Campbell R. Harvey,Anthony Ledford,Emidio Sciulli,Philipp Ustinov,Stefan Zohren
Impact costs occur when large buy or sell orders move market prices. The measurement of these costs is crucial for the evaluation of potential trading strategies and the successful execution of systematic investment strategies. However, common approaches suffer from a type of myopia: impact is only measured for the current transaction. In many cases, orders are correlated, and the impact of the first order will affect the execution of future orders. The authors propose a new measure that quantifies the long-term effects of market impact: expected future flow shortfall (EFFS). Their method is both intuitive and straightforward to implement. Importantly, the EFFS method performs competitively with far more complex and data-hungry approaches. The method should be useful for both the evaluation of execution methods and the sizing of orders.
{"title":"Quantifying Long-Term Market Impact","authors":"Campbell R. Harvey,Anthony Ledford,Emidio Sciulli,Philipp Ustinov,Stefan Zohren","doi":"10.3905/jpm.2021.1.324","DOIUrl":"https://doi.org/10.3905/jpm.2021.1.324","url":null,"abstract":"Impact costs occur when large buy or sell orders move market prices. The measurement of these costs is crucial for the evaluation of potential trading strategies and the successful execution of systematic investment strategies. However, common approaches suffer from a type of myopia: impact is only measured for the current transaction. In many cases, orders are correlated, and the impact of the first order will affect the execution of future orders. The authors propose a new measure that quantifies the long-term effects of market impact: expected future flow shortfall (EFFS). Their method is both intuitive and straightforward to implement. Importantly, the EFFS method performs competitively with far more complex and data-hungry approaches. The method should be useful for both the evaluation of execution methods and the sizing of orders.","PeriodicalId":501547,"journal":{"name":"The Journal of Portfolio Management","volume":"17 1","pages":"25-46"},"PeriodicalIF":0.0,"publicationDate":"2021-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138544007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The author closely examines the impact of adding intangibles to traditional book equity as a more meaningful value measure. This intangibles-adjusted value metric subsumes the traditional book-to-price metric in explaining cross-sectional equity returns and improves value factor performance across subsample periods and geographic regions. The author finds that knowledge capital (capitalized research and development expenditures) plays a more important role than organization capital (capitalized partial selling, general, and administrative expenditures). The improved value premium comes from both the long and short sides of intangibles-adjusted high-minus-low (HML), which is good news for investors under a long-only constraint and provides useful information for investors who choose to short or underweight certain names.
{"title":"Intangibles: The Missing Ingredient in Book Value","authors":"Feifei Li","doi":"10.3905/jpm.2021.1.322","DOIUrl":"https://doi.org/10.3905/jpm.2021.1.322","url":null,"abstract":"The author closely examines the impact of adding intangibles to traditional book equity as a more meaningful value measure. This intangibles-adjusted value metric subsumes the traditional book-to-price metric in explaining cross-sectional equity returns and improves value factor performance across subsample periods and geographic regions. The author finds that knowledge capital (capitalized research and development expenditures) plays a more important role than organization capital (capitalized partial selling, general, and administrative expenditures). The improved value premium comes from both the long and short sides of intangibles-adjusted high-minus-low (HML), which is good news for investors under a long-only constraint and provides useful information for investors who choose to short or underweight certain names.","PeriodicalId":501547,"journal":{"name":"The Journal of Portfolio Management","volume":"1 1","pages":"164-184"},"PeriodicalIF":0.0,"publicationDate":"2021-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The authors investigate how the interaction between entries and exits of informed institutional investors and market anomaly signals affects strategy performance. The long legs of anomalies earn more positive alphas following entries, whereas the short legs earn more negative alphas following exits. The enhanced anomaly-based strategies of buying stocks in the long legs of anomalies with entries and shorting stocks in the short legs with exits outperform the original anomalies, with an increase of 19–54 bps per month in the Fama–French five-factor alpha. The entries and exits of institutional investors capture informed trading and earnings surprises, thereby enhancing the anomalies.
{"title":"Changes in Ownership Breadth and Capital Market Anomalies","authors":"Yangru Wu,Weike Xu","doi":"10.3905/jpm.2021.1.317","DOIUrl":"https://doi.org/10.3905/jpm.2021.1.317","url":null,"abstract":"The authors investigate how the interaction between entries and exits of informed institutional investors and market anomaly signals affects strategy performance. The long legs of anomalies earn more positive alphas following entries, whereas the short legs earn more negative alphas following exits. The enhanced anomaly-based strategies of buying stocks in the long legs of anomalies with entries and shorting stocks in the short legs with exits outperform the original anomalies, with an increase of 19–54 bps per month in the Fama–French five-factor alpha. The entries and exits of institutional investors capture informed trading and earnings surprises, thereby enhancing the anomalies.","PeriodicalId":501547,"journal":{"name":"The Journal of Portfolio Management","volume":"25 1","pages":"185-198"},"PeriodicalIF":0.0,"publicationDate":"2021-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sander Gerber,Harry M. Markowitz,Philip A. Ernst,Yinsen Miao,Babak Javid,Paul Sargen
The purpose of this article is to introduce the Gerber statistic, a robust co-movement measure for covariance matrix estimation for the purpose of portfolio construction. The Gerber statistic extends Kendall’s Tau by counting the proportion of simultaneous co-movements in series when their amplitudes exceed data-dependent thresholds. Because the statistic is not affected by extremely large or extremely small movements, it is especially well suited for financial time series, which often exhibit extreme movements and a great amount of noise. Operating within the mean–variance portfolio optimization framework of Markowitz, we consider the performance of the Gerber statistic against two other commonly used methods for estimating the covariance matrix of stock returns: the sample covariance matrix (also called the historical covariance matrix) and shrinkage of the sample covariance matrix given by Ledoit and Wolf. Using a well-diversified portfolio of nine assets over a 30-year period (January 1990–December 2020), we find, empirically, that for almost all investment scenarios considered, the Gerber statistic’s returns dominate those achieved by both historical covariance and by the shrinkage method of Ledoit and Wolf.
{"title":"The Gerber Statistic: A Robust Co-Movement Measure for Portfolio Optimization","authors":"Sander Gerber,Harry M. Markowitz,Philip A. Ernst,Yinsen Miao,Babak Javid,Paul Sargen","doi":"10.3905/jpm.2021.1.316","DOIUrl":"https://doi.org/10.3905/jpm.2021.1.316","url":null,"abstract":"The purpose of this article is to introduce the Gerber statistic, a robust co-movement measure for covariance matrix estimation for the purpose of portfolio construction. The Gerber statistic extends Kendall’s Tau by counting the proportion of simultaneous co-movements in series when their amplitudes exceed data-dependent thresholds. Because the statistic is not affected by extremely large or extremely small movements, it is especially well suited for financial time series, which often exhibit extreme movements and a great amount of noise. Operating within the mean–variance portfolio optimization framework of Markowitz, we consider the performance of the Gerber statistic against two other commonly used methods for estimating the covariance matrix of stock returns: the sample covariance matrix (also called the historical covariance matrix) and shrinkage of the sample covariance matrix given by Ledoit and Wolf. Using a well-diversified portfolio of nine assets over a 30-year period (January 1990–December 2020), we find, empirically, that for almost all investment scenarios considered, the Gerber statistic’s returns dominate those achieved by both historical covariance and by the shrinkage method of Ledoit and Wolf.","PeriodicalId":501547,"journal":{"name":"The Journal of Portfolio Management","volume":"5 1","pages":"87-102"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article is an examination of the stock-picking behavior of nearly 1,500 hedge funds using regulatory mandated position-level data from the SEC (Form 13F). Using data from June 1999 to December 2018, abnormal excess alpha is found on both a gross and dollar basis. Breaking the 20-year sample into two periods, the authors note a significant decline in gross alpha after the 2008 global financial crisis. In contrast, dollar alphas remain economically and statistically significant. This finding coincides with an increase in aggregate assets in the post-crisis period, suggesting asset growth may be impeding gross alphas. To test this hypothesis, the authors analyze the Best Ideas within manager portfolios. They find no significant difference between the alphas generated by managers’ Best Ideas and the rest of their portfolios, suggesting asset growth is not a significant determinant of alpha deterioration. These findings broadly contrast with prior studies conducted on mutual funds, suggesting differences in portfolio construction and incentive effects.
{"title":"Aggregate Alpha in the Hedge Fund Industry: A Further Look at Best Ideas","authors":"F. Amir-Ghassemi,A. Papanicolaou,M. Perlow","doi":"10.3905/jpm.2021.1.313","DOIUrl":"https://doi.org/10.3905/jpm.2021.1.313","url":null,"abstract":"This article is an examination of the stock-picking behavior of nearly 1,500 hedge funds using regulatory mandated position-level data from the SEC (Form 13F). Using data from June 1999 to December 2018, abnormal excess alpha is found on both a gross and dollar basis. Breaking the 20-year sample into two periods, the authors note a significant decline in gross alpha after the 2008 global financial crisis. In contrast, dollar alphas remain economically and statistically significant. This finding coincides with an increase in aggregate assets in the post-crisis period, suggesting asset growth may be impeding gross alphas. To test this hypothesis, the authors analyze the Best Ideas within manager portfolios. They find no significant difference between the alphas generated by managers’ Best Ideas and the rest of their portfolios, suggesting asset growth is not a significant determinant of alpha deterioration. These findings broadly contrast with prior studies conducted on mutual funds, suggesting differences in portfolio construction and incentive effects.","PeriodicalId":501547,"journal":{"name":"The Journal of Portfolio Management","volume":"160 1","pages":"220-239"},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The authors examine government bond factor premiums in a deep global sample from 1800 to 2020, spanning the major markets and maturities. Bond factors (value, momentum, low-risk) offer attractive premiums that do not decay across samples, are persistent over time, and are consistent across various market and macroeconomic scenarios. The factor premiums are diversified to each other, as well as to bond or equity market risks. A combined multifactor bond strategy provides the strongest risk-adjusted returns. These results strongly show a consistent added value of government bond factor premiums over a passive bond portfolio.
{"title":"Factor Investing in Sovereign Bond Markets: Deep Sample Evidence","authors":"Guido Baltussen,Martin Martens,Olaf Penninga","doi":"10.3905/jpm.2021.1.311","DOIUrl":"https://doi.org/10.3905/jpm.2021.1.311","url":null,"abstract":"The authors examine government bond factor premiums in a deep global sample from 1800 to 2020, spanning the major markets and maturities. Bond factors (value, momentum, low-risk) offer attractive premiums that do not decay across samples, are persistent over time, and are consistent across various market and macroeconomic scenarios. The factor premiums are diversified to each other, as well as to bond or equity market risks. A combined multifactor bond strategy provides the strongest risk-adjusted returns. These results strongly show a consistent added value of government bond factor premiums over a passive bond portfolio.","PeriodicalId":501547,"journal":{"name":"The Journal of Portfolio Management","volume":"29 1","pages":"209-225"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chris Brightman,Forrest Henslee,Vitali Kalesnik,Feifei Li,Juhani Linnainmaa
By choosing investment strategies that intentionally create exposure to factor betas, investors may be obtaining uncompensated risks. The authors show, across a wide variety of factors and geographical markets, that factors constructed from fundamental characteristics have earned high returns, whereas those constructed from statistical betas have earned returns close to zero. When designing factor-based investment strategies, investors should seek exposure to the fundamental characteristics that define a factor and use statistical measures of factor betas to manage factor risks. Conversely, seeking to gain exposure to factor betas is a misguided means of obtaining the returns available from factor investing.
{"title":"Why Are High Exposures to Factor Betas Unlikely to Deliver Anticipated Returns?","authors":"Chris Brightman,Forrest Henslee,Vitali Kalesnik,Feifei Li,Juhani Linnainmaa","doi":"10.3905/jpm.2021.1.310","DOIUrl":"https://doi.org/10.3905/jpm.2021.1.310","url":null,"abstract":"By choosing investment strategies that intentionally create exposure to factor betas, investors may be obtaining uncompensated risks. The authors show, across a wide variety of factors and geographical markets, that factors constructed from fundamental characteristics have earned high returns, whereas those constructed from statistical betas have earned returns close to zero. When designing factor-based investment strategies, investors should seek exposure to the fundamental characteristics that define a factor and use statistical measures of factor betas to manage factor risks. Conversely, seeking to gain exposure to factor betas is a misguided means of obtaining the returns available from factor investing.","PeriodicalId":501547,"journal":{"name":"The Journal of Portfolio Management","volume":"68 1","pages":"144-163"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexander Swade,Harald Lohre,Mark Shackleton,Sandra Nolte,Scott Hixon,Jay Raol
Investors face similar macroeconomic risks and opportunities regardless of their individual investment preferences. To best navigate growth and inflation concerns, the authors propose building macro factor–mimicking portfolios diversified across asset classes and style factors. They focus on the macro factors growth, inflation, and defensive. Their approach allows for shaping the macroeconomic risk exposure of a given portfolio by applying systematic macro factor completion to effectively address specific economic outcomes.
{"title":"Macro Factor Investing with Style","authors":"Alexander Swade,Harald Lohre,Mark Shackleton,Sandra Nolte,Scott Hixon,Jay Raol","doi":"10.3905/jpm.2021.1.306","DOIUrl":"https://doi.org/10.3905/jpm.2021.1.306","url":null,"abstract":"Investors face similar macroeconomic risks and opportunities regardless of their individual investment preferences. To best navigate growth and inflation concerns, the authors propose building macro factor–mimicking portfolios diversified across asset classes and style factors. They focus on the macro factors growth, inflation, and defensive. Their approach allows for shaping the macroeconomic risk exposure of a given portfolio by applying systematic macro factor completion to effectively address specific economic outcomes.","PeriodicalId":501547,"journal":{"name":"The Journal of Portfolio Management","volume":"27 1","pages":"80-104"},"PeriodicalIF":0.0,"publicationDate":"2021-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eugene Pauksta,Karishma Kaul,Tom Parker,Scott Radell,Andrew Ang
The authors harvest factors—broad and persistent sources of returns—in US core fixed income in three ways. First, they take strategic over- and underweight positions in certain macro factors. Although strategic overweights to rates, or duration, and credit factors have historically resulted in outperforming fixed-income benchmarks, the authors find the long Treasury sector to be the most efficient way to capture rates exposure, and short-duration corporate bonds maximize risk-adjusted returns for credit exposure. Second, the authors time the allocation to rates and credit factors, along with changing high-yield and mortgage exposures. Third, the authors use style factors to select securities. They incorporate a value tilt in Treasuries and value and quality factors in investment-grade and high-yield sectors. Incorporating factors in these ways and building an optimized portfolio to control for deviations relative to the market index resulted in an information ratio of 1.67 over the period of January 2007 to March 2021.
{"title":"Investing in US Core Fixed Income with Macro and Style Factors","authors":"Eugene Pauksta,Karishma Kaul,Tom Parker,Scott Radell,Andrew Ang","doi":"10.3905/jpm.2021.1.309","DOIUrl":"https://doi.org/10.3905/jpm.2021.1.309","url":null,"abstract":"The authors harvest factors—broad and persistent sources of returns—in US core fixed income in three ways. First, they take strategic over- and underweight positions in certain macro factors. Although strategic overweights to rates, or duration, and credit factors have historically resulted in outperforming fixed-income benchmarks, the authors find the long Treasury sector to be the most efficient way to capture rates exposure, and short-duration corporate bonds maximize risk-adjusted returns for credit exposure. Second, the authors time the allocation to rates and credit factors, along with changing high-yield and mortgage exposures. Third, the authors use style factors to select securities. They incorporate a value tilt in Treasuries and value and quality factors in investment-grade and high-yield sectors. Incorporating factors in these ways and building an optimized portfolio to control for deviations relative to the market index resulted in an information ratio of 1.67 over the period of January 2007 to March 2021.","PeriodicalId":501547,"journal":{"name":"The Journal of Portfolio Management","volume":"1 1","pages":"45-65"},"PeriodicalIF":0.0,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Price informativeness measures how and when information is aggregated into asset prices. The authors study the price informativeness of realized earnings growth for US stocks, with a focus on exposures to factors that have historically outperformed the market index. Their study includes the largest 1,000 stocks from 1975 to 2019 and approximately 180,000 individual corporate net income observations aligned by report date. Stock returns are sensitive to concurrent and realized earnings growth reports up to 15 months into the future, but not to old earnings reports. The decomposition of value, momentum, small size, low beta, and profitability factor active returns into components that are explained and unexplained by earnings aids in understanding the anomalous nature of their positive market-relative performance. The active returns to momentum stocks are largely attributable to the growth of realized earnings over the next several quarters. Low beta, small size, and profitability stocks have little of their active returns explained by realized earnings, suggesting the anomalies are associated with other drivers, such as changes in expected long-term earnings growth and discount rates. In contrast, the active returns to value stocks explained by concurrent and future realized earnings are negative.
{"title":"Price Informativeness with Equity Market Factors","authors":"Roger Clarke,Harindra de Silva,Steven Thorley","doi":"10.3905/jpm.2021.1.303","DOIUrl":"https://doi.org/10.3905/jpm.2021.1.303","url":null,"abstract":"Price informativeness measures how and when information is aggregated into asset prices. The authors study the price informativeness of realized earnings growth for US stocks, with a focus on exposures to factors that have historically outperformed the market index. Their study includes the largest 1,000 stocks from 1975 to 2019 and approximately 180,000 individual corporate net income observations aligned by report date. Stock returns are sensitive to concurrent and realized earnings growth reports up to 15 months into the future, but not to old earnings reports. The decomposition of value, momentum, small size, low beta, and profitability factor active returns into components that are explained and unexplained by earnings aids in understanding the anomalous nature of their positive market-relative performance. The active returns to momentum stocks are largely attributable to the growth of realized earnings over the next several quarters. Low beta, small size, and profitability stocks have little of their active returns explained by realized earnings, suggesting the anomalies are associated with other drivers, such as changes in expected long-term earnings growth and discount rates. In contrast, the active returns to value stocks explained by concurrent and future realized earnings are negative.","PeriodicalId":501547,"journal":{"name":"The Journal of Portfolio Management","volume":"60 1","pages":"66-79"},"PeriodicalIF":0.0,"publicationDate":"2021-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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