{"id":11732,"date":"2021-03-02T11:44:04","date_gmt":"2021-03-02T10:44:04","guid":{"rendered":"https:\/\/quantpedia.com\/?p=11732"},"modified":"2025-06-04T14:28:19","modified_gmt":"2025-06-04T12:28:19","slug":"quantpedia-premium-update-1st-march-2021","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-1st-march-2021\/","title":{"rendered":"Quantpedia Premium Update \u2013 1st March 2021"},"content":{"rendered":"

New strategies:<\/strong><\/h4>\n\n\n\n

#596 \u2013 Editor Preference and Stock Returns<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1995-2016
Indicative performance:<\/strong> 12.68%
Estimated volatility:<\/strong> 17.6%<\/p>\n\n\n\n

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

Zheng, Hannan: Time-varying Media Coverage and Stock Returns
<\/strong>https:\/\/ssrn.com\/abstract=3748892
<\/a>Abstracto:
I show that news editors have state-dependent preference for different types of firms. Using the New York Times data and natural language processing techniques, I estimate the loadings of media coverage on eight common features of firms and construct the corresponding editor preference. I find that firms with higher editor preference earn higher returns than those with lower preference on average. It is consistent with the theory that if investors delegate their information selection to news editors, the state-dependent coverage decisions signal risky features and hence related firms are required more risk compensation. This excess return cannot be explained by mainstream risk factors and has an annualized alpha around 12%. Although this excess return is robust among more firms that are out of the scope of news data, it is bound to a short time horizon.<\/p>\n\n\n\n

#597 \u2013 Idiosyncratic Tail Risk<\/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 complex strategy
Backtest period:<\/strong> 2001-2016
Indicative performance:<\/strong> 8.21%
Estimated volatility:<\/strong> 9.71%<\/p>\n\n\n\n

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

Liu, Fred: Can the Premium for Idiosyncratic Tail Risk be Explained by Exposures to its Common Factor?
<\/strong>https:\/\/ssrn.com\/abstract=3711215
<\/a>Abstracto:
Stocks in the highest idiosyncratic tail risk decile earn 8% higher average annualized returns than in the lowest. I propose a risk-based explanation for this premium, in which shocks to intermediary funding cause idiosyncratic tail risk to follow a strong factor structure, and the factor, common idiosyncratic tail risk (CITR), comoves with intermediary funding. Consequently, if firms with high idiosyncratic tail risk have high exposure to CITR shocks, then they earn a risk premium due to their low returns when intermediary constraints tighten. To test my explanation, I create a novel measure of idiosyncratic tail risk that is estimated using high-frequency returns, and theoretically establish its time-aggregation properties. Consistent with my explanation, CITR shocks are procyclical, are correlated to intermediary factors, are priced in assets, and explain the idiosyncratic tail risk premium. Furthermore, volume tail risk also earns a premium, follows a strong factor structure, and its common factor is priced. This duality of idiosyncratic tail risk and volume tail risk provides evidence for my risk-based explanation, and further supports the hypothesis that intermediaries’ large trades cause idiosyncratic tail risk and volume tail risk from Gabaix et al. (2006).<\/p>\n\n\n\n

#598 \u2013 Lottery and Hot Potato Stocks<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Moderately complex strategy
Backtest period:<\/strong> 1972-2017
Indicative performance:<\/strong> 17.89%
Estimated volatility:<\/strong> 17.04%<\/p>\n\n\n\n

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

Mustafa O. Caglayanz and Robinson Reyes-Pe\u00f1a: Hot Potatoes: Overreaction to extreme negative returns
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3760190
<\/a>Abstracto:
Although it is well established that investors are willing to accept a negative premium for lotterylike stocks, it is puzzling that the opposite effect is not observed in stocks experiencing large daily losses. We find that stocks that experience large negative daily returns (MIN) also display large positive daily returns (MAX); therefore the MIN effect is subdued. Once stocks ranked as high-MAX within MIN deciles are removed, we find that MIN positively predicts future stock
returns. More importantly, a strategy that buys pure high-MIN stocks and simultaneously shorts pure high-MAX stocks generates superior returns compared to stand-alone MAX and MIN strategies.<\/p>\n\n\n\n

#599 \u2013 Barbells Strategy<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Weekly
Markets traded: <\/strong>equities, bonds
Instruments used for trading:<\/strong> ETFs, options, bonds
Complexity:<\/strong> Moderately complex strategy
Backtest period:<\/strong> 2002-2019
Indicative performance:<\/strong> 8.62%
Estimated volatility:<\/strong> 6.85%<\/p>\n\n\n\n

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

William Trainor, Dan Cupkovic, Indudeep Chhachhi, Chris Brown: Using Barbells to Lift Risk-Adjusted Return
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3753732
<\/a>Abstracto:
This study demonstrates how a barbell strategy invested primarily in fixed income assets coupled with in-the-money long-term call options on various equity asset classes can achieve a significant percentage of upside appreciation and significantly reduce downside risk. An examination of exchange-traded funds (ETFs) covering S&P 500, NASDAQ 100, mid-cap, small-cap, developed international, emerging, and real estate equities shows a barbell strategy of 88-percent bonds and 12-percent long-term call options captures 70\u2013124 percent of the geometric annual return of the underlying ETFs for December 2002\u2013November 2019.<\/p>\n\n\n\n

#600 \u2013 Break Risk Factor<\/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 complex strategy
Backtest period:<\/strong> 1950-2018
Indicative performance:<\/strong> 2.92%
Estimated volatility:<\/strong> 10.33%<\/p>\n\n\n\n

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

Simon C. Smith, Allan Timmermann: Instability in Risk Premia
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3728192
<\/a>Abstracto:
We apply a new methodology for identifying pervasive and discrete changes (“breaks”) in cross-sectional risk premia and find empirical evidence that these are economically important for understanding returns on US stocks. Risk premia on the market, size, and value factors have declined systematically over time with a particularly notable reduction after the 2008-09 Global Financial Crisis. We construct a new instability risk factor from cross-sectional differences in individual stocks\u2019 exposure to timevarying risk premia and show that this factor earns a premium comparable to that of commonly used risk factors. Using industry- and characteristics-sorted portfolios, we show that some breaks to the return premium process are broad-based, affecting all stocks regardless of industry- or firm characteristics, while others are limited to stocks with specific style characteristics. Moreover, we identify distinct lead-lag patterns in how breaks to the risk premium process impact stocks in different industries and with different style characteristic.<\/p>\n\n\n\n

New research papers related to existing strategies:<\/strong><\/h4>\n\n\n\n

#554 \u2013 Size factor in China<\/a><\/strong>
#555 \u2013 Value factor in China<\/a><\/strong><\/p>\n\n\n\n

Xu, Jin and Zhang, Shaojun, The Fama-French Three Factors in Chinese Stock Market
<\/strong>https:\/\/ssrn.com\/abstract=2367908
<\/a>Abstracto:
China is the largest emerging market and attracts a great deal of attention from investors and researchers worldwide. The Fama-French three-factor model is the outcome of decades of research on U.S. stock returns. To what extent the three factors explain the variation in Chinese stock returns is an intriguing question. This paper documents empirical evidence on this issue and identifies some pitfalls that arise in the application of the three-factor model to Chinese stock returns. We find that several special features in China affect the three factors considerably and also influence the explanatory power of the three-factor model.<\/p>\n\n\n\n

#554 \u2013 Size factor in China<\/a><\/strong>
#555 \u2013 Value factor in China<\/a><\/strong><\/p>\n\n\n\n

Chen, Jianguo and Kan, Kwong Leong and Anderson, Hamish D., Size: Book\/Market Ratio and Risk Factor Returns: Evidence from China A-Share Market
<\/strong>https:\/\/ssrn.com\/abstract=900474 
<\/a>Abstracto:
This study investigates the risk factors for A-shares listed on both Shenzhen and Shanghai Stock Exchange in China using variables from Akgun and Gibson (2001). By rearranging these risk variables into several principle components, we have run the cross-sectional regression on the orthogonal components. The results produced strong evidence that size and BM ratio could be well explained by these alternative risk variables. Additionally, the variables are better at explaining returns in terms of adjusted R-squares. As such the size and BM effects could be replaced with the risk factors identified that could enable enhanced pricing of risk for investors as well as greater control of the risk factors management.<\/p>\n\n\n\n

#460 \u2013 ESG Level Factor Investing Strategy<\/a><\/strong><\/p>\n\n\n\n

Khajenouri, Daniel and Schmidt, Jacob H: Standard or Sustainable – Which Offers Better Performance for the Passive Investor?
<\/strong>https:\/\/ssrn.com\/abstract=3733811
<\/a>Abstracto:
This research report studies the risk-adjusted performance of the major international equity indices against their ESG screened equivalents (MSCI World, MSCI USA, MSCI Emerging Markets, and MSCI Europe). The daily closing prices, returns, standard deviations, and Sharpe ratio characteristics are analyzed from 2013 to 2020. The current literature available from highly rated journals on the subject is also considered, which provided mixed results on the subject matter. We found no academic papers focusing specifically on analyzing the performance of indices and their ESG screened equivalents. With this paper, we intend to fill this gap in the current research available. We conclude that for the passive investor, choosing ESG screened indices over the conventional equivalent has consistently provided better risk-adjusted returns over the long-term period. These findings are robust with the consistently higher Sharpe ratios over the eight-year period for each index. We predict ESG investments may continue to outperform due to changing retail and institutional investor preferences.<\/p>\n\n\n\n

#27 \u2013 Market Timing with Aggregate and Idiosyncratic Stock Volatilities<\/a><\/strong><\/p>\n\n\n\n

Jiang, Ying and Pan, Jiening and Wang, Jianqiu and Wu, Ke: Disagreement, Speculation, and the Idiosyncratic Volatility
<\/strong>https:\/\/ssrn.com\/abstract=3731011
<\/a>Abstracto:
We propose that speculative trading arising from the joint effect of investor disagreement and short-sale constraints plays an important role in explaining the idiosyncratic volatility (IVOL) puzzle, the correlation among IVOL, market beta and trading volume, and the co-movement of IVOL. Empirical tests show that the return spread between high and low IVOL quintile portfolios is closely related to both aggregate and firm-level disagreement. The common IVOL (CIV) factor is strongly correlated with the aggregate disagreement. The correlation between the IVOL effect and disagreement presents mainly among stocks that are more likely to be short-sales constrained. We provide a mechanism that high aggregate disagreement can lead to more firm-level speculative trading, which is consistent with a factor structure in IVOL and positive correlation among stock beta, the IVOL effect, and the trading volume.<\/p>\n\n\n\n

#266 \u2013 Skewness Effect in Country Equity Indexes<\/a><\/strong><\/p>\n\n\n\n

Le, Trung H. and Kourtis, Apostolos and Markellos, Raphael N.: Modeling Skewness in Portfolio Choice
<\/strong>https:\/\/ssrn.com\/abstract=3708200
<\/a>Abstracto:
Despite half a century of research, we still do not know the best way to model skewness of financial returns. We address this question by comparing the predictive ability and associated portfolio performance of several prominent skewness models in a sample of ten international equity market indices. Models that employ information from the option markets provide the best outcomes overall. We develop an option-based model that accounts for the skewness risk premium. The new model produces the most informative forecasts of future skewness, the lowest prediction errors and the best portfolio performance in most of our tests.<\/p>\n\n\n\n

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

A Robust Approach to Multi-Factor Regression Analysis<\/a><\/strong><\/p>\n\n\n\n

Practitioners widely use asset pricing models such as CAPM or Fama French models to identify relationships between their portfolios and common factors. Moreover, each asset class has some widely-recognized asset pricing model, from equities through commodities to even cryptocurrencies. <\/p>\n\n\n\n

However, which model can we use if our portfolio is complex and consists of many asset classes? Which factors should we include and which should we omit? (Especially if we have a database that consists of several hundreds of potential factors<\/a>). Additionally, we know that equities influence bonds, commodities influence equities and vice versa. Hence the question, what about the cross-asset relationships? <\/p>\n\n\n\n

These are the problems and questions we faced when looking for a methodology for our Multi-Factor Analysis report in the Quantpedia Pro platform<\/a>. This blog post aims to introduce the model, its logic and the method we have decided to use. <\/p>\n\n\n\n


Accelerate Design of Multi-Factor Multi-Asset Models with Quantpedia Pro<\/a><\/strong><\/p>\n\n\n\n

We hinted in the past few blogs that we were preparing a small surprise. And now it\u2019s time to unveil what we have been cooking during the previous several months.<\/p>\n\n\n\n

Let us introduce Quantpedia Pro<\/a>.<\/p>\n\n\n\n

Quantpedia Pro is a new analytical platform built on top of our out-of-sample backtests of selected Quantpedia Premium strategies. It allows users to significantly speed up the process of building custom model multi-factor and multi-strategy portfolios. Instead of re-creating all ideas for systematic strategies in-house, users can explore ideas and do preliminary portfolio testing on Quantpedia Pro platform.<\/p>\n\n\n\n

Plus, the following six trading strategies have been backtested in\u00a0QuantConnect<\/a>\u00a0in the previous two weeks:<\/strong><\/h4>\n\n\n\n

#216 – Active Collar Strategy<\/a>
#462 – Market Breadth in Global Equities<\/a>
#585 – Trend Factor in China<\/a>
#592 – Volatility effect in the Chinese A-share market<\/a>
#593 – Value in Cryptocurrencies<\/a>
#594 – Size in Cryptocurrencies<\/a><\/p>\n\n\n\n

\n\n\n\n

\u00bfBuscas m\u00e1s estrategias para leer? Suscr\u00edbete a nuestro bolet\u00edn informativo<\/a> o visite nuestra Blog<\/a> o Evaluador<\/a><\/strong>.<\/p>\n\n\n\n

\u00bfQuieres saber m\u00e1s sobre el servicio Quantpedia Premium? Consulta C\u00f3mo funciona Quantpedia<\/a>, nuestra misi\u00f3n<\/a> y Oferta de precios premium<\/a>.<\/strong><\/p>\n\n\n\n

\u00bfQuieres saber m\u00e1s sobre el servicio Quantpedia Pro? Compru\u00e9balo descripci\u00f3n<\/a>, mirar videos<\/a>, revisar capacidades de generaci\u00f3n de informes<\/a> y visite nuestro oferta de precios<\/a>.<\/strong><\/p>\n\n\n\n

\u00bfBuscas datos hist\u00f3ricos o plataformas de backtesting? Consulta nuestra lista de Descuentos en Algo Trading<\/a><\/strong>.<\/p>\n\n\n\n

\u00bfTe gustar\u00eda tener acceso gratuito a? nuestros servicios<\/a>? Entonces, Abre una cuenta con Lightspeed.<\/a> y disfrute de un a\u00f1o de Quantpedia Premium sin costo alguno.<\/strong><\/p>\n\n\n\n

\n\n\n\n

O s\u00edguenos en:<\/strong><\/p>\n\n\n\n

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

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

Five new related research papers have been included into existing strategy reviews and two short free blog posts have been published during last few weeks. Plus, six trading strategies have been backtested in QuantConnect<\/strong><\/a> in the previous two weeks.<\/p>","protected":false},"author":21001,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-11732","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/11732","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\/21001"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=11732"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/11732\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=11732"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=11732"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=11732"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}