In this article, the authors present an analytical explanation for why it can be difficult to devise a successful market timing strategy. The authors derive formulas to estimate the minimum required information coefficient for a timing strategy to outperform a buy-and-hold market benchmark, both with and without an alpha target. They show that markets with high Sharpe ratios and those that have low volatility are by nature hard to time. They also show that having high market exposure in a market timing strategy is generally beneficial; however, there can be a critical point beyond which additional market exposure makes timing more difficult. The authors extend the model to cover practical considerations such as transaction costs, skewness and fat tails, and market timing with two correlated assets. Finally, they present a case study to illustrate how investors could apply their framework to choose the optimal market exposure in a market timing strategy using the S&P 500. Key Findings ▪ Under a bivariate normal framework, the authors show that the expected return of a timing strategy comes in two additive parts: one part driven by timing information and the other driven by average market exposure. ▪ There is generally a theoretical nonzero information threshold for a timing strategy to beat a buy-and-hold benchmark. This threshold can serve as a useful guide to determine whether a timing strategy is likely to succeed, complementing historical backtests. ▪ Although an investor can increase timing strategy return by increasing average market exposure without having more timing information, the difficulty of beating a buy-and-hold benchmark with an alpha target increases dramatically as average market exposure becomes very high.
{"title":"How Much Information Is Required to Time the Market?","authors":"Rongju Zhang,Henry Wong","doi":"10.3905/jpm.2021.1.299","DOIUrl":"https://doi.org/10.3905/jpm.2021.1.299","url":null,"abstract":"In this article, the authors present an analytical explanation for why it can be difficult to devise a successful market timing strategy. The authors derive formulas to estimate the minimum required information coefficient for a timing strategy to outperform a buy-and-hold market benchmark, both with and without an alpha target. They show that markets with high Sharpe ratios and those that have low volatility are by nature hard to time. They also show that having high market exposure in a market timing strategy is generally beneficial; however, there can be a critical point beyond which additional market exposure makes timing more difficult. The authors extend the model to cover practical considerations such as transaction costs, skewness and fat tails, and market timing with two correlated assets. Finally, they present a case study to illustrate how investors could apply their framework to choose the optimal market exposure in a market timing strategy using the S&P 500. Key Findings ▪ Under a bivariate normal framework, the authors show that the expected return of a timing strategy comes in two additive parts: one part driven by timing information and the other driven by average market exposure. ▪ There is generally a theoretical nonzero information threshold for a timing strategy to beat a buy-and-hold benchmark. This threshold can serve as a useful guide to determine whether a timing strategy is likely to succeed, complementing historical backtests. ▪ Although an investor can increase timing strategy return by increasing average market exposure without having more timing information, the difficulty of beating a buy-and-hold benchmark with an alpha target increases dramatically as average market exposure becomes very high.","PeriodicalId":501547,"journal":{"name":"The Journal of Portfolio Management","volume":"21 1","pages":"163-187"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543949","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}
Asset managers are increasingly incorporating equity factors that deviate from traditional academic definitions in their stock selection process. The authors show that these factors frequently exhibit strong industry biases, making it crucial to understand the interaction between factor exposure and traditional industry exposure. Industry exposure plays a major role in the risk profile of a portfolio, making unintended industry exposures costly. For an extensive set of 21 equity factors, beyond the standard academic factors, the authors examine which equity factors are rewarded for their industry allocation. This set spans the value, quality, momentum, low-volatility, and size investment styles. The authors use a global and liquid investment universe, as is commonly used by large institutional asset managers. They find that equity factors from the same investment style, most notably momentum and quality, exhibit strong differences in their returns from industry allocation. Understanding the interaction between factors and industry exposures can lead to higher return premiums and lower portfolio volatility without harming performance. Key Findings ▪ Asset managers are increasingly using nontraditional equity factors to select stocks. Many of these factors have biases toward and away from certain industries. ▪ Some equity factors are rewarded for industry exposure; for others, this is an unrewarded risk. We assess industry allocation efficacy for 21 equity factors. ▪ Industry allocation efficacy differs significantly across equity factors, even among factors associated with the same investment style.
{"title":"Should Equity Factors Be Betting on Industries?","authors":"Krishna Vyas,Michael van Baren","doi":"10.3905/jpm.2021.1.297","DOIUrl":"https://doi.org/10.3905/jpm.2021.1.297","url":null,"abstract":"Asset managers are increasingly incorporating equity factors that deviate from traditional academic definitions in their stock selection process. The authors show that these factors frequently exhibit strong industry biases, making it crucial to understand the interaction between factor exposure and traditional industry exposure. Industry exposure plays a major role in the risk profile of a portfolio, making unintended industry exposures costly. For an extensive set of 21 equity factors, beyond the standard academic factors, the authors examine which equity factors are rewarded for their industry allocation. This set spans the value, quality, momentum, low-volatility, and size investment styles. The authors use a global and liquid investment universe, as is commonly used by large institutional asset managers. They find that equity factors from the same investment style, most notably momentum and quality, exhibit strong differences in their returns from industry allocation. Understanding the interaction between factors and industry exposures can lead to higher return premiums and lower portfolio volatility without harming performance. Key Findings ▪ Asset managers are increasingly using nontraditional equity factors to select stocks. Many of these factors have biases toward and away from certain industries. ▪ Some equity factors are rewarded for industry exposure; for others, this is an unrewarded risk. We assess industry allocation efficacy for 21 equity factors. ▪ Industry allocation efficacy differs significantly across equity factors, even among factors associated with the same investment style.","PeriodicalId":501547,"journal":{"name":"The Journal of Portfolio Management","volume":"66 2 1","pages":"73-92"},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543953","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 authors show that the slope of the volatility decile portfolio’s return profile contains valuable information that can be used to time volatility under different market conditions in the United States. During good (bad) market conditions, the high- (low-) volatility portfolio produces the highest return. The authors proceed to devise a volatility timing strategy based on statistical tests on the slope of the volatility decile portfolio’s return profile. Volatility timing is achieved by being aggressive during strong growth periods and conservative during market downturns. Superior performance is obtained, with an additional return of 4.1% observed in the volatility timing strategy, resulting in a fivefold improvement on accumulated wealth, along with statistically significant improvement in the Sortini ratio and the information ratio. The authors also demonstrate that stocks in the high-volatility portfolio are more strongly correlated compared to stocks in the low-volatility portfolio. Hence, the profitability of the volatility timing strategy can be attributed to successfully holding a diversified portfolio during bear markets and holding a concentrated growth portfolio during bull markets. Key Findings ▪ The return profile of the volatility decile portfolio is time-varying. Its slope contains vital information on market condition—high-volatility portfolio outperforms low-volatility portfolio during good market condition, but underperforms during bad market condition. Since market regime and asset price behaviors are persistent, the slope parameter can be used to time volatility exposure. ▪ Holding the low-volatility portfolio benefits from the higher risk-adjusted return during general market condition. However, when the slope parameter is positive and statistically significant, it is optimal to hold the high-volatility portfolio for the subsequent period. This will ride on the higher return of high-volatility portfolio during strong growth periods. This leads to higher return and increased volatility, but both Sortini ratio and Information ratio exhibit statistically significant improvement. ▪ Stocks in the low-volatility portfolio are less correlated than stocks in the high-volatility portfolio. The outperformance of the volatility timing strategy formulated in this article can be attributed to holding a concentrated growth portfolio during good market conditions, and holding a diversified portfolio during bad market conditions, thus connecting the literature on low-volatility portfolio with studies on correlation structure and diversification.
{"title":"Volatility Timing under Low-Volatility Strategy","authors":"Poh Ling Neo,Chyng Wen Tee","doi":"10.3905/jpm.2021.1.293","DOIUrl":"https://doi.org/10.3905/jpm.2021.1.293","url":null,"abstract":"The authors show that the slope of the volatility decile portfolio’s return profile contains valuable information that can be used to time volatility under different market conditions in the United States. During good (bad) market conditions, the high- (low-) volatility portfolio produces the highest return. The authors proceed to devise a volatility timing strategy based on statistical tests on the slope of the volatility decile portfolio’s return profile. Volatility timing is achieved by being aggressive during strong growth periods and conservative during market downturns. Superior performance is obtained, with an additional return of 4.1% observed in the volatility timing strategy, resulting in a fivefold improvement on accumulated wealth, along with statistically significant improvement in the Sortini ratio and the information ratio. The authors also demonstrate that stocks in the high-volatility portfolio are more strongly correlated compared to stocks in the low-volatility portfolio. Hence, the profitability of the volatility timing strategy can be attributed to successfully holding a diversified portfolio during bear markets and holding a concentrated growth portfolio during bull markets. Key Findings ▪ The return profile of the volatility decile portfolio is time-varying. Its slope contains vital information on market condition—high-volatility portfolio outperforms low-volatility portfolio during good market condition, but underperforms during bad market condition. Since market regime and asset price behaviors are persistent, the slope parameter can be used to time volatility exposure. ▪ Holding the low-volatility portfolio benefits from the higher risk-adjusted return during general market condition. However, when the slope parameter is positive and statistically significant, it is optimal to hold the high-volatility portfolio for the subsequent period. This will ride on the higher return of high-volatility portfolio during strong growth periods. This leads to higher return and increased volatility, but both Sortini ratio and Information ratio exhibit statistically significant improvement. ▪ Stocks in the low-volatility portfolio are less correlated than stocks in the high-volatility portfolio. The outperformance of the volatility timing strategy formulated in this article can be attributed to holding a concentrated growth portfolio during good market conditions, and holding a diversified portfolio during bad market conditions, thus connecting the literature on low-volatility portfolio with studies on correlation structure and diversification.","PeriodicalId":501547,"journal":{"name":"The Journal of Portfolio Management","volume":"6 1","pages":"133-146"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138543950","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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