{"title":"INVITED EDITORIAL COMMENT","authors":"Marcos López de Prado","doi":"10.3905/jpm.2016.43.1.005","DOIUrl":"https://doi.org/10.3905/jpm.2016.43.1.005","url":null,"abstract":"1. Marcos Lopez de Prado\u0000 1. is a senior managing director at Guggenheim Partners in New York, NY, and a research fellow at the Lawrence Berkeley National Laboratory in Berkeley, CA. (lopezdeprado{at}lbl.gov) \u0000\u0000Economics (and, by extension, finance) is arguably one of the most mathematical","PeriodicalId":214661,"journal":{"name":"The Journal of Portfolio Management","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114363794","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}
{"title":"A Practitioner’s Guide to Market Microstructure Invariance","authors":"A. Kyle, Anna A. Obizhaeva, M. Kritzman","doi":"10.3905/jpm.2016.43.1.043","DOIUrl":"https://doi.org/10.3905/jpm.2016.43.1.043","url":null,"abstract":"The authors present a hypothesis of market microstructure invariance, which follows from the assumption that risk transfer and transaction costs are the same for all stocks when trades are converted to bets, calendar time is converted to business time, and return volatility is converted to dollar volatility. This hypothesis generates simple operational formulas for determining the distribution of bet sizes, trading patterns, and transaction costs as nonlinear functions of volume and volatility.","PeriodicalId":214661,"journal":{"name":"The Journal of Portfolio Management","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127664073","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}
{"title":"David and Goliath: Who Wins the Quantitative Battle?","authors":"John C. Bogle","doi":"10.3905/JPM.2016.43.1.127","DOIUrl":"https://doi.org/10.3905/JPM.2016.43.1.127","url":null,"abstract":"Today’s hedge fund managers, armed with PhDs, complex algorithms, massive databases, and lightning-fast trading capabilities—and high costs—are the Goliaths of the financial industry. The humble index fund is the David of investing, armed only with simple arithmetic and a buy-and-hold strategy but free of the heavy weight of high fees and expenses. Does the underdog David of investing stand a chance against the imposing Goliath? Or does Goliath’s heavy cost burden give David the edge in the battle for superior returns? This article discusses the history of index investing and evaluates the relative success of hedge funds and broad-market index funds.","PeriodicalId":214661,"journal":{"name":"The Journal of Portfolio Management","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129414350","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}
{"title":"An Asset Class Characterization of the U.S. Equity Index Volatility Risk Premium","authors":"William Fallon, James L. Park","doi":"10.3905/jpm.2016.43.1.072","DOIUrl":"https://doi.org/10.3905/jpm.2016.43.1.072","url":null,"abstract":"The authors use a novel 32-year return series to study the risk, return, and predictability of a strategy that sells one-month S&P 500 variance swaps with fixed ex-ante tail risk. They find that unconditional short exposure in their sample is characterized primarily by two features: (1) a very high Sharpe ratio exceeding 1.2 and (2) a severe but infrequent crash risk. From a forecasting perspective, the authors find a generally lower premium following market sell-offs and crashes. However, they fail to find significant evidence linking returns to the level of either implied or realized volatility.","PeriodicalId":214661,"journal":{"name":"The Journal of Portfolio Management","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115151289","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}
{"title":"Identifying Economic Regimes: Reducing Downside Risks for University Endowments and Foundations","authors":"J. Mulvey, Han Liu","doi":"10.3905/jpm.2016.43.1.100","DOIUrl":"https://doi.org/10.3905/jpm.2016.43.1.100","url":null,"abstract":"One of the most durable patterns in market behavior involves contagion—increases in correlation and volatility—during crash periods such as 2008. This condition can cause major problems for an investor when markets severely contract and anticipated diversification benefits vanish. To address contagion, the authors implement a machine-learning algorithm, trend filtering, to capture distinctive economic conditions. Over long horizons, they find that a multiregime simulation provides more accurate estimates of downside risk compared with traditional static portfolio models and can help in evaluating strategies for reducing the worst-case outcomes. The approach readily applies to nonprofit institutions that depend upon their endowment capital to fund liabilities and meet goals.","PeriodicalId":214661,"journal":{"name":"The Journal of Portfolio Management","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126478679","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}
{"title":"Uncloaking Campbell and Shiller’s CAPE: A Comprehensive Guide to Its Construction and Use","authors":"T. Philips, Cenk Ural","doi":"10.3905/jpm.2016.43.1.109","DOIUrl":"https://doi.org/10.3905/jpm.2016.43.1.109","url":null,"abstract":"Campbell and Shiller’s cyclically adjusted P/E (CAPE) has proven to be a powerful descriptor, and useful predictor, of long-term equity returns in the United States and some global markets. In recent years, though, it has been criticized for being overly pessimistic about the prospects for equity returns, lacking robustness to distortions in corporate earnings and overstating the predictability of returns at long horizons because of overlapping observations and endogeneity, particularly when estimated using ordinary least squares (OLS). This article explores various definitions of CAPE and presents new construction techniques that make it robust to a wide range of accounting and index construction biases, as well as to changing equity market fundamentals. The authors evaluate CAPE’s forecasts over various time periods using econometric methods that account for endogeneity, overlapping observations, and the presence of outliers. Many of these enhancements have minimal impact on CAPE and its U.S. equity market forecasts, but they prove to be useful in smaller markets and in markets that experienced significant dislocations. The authors also show that using accounting-flow variables such as cash flow and revenues in place of earnings and cyclically adjusted earnings can effectively supplement, and even enhance, CAPE’s market return forecasts. Finally, they show that CAPE and its variants forecast nominal returns more effectively than real returns and that the current 10-year forecast for the S&P 500 Index return is about 5.8% per annum.","PeriodicalId":214661,"journal":{"name":"The Journal of Portfolio Management","volume":"43 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127611844","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}
{"title":"A Trustee Guide to Factor Investing","authors":"K. Koedijk, Alfred Slager, Philip A. Stork","doi":"10.3905/jpm.2016.42.5.028","DOIUrl":"https://doi.org/10.3905/jpm.2016.42.5.028","url":null,"abstract":"Factor investing is rapidly becoming mainstream in the investment community. Although the technique has progressed impressively, the governance to implement it successfully has lagged behind. In this article, the authors analyze how pension funds and institutional investors implement new insights into factor investing and how they work with the factors that drive asset returns as a portfolio construction tool. The authors review hurdles in the implementation process, analyze the reasons behind them, and then develop a checklist with eight recommendations to consider when implementing factor investing. Looking forward, the authors explore three emerging approaches to factor investing and examine how these approaches could be implemented.","PeriodicalId":214661,"journal":{"name":"The Journal of Portfolio Management","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116284039","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}
{"title":"Adjusted Factor-Based Performance Attribution","authors":"Robert A. Stubbs, V. Jeet","doi":"10.3905/jpm.2016.42.5.067","DOIUrl":"https://doi.org/10.3905/jpm.2016.42.5.067","url":null,"abstract":"Factor-based performance attribution is frequently used in the asset management industry in both understanding and assessing the management of a portfolio. Unfortunately, in many cases the inferences from a standard attribution report can be misleading. One cause of this is the misclassification of factor contributions as asset-specific contributions or vice versa, due to missing factors or biased factor exposure estimates. The authors propose an adjusted factor-based performance attribution methodology that corrects for some types of biases by shifting the portion of the asset-specific contribution that is correlated with the factor contributions back into the factor portion. The authors find that, from a practical perspective, the proposed methodology results in more intuitive attributions that provide stronger support of factor-based investment mandates.","PeriodicalId":214661,"journal":{"name":"The Journal of Portfolio Management","volume":"358 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124517506","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}
{"title":"The Wisdom of Twitter Crowds: Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds","authors":"Pablo Azar, A. Lo","doi":"10.3905/jpm.2016.42.5.123","DOIUrl":"https://doi.org/10.3905/jpm.2016.42.5.123","url":null,"abstract":"With the rise of social media, investors have a new tool for measuring sentiment in real time. However, the nature of these data sources raises serious questions about its quality. Because anyone on social media can participate in a conversation about markets—whether the individual is informed or not—these data may have very little information about future asset prices. In this article, the authors show that this is not the case. They analyze a recurring event that has a high impact on asset prices—Federal Open Market Committee (FOMC) meetings—and exploit a new dataset of tweets referencing the Federal Reserve. The authors show that the content of tweets can be used to predict future returns, even after controlling for common asset pricing factors. To gauge the economic magnitude of these predictions, the authors construct a simple hypothetical trading strategy based on this data. They find that a tweet-based asset allocation strategy outperforms several benchmarks—including a strategy that buys and holds a market index, as well as a comparable dynamic asset allocation strategy that does not use Twitter information.","PeriodicalId":214661,"journal":{"name":"The Journal of Portfolio Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122233245","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}
{"title":"Seeking Alpha? It’s a Bad Guideline for Portfolio Optimization","authors":"Moshe Levy, Richard Roll","doi":"10.3905/jpm.2016.42.5.107","DOIUrl":"https://doi.org/10.3905/jpm.2016.42.5.107","url":null,"abstract":"Alpha is the most popular measure for evaluating the performance of both individual assets and funds. The alpha of an asset with respect to a given benchmark portfolio measures the change in the portfolio’s Sharpe ratio driven by a marginal increase in the asset’s portfolio weight. Thus, alpha indicates which assets should be marginally over- or underweighted relative to the benchmark weights, and by how much. In this article, the authors show that alpha is actually an ineffective guideline for portfolio optimization. The reason is that alpha only measures the effects of infinitesimal changes in the portfolio weights. For small but finite changes, which are those relevant to investors, the optimal weight adjustments are almost unrelated to the alphas. In fact, in many cases the optimal adjustment is in the opposite direction of alpha—it may be optimal to reduce the weight of an asset with a positive alpha, and vice versa. Rather than employing alphas as a guideline, the authors argue that investors can do much better by using direct optimization with the desired constraint on the distance from the benchmark portfolio weights.","PeriodicalId":214661,"journal":{"name":"The Journal of Portfolio Management","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128769187","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}