{"id":5582,"date":"2019-12-15T00:22:30","date_gmt":"2019-12-14T23:22:30","guid":{"rendered":"https:\/\/quantpedia.com\/?p=5582"},"modified":"2025-06-04T14:28:06","modified_gmt":"2025-06-04T12:28:06","slug":"quantpedia-premium-update-14th-december-2019","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-14th-december-2019\/","title":{"rendered":"Quantpedia Premium Update – 14th December 2019"},"content":{"rendered":"

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

#462 – Market Breadth in Global Equities <\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1973 – 2018
Indicative performance:<\/strong> 20.76%
Estimated volatility:<\/strong> 21.69%<\/p>\n\n\n\n

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

Zaremba, Szyszka, Karathanasopoulos, Mikutowski: Herding for Profits:<\/strong> Market Breadth and the Cross-Section of Global Equity Returns<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3444882<\/a>
Abstracto:
This paper shows that market breadth, i.e. the difference between the average number of rising stocks and the average number of falling stocks within a portfolio, is a robust predictor of future stock returns on market and industry portfolios for 64 countries for the period between 1973 and 2018. We link the market breadth with herd behavior and show that high market breadth portfolios significantly outperform low market breadth portfolios, and that this effect is robust to effects such as size, style, volatility, skewness, momentum, and trend-following signals. In addition, the role of market breadth is particularly strong among markets characterized by high limits to arbitrage, following bullish periods, and in collectivistic societies, supporting behavioral explanations of the phenomenon. We also examine practical implications of the effect and our results indicate that the effect may be employed for equity allocation and market timing, although frequent portfolio rebalancing can lead to higher transaction costs that may affect profitability. <\/p>\n\n\n\n

#463 – Price Gap Strategy in the US Stock Market<\/a><\/strong> <\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> futures, ETFs, CFDs
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2009 – 2018
Indicative performance:<\/strong> 5.9%
Estimated volatility:<\/strong> not stated<\/p>\n\n\n\n

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

Plastun, Sibande, Gupta, Wohar: Price Gap Anomaly in the US Stock Market: The Whole Story<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3461283<\/a>
Abstracto:
This paper analyses the price gap anomaly in the US stock market (comprised of the DJI, S&P 500 and NASDAQ) covering the period 1928 to 2018. This paper aims to investigate whether or not price gaps create market inefficiencies. Price gaps occur when the current day\u2019s opening price is different from the previous day\u2019s closing price due orders placed before the opening of the market. Several hypotheses are tested using various statistical tests (Student\u2019s t-test, ANOVA, Mann-Whitney test), regression analysis, and special methods, that is, the modified cumulative returns and the trading simulation approaches. We find strong evidence in favour of abnormal price movements after price gaps. We observe that during a gap day prices tend to change in the direction of the gap. A trading strategy based on this anomaly was efficient in that its results were not random, indicating that this market was not efficient. The momentum effect was found to be temporary and no evidence of seasonality in price gaps was found. Lastly, our results were also contrary to the myth that price gaps tend to get filled. <\/p>\n\n\n\n

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

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

Rein, Ruschendorf, Schmidt: Generalized Statistical Arbitrage Concepts and Related Gain Strategies<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3423944<\/a>
Abstract
Generalized statistical arbitrage concepts are introduced corresponding to trading strategies which yield positive gains on average in a class of scenarios rather than almost surely. The relevant scenarios or market states are specified via an information system given by a sigma-algebra and so this notion contains classical arbitrage as a special case. It also covers the notion of statistical arbitrage introduced in Bondarenko (2003). Relaxing these notions further we introduce generalized profitable strategies which include also static or semi-static strategies. Under standard no-arbitrage there may exist generalized gain strategies yielding positive gains on average under the specified scenarios. In the first part of the paper we characterize these generalized statistical no-arbitrage notions. In the second part of the paper we construct several profitable generalized strategies with respect to various choices of the information system. In particular, we consider several forms of embedded binomial strategies and follow-the-trend strategies as well as partition-type strategies. We study and compare their behaviour on simulated data. Additionally, we find good performance on market data of these simple strategies which makes them profitable candidates for real applications. <\/p>\n\n\n\n

#14 – Momentum Factor Effect in Stocks<\/a>
#26 – Value (Book-to-Market) Factor<\/a>
#130 – Investment Factor<\/a>
#224 – Profitability Factor Combined with Value Factor<\/a><\/strong><\/p>\n\n\n\n

Dong: Risk or Mispricing? Cross-Country Evidence on the Cross-Section of Stock Returns<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3478335<\/a>
Abstract
Using a novel collection of market characteristics from 40 countries, this paper test competing explanations behind five major anomalies classified in Hou, Xue, and Zhang (2015): momentum, value-growth, investment, profitability, and trading frictions. Results show that anomaly returns highly correlate with proxies for market efficiency, investor protection, limits-to-arbitrage, and investor irrationality. New to existing studies, results favor a limits-to-arbitrage explanation for momentum effect, and a mispricing explanation for value-growth and investment effects. Results also suggest that profitability effect may be a result of both rational risk pricing and market inefficiency while remain silent on the cause of trading frictions effect. These findings have new implications on return predictability in both U.S. and international markets. <\/p>\n\n\n\n

#151 – EBIDTA\/TEV Measure Effect<\/a><\/strong><\/p>\n\n\n\n

Zaremba, Szczygielski: And the Winner Is\u2026A Comparison of Valuation Measures for Country Asset Allocation<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3114803<\/a>
Abstract
The authors evaluate and compare the usefulness of various valuation ratios for country selection. To this end, the performance of 73 national equity indices is investigated for the period 1996 to 2017. The EBITDA-to-EV multiple is the best predictor of performance and outperforms other metrics. An equal-weighted portfolio that is long (short) in the tertile of countries with the highest (lowest) EBITDA-to-EV ratio produces a mean monthly return of 0.69% and a Sharpe ratio of 0.81. These are more than double the Sharpe ratios obtained from using traditional metrics such as the book-to-market ratio or dividend yield. Two major drawbacks of inter-country value strategies are identified: 1) payoffs are derived predominantly from emerging and frontier markets and 2) profitability has significantly declined in the last decade. <\/p>\n\n\n\n

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

How to Choose the Best Period for Indicators<\/a><\/strong><\/p>\n\n\n\n

Academic literature recognizes a large set of indicators or factors that are connected with the various assets. These indicators can be utilized in a variety of trading strategies, which means that such indicators are popular among practitioners who seek to invest their funds. Usually, the indicators are connected with some evaluation period.<\/strong><\/p>\n\n\n\n

This paper aims to show some possible approaches to find the optimal evaluation periods of indicators. This is a key question among practitioners and therefore we see it as crucial to shed a light on this topic. Although we are focused on momentum strategies, the information in this paper is widely applicable also in the construction of any other trading strategy where the investor has to decide indicator\u2019s period\u2026<\/strong><\/p>\n\n\n\n

Quant\u2019s Look on ESG Investing Strategies <\/a> <\/strong><\/p>\n\n\n\n

ESG Investing (sometimes called Socially Responsible Investing) is becoming a current trend, and its proponents characterize it as a modern, sustainable, and responsible way of investing. Some people love it, others see it as just another fad that will soon be forgotten. We at Quantpedia have decided to immerse in academic research related to this trend to understand it better. How are ESG scores measured? What are the common problems in ESG data? Are there any systematic ESG factor strategies that offer outperformance? These are some of the areas we wanted to explore, and we invite you on this journey with us \u2026<\/strong><\/p>\n\n\n\n

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\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

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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":"

Two 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.<\/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-5582","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/5582","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=5582"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/5582\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=5582"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=5582"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=5582"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}