalIF":1.4,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45789191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Regret and Optimal Portfolio Allocations","authors":"David Blanchett","doi":"10.3905/jpm.2023.1.464","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.464","url":null,"abstract":"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.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"143 - 154"},"PeriodicalIF":1.4,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49276736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Strategic Asset Allocation and Inflation Resilience","authors":"W. Phoa","doi":"10.3905/jpm.2023.1.465","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.465","url":null,"abstract":"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.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"45 - 63"},"PeriodicalIF":1.4,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45697725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, the authors study qualitative margin forecasts made by managers in their earnings conference calls as well as forecast revisions of gross margins by financial analysts. Maintaining margins in cases when the costs of input factors rise indicates strength because the firm can pass these increased costs onto its customers. Increased margins when sales increase indicate strong market power by the firm or a better utilization of fixed resources. Decreasing margins when revenues increase typically indicate a strategy of capturing market share. Due to the recent difficulties in supply chains caused by the pandemic and then by inflation pressures, it became more important to study changes in margins forecasted by managers and analysts. The authors provide evidence that these forecasts can improve portfolio returns, especially when used in conjunction with forecast revisions of sales and earnings.
{"title":"Margin Forecasts by Managers and Analysts","authors":"S. Levi, J. Livnat, Kate Suslava","doi":"10.3905/jpm.2023.1.463","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.463","url":null,"abstract":"In this article, the authors study qualitative margin forecasts made by managers in their earnings conference calls as well as forecast revisions of gross margins by financial analysts. Maintaining margins in cases when the costs of input factors rise indicates strength because the firm can pass these increased costs onto its customers. Increased margins when sales increase indicate strong market power by the firm or a better utilization of fixed resources. Decreasing margins when revenues increase typically indicate a strategy of capturing market share. Due to the recent difficulties in supply chains caused by the pandemic and then by inflation pressures, it became more important to study changes in margins forecasted by managers and analysts. The authors provide evidence that these forecasts can improve portfolio returns, especially when used in conjunction with forecast revisions of sales and earnings.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"45 - 57"},"PeriodicalIF":1.4,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47472587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Frank J. Fabozzi, S. Cavaglia, Stephen A. Gorman, Brian Jacobsen, Eugene Podkaminer
{"title":"webinar summary Multi-Asset Strategies Webinar","authors":"Frank J. Fabozzi, S. Cavaglia, Stephen A. Gorman, Brian Jacobsen, Eugene Podkaminer","doi":"10.3905/jpm.2023.1.462","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.462","url":null,"abstract":"","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"9 - 19"},"PeriodicalIF":1.4,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48374912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
What happened to major asset prices before 2022 and what changed in 2022? This article covers six key themes. 1) The backdrop of high asset valuations and low expected returns before 2022. 2) Investor responses to low expected returns, notably the boom in flows to private assets. 3) A revised picture after 2022: much higher expected returns at least for bonds (less for private assets so far) after the biggest inflation scare in a generation and central bankers’ attempt to contain it. 4) Contrasting fortunes and prospects for long-only assets and long–short strategies. 5) Understanding the rollercoaster ride of value-versus-growth stock selection strategies. 6) The important role of risk-mitigating strategies, especially trend following, amid protracted bear markets and elevated macro volatility.
{"title":"Investing in Interesting Times","authors":"A. Ilmanen","doi":"10.3905/jpm.2023.1.461","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.461","url":null,"abstract":"What happened to major asset prices before 2022 and what changed in 2022? This article covers six key themes. 1) The backdrop of high asset valuations and low expected returns before 2022. 2) Investor responses to low expected returns, notably the boom in flows to private assets. 3) A revised picture after 2022: much higher expected returns at least for bonds (less for private assets so far) after the biggest inflation scare in a generation and central bankers’ attempt to contain it. 4) Contrasting fortunes and prospects for long-only assets and long–short strategies. 5) Understanding the rollercoaster ride of value-versus-growth stock selection strategies. 6) The important role of risk-mitigating strategies, especially trend following, amid protracted bear markets and elevated macro volatility.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"21 - 35"},"PeriodicalIF":1.4,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49548042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David Blitz, T. Hoogteijling, Harald Lohre, Philip Messow
The emerging literature suggests that machine learning (ML) is beneficial in many asset pricing applications because of its ability to detect and exploit nonlinearities and interaction effects that tend to go unnoticed with simpler modeling approaches. In this article, the authors discuss the promises and pitfalls of applying machine learning to asset management by reviewing the existing ML literature from the perspective of a prudent practitioner. The focus is on the methodological design choices that can critically affect predictive outcomes and on an evaluation of the frequent claim that ML gives spectacular performance improvements. In light of the practical considerations, the apparent advantage of ML is reduced, but still likely to make a difference for investors who adhere to a sound research protocol to navigate the intrinsic pitfalls of ML.
{"title":"How Can Machine Learning Advance Quantitative Asset Management?","authors":"David Blitz, T. Hoogteijling, Harald Lohre, Philip Messow","doi":"10.3905/jpm.2023.1.460","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.460","url":null,"abstract":"The emerging literature suggests that machine learning (ML) is beneficial in many asset pricing applications because of its ability to detect and exploit nonlinearities and interaction effects that tend to go unnoticed with simpler modeling approaches. In this article, the authors discuss the promises and pitfalls of applying machine learning to asset management by reviewing the existing ML literature from the perspective of a prudent practitioner. The focus is on the methodological design choices that can critically affect predictive outcomes and on an evaluation of the frequent claim that ML gives spectacular performance improvements. In light of the practical considerations, the apparent advantage of ML is reduced, but still likely to make a difference for investors who adhere to a sound research protocol to navigate the intrinsic pitfalls of ML.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"78 - 95"},"PeriodicalIF":1.4,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43874237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alfie Brixton, J. Brooks, P. Hecht, A. Ilmanen, Thomas Maloney, Nicholas McQuinn
The relationship between stock and bond returns is a fundamental determinant of risk in traditional portfolios. For the first two decades of the 21st century, the stock–bond correlation was consistently negative and investors were largely able to rely on their bond investments for protection when equities sold off. But this was not the case in the previous century, and macroeconomic changes—such as higher inflation uncertainty—could lead to a reappearance of the positive stock–bond correlation of the 1970s, 80s, and 90s. This would have broad implications for investors, either increasing portfolio risk or forcing allocation changes likely to reduce expected returns. This article analyzes the implications for investors of a change in this “golden parameter” and presents a simple macroeconomic model to help understand its drivers, supported by international empirical evidence. Finally, it explores the role of alternatives in making up the potential diversification deficit in a positive stock–bond correlation world.
{"title":"A Changing Stock–Bond Correlation: Drivers and Implications","authors":"Alfie Brixton, J. Brooks, P. Hecht, A. Ilmanen, Thomas Maloney, Nicholas McQuinn","doi":"10.3905/jpm.2023.1.459","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.459","url":null,"abstract":"The relationship between stock and bond returns is a fundamental determinant of risk in traditional portfolios. For the first two decades of the 21st century, the stock–bond correlation was consistently negative and investors were largely able to rely on their bond investments for protection when equities sold off. But this was not the case in the previous century, and macroeconomic changes—such as higher inflation uncertainty—could lead to a reappearance of the positive stock–bond correlation of the 1970s, 80s, and 90s. This would have broad implications for investors, either increasing portfolio risk or forcing allocation changes likely to reduce expected returns. This article analyzes the implications for investors of a change in this “golden parameter” and presents a simple macroeconomic model to help understand its drivers, supported by international empirical evidence. Finally, it explores the role of alternatives in making up the potential diversification deficit in a positive stock–bond correlation world.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"64 - 80"},"PeriodicalIF":1.4,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48437458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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