Pub Date : 2019-11-29DOI: 10.3905/jii.2019.10.3.001
Brian R. Bruce
roBerT Dunn General Manager To open the Winter 2019 issue, Zorina, Khatri, Zhu, and Rowley. test two measures of market volatility for their potential relationship with growth in indexing assets and selected macroeconomic factors. The analysis demonstrates that macroeconomic factors have a strong correlation with and are useful predictors of market volatility; on the other hand, growth in indexing assets does not exhibit any causal relationship with market volatility. Bender, Nagori, and Tank revisit the long-documented index effect. Their findings show that the index effect is present in the global indices, particularly the MSCI World Small Cap and MSCI Emerging Markets Indices. Security characteristics matter as well, as the index effect is stronger for larger securities (relative to their index). They also find that the index effect appears to hold further ahead, for instance a month before the index rebalance date. Next, Esakia, Goltz, Luyten, and Sibbe evaluate whether the size factor still has its place in multi-factor portfolios. They suggest that the size factor improves model fit, delivers a significant positive premium in the presence of other factors, and contributes positively to the performance of multi-factor portfolios. Additionally, omitting the size factor has substantial cost to investors, which often exceeds that of omitting other popular factors. Crouse evaluates monthly leveraged investment products and shows that they improve returns because markets are less volatile on a monthly timescale, but they remain problematic as buy-and-hold investments due to the risks of large drawdowns and catastrophic losses. He characterizes these risks through higher-order moments and identifies attributes of LIPs to mitigate these risks to benefit both LIP investors and LIP sponsors. Ge studies the use of low-volatility assets for the purpose of retirement planning and the choice of ideal glidepaths. The article concludes that when equipped with proper low-volatility assets and carefully chosen glidepaths, retirement plan managers may both improve the odds that their plans succeed and increase the expected final wealth levels. To complete this issue, Malladi evaluates performance of three children-oriented indices and finds that the KIDS indices consistently outperformed the traditional S&P 500 market index in both absolute and risk-adjusted terms. The author suggests that these indices can be used in advancing financial literacy in high schools and among parents since they are easily understood due to their familiarity with composition and construction methods. As always, we welcome your submissions. Please encourage those you know who have papers or have made good presentations on indexing, ETFs, mutual funds, or related subjects to submit them for consideration. We value your comments and suggestions, so please email us at journals@investmentresearch.org.
roBerT Dunn总经理Zorina、Khatri、Zhu和Rowley为2019年冬季杂志揭幕。测试两种市场波动性指标与指数化资产增长和选定宏观经济因素的潜在关系。分析表明，宏观经济因素与市场波动具有很强的相关性，是市场波动的有用预测因素；另一方面，指数化资产的增长与市场波动没有任何因果关系。Bender、Nagori和Tank重新审视了长期记录的指数效应。他们的研究结果表明，指数效应存在于全球指数中，特别是MSCI世界小盘股指数和MSCI新兴市场指数。证券特征也很重要，因为指数效应对较大的证券（相对于其指数）更强。他们还发现，指数效应似乎进一步保持，例如在指数重新平衡日期前一个月。接下来，Esakia、Goltz、Luyten和Sibbe评估规模因素是否在多因素投资组合中仍有一席之地。他们认为，规模因素提高了模型拟合度，在存在其他因素的情况下提供了显著的正溢价，并对多因素投资组合的表现做出了积极贡献。此外，省略规模因素给投资者带来了巨大的成本，这往往超过了省略其他流行因素的成本。Crouse评估了每月的杠杆投资产品，并表明它们提高了回报，因为市场在每月的时间尺度上波动较小，但由于存在大量提款和灾难性损失的风险，它们仍然是买入和持有投资的问题。他通过高阶矩来描述这些风险，并确定LIP的属性，以减轻这些风险，从而使LIP投资者和LIP赞助商都受益。通用电气研究了低波动性资产的使用，用于退休计划和理想滑行道的选择。文章的结论是，当配备了适当的低波动性资产和精心选择的滑行道时，退休计划经理既可以提高计划成功的几率，也可以提高预期的最终财富水平。为了完成这一问题，Malladi评估了三个以儿童为导向的指数的表现，发现KIDS指数在绝对值和风险调整值方面一直优于传统的标准普尔500指数。作者认为，这些指数可以用于提高高中和家长的金融素养，因为他们熟悉组成和构建方法，很容易理解。一如既往，我们欢迎您提交意见。请鼓励那些你认识的在索引、ETF、共同基金或相关主题上有论文或做过良好演讲的人提交论文供考虑。我们重视您的意见和建议，请发送电子邮件至journals@investmentresearch.org.
{"title":"Editor’s Letter","authors":"Brian R. Bruce","doi":"10.3905/jii.2019.10.3.001","DOIUrl":"https://doi.org/10.3905/jii.2019.10.3.001","url":null,"abstract":"roBerT Dunn General Manager To open the Winter 2019 issue, Zorina, Khatri, Zhu, and Rowley. test two measures of market volatility for their potential relationship with growth in indexing assets and selected macroeconomic factors. The analysis demonstrates that macroeconomic factors have a strong correlation with and are useful predictors of market volatility; on the other hand, growth in indexing assets does not exhibit any causal relationship with market volatility. Bender, Nagori, and Tank revisit the long-documented index effect. Their findings show that the index effect is present in the global indices, particularly the MSCI World Small Cap and MSCI Emerging Markets Indices. Security characteristics matter as well, as the index effect is stronger for larger securities (relative to their index). They also find that the index effect appears to hold further ahead, for instance a month before the index rebalance date. Next, Esakia, Goltz, Luyten, and Sibbe evaluate whether the size factor still has its place in multi-factor portfolios. They suggest that the size factor improves model fit, delivers a significant positive premium in the presence of other factors, and contributes positively to the performance of multi-factor portfolios. Additionally, omitting the size factor has substantial cost to investors, which often exceeds that of omitting other popular factors. Crouse evaluates monthly leveraged investment products and shows that they improve returns because markets are less volatile on a monthly timescale, but they remain problematic as buy-and-hold investments due to the risks of large drawdowns and catastrophic losses. He characterizes these risks through higher-order moments and identifies attributes of LIPs to mitigate these risks to benefit both LIP investors and LIP sponsors. Ge studies the use of low-volatility assets for the purpose of retirement planning and the choice of ideal glidepaths. The article concludes that when equipped with proper low-volatility assets and carefully chosen glidepaths, retirement plan managers may both improve the odds that their plans succeed and increase the expected final wealth levels. To complete this issue, Malladi evaluates performance of three children-oriented indices and finds that the KIDS indices consistently outperformed the traditional S&P 500 market index in both absolute and risk-adjusted terms. The author suggests that these indices can be used in advancing financial literacy in high schools and among parents since they are easily understood due to their familiarity with composition and construction methods. As always, we welcome your submissions. Please encourage those you know who have papers or have made good presentations on indexing, ETFs, mutual funds, or related subjects to submit them for consideration. We value your comments and suggestions, so please email us at journals@investmentresearch.org.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48290823","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}
Mikheil Esakia, Felix Goltz, B. Luyten, Marcel Sibbe
The finance literature has established a size effect: stocks with small market capitalization outperform larger stocks over the long term. The size factor is included in asset-pricing models because of its explanatory power for cross-sectional differences in equity returns. However, recent studies recommend removing size from the factor menu, given its relatively weak performance. Instead of looking at the stand-alone performance, we account for cross-factor correlation to assess the impact of excluding the size factor. We consider three tests. First, we measure the impact on model fit of asset-pricing models. Second, we assess whether the size premium remains intact when accounting for implicit exposures to other factors. Third, we evaluate the impact of the size factor on the performance of optimal multifactor portfolios. Our results suggest that the size factor improves model fit, delivers a significant positive premium in the presence of other factors, and contributes positively to the performance of multifactor portfolios. Omitting the size factor has substantial cost to investors, which often exceeds that of omitting other popular factors. TOPICS: Analysis of individual factors/risk premia, factor-based models, style investing Key Findings • The size factor carries a significant premium after adjusting for implicit exposure to other factors. • Optimal factor portfolios allocate to the size factor even if the return assumption is extremely conservative. • The size factor improves diversification due to its low correlation with other factors and different exposure to macroeconomic conditions.
{"title":"Size Factor in Multifactor Portfolios: Does the Size Factor Still Have Its Place in Multifactor Portfolios?","authors":"Mikheil Esakia, Felix Goltz, B. Luyten, Marcel Sibbe","doi":"10.3905/jii.2019.1.078","DOIUrl":"https://doi.org/10.3905/jii.2019.1.078","url":null,"abstract":"The finance literature has established a size effect: stocks with small market capitalization outperform larger stocks over the long term. The size factor is included in asset-pricing models because of its explanatory power for cross-sectional differences in equity returns. However, recent studies recommend removing size from the factor menu, given its relatively weak performance. Instead of looking at the stand-alone performance, we account for cross-factor correlation to assess the impact of excluding the size factor. We consider three tests. First, we measure the impact on model fit of asset-pricing models. Second, we assess whether the size premium remains intact when accounting for implicit exposures to other factors. Third, we evaluate the impact of the size factor on the performance of optimal multifactor portfolios. Our results suggest that the size factor improves model fit, delivers a significant positive premium in the presence of other factors, and contributes positively to the performance of multifactor portfolios. Omitting the size factor has substantial cost to investors, which often exceeds that of omitting other popular factors. TOPICS: Analysis of individual factors/risk premia, factor-based models, style investing Key Findings • The size factor carries a significant premium after adjusting for implicit exposure to other factors. • Optimal factor portfolios allocate to the size factor even if the return assumption is extremely conservative. • The size factor improves diversification due to its low correlation with other factors and different exposure to macroeconomic conditions.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44358332","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}
Inna Zorina, Jamie Khatri, Carol Zhu, James J. Rowley
Some academics and market participants argue that the growth of indexing causes market volatility. However, while the percentage of assets in indexed strategies has grown over the past twenty-five years, market volatility has risen and fallen in a somewhat random pattern, peaking around economic and financial crises. In this article, we test two measures of market volatility for their potential relationship with growth in indexing assets and selected macroeconomic factors. Our analysis demonstrates that macroeconomic factors have a strong correlation with and are useful predictors of market volatility; on the other hand, growth in indexing assets does not exhibit any causal relationship with market volatility. TOPIC: Volatility measures Key Findings • Macroeconomic factors and market volatility have a strong positive correlation while correlation between market volatility and growth of indexing is negative and relatively small in absolute terms. • Granger causality tests suggest that macroeconomic factors do have a causal relationship with and are useful predictors of market volatility. Growth of indexing, however, does not have such a relationship and is not a useful predictor of market volatility. • Macroeconomic factors such as economic policy uncertainty—not the growth of indexing assets—are responsible for elevated market volatility.
{"title":"With Greater Uncertainty Comes Greater Volatility","authors":"Inna Zorina, Jamie Khatri, Carol Zhu, James J. Rowley","doi":"10.3905/jii.2019.1.077","DOIUrl":"https://doi.org/10.3905/jii.2019.1.077","url":null,"abstract":"Some academics and market participants argue that the growth of indexing causes market volatility. However, while the percentage of assets in indexed strategies has grown over the past twenty-five years, market volatility has risen and fallen in a somewhat random pattern, peaking around economic and financial crises. In this article, we test two measures of market volatility for their potential relationship with growth in indexing assets and selected macroeconomic factors. Our analysis demonstrates that macroeconomic factors have a strong correlation with and are useful predictors of market volatility; on the other hand, growth in indexing assets does not exhibit any causal relationship with market volatility. TOPIC: Volatility measures Key Findings • Macroeconomic factors and market volatility have a strong positive correlation while correlation between market volatility and growth of indexing is negative and relatively small in absolute terms. • Granger causality tests suggest that macroeconomic factors do have a causal relationship with and are useful predictors of market volatility. Growth of indexing, however, does not have such a relationship and is not a useful predictor of market volatility. • Macroeconomic factors such as economic policy uncertainty—not the growth of indexing assets—are responsible for elevated market volatility.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46359401","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}
Target-date funds (TDFs) have become a popular choice for retirement plans, and the concept of a glide path is essential for TDFs. One of the key assumption behind glide paths is that the main component of a retirement plan should be growth assets invested in the overall equity market—for example, the S&P 500 Index. Researchers have recently challenged this assumption and argued for using smart beta factors for retirement purposes. Among them, the factor of low volatility may be uniquely suitable for retirement investing. This article studies the use of low-volatility assets for the purpose of retirement planning and the choice of ideal glide paths. This study is agnostic about the means by which the low-volatility risk/return profile is achieved and analyzes four series with diminished volatility constructed with different methodologies. The article concludes that low-volatility assets may indeed help retirement investors achieve their objectives, though such investors must choose ideal glide paths carefully to suit the selected low-volatility series. When equipped with proper low-volatility assets and carefully chosen glide paths, retirement plan managers may both improve the odds that their plans succeed and increase the expected final wealth levels. TOPICS: Mutual fund performance, retirement, other real assets, wealth management Key Findings • Both historical and de novo Monte-Carlo simulations confirm that low-volatility assets can increase the certainty of achieving investing objectives for retirement plans. • The three tested glidepath choices, hockey stick, curved, and straight line, have similar investment outcomes. • Investors with low-volatility assets may use more aggressive glidepaths to not only reduce failure rates but also increase expected final wealth levels.
{"title":"Optimal Glide Path Selection for Low-Volatility Assets","authors":"Weili Ge","doi":"10.3905/jii.2019.1.073","DOIUrl":"https://doi.org/10.3905/jii.2019.1.073","url":null,"abstract":"Target-date funds (TDFs) have become a popular choice for retirement plans, and the concept of a glide path is essential for TDFs. One of the key assumption behind glide paths is that the main component of a retirement plan should be growth assets invested in the overall equity market—for example, the S&P 500 Index. Researchers have recently challenged this assumption and argued for using smart beta factors for retirement purposes. Among them, the factor of low volatility may be uniquely suitable for retirement investing. This article studies the use of low-volatility assets for the purpose of retirement planning and the choice of ideal glide paths. This study is agnostic about the means by which the low-volatility risk/return profile is achieved and analyzes four series with diminished volatility constructed with different methodologies. The article concludes that low-volatility assets may indeed help retirement investors achieve their objectives, though such investors must choose ideal glide paths carefully to suit the selected low-volatility series. When equipped with proper low-volatility assets and carefully chosen glide paths, retirement plan managers may both improve the odds that their plans succeed and increase the expected final wealth levels. TOPICS: Mutual fund performance, retirement, other real assets, wealth management Key Findings • Both historical and de novo Monte-Carlo simulations confirm that low-volatility assets can increase the certainty of achieving investing objectives for retirement plans. • The three tested glidepath choices, hockey stick, curved, and straight line, have similar investment outcomes. • Investors with low-volatility assets may use more aggressive glidepaths to not only reduce failure rates but also increase expected final wealth levels.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49105414","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}
We examine factors in a novel dataset on the cross-section of emerging market hard currency corporate bonds. We find that the size, low-risk, value, and momentum factors predict future excess returns. Single-factor and multi-factor portfolios obtain economically and statistically significant premiums. Further, alphas remain significant after controlling for exposures to developed market credit factors. The factor portfolios benefit from bottom-up allocations to countries, sectors, ratings, and maturity segments, as well as from bond selection within these segments. Higher risk-adjusted returns of factor portfolios also can be found within liquid subsamples of the market. TOPICS: Fixed income and structured finance, emerging markets, analysis of individual factors/risk premia, portfolio construction Key Findings ▪ We examine factors in the cross-section of emerging market hard currency corporate bonds and find that the size, value, momentum, and low-risk factors predict future excess returns. ▪ Factor portfolios yield significant alphas in the Capital Asset Pricing Model, and a multi-factor portfolio that allocates equally to the four factors shows even stronger results, due to the low pairwise correlations among the individual factors. ▪ Alphas remain significant versus developed market credit factors, and the results hold within countries, sectors, ratings, maturities, and liquid subsamples.
{"title":"Factor Investing in Emerging Market Credits","authors":"Lennart Dekker, P. Houweling, Frederik Muskens","doi":"10.2139/ssrn.3457127","DOIUrl":"https://doi.org/10.2139/ssrn.3457127","url":null,"abstract":"We examine factors in a novel dataset on the cross-section of emerging market hard currency corporate bonds. We find that the size, low-risk, value, and momentum factors predict future excess returns. Single-factor and multi-factor portfolios obtain economically and statistically significant premiums. Further, alphas remain significant after controlling for exposures to developed market credit factors. The factor portfolios benefit from bottom-up allocations to countries, sectors, ratings, and maturity segments, as well as from bond selection within these segments. Higher risk-adjusted returns of factor portfolios also can be found within liquid subsamples of the market. TOPICS: Fixed income and structured finance, emerging markets, analysis of individual factors/risk premia, portfolio construction Key Findings ▪ We examine factors in the cross-section of emerging market hard currency corporate bonds and find that the size, value, momentum, and low-risk factors predict future excess returns. ▪ Factor portfolios yield significant alphas in the Capital Asset Pricing Model, and a multi-factor portfolio that allocates equally to the four factors shows even stronger results, due to the low pairwise correlations among the individual factors. ▪ Alphas remain significant versus developed market credit factors, and the results hold within countries, sectors, ratings, maturities, and liquid subsamples.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42202769","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}
Volatility reduces any investment’s compound rate of return in what is termed volatility drag, a drawback of leveraged investment products (LIPs). In recent years “Version 2.0” LIPs that reset leverage monthly (monthly LIPs) have been created to lessen the impact of drag. We show that monthly LIPs improve returns because markets are less volatile on a monthly timescale. Nevertheless, monthly LIPs remain problematic as buy-and-hold investments because of the risks of large drawdowns and catastrophic losses. We characterize these risks through higher-order moments and identify attributes of LIPs to mitigate these risks to benefit both LIP investors and LIP sponsors. TOPICS: Exchange-traded funds and applications, volatility measures, performance measurement Key Findings • Leveraged investment products (LIPs) that rebalance leverage monthly provide higher returns than the standard LIPs that rebalance daily. Monthly leverage rebalancing reduces volatility drag because markets exhibit lower realized volatility on a monthly timescale. • Monthly LIPs remain problematic as buy-and-hold investments because of the risks of large drawdowns, excess leverage, and catastrophic losses—risks that are captured not by standard mean-variance and simulation analyses but by our model through higher-order moments. • Buy-and-hold investors should focus on monthly LIPs with broad diversification, low volatility, and intramonth leverage rebalancing that avoids outright liquidation in times of market stress. LIP sponsors should emphasize these same qualities in designing their investment products.
{"title":"Leveraged Investment Products: Monthly Rebalancing Boosts Performance, but Tail Risk Looms","authors":"M. Crouse","doi":"10.3905/jii.2019.1.074","DOIUrl":"https://doi.org/10.3905/jii.2019.1.074","url":null,"abstract":"Volatility reduces any investment’s compound rate of return in what is termed volatility drag, a drawback of leveraged investment products (LIPs). In recent years “Version 2.0” LIPs that reset leverage monthly (monthly LIPs) have been created to lessen the impact of drag. We show that monthly LIPs improve returns because markets are less volatile on a monthly timescale. Nevertheless, monthly LIPs remain problematic as buy-and-hold investments because of the risks of large drawdowns and catastrophic losses. We characterize these risks through higher-order moments and identify attributes of LIPs to mitigate these risks to benefit both LIP investors and LIP sponsors. TOPICS: Exchange-traded funds and applications, volatility measures, performance measurement Key Findings • Leveraged investment products (LIPs) that rebalance leverage monthly provide higher returns than the standard LIPs that rebalance daily. Monthly leverage rebalancing reduces volatility drag because markets exhibit lower realized volatility on a monthly timescale. • Monthly LIPs remain problematic as buy-and-hold investments because of the risks of large drawdowns, excess leverage, and catastrophic losses—risks that are captured not by standard mean-variance and simulation analyses but by our model through higher-order moments. • Buy-and-hold investors should focus on monthly LIPs with broad diversification, low volatility, and intramonth leverage rebalancing that avoids outright liquidation in times of market stress. LIP sponsors should emphasize these same qualities in designing their investment products.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41352413","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 : 2019-08-30DOI: 10.3905/jii.2019.10.2.063
Deepika Sharma, Muling Si, Josephine M. Smith
Deep value strategies hold concentrated stock portfolios that trade at significant discounts to their intrinsic value. Such stocks tend to have significant risks: stocks with high stock-specific volatilities may have transitory low price movements; stock prices may be low because of recent negative price trends; and stocks’ earnings quality may be poor giving rise to their low valuations. In addition, the risk of value traps—that low prices may remain low for a long time—is exacerbated with deep value strategies. The authors develop a systematic deep value strategy that is designed to combine the concept of deep value investing while mitigating these risks. Using hypothetical backtested data, the deep value strategy has exhibited attractive risk-adjusted returns, including offering diversification to traditional value strategies. TOPICS: Analysis of individual factors/risk premia, factor-based models, style investing
{"title":"Focused Value","authors":"Deepika Sharma, Muling Si, Josephine M. Smith","doi":"10.3905/jii.2019.10.2.063","DOIUrl":"https://doi.org/10.3905/jii.2019.10.2.063","url":null,"abstract":"Deep value strategies hold concentrated stock portfolios that trade at significant discounts to their intrinsic value. Such stocks tend to have significant risks: stocks with high stock-specific volatilities may have transitory low price movements; stock prices may be low because of recent negative price trends; and stocks’ earnings quality may be poor giving rise to their low valuations. In addition, the risk of value traps—that low prices may remain low for a long time—is exacerbated with deep value strategies. The authors develop a systematic deep value strategy that is designed to combine the concept of deep value investing while mitigating these risks. Using hypothetical backtested data, the deep value strategy has exhibited attractive risk-adjusted returns, including offering diversification to traditional value strategies. TOPICS: Analysis of individual factors/risk premia, factor-based models, style investing","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48911763","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 : 2019-08-30DOI: 10.3905/jii.2019.10.2.006
Ramu Thiagarajan, Richard F. Lacaille, Hanbin Im, Jingyan Wang
Advances in technology and introduction of new regulations have transformed the equity market over last few decades, giving rise to a new breed of trading—high-frequency trading (HFT). This article investigates transformations that have shaped today’s equity trading ecology in detail with a particular focus on HFT. HFT accounts for an increasingly significant share of equity trading volumes, and is associated with lower transaction costs, greater liquidity, improved price discovery, and overall market efficiency. At times of increasing order toxicity, however, HFTs may withdraw liquidity due to higher probability of adverse selection. In this new era of equity trading, further research on a complete ecosystem of liquidity provision that ensures full provision of liquidity even during stressed periods is much needed. TOPICS: Fundamental equity analysis, accounting and ratio analysis, technical analysis
{"title":"The Need for Speed: Does High-Frequency Trading Make or Break Equity Markets?","authors":"Ramu Thiagarajan, Richard F. Lacaille, Hanbin Im, Jingyan Wang","doi":"10.3905/jii.2019.10.2.006","DOIUrl":"https://doi.org/10.3905/jii.2019.10.2.006","url":null,"abstract":"Advances in technology and introduction of new regulations have transformed the equity market over last few decades, giving rise to a new breed of trading—high-frequency trading (HFT). This article investigates transformations that have shaped today’s equity trading ecology in detail with a particular focus on HFT. HFT accounts for an increasingly significant share of equity trading volumes, and is associated with lower transaction costs, greater liquidity, improved price discovery, and overall market efficiency. At times of increasing order toxicity, however, HFTs may withdraw liquidity due to higher probability of adverse selection. In this new era of equity trading, further research on a complete ecosystem of liquidity provision that ensures full provision of liquidity even during stressed periods is much needed. TOPICS: Fundamental equity analysis, accounting and ratio analysis, technical analysis","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48447782","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 : 2019-08-30DOI: 10.3905/jii.2019.10.2.001
Brian R. Bruce
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G. Giese, Linda-Eling Lee, D. Melas, Z. Nagy, Laura Nishikawa
There has been a wide range of research in academia and the asset management industry about the financial benefits of ESG investing. However, the equally important question about how to achieve consistency when integrating ESG and what methodologies to use has not received the same level of attention. As a result, ESG integration is often applied inconsistently and incompletely across portfolios. The authors provide a framework for the integration of ESG into benchmarks at various strategic levels—from the top policy benchmark level to the performance benchmark of individual allocations. In addition, they highlight the different investment objectives that asset owners may pursue when integrating ESG and how they can reflect these in their choice of ESG benchmarks. They find that integrating ESG into benchmarks makes sense as a framework to achieve consistency because benchmarks are not only used at different strategic levels but also across all areas of asset management—index-based, factor-based, and active management—to define the underlying investable universe and to provide a yardstick for performance. TOPICS: ESG investing, analysis of individual factors/risk premia, portfolio construction, portfolio management/multi-asset allocation
{"title":"Consistent ESG through ESG Benchmarks","authors":"G. Giese, Linda-Eling Lee, D. Melas, Z. Nagy, Laura Nishikawa","doi":"10.3905/jii.2019.1.072","DOIUrl":"https://doi.org/10.3905/jii.2019.1.072","url":null,"abstract":"There has been a wide range of research in academia and the asset management industry about the financial benefits of ESG investing. However, the equally important question about how to achieve consistency when integrating ESG and what methodologies to use has not received the same level of attention. As a result, ESG integration is often applied inconsistently and incompletely across portfolios. The authors provide a framework for the integration of ESG into benchmarks at various strategic levels—from the top policy benchmark level to the performance benchmark of individual allocations. In addition, they highlight the different investment objectives that asset owners may pursue when integrating ESG and how they can reflect these in their choice of ESG benchmarks. They find that integrating ESG into benchmarks makes sense as a framework to achieve consistency because benchmarks are not only used at different strategic levels but also across all areas of asset management—index-based, factor-based, and active management—to define the underlying investable universe and to provide a yardstick for performance. TOPICS: ESG investing, analysis of individual factors/risk premia, portfolio construction, portfolio management/multi-asset allocation","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48207936","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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