For many real estate investors, property-type and geography segmentations are the primary lens through which they measure and manage their portfolios. Whether it is defining allocations, constructing benchmarks, attributing performance, forecasting or modeling risk, segmentations built on property type and geographical classifications play an important role. In an analysis of over 26,000 UK properties between 2002 and 2022, however, the authors find that traditional property-type/geography segmentations explained an average of just 20% of asset-level total return variation. Testing six potential real estate style factors in a cross-sectional multifactor model, they were able to explain an additional 8% of asset-level variation, suggesting that real estate factors could play a role in helping investors manage their portfolios more systematically.
{"title":"Style Factors for Private Real Estate—Beyond Property Type and Location","authors":"Bryan Reid, Fritz Louw, W. Robson","doi":"10.3905/jpm.2023.1.529","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.529","url":null,"abstract":"For many real estate investors, property-type and geography segmentations are the primary lens through which they measure and manage their portfolios. Whether it is defining allocations, constructing benchmarks, attributing performance, forecasting or modeling risk, segmentations built on property type and geographical classifications play an important role. In an analysis of over 26,000 UK properties between 2002 and 2022, however, the authors find that traditional property-type/geography segmentations explained an average of just 20% of asset-level total return variation. Testing six potential real estate style factors in a cross-sectional multifactor model, they were able to explain an additional 8% of asset-level variation, suggesting that real estate factors could play a role in helping investors manage their portfolios more systematically.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"59 - 68"},"PeriodicalIF":1.4,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41936995","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}
Asset returns and factor exposures frequently exhibit small fractions of extreme outliers, which are often associated with fat-tailed distributions and can have very adverse influence on classical least-squares regression estimators and sample covariance matrices. Over a number of decades, a solid theoretical and computational foundation has been developed for alternative robust estimators that are not much influenced by outliers. Unfortunately, such methods have seen relatively little use in portfolio construction and analysis. An overarching goal of this article is to encourage the use of robust statistics by portfolio managers and analysts, minimally as a complement to classical estimators and in some cases as a replacement. In support of this goal, the authors briefly describe the main data and theoretical foundations of robust statistics, then introduce a best-of-breed robust regression estimator with applications to cross-sectional and time-series factor model data. They go on to describe a highly robust covariance matrix estimator and the closely related robust multidimensional distance measure for outlier detection and shrinkage, applied to stock return and factor exposure data with influential outliers. A unique aspect of the robust estimators and most of the data used in this article is that they are freely available in several open source R packages. Consequently, most of the exhibits are reproducible with R code that may be found at: https://github.com/robustport/PCRA/blob/main/README.md.
{"title":"Robust Statistics for Portfolio Construction and Analysis","authors":"R. Martin, Stoyan Stoyanov, Kirk Li, M. Shammaa","doi":"10.3905/jpm.2023.1.527","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.527","url":null,"abstract":"Asset returns and factor exposures frequently exhibit small fractions of extreme outliers, which are often associated with fat-tailed distributions and can have very adverse influence on classical least-squares regression estimators and sample covariance matrices. Over a number of decades, a solid theoretical and computational foundation has been developed for alternative robust estimators that are not much influenced by outliers. Unfortunately, such methods have seen relatively little use in portfolio construction and analysis. An overarching goal of this article is to encourage the use of robust statistics by portfolio managers and analysts, minimally as a complement to classical estimators and in some cases as a replacement. In support of this goal, the authors briefly describe the main data and theoretical foundations of robust statistics, then introduce a best-of-breed robust regression estimator with applications to cross-sectional and time-series factor model data. They go on to describe a highly robust covariance matrix estimator and the closely related robust multidimensional distance measure for outlier detection and shrinkage, applied to stock return and factor exposure data with influential outliers. A unique aspect of the robust estimators and most of the data used in this article is that they are freely available in several open source R packages. Consequently, most of the exhibits are reproducible with R code that may be found at: https://github.com/robustport/PCRA/blob/main/README.md.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"105 - 139"},"PeriodicalIF":1.4,"publicationDate":"2023-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44211256","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}
Yongjae Lee, John R.J. Thompson, J. Kim, W. Kim, F. Fabozzi
Machine learning has been widely used in the asset management industry to improve operations and make data-driven decisions. This article provides an overview of machine learning for asset management by presenting various machine learning models in the context of their applications, including general classification and regression, time-series forecasting, natural language processing, dimension reduction, reinforcement learning, data generation, recommendation, and clustering. Additionally, it highlights the challenges of implementing machine learning in asset management, such as data quality and quantity, interpretability, and fairness.
{"title":"An Overview of Machine Learning for Asset Management","authors":"Yongjae Lee, John R.J. Thompson, J. Kim, W. Kim, F. Fabozzi","doi":"10.3905/jpm.2023.1.526","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.526","url":null,"abstract":"Machine learning has been widely used in the asset management industry to improve operations and make data-driven decisions. This article provides an overview of machine learning for asset management by presenting various machine learning models in the context of their applications, including general classification and regression, time-series forecasting, natural language processing, dimension reduction, reinforcement learning, data generation, recommendation, and clustering. Additionally, it highlights the challenges of implementing machine learning in asset management, such as data quality and quantity, interpretability, and fairness.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"31 - 63"},"PeriodicalIF":1.4,"publicationDate":"2023-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45651302","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}
Gueorgui S. Konstantinov, Irene E. Aldridge, Hossein Kazemi
This article aims to provide information on how networks gauge and visualize complex interactions and relationships between assets, factors, or other economic variables. The authors show that networks are helpful in portfolio and risk management and explain the important properties and metrics that describe networks and show examples of network applications. They discuss how the different types of networks—information, technological, social, and biological—have common properties that find their justification in finance and can be used in portfolio and risk management. Understanding the building elements of graphs and appropriate metrics provides valuable tools for researchers to deal with interacting risk entities. The article highlights and provides examples of how networks can be among the most complex graphs, and their use in portfolio management is bright and promising.
{"title":"Financial Networks and Portfolio Management","authors":"Gueorgui S. Konstantinov, Irene E. Aldridge, Hossein Kazemi","doi":"10.3905/jpm.2023.1.525","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.525","url":null,"abstract":"This article aims to provide information on how networks gauge and visualize complex interactions and relationships between assets, factors, or other economic variables. The authors show that networks are helpful in portfolio and risk management and explain the important properties and metrics that describe networks and show examples of network applications. They discuss how the different types of networks—information, technological, social, and biological—have common properties that find their justification in finance and can be used in portfolio and risk management. Understanding the building elements of graphs and appropriate metrics provides valuable tools for researchers to deal with interacting risk entities. The article highlights and provides examples of how networks can be among the most complex graphs, and their use in portfolio management is bright and promising.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"190 - 216"},"PeriodicalIF":1.4,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47293820","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":"Editor’s Introduction to the 2023 Special Issue on Performance Analysis","authors":"Frank J. Fabozzi","doi":"10.3905/jpm.2023.1.523","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.523","url":null,"abstract":"","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"1 - 4"},"PeriodicalIF":1.4,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42225036","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 author develops a parsimonious attribution framework for evaluating the environmental, social, and governance (ESG) performance of a portfolio. The attribution model decomposes portfolio ESG performance into three principal components: a value effect, a weighting effect, and an interaction effect. The author illustrates his approach using the equity portfolios of US public pension funds over time and finds that US public pensions’ positive ESG performance over the past decade is mainly due to their underlying holdings boosting their ESG scores over this period. By contrast, pension portfolio weight changes in high and low ESG-scoring firms over this period contributed negatively to their ESG performance, both in absolute terms and relative to the market portfolio. Furthermore, public pensions’ portfolio weighting behavior (the weighting effect) explains most of the variation in their ESG performance. The findings suggest that the proposed ESG attribution framework can help meet the demand for transparency regarding the ESG performance of investment assets.
{"title":"Determinants of Portfolio ESG Performance: An Attribution Framework","authors":"James J. Li","doi":"10.3905/jpm.2023.1.524","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.524","url":null,"abstract":"In this article, the author develops a parsimonious attribution framework for evaluating the environmental, social, and governance (ESG) performance of a portfolio. The attribution model decomposes portfolio ESG performance into three principal components: a value effect, a weighting effect, and an interaction effect. The author illustrates his approach using the equity portfolios of US public pension funds over time and finds that US public pensions’ positive ESG performance over the past decade is mainly due to their underlying holdings boosting their ESG scores over this period. By contrast, pension portfolio weight changes in high and low ESG-scoring firms over this period contributed negatively to their ESG performance, both in absolute terms and relative to the market portfolio. Furthermore, public pensions’ portfolio weighting behavior (the weighting effect) explains most of the variation in their ESG performance. The findings suggest that the proposed ESG attribution framework can help meet the demand for transparency regarding the ESG performance of investment assets.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"146 - 162"},"PeriodicalIF":1.4,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48508371","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}
J. Kim, W. Kim, Yongjae Lee, Bong-Geun Choi, Frank J. Fabozzi
Portfolio optimization is the basic quantitative approach for finding optimal portfolio weights. It has become increasingly important as portfolio construction involves more and more data and automated approaches. The inherent uncertainty in financial markets has led to consistent demand for improved robustness of portfolio models. In this article, the authors discuss the importance of robustness in portfolio optimization and present powerful methods that include robust estimators, robust portfolio optimization, distributionally robust optimization, and scenario-based optimization. They also review data-driven methods, machine learning–based models, and practical approaches for improving portfolio robustness.
{"title":"Robustness in Portfolio Optimization","authors":"J. Kim, W. Kim, Yongjae Lee, Bong-Geun Choi, Frank J. Fabozzi","doi":"10.3905/jpm.2023.1.522","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.522","url":null,"abstract":"Portfolio optimization is the basic quantitative approach for finding optimal portfolio weights. It has become increasingly important as portfolio construction involves more and more data and automated approaches. The inherent uncertainty in financial markets has led to consistent demand for improved robustness of portfolio models. In this article, the authors discuss the importance of robustness in portfolio optimization and present powerful methods that include robust estimators, robust portfolio optimization, distributionally robust optimization, and scenario-based optimization. They also review data-driven methods, machine learning–based models, and practical approaches for improving portfolio robustness.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"140 - 159"},"PeriodicalIF":1.4,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44834772","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}
Models in asset management require consideration of uncertainty. Monte Carlo simulation is a popular quantitative tool that assigns random values to input variables in order to draw inferences about an uncertain outcome. This article explains and illustrates the main characteristics of Monte Carlo simulation and presents examples for its application in option pricing, portfolio insurance, and portfolio risk management.
{"title":"Monte Carlo Simulation in Financial Modeling","authors":"K. Simsek","doi":"10.3905/jpm.2023.1.521","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.521","url":null,"abstract":"Models in asset management require consideration of uncertainty. Monte Carlo simulation is a popular quantitative tool that assigns random values to input variables in order to draw inferences about an uncertain outcome. This article explains and illustrates the main characteristics of Monte Carlo simulation and presents examples for its application in option pricing, portfolio insurance, and portfolio risk management.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"178 - 188"},"PeriodicalIF":1.4,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48316925","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 surveys the evolution of machine learning from linear regression through ChatGPT to fully unsupervised learning. We illustrate the advantages of artificial intelligence (AI) over traditional methods with simple intuitive examples for the US equities markets. We also show that the AI inferences are consistent with classical finance models, such as the capital asset pricing model. We also describe how, unlike machine learning, true AI unsupervised models satisfy the optimal modeling characteristics. Most importantly, we show step by step how AI identifies and extracts signals from data.
{"title":"The AI Revolution: From Linear Regression to ChatGPT and beyond and How It All Connects to Finance","authors":"Irene E. Aldridge","doi":"10.3905/jpm.2023.1.519","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.519","url":null,"abstract":"This article surveys the evolution of machine learning from linear regression through ChatGPT to fully unsupervised learning. We illustrate the advantages of artificial intelligence (AI) over traditional methods with simple intuitive examples for the US equities markets. We also show that the AI inferences are consistent with classical finance models, such as the capital asset pricing model. We also describe how, unlike machine learning, true AI unsupervised models satisfy the optimal modeling characteristics. Most importantly, we show step by step how AI identifies and extracts signals from data.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"64 - 77"},"PeriodicalIF":1.4,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45214486","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}
Relevance-based prediction is a new approach to data-driven forecasting that serves as a favorable alternative to both linear regression analysis and machine learning. It follows from two seminal scientific innovations: Prasanta Mahalanobis’ distance measure and Claude Shannon’s information theory. Relevance-based prediction rests on three key tenets: 1) relevance, which measures the importance of an observation to a prediction; 2) fit, which measures the reliability of each individual prediction task; and 3) codependence, which holds that the choice of observations and predictive variables should be determined jointly for each individual prediction task.
{"title":"An Intuitive Guide to Relevance-Based Prediction","authors":"M. Czasonis, M. Kritzman, D. Turkington","doi":"10.3905/jpm.2023.1.518","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.518","url":null,"abstract":"Relevance-based prediction is a new approach to data-driven forecasting that serves as a favorable alternative to both linear regression analysis and machine learning. It follows from two seminal scientific innovations: Prasanta Mahalanobis’ distance measure and Claude Shannon’s information theory. Relevance-based prediction rests on three key tenets: 1) relevance, which measures the importance of an observation to a prediction; 2) fit, which measures the reliability of each individual prediction task; and 3) codependence, which holds that the choice of observations and predictive variables should be determined jointly for each individual prediction task.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"96 - 104"},"PeriodicalIF":1.4,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45990981","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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