{"id":7098,"date":"2020-05-16T23:13:28","date_gmt":"2020-05-16T21:13:28","guid":{"rendered":"https:\/\/quantpedia.com\/?p=7098"},"modified":"2025-06-04T14:20:36","modified_gmt":"2025-06-04T12:20:36","slug":"quantpedia-premium-update-16th-may-2020","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-16th-may-2020\/","title":{"rendered":"Quantpedia Premium Update \u2013 16th May 2020"},"content":{"rendered":"

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

#497 – Monetary (FOMC) Momentum in Stocks<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> CFDs, ETFs, futures
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 1994-2019
Indicative performance:<\/strong> 6.02%
Estimated volatility:<\/strong> 10.56%<\/p>\n\n\n\n

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

Andreas Neuhierl and Michael Weber: Monetary Momentum<\/strong>
https:\/\/bfi.uchicago.edu\/wp-content\/uploads\/BFI_WP_202039.pdf<\/a>
Abstracto:
We document a large return drift around monetary policy announcements by the Federal Open Market Committee (FOMC). Stock returns start drifting up 25 days before expansionary monetary policy surprises, whereas they decrease before contractionary surprises. The cumulative return di\ufb00erence across expansionary and contractionary policy decisions amounts to 2.5% until the day of the policy decision and continues to increase to more than 4.5% 15 days after the meeting. The drift is more pronounced during periods of high uncertainty, it is a market-wide phenomenon, and it is present in all industries and many international equity markets. Standard returns factors and time-series momentum do not span the return drift around FOMC policy decisions. A simple trading strategy exploiting the drift around FOMC meetings increases Sharpe ratios relative to a buy-and-hold investment by a factor of 4. The cumulative returns before FOMC meetings signi\ufb01cantly predict the subsequent policy surprise.<\/p>\n\n\n\n

#498 – Value in Anomalies<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Yearly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1975 – 2014
Indicative performance:<\/strong> 10.27%
Estimated volatility:<\/strong> 9.68%<\/p>\n\n\n\n

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

Anginer, Deniz and Ray, Sugata and Seyhun, H. Nejat and Xu, Luqi: Value and Momentum in Anomalies<\/strong>
https:\/\/ssrn.com\/abstract=3537724<\/a>
Abstracto:
We document value and momentum across thirteen well-known stock market anomalies. We find anomalies that have performed well in the past month continue to outperform those that have performed poorly by about 60bp per month. These results hold for both relative momentum and absolute momentum across the anomalies. Similarly, we investigate future abnormal returns when anomalies exhibit a value or growth orientation with respect to historical levels. We find anomalies that exhibit a value orientation outperform anomalies that exhibit a growth orientation going forward by about 30bp per month. Furthermore, we find favorable anomalies based on combined momentum and value principles outperform unfavorable anomalies by about 90 bp per month. Our findings further corroborate the hypothesis that mispricing is an important source of anomaly profits.<\/p>\n\n\n\n

#499 \u2013 ETF Momentum<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2004-2018
Indicative performance:<\/strong> 16.08%
Estimated volatility:<\/strong> 25.78%<\/p>\n\n\n\n

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

Weikai Li, Melvyn Teo, and Chloe Yang: ETF Momentum<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3468556<\/a>
Abstracto:
We document economically large momentum profits when sorting ETFs on returns over the past two to four years. A value-weighted, long-short strategy based on ETF momentum delivers Carhart (1997) four-factor alphas of up to 1.20% per month. Neither cross-sectional stock momentum nor co-variation with macroeconomic and liquidity risks can explain ETF momentum. Instead, the post-holding period returns are most consonant with the behavioral story of delayed overreaction. While ETF momentum survives multiple adjustments for transaction costs, it may be difficult to arbitrage as the profits are volatile and concentrated in ETFs with high idiosyncratic volatility or that hold low-analyst-coverage stocks.<\/p>\n\n\n\n

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

#377 – Trading Futures Using Basis Indicator<\/a><\/strong><\/p>\n\n\n\n

Hazelkorn, Moskowitz, Vasudevan: Beyond Basis Basics: Leverage Demand and Deviations from the Law of One Price<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3264926<\/a>
Abstracto:
Deviations from the law of one price between futures and spot prices, known as bases, reflect the difference between interest rates implied in futures prices and benchmark borrowing rates. These differences are driven by intermediaries\u2019 cost of capital and the amount of leverage demand for an asset. Focusing on leverage demand, we find that bases negatively predict futures and spot market returns with the same sign in both global equities and currencies. This evidence is consistent with bases capturing uninformed leverage demand. We investigate the source of this demand in both markets using dealer and institutional positions data, securities lending fees, and foreign capital flows and find that the return predictability represents compensation to intermediaries for meeting liquidity and hedging demand. Our results have broader implications for understanding the interest rates embedded in derivatives prices.<\/p>\n\n\n\n

#7 – Low Volatility Factor Effect in Stocks \u2013 Long-Only Version<\/a><\/strong><\/p>\n\n\n\n

Alquist, Frazzini, Ilmanen, Pedersen: Fact and Fiction about Low-Risk Investing<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3539452<\/a>
Abstracto:
Low-risk investing within equities and other asset classes has received a lot of attention over the past decade. An intensive academic debate has spurred, and been spurred by, the growing market for low-risk strategies. This article presents five fact and dispels five fictions about low-risk investing. The facts are: Low-risk returns have been 1) strong historically, 2) highly significant out-of-sample, 3) robust across many countries and asset classes, and 4) backed by strong economic theory, but, nevertheless, 5) can be negative when the market is down. The fictions that this article dispels are that low-risk investing 1) delivers weaker returns than other common factor premia, 2) is mostly about betting on bond-like industries, 3) is especially sensitive to transaction costs and only works among small-cap stocks, and 4) have become so expensive that they cannot do well going forward. Lastly, the article dispels the fiction 5) that CAPM is dead and so is low-risk investing \u2013 this statement is a contradiction; If the CAPM is dead, then low-risk investing is alive.<\/p>\n\n\n\n

#12 – Pairs Trading with Stocks<\/a><\/strong><\/p>\n\n\n\n

Ramos-Requena, Trinidad-Segovia, S\u00e1nchez-Granero: Some Notes on the Formation of a Pair in Pairs Trading<\/strong>
https:\/\/www.mdpi.com\/2227-7390\/8\/3\/348\/pdf<\/a>
Abstracto:
The main goal of the paper is to introduce different models to calculate the amount of money that must be allocated to each stock in a statistical arbitrage technique known as pairs trading. The traditional allocation strategy is based on an equal weight methodology. However, we will show how, with an optimal allocation, the performance of pairs trading increases significantly. Four methodologies are proposed to set up the optimal allocation. These methodologies are based on distance, correlation, cointegration and Hurst exponent (mean reversion). It is showed that the new methodologies provide an improvement in the obtained results with respect to an equal weighted strategy.<\/p>\n\n\n\n

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

Backtesting ESG Factor Investing Strategies<\/a><\/strong><\/p>\n\n\n\n

Socially Responsible Investing (also called ESG Factor Investing) grows in popularity. More and more investors enter the stock market not just to invest their savings, but they are also want to support companies that bring positive social or environmental change. ESG factor investing can bring satisfaction to those investors. But does it also brings a real outperformance in a financial sense? Is there some ESG factor alpha? How big is it? These are some of the questions we have decided to investigate \u2013 we obtained data, identified ESG factor strategies and tested them. Feel free to explore them with us\u2026<\/p>\n\n\n\n

YTD Performance of Equity Factors \u2013 Update After Two Months<\/a><\/strong><\/p>\n\n\n\n

Nearly two months ago, in a time of the highest turmoil during the current pandemic crisis, we performed a quick assessment of the status of performance of equity factor strategies. The world has still not been able to ward-off health-care crisis completely, but a lot of countries have made significant progress (on the other hand, there are still a lot of countries in a worse state than a few months ago). Equity indexes have rebounded from the March lows and have removed some of the losses. Therefore, we have received multiple inquiries about the current situation of equity factor strategies.<\/p>\n\n\n\n

So it may be a good time to revisit once again how they are performing.<\/p>\n\n\n\n

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

#330 – Pre-Earnings Announcement Drift<\/a>
#331 – Timing Betting-Against-Beta (BAB) Anomaly<\/a>
#338 – Timing of Option Returns<\/a>
#349 – Trading Options During Expiration Weekends<\/a>
#368 – Betting Against Alpha<\/a>
#386 – Enhanced Betting Against Beta Strategy in Equities<\/a>
#393 – Oil Surprise Factor in Equities<\/a>
#405 – Using VIX to Time Options Writing<\/a><\/p>\n\n\n\n

\n\n\n\n

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\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

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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":"

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> have been published during last few weeks. Plus, eight trading strategies have been backtested in QuantConnect<\/strong><\/a> in the previous two 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-7098","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/7098","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=7098"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/7098\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=7098"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=7098"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=7098"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}