Pub Date : 2018-05-31DOI: 10.3905/jii.2018.9.1.001
Brian R. Bruce
{"title":"Editor’s Letter","authors":"Brian R. Bruce","doi":"10.3905/jii.2018.9.1.001","DOIUrl":"https://doi.org/10.3905/jii.2018.9.1.001","url":null,"abstract":"","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43552582","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 : 2018-05-31DOI: 10.3905/jii.2018.9.1.084
A. Ma, Lanston Lane Chun Yeung
In this article, we construct a risk measure of an investment, called the D-Index, which measures the fraction of time the investment is in a drawdown. The D-Index isolates the time dimension from the returns when measuring risk, and this property uniquely characterizes it from other performance measures. An explicit analytical form of the ex-ante D-Index is provided for the case of a buy-and-hold strategy with price dynamics following a Black-Scholes model. Numerical evidence based on 32,642 funds across three asset classes shows that the D-Index does not exhibit strong rank correlation with the Sharpe ratio across funds, in contrast with most other existing risk-measures. Coupled with evidence from experimental psychology, this new perspective motivates us to consider its importance in investment decision making in the real-world environment.
{"title":"D-Index: A Risk Measure in a New Dimension","authors":"A. Ma, Lanston Lane Chun Yeung","doi":"10.3905/jii.2018.9.1.084","DOIUrl":"https://doi.org/10.3905/jii.2018.9.1.084","url":null,"abstract":"In this article, we construct a risk measure of an investment, called the D-Index, which measures the fraction of time the investment is in a drawdown. The D-Index isolates the time dimension from the returns when measuring risk, and this property uniquely characterizes it from other performance measures. An explicit analytical form of the ex-ante D-Index is provided for the case of a buy-and-hold strategy with price dynamics following a Black-Scholes model. Numerical evidence based on 32,642 funds across three asset classes shows that the D-Index does not exhibit strong rank correlation with the Sharpe ratio across funds, in contrast with most other existing risk-measures. Coupled with evidence from experimental psychology, this new perspective motivates us to consider its importance in investment decision making in the real-world environment.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2018.9.1.084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43318973","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 : 2017-02-28DOI: 10.3905/jii.2017.7.4.001
Brian R. Bruce
{"title":"Editor’s Letter","authors":"Brian R. Bruce","doi":"10.3905/jii.2017.7.4.001","DOIUrl":"https://doi.org/10.3905/jii.2017.7.4.001","url":null,"abstract":"","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46407943","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 : 2017-02-28DOI: 10.3905/jii.2017.7.4.030
Taie Wang
Thematic indexes that capture themes ranging from lowering carbon emissions to corporate governance have expanded in recent years and have received increasing interest from investors. Gender equality, however, has received relatively little treatment to date, even though evidence shows that firms with higher rates of female participation at the senior manager level have delivered higher return on equity, better corporate governance, and, in some cases, higher returns. Inspired by a need to address gender inequity in the workplace through Environmental, Social, and Governance investing, we researched the viability of creating an index that captures gender diversity. Here, we present our findings on gender diversity characteristics for U.S. large-cap companies and propose a rules-based portfolio construction framework for a gender diversity index.
{"title":"Social Change through Indexing: A Gender Diversity Index","authors":"Taie Wang","doi":"10.3905/jii.2017.7.4.030","DOIUrl":"https://doi.org/10.3905/jii.2017.7.4.030","url":null,"abstract":"Thematic indexes that capture themes ranging from lowering carbon emissions to corporate governance have expanded in recent years and have received increasing interest from investors. Gender equality, however, has received relatively little treatment to date, even though evidence shows that firms with higher rates of female participation at the senior manager level have delivered higher return on equity, better corporate governance, and, in some cases, higher returns. Inspired by a need to address gender inequity in the workplace through Environmental, Social, and Governance investing, we researched the viability of creating an index that captures gender diversity. Here, we present our findings on gender diversity characteristics for U.S. large-cap companies and propose a rules-based portfolio construction framework for a gender diversity index.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2017.7.4.030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42076311","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 : 2017-02-28DOI: 10.3905/jii.2017.7.4.083
W. Pennington, John Evans
A private, permissioned, peer-to-peer capital markets index data distribution architecture would streamline the provision and ingestion of securities index data from index provider to index subscribers. Limiting access on the blockchain network to trusted parties (subscribers) reduces the overhead needed to secure consensus in writing and validating new blocks of data and eliminates the need for incentives and the traditional “mining” associated with public peer-to-peer blockchain architectures. Applying defined permissioning to the different participants in the blockchain further refines activities and access abilities, such as regulator view all; subscriber read and propose records; and provider read, write, and approve new records proposed by subscribers. Various consensus models could be employed, depending on the implementation environment and underlying choice of blockchain base code, so long as access is enforced primarily at the network environment level and permissions are enforced at the blockchain level.
{"title":"Blockchain-Enabled, Subscriber-Based Capital Markets Index Data Distribution","authors":"W. Pennington, John Evans","doi":"10.3905/jii.2017.7.4.083","DOIUrl":"https://doi.org/10.3905/jii.2017.7.4.083","url":null,"abstract":"A private, permissioned, peer-to-peer capital markets index data distribution architecture would streamline the provision and ingestion of securities index data from index provider to index subscribers. Limiting access on the blockchain network to trusted parties (subscribers) reduces the overhead needed to secure consensus in writing and validating new blocks of data and eliminates the need for incentives and the traditional “mining” associated with public peer-to-peer blockchain architectures. Applying defined permissioning to the different participants in the blockchain further refines activities and access abilities, such as regulator view all; subscriber read and propose records; and provider read, write, and approve new records proposed by subscribers. Various consensus models could be employed, depending on the implementation environment and underlying choice of blockchain base code, so long as access is enforced primarily at the network environment level and permissions are enforced at the blockchain level.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2017.7.4.083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45176441","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 : 2017-02-28DOI: 10.3905/jii.2017.7.4.041
Andrew Ang, Ananth Madhavan, A. Sobczyk
Minimum volatility (min vol) strategies are smart beta strategies that are designed to minimize risk. In the past 12 months, ending June 30, 2016, there were $7.8 billion flows into a total of 17 U.S.-equity exchange-traded fund min vol strategies. This represents just 0.04% of total equity market capitalization of the underlying securities. Capacity in these strategies is large because traditional active mutual funds tend to overweight high-volatility stocks. Were these funds to move to a neutral position in high-volatility securities, a shift of roughly $600 billion to min vol would take place. Current valuations of min vol strategies are not high relative to historical norms and are consistent with the observed outperformance of these strategies during periods of high uncertainty.
{"title":"Crowding, Capacity, and Valuation of Minimum Volatility Strategies","authors":"Andrew Ang, Ananth Madhavan, A. Sobczyk","doi":"10.3905/jii.2017.7.4.041","DOIUrl":"https://doi.org/10.3905/jii.2017.7.4.041","url":null,"abstract":"Minimum volatility (min vol) strategies are smart beta strategies that are designed to minimize risk. In the past 12 months, ending June 30, 2016, there were $7.8 billion flows into a total of 17 U.S.-equity exchange-traded fund min vol strategies. This represents just 0.04% of total equity market capitalization of the underlying securities. Capacity in these strategies is large because traditional active mutual funds tend to overweight high-volatility stocks. Were these funds to move to a neutral position in high-volatility securities, a shift of roughly $600 billion to min vol would take place. Current valuations of min vol strategies are not high relative to historical norms and are consistent with the observed outperformance of these strategies during periods of high uncertainty.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2017.7.4.041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42879936","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 : 2017-02-28DOI: 10.3905/jii.2017.7.4.016
Geng Deng, C. McCann, M. Yan
In the early days of structured products, issuers issued, underwriters underwrote, and index providers provided indexes. In the 1990s, underwriters bypassed operating companies and began issuing debt linked to operating company stocks or to stock indexes. In recent years, investment banks have gone a step further and issued structured products linked to proprietary indexes of stocks, commodities, currencies, and volatility, including the two VIX-derived proprietary indexes discussed herein, rather than just linking to standardized indexes from S&P and other index providers. When brokerage firms include hypothetical trading costs in their proprietary indexes—costs that are absent from third-party indexes—they render comparisons of disclosed costs at the structured product level uninformative. This mischief would not be possible if issuers linked to indexes provided by third-party vendors who had no interest in the payoffs from structured products linked to their indexes. We illustrate the problems with self-indexing structured products using proprietary volatility indexes from Bank of America and J.P. Morgan, although the conflicts we highlight arise equally with the proprietary indexes of other underlying assets, including commodities and currencies.
在结构化产品的早期，发行人发行，承销商承销，指数提供商提供指数。上世纪90年代，承销商绕过运营公司，开始发行与运营公司股票或股指挂钩的债券。近年来，投资银行更进一步，发行了与股票、大宗商品、货币和波动率的专有指数挂钩的结构性产品，包括本文讨论的两个衍生于vix的专有指数，而不仅仅是与标准普尔和其他指数提供商的标准化指数挂钩。当经纪公司将假设的交易成本(第三方指数中没有的成本)纳入其自营指数时，它们就会使结构性产品层面的披露成本的比较缺乏信息。如果与第三方供应商提供的指数挂钩的发行人对与其指数挂钩的结构性产品的收益不感兴趣，那么这种恶作剧就不可能发生。我们使用美国银行(Bank of America)和摩根大通(J.P. Morgan)的专有波动率指数来说明自指数结构化产品的问题，尽管我们强调的冲突同样出现在其他基础资产(包括大宗商品和货币)的专有指数上。
{"title":"Structured Products and the Mischief of Self-Indexing","authors":"Geng Deng, C. McCann, M. Yan","doi":"10.3905/jii.2017.7.4.016","DOIUrl":"https://doi.org/10.3905/jii.2017.7.4.016","url":null,"abstract":"In the early days of structured products, issuers issued, underwriters underwrote, and index providers provided indexes. In the 1990s, underwriters bypassed operating companies and began issuing debt linked to operating company stocks or to stock indexes. In recent years, investment banks have gone a step further and issued structured products linked to proprietary indexes of stocks, commodities, currencies, and volatility, including the two VIX-derived proprietary indexes discussed herein, rather than just linking to standardized indexes from S&P and other index providers. When brokerage firms include hypothetical trading costs in their proprietary indexes—costs that are absent from third-party indexes—they render comparisons of disclosed costs at the structured product level uninformative. This mischief would not be possible if issuers linked to indexes provided by third-party vendors who had no interest in the payoffs from structured products linked to their indexes. We illustrate the problems with self-indexing structured products using proprietary volatility indexes from Bank of America and J.P. Morgan, although the conflicts we highlight arise equally with the proprietary indexes of other underlying assets, including commodities and currencies.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2017.7.4.016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41575593","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 : 2017-02-28DOI: 10.3905/jii.2017.7.4.060
E. Pong, Peter Gunthorp, A. Chen
With the recent revisions of the Qualified Foreign Institutional Investor (QFII) and Renminbi Qualified Foreign Institutional Investor (RQFII) programs, together with the introduction of the Stock Connect program, the mutual accessibility of the Chinese onshore and offshore equity markets is expected to be enhanced. We examine the characteristics of the A- and H-shares price differentials using the data for dual-listed companies during the 2006–2015 period. We find that the price differential narrows in the latter part of the sample period, which coincides with the enhancement of mutual access of both markets. We propose an A/H-share selection mechanism to identify the share class with the lower price and form an A/H-share class index. We show that the mechanism can deliver improved index characteristics compared with a market-capitalization-weighted China A-shares benchmark. The results are robust to different choices of parameters in terms of buffer zone and rebalancing frequency.
{"title":"Capturing the Chinese A-Shares and H-Shares Price Anomaly","authors":"E. Pong, Peter Gunthorp, A. Chen","doi":"10.3905/jii.2017.7.4.060","DOIUrl":"https://doi.org/10.3905/jii.2017.7.4.060","url":null,"abstract":"With the recent revisions of the Qualified Foreign Institutional Investor (QFII) and Renminbi Qualified Foreign Institutional Investor (RQFII) programs, together with the introduction of the Stock Connect program, the mutual accessibility of the Chinese onshore and offshore equity markets is expected to be enhanced. We examine the characteristics of the A- and H-shares price differentials using the data for dual-listed companies during the 2006–2015 period. We find that the price differential narrows in the latter part of the sample period, which coincides with the enhancement of mutual access of both markets. We propose an A/H-share selection mechanism to identify the share class with the lower price and form an A/H-share class index. We show that the mechanism can deliver improved index characteristics compared with a market-capitalization-weighted China A-shares benchmark. The results are robust to different choices of parameters in terms of buffer zone and rebalancing frequency.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2017.7.4.060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45743451","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 : 2017-02-28DOI: 10.3905/jii.2017.7.4.006
P. Mackintosh
It looks like exchange-traded funds (ETFs) are eating mutual funds’ lunch. Although we think this is more an index versus active story, it has not stopped mutual funds from looking to convert their active strategies into ETFs. The active ETF space is already alive and well: Active strategies already exist in the form of transparent active and smart beta funds. Although assets in these funds remain small, they are on a growth path that is consistent with that of index funds. The arbitrage mechanism helps active and smart beta ETFs track their net asset values very well, which is good for investors. Nontransparent active ETFs would help larger, active managers hide their trades from predatory traders and might also lower transaction and holding costs to investors. To date, none has been approved because of concerns over their tradeability. We think that they would likely have wider spreads than current ETFs but would trade much better than closed-end funds (which also trade on exchange intraday).
{"title":"It’s All about Active ETFs","authors":"P. Mackintosh","doi":"10.3905/jii.2017.7.4.006","DOIUrl":"https://doi.org/10.3905/jii.2017.7.4.006","url":null,"abstract":"It looks like exchange-traded funds (ETFs) are eating mutual funds’ lunch. Although we think this is more an index versus active story, it has not stopped mutual funds from looking to convert their active strategies into ETFs. The active ETF space is already alive and well: Active strategies already exist in the form of transparent active and smart beta funds. Although assets in these funds remain small, they are on a growth path that is consistent with that of index funds. The arbitrage mechanism helps active and smart beta ETFs track their net asset values very well, which is good for investors. Nontransparent active ETFs would help larger, active managers hide their trades from predatory traders and might also lower transaction and holding costs to investors. To date, none has been approved because of concerns over their tradeability. We think that they would likely have wider spreads than current ETFs but would trade much better than closed-end funds (which also trade on exchange intraday).","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2017.7.4.006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46167256","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 : 2017-02-28DOI: 10.3905/jii.2017.7.4.075
W. Fong
Big data such as Google Trends has stimulated much interest in the use of search query volumes for predicting social, business, and financial market trends. A recent paper by Preis, Moat, and Stanley [2013] claimed that a simple trading strategy using the Google Trends keyword debt powerfully predicts the Dow Jones Industrial Average stock index one week ahead and outperforms the buy-and-hold strategy by a factor of 20. Using the same sample period used by Preis, Moat, and Stanley, we show that debt completely loses its predictive power once look-ahead bias is eliminated. We find a similar result with a more recent sample period, from 2011 to 2016. Search terms that do outperform the buy-and-hold strategy generally have no economic meaning and are most likely spurious.
{"title":"Big Data, Small Pickings: Predicting the Stock Market with Google Trends","authors":"W. Fong","doi":"10.3905/jii.2017.7.4.075","DOIUrl":"https://doi.org/10.3905/jii.2017.7.4.075","url":null,"abstract":"Big data such as Google Trends has stimulated much interest in the use of search query volumes for predicting social, business, and financial market trends. A recent paper by Preis, Moat, and Stanley [2013] claimed that a simple trading strategy using the Google Trends keyword debt powerfully predicts the Dow Jones Industrial Average stock index one week ahead and outperforms the buy-and-hold strategy by a factor of 20. Using the same sample period used by Preis, Moat, and Stanley, we show that debt completely loses its predictive power once look-ahead bias is eliminated. We find a similar result with a more recent sample period, from 2011 to 2016. Search terms that do outperform the buy-and-hold strategy generally have no economic meaning and are most likely spurious.","PeriodicalId":36431,"journal":{"name":"Journal of Index Investing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3905/jii.2017.7.4.075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43537277","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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