Using filtering techniques, spectral analysis, and Markov chain models, the author documents trends and cycles of factors that have significantly changed over the period to December 2000 compared with the period post-January 2001. The recent weaker performance of the value factor in the 21st century, including the value drawdown over 2017 to 2022, which is the worst value drawdown ever experienced, can be attributed to both a decreasing trend component and downturns in cyclical components. Momentum performance has also declined in the post-2001 period due to decreasing trends, while the trends of the quality and size factors have increased. Low-volatility portfolios still significantly reduce equity market risk in the 21st century, but the factor spends slightly longer durations in a low return regime.
{"title":"Trends and Cycles of Style Factors in the 20th and 21st Centuries","authors":"Andrew Ang","doi":"10.2139/ssrn.4279022","DOIUrl":"https://doi.org/10.2139/ssrn.4279022","url":null,"abstract":"Using filtering techniques, spectral analysis, and Markov chain models, the author documents trends and cycles of factors that have significantly changed over the period to December 2000 compared with the period post-January 2001. The recent weaker performance of the value factor in the 21st century, including the value drawdown over 2017 to 2022, which is the worst value drawdown ever experienced, can be attributed to both a decreasing trend component and downturns in cyclical components. Momentum performance has also declined in the post-2001 period due to decreasing trends, while the trends of the quality and size factors have increased. Low-volatility portfolios still significantly reduce equity market risk in the 21st century, but the factor spends slightly longer durations in a low return regime.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"49 1","pages":"33 - 56"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41491250","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}
Index tracking has long been of interest for both industry of fund management and academia. Various methods have been proposed and tested and various issues are discussed throughout the past 30 years. Yet one issue remains unresolved is how to perform stock selection optimally. In this article, I propose to use an artificial intelligent method—particle swarm optimization (or PSO) to select the most effective stocks to track a target index most closely. I track the S&P 500 index using a small number of its constituents from 1990 till 2019. Practical constraints such as liquidity (in a form of bid-ask spread), transaction costs (commission), and capital requirement are considered. The overall out-of-sample error is consistent with the literature and shown to be greatly reduced if the rebalancing horizon is shorter and the number of stocks is increased. Also, turnovers are lower if rebalancing is more frequent and if more stocks are chosen. Hence, there is a clear tradeoff between rebalancing cost and tracking accuracy.
{"title":"Index Tracking: A Stock Selection Model Using Particle Swarm Optimization","authors":"Ren‐Raw Chen","doi":"10.2139/ssrn.4109603","DOIUrl":"https://doi.org/10.2139/ssrn.4109603","url":null,"abstract":"Index tracking has long been of interest for both industry of fund management and academia. Various methods have been proposed and tested and various issues are discussed throughout the past 30 years. Yet one issue remains unresolved is how to perform stock selection optimally. In this article, I propose to use an artificial intelligent method—particle swarm optimization (or PSO) to select the most effective stocks to track a target index most closely. I track the S&P 500 index using a small number of its constituents from 1990 till 2019. Practical constraints such as liquidity (in a form of bid-ask spread), transaction costs (commission), and capital requirement are considered. The overall out-of-sample error is consistent with the literature and shown to be greatly reduced if the rebalancing horizon is shorter and the number of stocks is increased. Also, turnovers are lower if rebalancing is more frequent and if more stocks are chosen. Hence, there is a clear tradeoff between rebalancing cost and tracking accuracy.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"32 1","pages":"53 - 73"},"PeriodicalIF":0.0,"publicationDate":"2022-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42471969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexander Cheema-Fox, George Serafeim, Hui (Stacie) Wang
An increasing number of companies are providing products and services that help reduce carbon emissions in the economy. The authors develop a methodology to identify those companies and create a sample of publicly listed climate solutions companies, allowing the authors to study their geographic composition, accounting fundamentals, valuation ratios, and stock performance over time. The sample is equally split between developed and emerging markets, with a significant number of companies located in China. A portfolio of climate solutions companies exhibits higher revenue growth, higher investments in research and development and talent, and lower profitability margin. Portfolio returns are higher for solutions in energy, fuels, battery, and transportation themes and exhibit very little correlation with the returns of portfolios that seek to reduce their carbon emissions by underweighting high-carbon-emission companies, suggesting that climate solutions portfolios are distinct from low-carbon-emission indexes.
{"title":"Climate Solutions Investments","authors":"Alexander Cheema-Fox, George Serafeim, Hui (Stacie) Wang","doi":"10.2139/ssrn.4027579","DOIUrl":"https://doi.org/10.2139/ssrn.4027579","url":null,"abstract":"An increasing number of companies are providing products and services that help reduce carbon emissions in the economy. The authors develop a methodology to identify those companies and create a sample of publicly listed climate solutions companies, allowing the authors to study their geographic composition, accounting fundamentals, valuation ratios, and stock performance over time. The sample is equally split between developed and emerging markets, with a significant number of companies located in China. A portfolio of climate solutions companies exhibits higher revenue growth, higher investments in research and development and talent, and lower profitability margin. Portfolio returns are higher for solutions in energy, fuels, battery, and transportation themes and exhibit very little correlation with the returns of portfolios that seek to reduce their carbon emissions by underweighting high-carbon-emission companies, suggesting that climate solutions portfolios are distinct from low-carbon-emission indexes.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"49 1","pages":"72 - 96"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49244591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article focuses on the management of endowments that are associated with private and community charitable foundations. Endowment portfolio managers face a difficult environment due to low interest rates and high inflation. We construct a novel return hurdle for foundations that is forward-looking and market based. This return hurdle indicates that the total and excess returns necessary to meet typical foundation portfolio objectives are near all-time highs. There are no riskless spending policies available for foundations that desire to operate in perpetuity. While some investment committees and portfolio managers will be compelled to “risk up” to try to meet their return and distribution objectives, doing so creates a nuanced set of trade-offs that impact the broader objectives that most foundations consider when determining success or failure. We illustrate the tradeoffs and offer some suggestions for improving outcomes in the post-COVID environment.
{"title":"Mission Impossible: Foundation Investment Policy in the Post-COVID World","authors":"M. Crook","doi":"10.2139/ssrn.4178186","DOIUrl":"https://doi.org/10.2139/ssrn.4178186","url":null,"abstract":"This article focuses on the management of endowments that are associated with private and community charitable foundations. Endowment portfolio managers face a difficult environment due to low interest rates and high inflation. We construct a novel return hurdle for foundations that is forward-looking and market based. This return hurdle indicates that the total and excess returns necessary to meet typical foundation portfolio objectives are near all-time highs. There are no riskless spending policies available for foundations that desire to operate in perpetuity. While some investment committees and portfolio managers will be compelled to “risk up” to try to meet their return and distribution objectives, doing so creates a nuanced set of trade-offs that impact the broader objectives that most foundations consider when determining success or failure. We illustrate the tradeoffs and offer some suggestions for improving outcomes in the post-COVID environment.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"32 1","pages":"97 - 107"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41955457","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}
Long-term investors tilt their portfolios given their views on the evolving investment landscape. In the literature, portfolio tilting is often implemented with methodologies that use investors’ views on point estimates of conditional assets’ expected returns. These conditional return expectations are notoriously difficult to estimate, and using them often results in unstable portfolio weights when existing methodologies are applied. The authors avoid such shortcomings by providing a methodology that incorporates views on the likelihood of economic regimes (e.g., growth and inflation surprises) instead. Using data on equities, bonds, and commodities, the authors show—both in simulation and empirically—that this approach generates stable portfolio weights and outperformance that is minimally affected by forecast errors.
{"title":"Portfolio Tilts Using Views on Macroeconomic Regimes","authors":"Redouane Elkamhi, Jacky Lee, M. Salerno","doi":"10.2139/ssrn.3810877","DOIUrl":"https://doi.org/10.2139/ssrn.3810877","url":null,"abstract":"Long-term investors tilt their portfolios given their views on the evolving investment landscape. In the literature, portfolio tilting is often implemented with methodologies that use investors’ views on point estimates of conditional assets’ expected returns. These conditional return expectations are notoriously difficult to estimate, and using them often results in unstable portfolio weights when existing methodologies are applied. The authors avoid such shortcomings by providing a methodology that incorporates views on the likelihood of economic regimes (e.g., growth and inflation surprises) instead. Using data on equities, bonds, and commodities, the authors show—both in simulation and empirically—that this approach generates stable portfolio weights and outperformance that is minimally affected by forecast errors.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"49 1","pages":"7 - 24"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42390588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article documents a new stock market anomaly that seems to have escaped the attention of both investment professionals and academics. For more than a century, the monthly market return has been predicted by the monthly market return at lag 5. This predictability is marketwide and is most evident in the returns of portfolios of large and growth stocks. The trading strategy incorporating this predictability yields superior performance that cannot be attributed to common risk factors. A closer investigation of the new anomaly reveals that not each calendar month possesses predictive ability. Therefore, there is a link between the new anomaly and calendar effects in stock returns.
{"title":"A New Predictability Pattern in the US Stock Market Returns","authors":"Valeriy Zakamulin","doi":"10.2139/ssrn.4053277","DOIUrl":"https://doi.org/10.2139/ssrn.4053277","url":null,"abstract":"This article documents a new stock market anomaly that seems to have escaped the attention of both investment professionals and academics. For more than a century, the monthly market return has been predicted by the monthly market return at lag 5. This predictability is marketwide and is most evident in the returns of portfolios of large and growth stocks. The trading strategy incorporating this predictability yields superior performance that cannot be attributed to common risk factors. A closer investigation of the new anomaly reveals that not each calendar month possesses predictive ability. Therefore, there is a link between the new anomaly and calendar effects in stock returns.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"49 1","pages":"169 - 183"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48428895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article examines the exposures of low-volatility portfolios to various sources of systematic risk. The analysis includes interest rate, implied volatility, liquidity, commodity, sentiment, macroeconomic, and climate risk factors. The author finds that low-volatility portfolios lower the exposure to all significant drivers of systematic risk. The risk reductions vary from a minimum of 20% to over 90% across the various risk factors. Although low-volatility portfolios are very effective at dampening known structural risk factors, the 2020 COVID-19 pandemic episode illustrates that event risk is harder to control for data-driven methods.
{"title":"Macro Risk of Low-Volatility Portfolios","authors":"David Blitz","doi":"10.2139/ssrn.4213589","DOIUrl":"https://doi.org/10.2139/ssrn.4213589","url":null,"abstract":"This article examines the exposures of low-volatility portfolios to various sources of systematic risk. The analysis includes interest rate, implied volatility, liquidity, commodity, sentiment, macroeconomic, and climate risk factors. The author finds that low-volatility portfolios lower the exposure to all significant drivers of systematic risk. The risk reductions vary from a minimum of 20% to over 90% across the various risk factors. Although low-volatility portfolios are very effective at dampening known structural risk factors, the 2020 COVID-19 pandemic episode illustrates that event risk is harder to control for data-driven methods.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"49 1","pages":"25 - 35"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42906580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research addresses a simple but important unanswered question in the factor investing literature: How do the factor exposures of equity factor strategies decay over time? The answer to this question has two important practical consequences. First, understanding how a strategy’s factor exposures change over time informs the optimal rebalancing period. Second, when coupled with factor risk premia estimates, it describes the term structure of expected returns per factor strategy. To answer this question, the authors conduct a large-scale, empirical study of five well-known factors—value, momentum, quality, investment, and low volatility—across 12 developed and emerging markets over the last 20 years. They calculate factor exposure, or information, distributions per market for both pure and quartile long–short factor portfolios and then analyze how these distributions decay over a 36-month holding period. In order to formally measure the rate of information decay, they introduce the idea of a factor half-life metric and use the global half-life results to propose optimal rebalancing periods per factor.
{"title":"Factor Information Decay: A Global Study","authors":"Emlyn Flint, Rademeyer Vermaak","doi":"10.2139/ssrn.3986499","DOIUrl":"https://doi.org/10.2139/ssrn.3986499","url":null,"abstract":"This research addresses a simple but important unanswered question in the factor investing literature: How do the factor exposures of equity factor strategies decay over time? The answer to this question has two important practical consequences. First, understanding how a strategy’s factor exposures change over time informs the optimal rebalancing period. Second, when coupled with factor risk premia estimates, it describes the term structure of expected returns per factor strategy. To answer this question, the authors conduct a large-scale, empirical study of five well-known factors—value, momentum, quality, investment, and low volatility—across 12 developed and emerging markets over the last 20 years. They calculate factor exposure, or information, distributions per market for both pure and quartile long–short factor portfolios and then analyze how these distributions decay over a 36-month holding period. In order to formally measure the rate of information decay, they introduce the idea of a factor half-life metric and use the global half-life results to propose optimal rebalancing periods per factor.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"49 1","pages":"125 - 140"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46872078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The academic value factor (long cheap stocks, short expensive stocks) earns higher returns among small-cap stocks. When viewed through the lens of a long-only value investor, however, size is a less important factor. For example, equally weighted large-cap value portfolios have historically earned similar returns as small-cap value portfolios. This finding is robust to different value measures and markets. Despite realized returns being statistically similar, the liquidity profile of the two value portfolios is dramatically different: Equally weighted large-cap value portfolios have approximately 11 times (or more) the liquidity of small-cap value portfolios.
{"title":"Long-Only Value Investing: Does Size Matter?","authors":"Jack R. Vogel","doi":"10.2139/ssrn.4078256","DOIUrl":"https://doi.org/10.2139/ssrn.4078256","url":null,"abstract":"The academic value factor (long cheap stocks, short expensive stocks) earns higher returns among small-cap stocks. When viewed through the lens of a long-only value investor, however, size is a less important factor. For example, equally weighted large-cap value portfolios have historically earned similar returns as small-cap value portfolios. This finding is robust to different value measures and markets. Despite realized returns being statistically similar, the liquidity profile of the two value portfolios is dramatically different: Equally weighted large-cap value portfolios have approximately 11 times (or more) the liquidity of small-cap value portfolios.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"13 1","pages":"107 - 121"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45868229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article investigates forecasts of long-term volatility for the fast-growing field of long–short factor strategies in an extensive in-sample and out-of-sample framework. The author follows previous work by empirically comparing various forecast configurations to provide guidance for academics and practitioners on how to form accurate predictions of future volatility for various established factors. The set spans 21 factor return time series over multiple asset classes, factor styles, and a long historical data period. Both in-sample and out-of-sample results suggest monotonically increasing forecast accuracy for longer historical lookback periods, longer forecasting windows, and more-sophisticated models (considering short-term volatility clustering and external predictors motivated by the asset-pricing literature), while the findings appear less pronounced in a real-time setting than observed in-sample. Moreover, investors engaging in carry-styled factor strategies and multifactor portfolios (rather than single factors) achieve more-reliable forecasts, on average, as confirmed by the out-of-sample analysis.
{"title":"Forecasting Long-Horizon Factor Volatility","authors":"T. O. K. Zeissler","doi":"10.2139/ssrn.4092032","DOIUrl":"https://doi.org/10.2139/ssrn.4092032","url":null,"abstract":"This article investigates forecasts of long-term volatility for the fast-growing field of long–short factor strategies in an extensive in-sample and out-of-sample framework. The author follows previous work by empirically comparing various forecast configurations to provide guidance for academics and practitioners on how to form accurate predictions of future volatility for various established factors. The set spans 21 factor return time series over multiple asset classes, factor styles, and a long historical data period. Both in-sample and out-of-sample results suggest monotonically increasing forecast accuracy for longer historical lookback periods, longer forecasting windows, and more-sophisticated models (considering short-term volatility clustering and external predictors motivated by the asset-pricing literature), while the findings appear less pronounced in a real-time setting than observed in-sample. Moreover, investors engaging in carry-styled factor strategies and multifactor portfolios (rather than single factors) achieve more-reliable forecasts, on average, as confirmed by the out-of-sample analysis.","PeriodicalId":74863,"journal":{"name":"SSRN","volume":"13 1","pages":"54 - 106"},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46931466","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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