A. Alankar, Daniel Ding, Allan Z. Maymin, Philip Z. Maymin, M. Scholes
The authors identify, examine, and categorize the largest downside tail events in the US equity market over the past 150 years using monthly data. They define a downside tail event as any peak-to-trough loss of at least 15%. Using Gaussian mixture models to cluster the tail events based on predrawdown observables, five distinct environments emerge: hiking, easing, disinflationary, inflationary, and exuberance. The authors further distinguish gamma events as those in which a hedging policy of rolling short-term option positions would have recovered more than half of the drawdown and find that they all occur only in hiking or exuberance environments, with the exception of the 2020 COVID-19 lockdown event, which can be thought of as a “known unknown”. Such gamma events can be distinguished ex-ante from nongamma events with a two-factor logistic model based on the equity-fixed income correlation and the change in the geopolitical risk index over the year preceding the starting peak of the drawdown. A large increase in geopolitical risk and/or equity-fixed income correlation, reflective of a market environment driven by fewer factors and hence more fragile, indicates a greater likelihood that a future drawdown is of a gamma type. This model can help recommend if gamma or delta protection should be sought. Finally, in addition to categorizing and explaining the causes and drivers of downside tail events, we also determine which types of tail hedge strategies worked, and how well, for each tail event. The analysis provides important information to guide the use of tail-hedging strategies, which can accelerate compounding.
{"title":"Fairy Tails: Lessons from 150 Years of Drawdowns","authors":"A. Alankar, Daniel Ding, Allan Z. Maymin, Philip Z. Maymin, M. Scholes","doi":"10.3905/jpm.2023.1.503","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.503","url":null,"abstract":"The authors identify, examine, and categorize the largest downside tail events in the US equity market over the past 150 years using monthly data. They define a downside tail event as any peak-to-trough loss of at least 15%. Using Gaussian mixture models to cluster the tail events based on predrawdown observables, five distinct environments emerge: hiking, easing, disinflationary, inflationary, and exuberance. The authors further distinguish gamma events as those in which a hedging policy of rolling short-term option positions would have recovered more than half of the drawdown and find that they all occur only in hiking or exuberance environments, with the exception of the 2020 COVID-19 lockdown event, which can be thought of as a “known unknown”. Such gamma events can be distinguished ex-ante from nongamma events with a two-factor logistic model based on the equity-fixed income correlation and the change in the geopolitical risk index over the year preceding the starting peak of the drawdown. A large increase in geopolitical risk and/or equity-fixed income correlation, reflective of a market environment driven by fewer factors and hence more fragile, indicates a greater likelihood that a future drawdown is of a gamma type. This model can help recommend if gamma or delta protection should be sought. Finally, in addition to categorizing and explaining the causes and drivers of downside tail events, we also determine which types of tail hedge strategies worked, and how well, for each tail event. The analysis provides important information to guide the use of tail-hedging strategies, which can accelerate compounding.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"8 - 34"},"PeriodicalIF":1.4,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49493279","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 explains the methods for altering critical facets of the traditional performance measurement framework to handle the unique attributes and challenges associated with alternative investments. The practices described in this article are already used in the alternative investment industry. Due to the expansion of traditional asset manager product lines to alternatives and the expanded use of vehicles enabling wealth managers and individual investors access to private markets, many analysts are interested in a primer on enhancing their performance measurement process.
{"title":"Performance Measurement for Alternative Investments Portfolios","authors":"Bruce J. Feibel","doi":"10.3905/jpm.2023.1.506","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.506","url":null,"abstract":"In this article, the author explains the methods for altering critical facets of the traditional performance measurement framework to handle the unique attributes and challenges associated with alternative investments. The practices described in this article are already used in the alternative investment industry. Due to the expansion of traditional asset manager product lines to alternatives and the expanded use of vehicles enabling wealth managers and individual investors access to private markets, many analysts are interested in a primer on enhancing their performance measurement process.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"51 9","pages":"185 - 197"},"PeriodicalIF":1.4,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41268657","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 authors first characterize a variety of systematic strategies in terms of timing and sizing skills based on their frequencies and magnitudes of gains and losses over time and then move on to analyze how these characteristics differ over macroeconomic regimes, such as inflationary and recessionary periods. The results are based on new methodologies for significance testing of gain- and loss-based performance measures and complement previous results based on analysis of the Sharpe ratio of these strategies. The empirical results have implications for outcome-orientated portfolio construction as well as strategic and tactical asset allocation, because strategies that have desirable properties—as well as strategies with problematic performance—are identified under each regime. This approach also allows for identification and attribution of the inherent sources of these differences.
{"title":"Timing and Sizing Skills of Systematic Strategies across Time and Economic Regimes","authors":"S. Browne, Andreas Farmakas, Spyros Mesomeris","doi":"10.3905/jpm.2023.1.505","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.505","url":null,"abstract":"The authors first characterize a variety of systematic strategies in terms of timing and sizing skills based on their frequencies and magnitudes of gains and losses over time and then move on to analyze how these characteristics differ over macroeconomic regimes, such as inflationary and recessionary periods. The results are based on new methodologies for significance testing of gain- and loss-based performance measures and complement previous results based on analysis of the Sharpe ratio of these strategies. The empirical results have implications for outcome-orientated portfolio construction as well as strategic and tactical asset allocation, because strategies that have desirable properties—as well as strategies with problematic performance—are identified under each regime. This approach also allows for identification and attribution of the inherent sources of these differences.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"199 - 220"},"PeriodicalIF":1.4,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44814005","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}
One simple way for an asset owner to update an estimate of the expected performance of an investment manager is to apply the Bayes rule. In its simplest form, this involves no information other than an estimate of the prior distribution and historical data on manager performance. However, this direct method of updating expectations is inconsistent with finance theory. This short note draws out the distinction.
{"title":"Bayes Rule and the Selection of Investment Managers","authors":"Bradford Cornell","doi":"10.3905/jpm.2023.1.504","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.504","url":null,"abstract":"One simple way for an asset owner to update an estimate of the expected performance of an investment manager is to apply the Bayes rule. In its simplest form, this involves no information other than an estimate of the prior distribution and historical data on manager performance. However, this direct method of updating expectations is inconsistent with finance theory. This short note draws out the distinction.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"221 - 223"},"PeriodicalIF":1.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46734247","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 for the 2023 Special Issue on Investing in Non-US Financial Markets","authors":"Frank J. Fabozzi","doi":"10.3905/jpm.2023.1.502","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.502","url":null,"abstract":"","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":" ","pages":"1 - 3"},"PeriodicalIF":1.4,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43853962","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 contributes to the longstanding debate about the relative merits of building multifactor portfolios using a bottom-up approach, informed by factor-based expected returns, and a top-down approach that allocates across factor portfolios. Its main contribution is to prove formally that the solution of the mean–variance optimization solved by a stock picker who uses factors to select securities and that of a mean–variance-efficient allocation across factors are in fact largely equivalent. This finding is corroborated empirically and holds under stringent investment constraints. Moreover, while demonstrating this equivalence, an alternative methodology emerges that makes the best of both approaches.
{"title":"Reconciling Stock Selection and Factor Allocation","authors":"Xavier Gérard","doi":"10.3905/jpm.2023.1.500","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.500","url":null,"abstract":"This article contributes to the longstanding debate about the relative merits of building multifactor portfolios using a bottom-up approach, informed by factor-based expected returns, and a top-down approach that allocates across factor portfolios. Its main contribution is to prove formally that the solution of the mean–variance optimization solved by a stock picker who uses factors to select securities and that of a mean–variance-efficient allocation across factors are in fact largely equivalent. This finding is corroborated empirically and holds under stringent investment constraints. Moreover, while demonstrating this equivalence, an alternative methodology emerges that makes the best of both approaches.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"93 - 115"},"PeriodicalIF":1.4,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47936405","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 explain non-normal probability distributions and the reasons it is important to properly model the tails of one or more distributions in applications to asset management. The authors illustrate the types of quantitative models needed in asset management and provide some basic concepts on random variables and stochastic processes useful to understand non-normal models. After having reviewed the stylized facts of log-returns, the authors describe, in nontechnical terms and with only a few formulas, univariate and multivariate non-normal models that are able to explain the fat (and heavy) tails empirically observed in the distribution of asset and portfolio log-returns.
{"title":"Fat and Heavy Tails in Asset Management","authors":"M. L. Bianchi, G. Tassinari, Frank J. Fabozzi","doi":"10.3905/jpm.2023.1.501","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.501","url":null,"abstract":"In this article, the authors explain non-normal probability distributions and the reasons it is important to properly model the tails of one or more distributions in applications to asset management. The authors illustrate the types of quantitative models needed in asset management and provide some basic concepts on random variables and stochastic processes useful to understand non-normal models. After having reviewed the stylized facts of log-returns, the authors describe, in nontechnical terms and with only a few formulas, univariate and multivariate non-normal models that are able to explain the fat (and heavy) tails empirically observed in the distribution of asset and portfolio log-returns.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"236 - 263"},"PeriodicalIF":1.4,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42403531","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}
How should active investors calibrate their investment decisions, when the goal is to reach an outperformance target over the long term, without succumbing to large drawdowns in the interim? Closed-form expressions for the probability of consistent long-term alpha are derived and show that maximizing this probability requires a thoughtful calibration of skill, volatility, decision frequency, and costs. The article derives the minimum skill level for which the probability of consistent long-term alpha trends to its maximum for long investment horizons. This minimum skill level is not typically large, at only marginally above a fair coin toss. Unfortunately, the maximum probability of consistent long-term alpha is also not very large, even when skill is high and the investment horizon is long. The reason is that, although the probability of reaching the long-term goal rises with the time horizon for sufficiently high skill, so does the probability of experiencing the intertemporal loss that can force a stop-out in the interim.
{"title":"Investment Skill and Consistent Long-Term Alpha","authors":"Ronald J. M. van Loon","doi":"10.3905/jpm.2023.1.499","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.499","url":null,"abstract":"How should active investors calibrate their investment decisions, when the goal is to reach an outperformance target over the long term, without succumbing to large drawdowns in the interim? Closed-form expressions for the probability of consistent long-term alpha are derived and show that maximizing this probability requires a thoughtful calibration of skill, volatility, decision frequency, and costs. The article derives the minimum skill level for which the probability of consistent long-term alpha trends to its maximum for long investment horizons. This minimum skill level is not typically large, at only marginally above a fair coin toss. Unfortunately, the maximum probability of consistent long-term alpha is also not very large, even when skill is high and the investment horizon is long. The reason is that, although the probability of reaching the long-term goal rises with the time horizon for sufficiently high skill, so does the probability of experiencing the intertemporal loss that can force a stop-out in the interim.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"44 - 63"},"PeriodicalIF":1.4,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48769542","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}
Lauren Stagnol, Marc-ali Ben Abdallah, Patrick Herfroy
In this article, the authors intend to gain an understanding of the drivers of stock convexity, also known as gamma. First, using a bottom-up—firm-level—approach, they show that stock fundamentals, particularly metrics related to value (captured by the price-to-book ratio) and historical volatility, allow us to efficiently discriminate between convex and concave stocks. Building on this result, they investigate the ties between the gamma premium and traditional risk factors. Second, they adopt a top-down—macroeconomic-driven—framework to understand which economic environment is the most favorable to convexity: They highlight the importance of the short-term interest rate, the VIX, but also oil price dynamics in a univariate cointegrating vector. These variables share long-term relationships. The authors then evaluate the ability of different models to forecast future convexity premium dynamics. Finally, they seek to employ these signals in the design of a systematic long convexity strategy and show that it leads to significantly improved risk-adjusted returns compared with a capitalization-weighted benchmark, especially in turbulent markets. Convexity exposure appears particularly relevant in a context of monetary policy normalization.
{"title":"Equity Convexity under Major Monetary Policy Shift","authors":"Lauren Stagnol, Marc-ali Ben Abdallah, Patrick Herfroy","doi":"10.3905/jpm.2023.1.498","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.498","url":null,"abstract":"In this article, the authors intend to gain an understanding of the drivers of stock convexity, also known as gamma. First, using a bottom-up—firm-level—approach, they show that stock fundamentals, particularly metrics related to value (captured by the price-to-book ratio) and historical volatility, allow us to efficiently discriminate between convex and concave stocks. Building on this result, they investigate the ties between the gamma premium and traditional risk factors. Second, they adopt a top-down—macroeconomic-driven—framework to understand which economic environment is the most favorable to convexity: They highlight the importance of the short-term interest rate, the VIX, but also oil price dynamics in a univariate cointegrating vector. These variables share long-term relationships. The authors then evaluate the ability of different models to forecast future convexity premium dynamics. Finally, they seek to employ these signals in the design of a systematic long convexity strategy and show that it leads to significantly improved risk-adjusted returns compared with a capitalization-weighted benchmark, especially in turbulent markets. Convexity exposure appears particularly relevant in a context of monetary policy normalization.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"179 - 200"},"PeriodicalIF":1.4,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46509254","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}
Developing an excellent quantitative trading strategy to obtain a high Sharpe ratio requires optimizing several parameters at the same time. Example parameters include the window length of a moving average sequence, the choice of trading instruments, and the thresholds used to generate trading signals. Simultaneously optimizing all these parameters to seek a high Sharpe ratio is a daunting and time-consuming task, partly because of the unknown mechanism determining the Sharpe ratio. This article proposes using Bayesian optimization to systematically search for the optimal parameter configuration that leads to a high Sharpe ratio. The author shows that the proposed intelligent search strategy performs better than manual search, a common practice that proves to be inefficient. The author’s framework also can easily be extended to other parameter selection tasks in portfolio optimization and risk management.
{"title":"Seeking Better Sharpe Ratio via Bayesian Optimization","authors":"Peng Liu","doi":"10.3905/jpm.2023.1.497","DOIUrl":"https://doi.org/10.3905/jpm.2023.1.497","url":null,"abstract":"Developing an excellent quantitative trading strategy to obtain a high Sharpe ratio requires optimizing several parameters at the same time. Example parameters include the window length of a moving average sequence, the choice of trading instruments, and the thresholds used to generate trading signals. Simultaneously optimizing all these parameters to seek a high Sharpe ratio is a daunting and time-consuming task, partly because of the unknown mechanism determining the Sharpe ratio. This article proposes using Bayesian optimization to systematically search for the optimal parameter configuration that leads to a high Sharpe ratio. The author shows that the proposed intelligent search strategy performs better than manual search, a common practice that proves to be inefficient. The author’s framework also can easily be extended to other parameter selection tasks in portfolio optimization and risk management.","PeriodicalId":53670,"journal":{"name":"Journal of Portfolio Management","volume":"49 1","pages":"35 - 43"},"PeriodicalIF":1.4,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49110871","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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