{"id":7969,"date":"2020-08-18T16:12:58","date_gmt":"2020-08-18T14:12:58","guid":{"rendered":"https:\/\/quantpedia.com\/?p=7969"},"modified":"2025-06-04T14:33:43","modified_gmt":"2025-06-04T12:33:43","slug":"quantpedia-premium-update-18th-august-2020","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-18th-august-2020\/","title":{"rendered":"Quantpedia Premium Update \u2013 18th August 2020"},"content":{"rendered":"

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

#525 \u2013 Double-Sorting all Possible Strategies<\/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> 1988-2017
Indicative performance:<\/strong> 38.64%
Estimated volatility:<\/strong> 28.00%<\/p>\n\n\n\n

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

M\u00fcller, Karsten and Schmickler, Simon: Interacting Anomalies
<\/strong>https:\/\/ssrn.com\/abstract=3646417
<\/a>Abstracto:
An extensive literature studies interactions of stock market anomalies using double-sorted portfolios. But given hundreds of known candidate anomalies, examining selected interactions is subject to a data mining critique. In this paper, we conduct a comprehensive analysis of all possible double-sorted portfolios constructed from 102 underlying anomalies. We find hundreds of statistically significant anomaly interactions, even after accounting for multiple hypothesis testing. An out-of-sample trading strategy based on double-sorted portfolios performs on par with state-of-the-art machine learning strategies, suggesting that simple combinations of characteristics can capture a similar amount of variation in expected returns.<\/p>\n\n\n\n

#526 \u2013 Principal Portfolios<\/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> 1963-2019
Indicative performance:<\/strong> 7.2%
Estimated volatility:<\/strong> 10%<\/p>\n\n\n\n

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

Kelly, Bryan T. and Malamud, Semyon and Pedersen, Lasse Heje: Principal Portfolios<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3623983<\/a>
<\/a>Abstracto:
We propose a new asset-pricing framework in which all securities\u2019 signals are used to predict each individual return. While the literature focuses on each security\u2019s own- signal predictability, assuming an equal strength across securities, our framework is flexible and includes cross-predictability\u2014leading to three main results. First, we derive the optimal strategy in closed form. It consists of eigenvectors of a \u201cprediction matrix,\u201d which we call \u201cprincipal portfolios.\u201d Second, we decompose the problem into alpha and beta, yielding optimal strategies with, respectively, zero and positive factor exposure. Third, we provide a new test of asset pricing models. Empirically, principal portfolios deliver significant out-of-sample alphas to standard factors in several data sets.<\/p>\n\n\n\n

#527 \u2013 Employee Satisfaction, ESG 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> 2011-2019
Indicative performance:<\/strong> 5.83%
Estimated volatility:<\/strong> not stated<\/p>\n\n\n\n

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

Kyle Welch, Aaron Yoon: Corporate Sustainability and Stock Returns: Evidence from Employee Satisfaction<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3616486
<\/a>Abstracto:
We provide evidence that ESG improves shareholder value when employees are satisfied. Using calendar-time portfolio stock returns and firm-level panel regressions, we find that firms with high ratings on both ESG and employee satisfaction significantly outperform those with low ratings on both and with high employee satisfaction alone. Our results are confirmed when we analyze future changes in accounting performance. Overall, results suggest that ESG coupled with employee satisfaction enhance shareholder value and these findings have implications not only for asset managers who integrate ESG factors into their portfolios but also for firm managers who implement ESG practices.<\/p>\n\n\n\n

#528 \u2013 Equity Factors and Corporate Bonds<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>bonds
Instruments used for trading:<\/strong> bonds
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1996-2016
Indicative performance:<\/strong> 6.61%
Estimated volatility:<\/strong> 11.27%<\/p>\n\n\n\n

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

Bekti\u0107 Demir, Wenzler Josef-Stefan, Wegener Michael, Schiereck Dirk and Timo Spielmann: Extending Fama\u2013French Factors to Corporate Bond Markets<\/strong>
http:\/\/wp.lancs.ac.uk\/fofi2018\/files\/2018\/03\/FoFI-2017-0041-Demir-Bektic.pdf
<\/a>Abstracto:
The explanatory power of size, value, profitability and investment has been extensively studied for equity markets. Yet, the relevance of these factors in global credit markets is less explored although equities and bonds should be related according to structural credit risk models. We investigate the impact of the four Fama\u2013French factors in the U.S. and European credit space. While all factors exhibit economically and statistically significant excess returns in the U.S. high yield market, we find mixed evidence for U.S. and European investment grade markets. Nevertheless, we show that investable multi-factor portfolios outperform the corresponding corporate bond benchmarks on a risk-adjusted basis. Finally, our results highlight the impact of company level characteristics on the joint return dynamics of equities and corporate bonds.<\/p>\n\n\n\n

#529 \u2013 Identfying Smart Money with Options<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 2014-2018
Indicative performance:<\/strong> 13.87%
Estimated volatility:<\/strong> 8.1%<\/p>\n\n\n\n

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

Jiang, George and Strong, Cuyler: Unusual Option Activity: Is it Smart to Follow \u2018Smart Money\u2019?<\/strong>
https:\/\/ssrn.com\/abstract=3618427
<\/a>Abstracto:
CNBC\u2019s \u201cFast Money\u201d regularly covers unusual option activity and refers to it as \u201csmart money\u201d. We investigate the impact of the CNBC coverage on underlying stock prices and whether investors can profit by following the \u201csmart money\u201d. We document an immediate spike in trading volume and abnormal returns at the time of the CNBC coverage. While options trades significantly predict stock returns prior to the CNBC coverage, there is a significant reversal in underlying stock prices following the CNBC coverage. Using similar criteria, we identify unusual option activities for a large sample of stocks. We show that options trades significantly predict underlying stock returns and there is no evidence of reversal in underlying stock price. Our findings suggest that the CNBC coverage of unusual option activity has a destabilizing effect on underlying stock prices and investors cannot profit by simply following the CNBC reporting on the \u201csmart money\u201d.<\/p>\n\n\n\n

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

#14 \u2013 Momentum Factor Effect in Stocks<\/a>
#7 \u2013 Low Volatility Factor Effect in Stocks<\/a><\/strong><\/strong><\/p>\n\n\n\n

Joshipura, Mayank and Joshipura, Nehal: Low-Risk Effect: Evidence, Explanations and Approaches to Enhancing the Performance of Low-Risk Investment Strategies<\/strong>
https:\/\/ssrn.com\/abstract=3613658
<\/a>Abstracto:
The authors offer evidence for low-risk effect from the Indian stock market using the top-500 liquid stocks listed on the National Stock Exchange (NSE) of India for the period from January 2004 to December 2018. Finance theory predicts a positive risk-return relationship. However, empirical studies show that low-risk stocks outperform high-risk stocks on a risk-adjusted basis, and it is called low-risk anomaly or low-risk effect. Persistence of such an anomaly is one of the biggest mysteries in modern finance. The authors find strong evidence in favor of a low-risk effect with a flat (negative) risk-return relationship based on the simple average (compounded) returns. It is documented that low-risk effect is independent of size, value, and momentum effects, and it is robust after controlling for variables like liquidity and ticket-size of stocks. It is further documented that low-risk effect is a combination of stock and sector level effects, and it cannot be captured fully by concentrated sector exposure. By integrating the momentum effect with the low-volatility effect, the performance of a low-risk investment strategy can be improved both in absolute and risk-adjusted terms. The paper contributed to the body of knowledge by offering evidence for: a) robustness of low-risk effect for liquidity and ticket-size of stocks and sector exposure, b) how one can benefit from combining momentum and low-volatility effects to create a long-only investment strategy that offers higher risk-adjusted and absolute returns than plain vanilla, long-only, low-risk investment strategy.<\/p>\n\n\n\n

#7 \u2013 Low Volatility Factor Effect in Stocks<\/a><\/strong><\/strong><\/p>\n\n\n\n

Driessen, Joost, Kuiper, Ivo and Beilo, Robbert: Does interest rate exposure explain the low-volatility anomaly?<\/strong>
http:\/\/wp.lancs.ac.uk\/fofi2018\/files\/2018\/03\/FoFI-2017-0003-Ivo-Kuiper.pdf
<\/a>Abstracto:
We show that part of the outperformance of low-volatility stocks can be explained by a premium for interest rate exposure. Low-volatility stock portfolios have negative exposure to interest rates, whereas the more volatile stocks have positive exposure. Incorporating an interest rate premium explains part of the anomaly. Depending on assumptions about the interest rate premium, interest rate exposure explains between 20% and 80% of the unexplained excess return. We also find that the interest rate risk premium in equity markets exhibits time variation similar to bond markets.<\/p>\n\n\n\n

#505 \u2013 Systematic Investing in Emerging Market Debt<\/a><\/strong><\/p>\n\n\n\n

Dekker, Lennart and Houweling, Patrick and Muskens, Frederik: Factor Investing in Emerging Market Credits
<\/strong>https:\/\/ssrn.com\/abstract=3457127
<\/a>Abstracto:
We examine factor investing in emerging market hard currency corporate bonds. Size, low-risk, value, and momentum factor portfolios obtain significantly higher Sharpe ratios than the market. We find the strongest results when the four factors are combined in a multi-factor portfolio. In several tests, alphas remain significant after controlling for exposures to developed market credit factors or equity factors. The factor portfolios benefit from bottom-up allocations to countries, sectors, ratings, and maturity segments, but most alphas remain significant after controlling for these allocation effects. Higher risk-adjusted returns of factor portfolios can also be found within liquid subsamples of the market.
<\/p>\n\n\n\n

#69 \u2013 Post-Earnings Announcement Drift Combined with Strong Momentum<\/a>
#94 \u2013 Trading on Earnings Announcements<\/a><\/strong><\/strong><\/p>\n\n\n\n

Medhat, Mamdouh and Schmeling, Maik: Dissecting Announcement Returns<\/strong>
http:\/\/wp.lancs.ac.uk\/fofi2018\/files\/2018\/04\/FoFI-2018-0098-Mamdouh-Medhat-No2.pdf
<\/a>Abstracto:
We develop a model with heterogeneous beliefs about a public and a private signal to understand return predictability around earnings announcements. We find evidence consistent with all of the model\u2019s key predictions: (1) Stock prices increase on average on earnings announcement days even though all signals are mean zero; (2) Firms with more fundamental uncertainty have higher announcement day returns on average; (3) Announcements day returns predict fundamental growth rates and stock returns; (4) The part of the announcement return unrelated to the public signal is more informative about future price drifts and fundamental growth rates than the part related to the public signal. Moreover, a factor based on announcement returns unrelated to the public signal should deliver significant returns that cannot be explained by standard risk factors. We find strong evidence for this and show that such a factor subsumes momentum returns.<\/p>\n\n\n\n

#117 \u2013 Lottery Effect in Stocks<\/a><\/strong><\/strong><\/p>\n\n\n\n

Jiang, Lei and Wen, Quan and Zhou, Guofu and Zhu, Yifeng: Lottery Preference and Anomalies<\/strong>
https:\/\/ssrn.com\/abstract=3595419
<\/a>Abstracto:
We construct a lottery factor that aggregates the information of 16 commonly used lottery features. The lottery factor significantly improves the explanatory power of the four-factor q model in Hou, Xue, and Zhang (2015) and explains all but a few major anomaly returns. In assessing the implication of lottery preference on profitability of anomaly-based trading strategies, we find that anomaly returns are significantly stronger among stocks with strong lottery preference. Moreover, the anomaly spread portfolios are mainly driven by the short leg among stocks with stronger lottery preference. The effect of lottery feature on anomalies is not driven by financial distress and is related to investors being reluctant to short sell stocks with high lottery features due to the high upside risk.<\/p>\n\n\n\n

And two interesting free blog posts have been published during last 2 weeks:<\/strong><\/h4>\n\n\n\n

<\/a>Reverse Flight to Liquidity in Fixed Income<\/a><\/strong><\/p>\n\n\n\n

Recent corona-crisis turbulence brought us many unexpected things, and one observation is connected with the fixed-income market. The conventional wisdom says that there is a flight to liquidity during troubled times and crises. Traditionally, liquid assets are US Treasuries or high-quality corporate bonds. Therefore, in theory, the pandemic should have been connected with buying pressure of high-quality liquid assets. However, as shown by a novel, insightful research from Ha, Xiao and Zeng, the exact opposite held. There was a very unusual sellout of liquid assets such as high quality fixed income as mutual funds tried to meet their redemption requests.<\/p>\n\n\n\n

<\/a>Implied Equity Duration as a Measure of Pandemic Shutdown Risk<\/a><\/strong><\/p>\n\n\n\n

Some companies have relatively more of their value in near-term cash flow (for ex. General Motors Corporation). Others (for ex. Tesla), are growth stocks, with a greater proportion of their market value based on long-term expected future cash flow. It seems that coronavirus pandemic has hit mainly the first group, the \u201clow equity duration\u201d companies. A new academic research paper written by Dechow, Erhard, Sloan, and Soliman explains how the equity duration factor can be used to assess how are companies exposed to short-term unexpected macroeconomic events (like COVID-19 pandemic), and how equity duration sensitivity can also explain relative underperformance of value vs growth stocks during the last bear market.<\/p>\n\n\n\n

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

#70 – Combining Post-Earnings Announcement Drift with Accrual Anomaly<\/a>
#131 – Bonds Market Timing<\/a>
#184 – Timing Carry Trade<\/a>
#261 – Trading Commodity Calendar Spreads<\/a>
#310 – Headquarter Location Momentum<\/a>
#314 – Sector Rotation Strategy Based on Multivariate Regression Analysis<\/a>
#326 – Volatility Investing Across Asset Classes<\/a>
#339 – Expected Investment Growth within the Cross-section of Stocks Returns<\/a>
#366 – Daily Box Office Earnings and Aggregate Stock Returns<\/a><\/p>\n\n\n\n

\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":"

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, nine 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-7969","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/7969","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=7969"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/7969\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=7969"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=7969"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=7969"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}