Pub Date : 2016-08-31DOI: 10.3905/jii.2016.7.2.119
J. Staines, Wei (Victor) Li, Yazann S. Romahi
Within the investment industry, diversification now refers to not only the division of capital among a large number of securities but also the avoidance of risk concentration in any of a number of dimensions. Market-capitalization-weighted indexes often fail this requirement. The authors thus argue that although capitalization weighting makes a suitable benchmark, smart beta can provide a way to build indexes more suitable for investment. The authors present a methodology to measure and hence maximize diversification simultaneously across multiple dimensions. They show the practical value of this measure by using it to backtest equity portfolios. This provides an example of how the properties of assets, rather than historical returns, can be used to systematically construct well-diversified portfolios.
{"title":"Dimensions of Diversification","authors":"J. Staines, Wei (Victor) Li, Yazann S. Romahi","doi":"10.3905/jii.2016.7.2.119","DOIUrl":"https://doi.org/10.3905/jii.2016.7.2.119","url":null,"abstract":"Within the investment industry, diversification now refers to not only the division of capital among a large number of securities but also the avoidance of risk concentration in any of a number of dimensions. Market-capitalization-weighted indexes often fail this requirement. The authors thus argue that although capitalization weighting makes a suitable benchmark, smart beta can provide a way to build indexes more suitable for investment. The authors present a methodology to measure and hence maximize diversification simultaneously across multiple dimensions. They show the practical value of this measure by using it to backtest equity portfolios. This provides an example of how the properties of assets, rather than historical returns, can be used to systematically construct well-diversified portfolios.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2016.7.2.119","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70090275","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}
Pub Date : 2016-08-31DOI: 10.3905/jii.2016.7.2.016
Gerasimos G. Rompotis
This article examines various issues concerning the performance of commodity exchange-traded funds (ETFs) by taking into account whether these funds adopt a physical or a synthetic replication technique. The analysis first demonstrates that the physically backed ETFs perform better than their futures-based counterparts but they are riskier than them. Moreover, it is shown that the pricing of commodity ETFs, and especially the pricing of futures-based commodity ETFs, is somehow affected by developments in the equity market. The return of commodity ETFs is further affected by the implied and contemporaneous volatility of equity market as well as daily changes in the exchange rates of USD with such basic currencies as EUR and JPY. The tracking error of commodity ETFs is influenced by these market factors too. Finally, it is revealed that the tracking error of futures-based commodity ETFs is significantly higher than the tracking error of commodity ETFs that invest in the underlying commodities directly. This pattern applies both to bear and bull markets. In addition, the tracking error of the majority of commodity ETFs displays a mean-reverting behavior.
{"title":"Physical versus Futures-Based Replication: The Case of Commodity ETFs","authors":"Gerasimos G. Rompotis","doi":"10.3905/jii.2016.7.2.016","DOIUrl":"https://doi.org/10.3905/jii.2016.7.2.016","url":null,"abstract":"This article examines various issues concerning the performance of commodity exchange-traded funds (ETFs) by taking into account whether these funds adopt a physical or a synthetic replication technique. The analysis first demonstrates that the physically backed ETFs perform better than their futures-based counterparts but they are riskier than them. Moreover, it is shown that the pricing of commodity ETFs, and especially the pricing of futures-based commodity ETFs, is somehow affected by developments in the equity market. The return of commodity ETFs is further affected by the implied and contemporaneous volatility of equity market as well as daily changes in the exchange rates of USD with such basic currencies as EUR and JPY. The tracking error of commodity ETFs is influenced by these market factors too. Finally, it is revealed that the tracking error of futures-based commodity ETFs is significantly higher than the tracking error of commodity ETFs that invest in the underlying commodities directly. This pattern applies both to bear and bull markets. In addition, the tracking error of the majority of commodity ETFs displays a mean-reverting behavior.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2016.7.2.016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70090356","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}
Pub Date : 2016-08-31DOI: 10.3905/jii.2016.7.2.057
Carmine de Franco, B. Monnier, Johann Nicolle, K. Rulik
In this article, the authors use a quantitative approach to compare different alternative beta strategies, based on statistical relationships among their returns. Using correlations, principal component analysis, regression factor models, and minimum spanning tree graphs, they identify and quantify statistical closeness of these portfolios. The results show that when measured by return comovements and common systematic risk exposures, different alternative beta portfolios are, on average, quite close to each other. Surprisingly, in some cases, returns of portfolios with different strategic approaches can be more similar than those of two portfolios representing different variations of the same approach. Using a formal clustering technique, the authors show how to identify distinct clusters within a set of alternative beta portfolios. Given potential redundancy of alternative beta, their clusters can give a better diversified set of building blocks for multi-strategy allocations than individual strategies themselves. The authors build several portfolio allocations using clusters of alternative beta strategies as building blocks and compare individual strategy-based and cluster-based allocations, within both static and dynamic allocation frameworks. They find that the cluster-based allocations have a better risk–return profile with respect to the portfolios based on individual strategies.
{"title":"How Different Are Alternative Beta Strategies?","authors":"Carmine de Franco, B. Monnier, Johann Nicolle, K. Rulik","doi":"10.3905/jii.2016.7.2.057","DOIUrl":"https://doi.org/10.3905/jii.2016.7.2.057","url":null,"abstract":"In this article, the authors use a quantitative approach to compare different alternative beta strategies, based on statistical relationships among their returns. Using correlations, principal component analysis, regression factor models, and minimum spanning tree graphs, they identify and quantify statistical closeness of these portfolios. The results show that when measured by return comovements and common systematic risk exposures, different alternative beta portfolios are, on average, quite close to each other. Surprisingly, in some cases, returns of portfolios with different strategic approaches can be more similar than those of two portfolios representing different variations of the same approach. Using a formal clustering technique, the authors show how to identify distinct clusters within a set of alternative beta portfolios. Given potential redundancy of alternative beta, their clusters can give a better diversified set of building blocks for multi-strategy allocations than individual strategies themselves. The authors build several portfolio allocations using clusters of alternative beta strategies as building blocks and compare individual strategy-based and cluster-based allocations, within both static and dynamic allocation frameworks. They find that the cluster-based allocations have a better risk–return profile with respect to the portfolios based on individual strategies.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2016.7.2.057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70090110","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}
Pub Date : 2016-08-31DOI: 10.3905/jii.2016.7.2.087
M. Alighanbari, C. Chia
Multifactor index fund allocations are increasingly becoming the preferred approach to factor investing. This article examines the return–risk characteristics of nine static and dynamic weighting strategies over a 36-year period. The results highlight that a simple strategy that equal weights multiple factor indexes has historically proved more effective than many of the more complex approaches—pointing to its potential as a way to combine factors, especially in the absence of active investment views and skills. However, a dynamic factor-weighting strategy based on fundamental signals also has merit if the investor believes she has the insight or skills required.
{"title":"Multifactor Indexes Made Simple: A Review of Static and Dynamic Approaches","authors":"M. Alighanbari, C. Chia","doi":"10.3905/jii.2016.7.2.087","DOIUrl":"https://doi.org/10.3905/jii.2016.7.2.087","url":null,"abstract":"Multifactor index fund allocations are increasingly becoming the preferred approach to factor investing. This article examines the return–risk characteristics of nine static and dynamic weighting strategies over a 36-year period. The results highlight that a simple strategy that equal weights multiple factor indexes has historically proved more effective than many of the more complex approaches—pointing to its potential as a way to combine factors, especially in the absence of active investment views and skills. However, a dynamic factor-weighting strategy based on fundamental signals also has merit if the investor believes she has the insight or skills required.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2016.7.2.087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70090178","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}
Pub Date : 2016-08-31DOI: 10.3905/jii.2016.7.2.109
J. Bender, Xiaole Sun, Taie Wang
What does success look like for smart beta indexes? The authors argue that success is the ability of an index to provide a strong and consistent level of exposure to the targeted factor(s). Moreover, exposure should be achieved in a risk-aware way. Risk taken relative to the capitalization-weighted benchmark is active risk, and investors should be compensated with greater exposure to their targeted factor(s). The authors provide guidance on how indexes can be built with this measure in mind via screening and weighting decisions. Because screening and weighting decisions move tracking error and exposure in the same direction, most rules-based indexes naturally provide reasonably strong exposure per unit of tracking error.
{"title":"A New Metric for Smart Beta: Factor Exposure per Unit of Tracking Error","authors":"J. Bender, Xiaole Sun, Taie Wang","doi":"10.3905/jii.2016.7.2.109","DOIUrl":"https://doi.org/10.3905/jii.2016.7.2.109","url":null,"abstract":"What does success look like for smart beta indexes? The authors argue that success is the ability of an index to provide a strong and consistent level of exposure to the targeted factor(s). Moreover, exposure should be achieved in a risk-aware way. Risk taken relative to the capitalization-weighted benchmark is active risk, and investors should be compensated with greater exposure to their targeted factor(s). The authors provide guidance on how indexes can be built with this measure in mind via screening and weighting decisions. Because screening and weighting decisions move tracking error and exposure in the same direction, most rules-based indexes naturally provide reasonably strong exposure per unit of tracking error.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2016.7.2.109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70090225","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}
Pub Date : 2016-08-31DOI: 10.3905/jii.2016.7.2.039
N. Amenc, Felix Goltz
This article analyzes what academic research has to say on equity factors. The objective is to understand what lessons can be learned from such research on designing and evaluating factor indexes. When analyzing academic publications on equity factor investing, five important lessons emerge, which provide useful perspective on practical questions about factor indexes. This article looks at the empirical analysis that is required to identify rewarded factors. It then turns to the economic rationale behind factors and looks into the role of diversification for a given factor tilt. Moreover, it discusses the issue of implementation costs and addresses the question of crowding risks. Finally, the article discusses how popular practical implementations relate to the academic grounding.
{"title":"Long-Term Rewarded Equity Factors: What Can Investors Learn from Academic Research?","authors":"N. Amenc, Felix Goltz","doi":"10.3905/jii.2016.7.2.039","DOIUrl":"https://doi.org/10.3905/jii.2016.7.2.039","url":null,"abstract":"This article analyzes what academic research has to say on equity factors. The objective is to understand what lessons can be learned from such research on designing and evaluating factor indexes. When analyzing academic publications on equity factor investing, five important lessons emerge, which provide useful perspective on practical questions about factor indexes. This article looks at the empirical analysis that is required to identify rewarded factors. It then turns to the economic rationale behind factors and looks into the role of diversification for a given factor tilt. Moreover, it discusses the issue of implementation costs and addresses the question of crowding risks. Finally, the article discusses how popular practical implementations relate to the academic grounding.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2016.7.2.039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70090044","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}
Pub Date : 2016-08-31DOI: 10.3905/jii.2016.7.2.001
Brian R. Bruce
{"title":"Editor’s Letter","authors":"Brian R. Bruce","doi":"10.3905/jii.2016.7.2.001","DOIUrl":"https://doi.org/10.3905/jii.2016.7.2.001","url":null,"abstract":"","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2016.7.2.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70089653","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}
Pub Date : 2016-08-31DOI: 10.3905/jii.2016.7.2.100
Michael R. Hunstad
Equity factors such as value, size, and momentum have notoriously cyclical return patterns that may make them inappropriate investments depending on one’s time horizon. In this article, the author sizes the duration of these cycles and shows that cycle length varies considerably across factors. A simple rule of thumb emerges from this analysis: A factor should not be considered if the intended holding period is less than the cycle length. In other words, time diversification is a key consideration in choosing factors. He then uses a multiperiod optimization algorithm incorporating factor cycle lengths to select optimal factor allocations. As expected, the model tends to align factor cycle length and time to liquidation. That is, longer cycle factors were more prevalent in the optimal portfolio the longer the time to liquidation, and vice versa. These results add to the existing literature in helping to answer the question of which factor is best. Interestingly, the results suggest that we may be asking the wrong question—that we should be asking not which? but when?
{"title":"Choosing Factors: Not “Which?” but “When?”","authors":"Michael R. Hunstad","doi":"10.3905/jii.2016.7.2.100","DOIUrl":"https://doi.org/10.3905/jii.2016.7.2.100","url":null,"abstract":"Equity factors such as value, size, and momentum have notoriously cyclical return patterns that may make them inappropriate investments depending on one’s time horizon. In this article, the author sizes the duration of these cycles and shows that cycle length varies considerably across factors. A simple rule of thumb emerges from this analysis: A factor should not be considered if the intended holding period is less than the cycle length. In other words, time diversification is a key consideration in choosing factors. He then uses a multiperiod optimization algorithm incorporating factor cycle lengths to select optimal factor allocations. As expected, the model tends to align factor cycle length and time to liquidation. That is, longer cycle factors were more prevalent in the optimal portfolio the longer the time to liquidation, and vice versa. These results add to the existing literature in helping to answer the question of which factor is best. Interestingly, the results suggest that we may be asking the wrong question—that we should be asking not which? but when?","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2016.7.2.100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70090215","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}
Pub Date : 2016-08-31DOI: 10.3905/jii.2016.7.2.078
Rey Santodomingo, V. Nemtchinov, Tianchuan Li
The risk-adjusted returns of factor strategies can look quite attractive. However, the turnover associated with them can significantly reduce their after-tax excess returns. In this article, the authors report the results of their after-tax study of these strategies. They find that material pre-tax excess return can be gained through exposure to popular factors—up to 2.4% net of management fees. From an after-tax perspective, they find that taxes can erode much of this return unless a systematic tax management process is applied.
{"title":"Tax Management of Factor-Based Portfolios","authors":"Rey Santodomingo, V. Nemtchinov, Tianchuan Li","doi":"10.3905/jii.2016.7.2.078","DOIUrl":"https://doi.org/10.3905/jii.2016.7.2.078","url":null,"abstract":"The risk-adjusted returns of factor strategies can look quite attractive. However, the turnover associated with them can significantly reduce their after-tax excess returns. In this article, the authors report the results of their after-tax study of these strategies. They find that material pre-tax excess return can be gained through exposure to popular factors—up to 2.4% net of management fees. From an after-tax perspective, they find that taxes can erode much of this return unless a systematic tax management process is applied.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2016.7.2.078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70090119","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}
Pub Date : 2016-08-01DOI: 10.3905/jii.2016.7.3.043
David Blitz
The added value of smart beta indexes is known to be explained by exposures to established factor premiums, but does that make these indexes suitable for implementing a factor investing strategy? This article finds that the amount of factor exposure provided by popular smart beta strategies differs considerably, as does their degree of focus on a single target factor. It also provides insight into how “quality” and “high dividend” indexes relate to academic factors. Smart beta indexes exhibit a performance that is in line with the amount of factor exposure provided, but it seems that they do not unlock the full potential offered by factor premiums. Altogether, these results imply that factor investing with smart beta indexes is not as straightforward as one might think.
{"title":"Factor Investing with Smart Beta Indices","authors":"David Blitz","doi":"10.3905/jii.2016.7.3.043","DOIUrl":"https://doi.org/10.3905/jii.2016.7.3.043","url":null,"abstract":"The added value of smart beta indexes is known to be explained by exposures to established factor premiums, but does that make these indexes suitable for implementing a factor investing strategy? This article finds that the amount of factor exposure provided by popular smart beta strategies differs considerably, as does their degree of focus on a single target factor. It also provides insight into how “quality” and “high dividend” indexes relate to academic factors. Smart beta indexes exhibit a performance that is in line with the amount of factor exposure provided, but it seems that they do not unlock the full potential offered by factor premiums. Altogether, these results imply that factor investing with smart beta indexes is not as straightforward as one might think.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2016.7.3.043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70090431","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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