The authors develop a model whose aim is to study the relationship between crowding and liquidity shocks. One of the main results of that model is that crowding is associated with a larger exposure to broader liquidity shocks on arbitrageurs. The authors confirm this link empirically by studying equity long–short strategies. They use short interest data both to identify liquidity shocks impacting sophisticated equity investors and to infer crowdedness for some of the well-known long–short equity factors. When liquidity shocks (such as the 2007 quant crisis or the more recent 2020 COVID-19–induced quant deleverage) occur, crowded strategies indeed tend to underperform.
{"title":"Crowding and Liquidity Shocks","authors":"Hector Chan, Tony Tan","doi":"10.3905/jpm.2022.1.448","DOIUrl":"https://doi.org/10.3905/jpm.2022.1.448","url":null,"abstract":"The authors develop a model whose aim is to study the relationship between crowding and liquidity shocks. One of the main results of that model is that crowding is associated with a larger exposure to broader liquidity shocks on arbitrageurs. The authors confirm this link empirically by studying equity long–short strategies. They use short interest data both to identify liquidity shocks impacting sophisticated equity investors and to infer crowdedness for some of the well-known long–short equity factors. When liquidity shocks (such as the 2007 quant crisis or the more recent 2020 COVID-19–induced quant deleverage) occur, crowded strategies indeed tend to underperform.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"36 - 61"},"PeriodicalIF":1.4,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46549104","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}
{"title":"Factor Investing: The Best Is Yet to Come","authors":"David Blitz","doi":"10.3905/jpm.2022.1.445","DOIUrl":"https://doi.org/10.3905/jpm.2022.1.445","url":null,"abstract":"","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"10 - 18"},"PeriodicalIF":1.4,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46315531","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 establishing the foundation for their investment process, investors typically set up the investment framework first by dividing their investment universes into different buckets along the combinations of multiple sensible dimensions such as geography and industry. Because the framework is applied to the entire investment process including alpha generation, portfolio construction, and risk management, it is fundamentally important for investment outcomes. Contrary to the current ad hoc approaches, the author proposes a methodology guided by economic intuitions to select the optimal framework among the feasible ones, using global equities as the example. The author finds that the region sector framework is generally the optimal one among the possible combinations of the geography and industry dimensions for both developed and emerging markets. These results are important to both stock selection and asset allocation investing and to the academic research that often uses the country framework. The methodology can be easily adapted to other investment universes beyond global equities.
{"title":"Selecting Investment Analytic Framework for Both Top-Down and Bottom-Up Investors: Using Global Equity as the Example","authors":"Xi Li","doi":"10.3905/jpm.2022.1.444","DOIUrl":"https://doi.org/10.3905/jpm.2022.1.444","url":null,"abstract":"In establishing the foundation for their investment process, investors typically set up the investment framework first by dividing their investment universes into different buckets along the combinations of multiple sensible dimensions such as geography and industry. Because the framework is applied to the entire investment process including alpha generation, portfolio construction, and risk management, it is fundamentally important for investment outcomes. Contrary to the current ad hoc approaches, the author proposes a methodology guided by economic intuitions to select the optimal framework among the feasible ones, using global equities as the example. The author finds that the region sector framework is generally the optimal one among the possible combinations of the geography and industry dimensions for both developed and emerging markets. These results are important to both stock selection and asset allocation investing and to the academic research that often uses the country framework. The methodology can be easily adapted to other investment universes beyond global equities.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"106 - 128"},"PeriodicalIF":1.4,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43992528","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}
Constant adaptation to real markets (CARMa) is an equity risk premium framework designed to help investors value stocks given real-world conditions. It measures risks that investors are likely to encounter by owning a specific stock, such as shifting consumer preferences, evolving investor psychology, or potential illiquidity. The need for CARMa centers around the fact that the techniques most commonly used by practitioners today (i.e., capital asset pricing model–based) work well when stock-specific risk does not change much but struggle when it does. CARMa is designed to measure a stock’s intrinsic value in the context of its future risk and evolve as its risk profile does. The fundamental difference between CARMa and convention centers on linkage. Convention is built on the concept that stock-specific risk is linked to the overall market, and this relationship is predetermined (via beta). In the CARMa approach, stock-specific and systematic risks are measured independently; there is no predetermined connection. As such, CARMa can evolve in tandem with changes in the stock’s particular risk profile.
{"title":"Use CARMa to Price Your Stock: Equity Risk Premiums Reinvented with Exchange-Traded Funds","authors":"Stephen J. Antczak","doi":"10.3905/jpm.2022.1.442","DOIUrl":"https://doi.org/10.3905/jpm.2022.1.442","url":null,"abstract":"Constant adaptation to real markets (CARMa) is an equity risk premium framework designed to help investors value stocks given real-world conditions. It measures risks that investors are likely to encounter by owning a specific stock, such as shifting consumer preferences, evolving investor psychology, or potential illiquidity. The need for CARMa centers around the fact that the techniques most commonly used by practitioners today (i.e., capital asset pricing model–based) work well when stock-specific risk does not change much but struggle when it does. CARMa is designed to measure a stock’s intrinsic value in the context of its future risk and evolve as its risk profile does. The fundamental difference between CARMa and convention centers on linkage. Convention is built on the concept that stock-specific risk is linked to the overall market, and this relationship is predetermined (via beta). In the CARMa approach, stock-specific and systematic risks are measured independently; there is no predetermined connection. As such, CARMa can evolve in tandem with changes in the stock’s particular risk profile.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"185 - 199"},"PeriodicalIF":1.4,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46531579","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}
{"title":"A Tour of the Factor Funhouse","authors":"Jennifer R. Bender","doi":"10.3905/jpm.2022.1.443","DOIUrl":"https://doi.org/10.3905/jpm.2022.1.443","url":null,"abstract":"","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"19 - 25"},"PeriodicalIF":1.4,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43620862","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}
This article presents a generalization of the concept of operating leverage to include leverage factors/multipliers that can be used to estimate the earnings impact from any changes (including inflation-driven changes) in unit sales, prices, or costs. For a given firm, these price, cost, and sales multipliers may be synchronous or offsetting, resulting in a wide range of net leverage effects. In general, operating leverage increases as sales decline, so an adverse environment will find a firm confronted with greater leverage reactions—at just the wrong time. The resulting convexity effect can exacerbate the amplitude of any sales-driven earnings cycle. Incorporating specific inflation flow-through factors in the operating leverage/multiplier model allows the earnings impact of different forms of inflation to be more readily envisioned. This more granular approach often leads to surprising results. This article presents examples that illustrate how some forms of inflation can have a positive earnings impact, whereas other combinations can be quite devastating, especially in terms of real earnings.
{"title":"Operating Leverage and Inflation","authors":"Martin L. Leibowitz, S. Kogelman","doi":"10.3905/jpm.2022.1.441","DOIUrl":"https://doi.org/10.3905/jpm.2022.1.441","url":null,"abstract":"This article presents a generalization of the concept of operating leverage to include leverage factors/multipliers that can be used to estimate the earnings impact from any changes (including inflation-driven changes) in unit sales, prices, or costs. For a given firm, these price, cost, and sales multipliers may be synchronous or offsetting, resulting in a wide range of net leverage effects. In general, operating leverage increases as sales decline, so an adverse environment will find a firm confronted with greater leverage reactions—at just the wrong time. The resulting convexity effect can exacerbate the amplitude of any sales-driven earnings cycle. Incorporating specific inflation flow-through factors in the operating leverage/multiplier model allows the earnings impact of different forms of inflation to be more readily envisioned. This more granular approach often leads to surprising results. This article presents examples that illustrate how some forms of inflation can have a positive earnings impact, whereas other combinations can be quite devastating, especially in terms of real earnings.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"159 - 168"},"PeriodicalIF":1.4,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45058351","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}
This article is an in-depth large-scale analysis of the supply chain network and its bearing on the correlation structure of stock returns. The authors show that the stock returns of companies that are connected through the supply chain network exhibit a correlation structure that differs significantly from that of random pairs of stocks. This effect is observed for companies that are connected directly as well as through a common third party. A clustering approach is used to yield some interesting easier-to-exploit results with a view toward risk modeling. The authors also perform an analysis of rare negative events, highlighting some lead-lag relationships.
{"title":"Supply Chain and Correlations","authors":"F. Abergel, Adrien Akar","doi":"10.3905/jpm.2022.1.440","DOIUrl":"https://doi.org/10.3905/jpm.2022.1.440","url":null,"abstract":"This article is an in-depth large-scale analysis of the supply chain network and its bearing on the correlation structure of stock returns. The authors show that the stock returns of companies that are connected through the supply chain network exhibit a correlation structure that differs significantly from that of random pairs of stocks. This effect is observed for companies that are connected directly as well as through a common third party. A clustering approach is used to yield some interesting easier-to-exploit results with a view toward risk modeling. The authors also perform an analysis of rare negative events, highlighting some lead-lag relationships.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"138 - 158"},"PeriodicalIF":1.4,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46929096","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}
The role of factors in macro investing has come into question after mediocre performance during the past decade. In this article, the authors confirm this decline in profitability and examine the importance and relevance of macro factors via three different approaches, analyzing their explaining power for asset risks and cross-sectional return variations. They find no evidence of declining importance over time. They discuss a few possible explanations for the apparently unreliable risk premia associated with these factors in the recent decade.
{"title":"The Lost Decade: Have Macro Factor Risk Premia Become Irrelevant?","authors":"Chenfei Ma, Eddie Cheng, Wai Lee","doi":"10.3905/jpm.2022.1.439","DOIUrl":"https://doi.org/10.3905/jpm.2022.1.439","url":null,"abstract":"The role of factors in macro investing has come into question after mediocre performance during the past decade. In this article, the authors confirm this decline in profitability and examine the importance and relevance of macro factors via three different approaches, analyzing their explaining power for asset risks and cross-sectional return variations. They find no evidence of declining importance over time. They discuss a few possible explanations for the apparently unreliable risk premia associated with these factors in the recent decade.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"95 - 110"},"PeriodicalIF":1.4,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42958438","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}
Nanqing Dong, Luka M. Jankovic, A. Stewart, Scott D. Stewart
Practitioners and researchers seek to accurately estimate the value added by active equity fund managers. The authors hypothesize that the asset pricing models used to study equity funds may better capture nonlinearity in stock returns across market capitalizations by replacing the commonly used single size factor with two new size factors. This extension is important for explaining equity mutual fund returns because active fund holdings are weighted toward mid- and small-cap stocks to a greater extent than holdings of cap-weighted market indexes. In tests designed to minimize data mining issues, two size factors explain equity fund returns better than do the Fama–French single-size and style factors. Augmented Fama–French models explain over 25% of unexplained variance and yield superior adjusted R-squares for more than 75% of equity funds in the CRSP mutual fund database. Applied tests supplement these broad statistical analyses and confirm the technique’s value for practice. Also of benefit, the two proposed factor return series are readily available on the Internet to researchers and practitioners alike.
{"title":"Improving Equity Fund Alpha Estimates with a Second Size Factor","authors":"Nanqing Dong, Luka M. Jankovic, A. Stewart, Scott D. Stewart","doi":"10.3905/jpm.2022.1.435","DOIUrl":"https://doi.org/10.3905/jpm.2022.1.435","url":null,"abstract":"Practitioners and researchers seek to accurately estimate the value added by active equity fund managers. The authors hypothesize that the asset pricing models used to study equity funds may better capture nonlinearity in stock returns across market capitalizations by replacing the commonly used single size factor with two new size factors. This extension is important for explaining equity mutual fund returns because active fund holdings are weighted toward mid- and small-cap stocks to a greater extent than holdings of cap-weighted market indexes. In tests designed to minimize data mining issues, two size factors explain equity fund returns better than do the Fama–French single-size and style factors. Augmented Fama–French models explain over 25% of unexplained variance and yield superior adjusted R-squares for more than 75% of equity funds in the CRSP mutual fund database. Applied tests supplement these broad statistical analyses and confirm the technique’s value for practice. Also of benefit, the two proposed factor return series are readily available on the Internet to researchers and practitioners alike.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"175 - 187"},"PeriodicalIF":1.4,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42883891","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}
The author examines the statistical significance of the five Fama–French factors and several macroeconomic variables by decade (since the 1960s) and industry. The main findings indicate that not all factors were significant in each decade and for each industry. Also, when the Fama–French factors were present in the regressions, the macroeconomic variables often lost their significance for these industries in each decade. Finally, when constructing factors out of the macro variables, it was found that they were significant for many industries, mainly from the 1970s through the 1990s and part of the 2010s. These findings have implications for portfolio managers when selecting industries based on factor models.
{"title":"When Do and Which Fama–French Factors Explain Industry Returns?","authors":"N. Laopodis","doi":"10.3905/jpm.2022.1.432","DOIUrl":"https://doi.org/10.3905/jpm.2022.1.432","url":null,"abstract":"The author examines the statistical significance of the five Fama–French factors and several macroeconomic variables by decade (since the 1960s) and industry. The main findings indicate that not all factors were significant in each decade and for each industry. Also, when the Fama–French factors were present in the regressions, the macroeconomic variables often lost their significance for these industries in each decade. Finally, when constructing factors out of the macro variables, it was found that they were significant for many industries, mainly from the 1970s through the 1990s and part of the 2010s. These findings have implications for portfolio managers when selecting industries based on factor models.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"141 - 161"},"PeriodicalIF":1.4,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48314800","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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