{"id":5493,"date":"2019-12-01T00:48:47","date_gmt":"2019-11-30T23:48:47","guid":{"rendered":"https:\/\/quantpedia.com\/?p=5493"},"modified":"2025-06-04T14:27:00","modified_gmt":"2025-06-04T12:27:00","slug":"quantpedia-premium-update-30th-november-2019","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-30th-november-2019\/","title":{"rendered":"Quantpedia Premium Update – 30th November 2019"},"content":{"rendered":"

New strategies:<\/strong><\/p>\n\n\n\n

#459 – Machine Learning and Currency Carry Strategy<\/a> <\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>curencies
Instruments used for trading:<\/strong> futures, CFDs, swaps, forwards
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 1995 – 2017
Indicative performance:<\/strong> 10.72%
Estimated volatility:<\/strong> 19.65%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Filippou, Ilias and Rapach, David and Taylor, Mark Peter and Zhou, Guofu: The Rise and Fall of the Carry Trade: Links to Exchange Rate Predictability<\/strong>
https:\/\/ssrn.com\/abstract=3455713<\/a>
Abstracto:
We investigate out-of-sample exchange rate predictability in a high-dimensional panel predictive regression model that includes numerous country characteristics and their interactions with a variety of global variables. To avoid the overfitting problem that plagues conventional estimation of high-dimensional models, we estimate the panel predictive regression model via the elastic net, a machine learning technique based on penalized regression. The elastic net forecasts significantly outperform the no-change benchmark forecast that has proven difficult to beat in the literature. Out-of-sample exchange rate predictability becomes considerably stronger starting in the fall of 2008 during the worst stage of the global financial crisis. We show that exchange rate predictability can substantially improve the performance of conventional carry trade strategies: a smart carry portfolio that incorporates the information in the elastic net forecasts avoids the crash experienced by a conventional carry portfolio in late 2008 and markedly improves portfolio performance thereafter.<\/p>\n\n\n\n

#460 – ESG Factor Investing Strategy <\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Yearly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 2002 – 2011
Indicative performance:<\/strong> 3.25%
Estimated volatility:<\/strong> not stated<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Dorfleitner, Gregor and Utz, Sebastian and Wimmer, Maximilian: Where and When Does It Pay to Be Good? A Global Long-Term Analysis of ESG Investing<\/strong>
https:\/\/ssrn.com\/abstract=2311281<\/a>
Abstract
This paper explores the long-term performance of stocks with high corporate social performance (CSP), measured by so-called ESG scores depicting the environmental (E), social (S), and governance (G) dimension. We investigate the buy-and-hold abnormal returns of a long\/short investment strategy including the top and low 20% stocks with respect to each of the ESG dimensions. The results of the bootstrap tests in a world-wide perspective indicate that financial markets are not capable to price different levels of CSP in the short run and in particular in the long run properly. The zero investment strategy produces significantly positive abnormal returns up to 20% in North America and Europe in a five year period. We also identify regional differences, for instance, a high social score does not pay in Japan and strong corporate governance yields significantly negative abnormal returns in Asia Pacific. <\/p>\n\n\n\n

#461 – ESG  Momentum <\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Very comples strategy
Backtest period:<\/strong> 2007 – 2015
Indicative performance:<\/strong> 2.23%
Estimated volatility:<\/strong> 2.50%<\/p>\n\n\n\n

Source paper:<\/strong><\/p>\n\n\n\n

Zolt\u00e1n Nagy, Altaf Kassam, Linda-Eling Lee: CAN ESG ADD ALPHA?<\/strong>
https:\/\/www.semanticscholar.org\/paper\/Can-ESG-Add-Alpha-An-Analysis-of-ESG-Tilt-and-Nagy-Kassam\/64f77da4f8ce5906a73ffe4e9eec7c49c0960acc<\/a>
Abstracto:
Do institutional investors sacrifice risk-adjusted returns by incorporating environmental, social, and corporate governance (ESG) considerations? The authors analyze two relatively hightracking-error global strategies constructed using ESG data\u2014a tilt strategy and a momentum strategy and find that both model portfolios outperformed the MSCI World Index over the past eight years, while also improving the ESG profile of the portfolios. These backtested model portfolios provide an example of how institutional investors with the tolerance to take some active risk, while at the same time looking to improve the ESG profile of their portfolios on a systematic basis, can incorporate such strategies in their investment processes.<\/p>\n\n\n\n

New research papers related to existing strategies:<\/strong><\/p>\n\n\n\n

#167 – Idiosyncratic Momentum in Stocks<\/a><\/strong><\/p>\n\n\n\n

Matthias Hanauer and Steffen Windmueller: Enhanced Momentum Strategies<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3437919<\/a>
Abstract
This paper compares the performance of three momentum risk management techniques proposed in the literature \u2014 idiosyncratic momentum, constant volatility-scaling and dynamic scaling. Using data for individual stocks from the U.S. and across 48 international countries, we find that all three approaches decrease momentum crashes, lead to higher risk-adjusted returns and raise break even transaction costs. In a multiple model comparison test that also controls for other factors, idiosyncratic momentum emerges as the best momentum strategy. Finally, we find that the alpha stemming from volatility-scaling is distinctive from the idiosyncratic momentum alpha.<\/p>\n\n\n\n

#212 – Scheduled Economic Announcements Effect in Stocks<\/a> <\/strong><\/p>\n\n\n\n

Ernst, Gilbert, Hrdlicka: More Than 100% of the Equity Premium: How Much Is Really Earned on Macroeconomic Announcement Days?<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3469703<\/a>
Abstracto:
One can earn well over 100% of the equity risk premium on macroeconomic announcement days identified by the prior literature. This is a robust phenomenon present across many other subsets of macroeconomic variables. We show how inadvertent sample selection along with the timing of macroeconomic announcements throughout the month produces this too-much-return puzzle. Looking at the entire distribution of macroeconomic variables eliminates this sample selection bias, while including day-of-the-month fixed effects controls for the announcement timing. We find that expected macroeconomic announcements as a whole are responsible for about half of the equity premium. This smaller premium earned over more days means Sharpe ratios are similar on announcement and non-announcement days. We also show that the fit of the CAPM on macroeconomic announcement days is not evidence that those days are special, but only a by-product of those days\u2019 high ex-post market returns. <\/p>\n\n\n\n

384 – <\/strong>Time-Series Momentum and Carry<\/strong> <\/a><\/p>\n\n\n\n

Molyboga, Qian, He: Carry and Time-Series Momentum: A Match Made in Heaven<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3470864<\/a>
Abstract
This paper introduces a novel approach to combining time-series momentum and carry trade by conditioning trading signals of time-series momentum on the sign of the basis, a key input for carry trade. We find that this new technique applied to four major asset classes improves the Sharpe ratio of time-series momentum by approximately 0.17 net of fees. The improvement in performance is greater during recessions and, therefore, conditioning time-series momentum signals on the sign of the basis improves performance when it matters the most. Thus, the new approach has practical importance for investors and asset managers who attempt to improve their long-term performance without increasing downside risk during periods of market turbulence.<\/p>\n\n\n\n

And two short free blog posts have been published during last 2 weeks:<\/strong><\/p>\n\n\n\n

Quantitative Easing policy in the US triggered a massive inflow of liquidity to financial markets. This liquidity, combined with the growing popularity of commodities as an asset class, is a cause for a higher inter-connectedness among equity and commodity markets. A recent academic study written by Ordu-Akkaya and Soytas shows that commodities are not such a good diversifier as they used to be in the past. Moreover, commodity markets are also affected, as periods of higher equity volatility impact commodities significantly more \u2026<\/strong> <\/p>\n\n\n\n

Ordu-Akkaya, Soytas: Unconventional Monetary Policy and Financialization of Commodities<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3441666<\/a>
Abstracto:
We examine whether volatility spillover between US equity and commodity markets has significantly changed with the heavy influx of index traders in commodity derivatives markets, which is a phenomenon referred to as financialization. Previous findings show that institutional traders enter commodity markets at high liquidity episodes. Given that quantitative easing episode was highly criticized for providing excessive liquidity to the market, we investigate the impact of US quantitative easing policy on spillover between commodity and US stocks. Our results indicate that during post-financialization period, spillover from stocks to commodities have significantly increased for almost all commodities. More importantly, we show that quantitative easing is one of the underlying reasons for increasing volatility spillover between markets. Including interest rate, currency factors or default spread does not diminish the explicit role of quantitative easing on spillovers. <\/p>\n\n\n\n

Professor Roger Ibbotson is one of the most respected and influential researchers of the current era. His book \u201cStocks, Bonds, Bills, and Inflation\u201d is a classic and often serves as a reference for information about capital market returns. Therefore we always pay attention to his publications. His actual work, \u201cPopularity \u2013 A Bridge between Classical and Behavioral Finance\u201d, which is written with Thomas M. Idzorek, Paul D. Kaplan, and James X. Xiong, is now available on SSRN.<\/strong> In their work, authors explain the term \u201cPopularity\u201d from an asset pricing point and show how \u201cPopularity\u201d can be a broad umbrella under which nearly all market premiums and anomalies (including the traditional value and small-cap) can fall. They develop a formal asset pricing model that incorporates the central idea of \u201cPopularity\u201d, which they call the \u201cpopularity asset pricing model\u201d (PAPM). Based on this model, they predict characteristics as a company\u2019s brand, reputation, and perceived competitive advantage to be new equity factors.<\/strong> It\u2019s a long read, but we at Quantpedia really recommended it for all equity portfolio managers \u2026<\/strong> <\/p>\n\n\n\n

Ibbotson, Idzorek, Kaplan, Xiong: Popularity: A Bridge between Classical and Behavioral Finance<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3474546<\/a>
Abstracto:
Popularity is a word that embraces how much anything is liked, recognized, or desired. Popularity drives demand. In this book, we apply this concept to assets and securities to explain the premiums and so-called anomalies in security markets, especially the stock market. Most assets and securities have a relatively fixed supply over the short or intermediate term. Popularity represents the demand for a security \u2014 or perhaps the set of reasons why a security is demanded to the extent that it is \u2014 and thus is an important determinant of prices for a given set of expected cash flows. A common belief in the finance literature is that premiums in the market are payoffs for the risk of securities \u2014 that is, they are \u201crisk\u201d premiums. In classical finance, investors are risk averse, and market frictions are usually assumed away. In the broadest context, risk is unpopular. The largest risk premium is the equity risk premium (i.e., the extra expected return for investing in equities rather than bonds or risk-free assets). Other risk premiums include, for example, the interest rate term premium (because of the greater risk of longer-term bonds) and the default risk premium in bond markets. There are many premiums in the market that may or may not be related to risk, but all are related to investing in something that is unpopular in some way. We consider premiums to be the result of characteristics that are systematically unpopular \u2014 that is, popularity makes the price of a security higher and the expected return lower, all other things being equal. Preferences that influence relative popularity can and do change over time. These premiums include the size premium, the value premium, the liquidity premium, the severe downside premium, low volatility and low beta premiums, ESG premiums and discounts, competitive advantage, brand, and reputation. In general, any type of security with characteristics that tend to be overlooked or unwanted can have a premium. The title of this book refers to a bridge between classical and behavioral finance. Both approaches to finance rest on investor preferences, which we cast as popularity.<\/p>\n\n\n\n

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Facebook Grupo<\/a>, Facebook P\u00e1gina<\/a>, Gorjeo<\/a>, LinkedIn<\/a>, Medio<\/a> o YouTube<\/a><\/strong><\/p>","protected":false},"excerpt":{"rendered":"

Three new strategies have been added.<\/p>\n

Three new related research papers have been included into existing strategy reviews. And two short free blog posts<\/strong><\/a> about interesting related research papers have been published during last few weeks.<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-5493","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/5493","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=5493"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/5493\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=5493"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=5493"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=5493"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}