{"id":13889,"date":"2021-08-02T21:59:43","date_gmt":"2021-08-02T19:59:43","guid":{"rendered":"https:\/\/quantpedia.com\/?p=13889"},"modified":"2025-06-04T14:30:26","modified_gmt":"2025-06-04T12:30:26","slug":"quantpedia-premium-update-2nd-august-2021","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-2nd-august-2021\/","title":{"rendered":"Quantpedia Premium Update \u2013 2nd August 2021"},"content":{"rendered":"

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

#645 \u2013 Statistical Arbitrage With CNN and Transformer Networks<\/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> 1998-2016
Indicative performance:<\/strong> 5.5%
Estimated volatility:<\/strong> 5%<\/p>\n\n\n\n

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

Guijarro-Ordonez, J., Pelger, M., & Zanotti, G.: Deep Learning Statistical Arbitrage
<\/strong>https:\/\/ssrn.com\/abstract=3862004
<\/a>Abstracto:
Statistical arbitrage identifies and exploits temporal price differences between similar as-sets. We propose a unifying conceptual framework for statistical arbitrage and develop a novel deep learning solution, which finds commonality and time-series patterns from large panels in a data-driven and flexible way. First, we construct arbitrage portfolios of similar assets as resid- ual portfolios from conditional latent asset pricing factors. Second, we extract the time series signals of these residual portfolios with one of the most powerful machine learning time-series solutions, a convolutional transformer. Last, we use these signals to form an optimal trading policy, that maximizes risk-adjusted returns under constraints. We conduct a comprehensive empirical comparison study with daily large cap U.S. stocks. Our optimal trading strategy obtains a consistently high out-of-sample Sharpe ratio and substantially outperforms all bench- mark approaches. It is orthogonal to common risk factors, and exploits asymmetric local trend and reversion patterns. Our strategies remain profitable after taking into account trading fric- tions and costs. Our findings suggest a high compensation for arbitrageurs to enforce the law of one price.<\/p>\n\n\n\n

#646 \u2013 Post-Earnings-Annoucement Drift Using NLP on Earnings Calls<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Quarterly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 2008-2019
Indicative performance:<\/strong> 16.53%
Estimated volatility:<\/strong> 5.06%<\/p>\n\n\n\n

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

Meursault, Vitaly and Liang, Pierre Jinghong and Routledge, Bryan R. and Scanlon, Madeline: PEAD.txt: Post-Earnings-Announcement Drift Using Text
<\/strong>https:\/\/ssrn.com\/abstract=3778798
<\/a>Abstracto:
We construct a new numerical measure of earnings announcement surprises, standardized unexpected earnings call text (SUE.txt), that does not explicitly incorpo- rate the reported earnings value. SUE.txt generates a text-based post-earnings- announcement drift (PEAD.txt) larger than the classic PEAD and can be used to create a profitable trading strategy. The magnitude of PEAD.txt is considerable even in recent years when the classic PEAD is close to zero. Leveraging the prediction model underlying SUE.txt, we propose new tools to study the news content of text: paragraph-level SUE.txt and paragraph classification scheme based on the business curriculum. With these tools, we document many asymmetries in the distribution of news across content types, demonstrating that earnings calls contain a wide range of news about firms and their environment.<\/p>\n\n\n\n

#647 \u2013 Equity Duration<\/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> 1998-2019
Indicative performance:<\/strong> 5.8%
Estimated volatility:<\/strong> 16.5%<\/p>\n\n\n\n

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

Mullins, Gary: Equity Duration
<\/strong>https:\/\/ssrn.com\/abstract=3742725
<\/a>Abstracto:
The concept of bond duration was originally introduced by Macaulay (1938) and nowadays is well- established in the fixed-income literature. In this paper, I lift the same concepts from the fixed-income asset class and apply them to equities. I derive three candidate models for estimating the duration of a stock. The models are vastly different in their theoretical underpinnings, yet there is strong empirical evidence of positive co-movements between all three models in my sample. Furthermore, I investigate the relationship between the equity duration factor and various common equity factors. Empirical evidence suggests that low-duration stocks are also high-value, high-profitability, low-investment and low-risk stocks. In particular, there is a strong link between duration and the classical value factor \u2013 both theoretically and empirically. Importantly, however, the correspondence between the two factors is not one-to-one in my sample. I perform numerous empirical tests suggesting that a duration strategy out-performed a value-strategy in the period following the Great Financial Crisis (2007\u201308).<\/p>\n\n\n\n

#648 \u2013 Mean Absolute Daily Return in Cryptos<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Weekly
Markets traded: <\/strong>cryptos
Instruments used for trading:<\/strong> cryptos
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2014-2020
Indicative performance:<\/strong> 47.32%
Estimated volatility:<\/strong> 22.46%<\/p>\n\n\n\n

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

Han, Weihao and Newton, David and Platanakis, Emmanouil and Sutcliffe, Charles M. and Ye, Xiaoxia: Cryptocurrency Factor Portfolios: Performance, Decomposition and Pricing Models
<\/strong>https:\/\/ssrn.com\/abstract=3857315
<\/a>Abstract: The empirical distributions of cryptocurrency returns are highly non-normal, casting doubt on the performance metrics. So we apply almost stochastic dominance (ASD), which does not require any assumption about the return distribution, to examine cryptocurrency factor portfolios. Using portfolios based on factors that can be constructed from available market information, we find 13 factor portfolios that dominate our four benchmarks. The long-only strategy contributes more to this dominance than does the short-only strategy. We test whether returns on the 13 dominant factor portfolios can be explained by a coin market three-factor model. This model has limited success, and its performance is significantly improved by the inclusion of a mispricing factor.<\/p>\n\n\n\n

#649 \u2013 Scaled Volume in Cryptos<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Weekly
Markets traded: <\/strong>cryptos
Instruments used for trading:<\/strong> cryptos
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2014-2020
Indicative performance:<\/strong> 16.80%
Estimated volatility:<\/strong> 11.97%<\/p>\n\n\n\n

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

Han, Weihao and Newton, David and Platanakis, Emmanouil and Sutcliffe, Charles M. and Ye, Xiaoxia: Cryptocurrency Factor Portfolios: Performance, Decomposition and Pricing Models
<\/strong>https:\/\/ssrn.com\/abstract=3857315
<\/a>Abstract: The empirical distributions of cryptocurrency returns are highly non-normal, casting doubt on the performance metrics. So we apply almost stochastic dominance (ASD), which does not require any assumption about the return distribution, to examine cryptocurrency factor portfolios. Using portfolios based on factors that can be constructed from available market information, we find 13 factor portfolios that dominate our four benchmarks. The long-only strategy contributes more to this dominance than does the short-only strategy. We test whether returns on the 13 dominant factor portfolios can be explained by a coin market three-factor model. This model has limited success, and its performance is significantly improved by the inclusion of a mispricing factor.<\/p>\n\n\n\n

#650 \u2013 Volatility Effect in Cryptos<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Weekly
Markets traded: <\/strong>cryptos
Instruments used for trading:<\/strong> cryptos
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2014-2020
Indicative performance:<\/strong> 17.06%
Estimated volatility:<\/strong> 25.56%<\/p>\n\n\n\n

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

Han, Weihao and Newton, David and Platanakis, Emmanouil and Sutcliffe, Charles M. and Ye, Xiaoxia: Cryptocurrency Factor Portfolios: Performance, Decomposition and Pricing Models
<\/strong>https:\/\/ssrn.com\/abstract=3857315
<\/a>Abstract: The empirical distributions of cryptocurrency returns are highly non-normal, casting doubt on the performance metrics. So we apply almost stochastic dominance (ASD), which does not require any assumption about the return distribution, to examine cryptocurrency factor portfolios. Using portfolios based on factors that can be constructed from available market information, we find 13 factor portfolios that dominate our four benchmarks. The long-only strategy contributes more to this dominance than does the short-only strategy. We test whether returns on the 13 dominant factor portfolios can be explained by a coin market three-factor model. This model has limited success, and its performance is significantly improved by the inclusion of a mispricing factor.<\/p>\n\n\n\n

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

#35 \u2013 Insiders Trading Effect in Stocks<\/a><\/strong><\/p>\n\n\n\n

Cheong, Harvey and Kim, Joon Ho and M\u00fcnkel, Florian and Spilker III, Harold D., Do Social Networks Facilitate Informed Option Trading? Evidence from Alumni Reunion Networks
<\/strong>https:\/\/ssrn.com\/abstract=3795685
<\/a>Abstracto:
Material private information transmits through social networks. Using manually collected information on networks of alumni reunion cohorts, we show that hedge fund managers connected to directors of firms engaged in merger deals increase call option holdings on target firms before deal announcements. Effects are larger when reunion events for connected cohorts occur just before announcements. Independent directors, directors with short tenure, and directors with low stock ownership are more likely to transmit information. Our results are robust to confounding factors and alternative specifications. These findings highlight the role of social networks as channels of private information dissemination.<\/p>\n\n\n\n

#35 \u2013 Insiders Trading Effect in Stocks<\/a><\/strong><\/p>\n\n\n\n

Bushee, Brian J. and Taylor, Daniel and Zhu, Christina, The Dark Side of Investor Conferences: Evidence of Managerial Opportunism
<\/strong>https:\/\/ssrn.com\/abstract=3701977
<\/a>Abstracto:
While the shareholder benefits of investor conferences are well-documented, evidence on whether these conferences facilitate managerial opportunism is scarce. In this paper, we examine whether managers opportunistically exploit heightened attention around the conference to “hype” the stock. Consistent with hype, we find that managers increase the quantity of voluntary disclosure over the ten days prior to the conference, and that these disclosures increase prices to a greater extent than post-conference disclosures. Investigating managers\u2019 incentives for pre-conference disclosure, we find that the increase in pre-conference disclosure is more pronounced when insiders sell their shares immediately prior to the conference. In those circumstances where pre-conference disclosures coincide with pre-conference insider selling, we find evidence of a significant return reversal: large positive returns before the conference, and large negative returns after the conference. Collectively, our findings are consistent with some managers hyping the stock prior to the conference and selling their shares at inflated prices.<\/p>\n\n\n\n

#137 \u2013 Trend-following in Futures Markets<\/a><\/strong><\/p>\n\n\n\n

Bucher, Chris and Osterrieder, Joerg, Risk Parity for Multi-Asset Futures Allocation \u2013 A Practical Analysis of the Equal Risk Contribution Portfolio
<\/strong>https:\/\/ssrn.com\/abstract=3858730
<\/a>Abstracto:
Since the early beginning of investing as it was commonly seen as a form of gambling for the rich and wealthy, the idea of Harry Markowitz was revolutionising the way of thinking and how portfolios should be constructed. However, today the traditional mean-variance portfolios are still not fully adopted by practitioners. After the financial crisis of 2008, a type of portfolio called risk parity arise and attracted the attention of numerous investors. In this paper, a risk parity portfolio named equal risk contribution portfolio is constructed based on a rolling window of 300 days. The portfolio is built on 21 future contracts downloaded from Quandl. It includes assets from four asset classes with a data range from June 2005 to March 2020. A performance and risk analysis is made for each asset, asset class and year. Many findings from the literature are reflected in our results, such as strong diversification and a higher Sharpe Ratio than the equally weighted Benchmark. The impact of the financial crisis of 2008 and the good performance of risk parity during this period can also be seen. To a certain extent, the COVID-19 crisis, in which our risk parity portfolio performs well, can also be observed.<\/p>\n\n\n\n

#136 \u2013 Residual Momentum Factor<\/a><\/strong>
#77 \u2013 Betting Against Beta Factor in Stocks<\/a><\/strong><\/p>\n\n\n\n

Ehsani, Sina and Linnainmaa, Juhani T., The Invisible Portfolio
https:\/\/ssrn.com\/abstract=3855066
Abstracto:
A portfolio sorted on the intercepts of a multi-factor model – the invisible portfolio – is the optimal portfolio for improving the model’s mean-variance efficiency. This portfolio, similar to the betting-against-beta (BAB) factor, benefits from the distortions in the security market (or factor) lines. Whereas the BAB factor adjusts for the flatness in any one factor’s security factor line, the invisible portfolio optimally adjusts for all such distortions. The invisible portfolio increases the five-factor model’s out-of-sample maximum squared Sharpe ratio from 0.98 to 1.38. The invisible portfolio is an intuitive and theoretically founded method for improving all factor models.<\/p>\n\n\n\n

#473 \u2013 Cross-Asset Skewness Effect<\/a><\/strong><\/p>\n\n\n\n

Bauer, Michael and Chernov, Mikhail, Interest Rate Skewness and Biased Beliefs
<\/strong>https:\/\/ssrn.com\/abstract=3875121
<\/a>Abstracto:
Conditional yield skewness is an important summary statistic of the state of the economy. It exhibits pronounced variation over the business cycle and with the stance of monetary policy, and a tight relationship with the slope of the yield curve. Most importantly, variation in yield skewness has substantial forecasting power for future bond excess returns, high-frequency interest rate changes around FOMC announcements, and consensus survey forecast errors for the ten-year Treasury yield. The COVID pandemic did not disrupt these relations: historically high skewness correctly anticipated the run-up in long-term Treasury yields starting in late 2020. The connection between skewness, survey forecast errors, excess returns, and departures of yields from normality is consistent with a theoretical framework where one of the agents has biased beliefs.<\/p>\n\n\n\n

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

Do SPACs Generate Abnormal Returns?<\/a><\/strong><\/p>\n\n\n\n

Special Purpose Acquisition Companies (SPACs) raise capital through IPO under special conditions intending to acquire an existing company (private equity). On the one hand, it looks like an attractive opportunity for investors \u2013 SPACs bring a lot of excitement and prospects of large profits since the management can find a valuable opportunity. If no acquisition is made, then investors simply get their money back. For firms that are being acquired, it is a much easier and faster way how to get publicly traded \u2013 without investment banks and IPOs. On the other hand, SPACs are very speculative and even frequently overpriced, which attracts many critiques. While SPACs are nothing new, recently they have got quite popular, which raises several questions: are they worth attention or do they bring abnormal profits? A fascinating insight into SPACs provides a novel research paper of Chong et al. (2021). The study explains the fundamental principles of SPACs, but most importantly, it shows us the risks and returns of such investments. Despite the popularity and the seemingly attractive opportunity of SPACs, results show us that the invested capital could be instead used elsewhere. Although the success depends on the sector in which is the SPAC interested or whether the acquisition was successful, overall, it is hard to find abnormal returns in these investments.<\/p>\n\n\n\n

Five Small Shards of Insight Hidden in Data<\/a><\/strong><\/p>\n\n\n\n

This blog post will give you a short recapitulation of the five quick market\/portfolio insights built from Quantpedia Pro reporting<\/a>.<\/p>\n\n\n\n

\u2013 Gold displays a strong seasonal tendency<\/a> in returns in days around US public holidays.<\/p>\n\n\n\n

\u2013 The performance of Bitcoin is usually the worst during the same time as stock market experiences the bear market<\/a>.<\/p>\n\n\n\n

\u2013 Cryptocurrency market correlation<\/a> slowly increases, and we can\u2019t rule out the financialization of the crypto market (the same process that happened in commodities approximately ten years ago).<\/p>\n\n\n\n

\u2013 Skewness-based trading strategies could serve as a practical hedge\/diversification during stock market drawdowns<\/a>.<\/p>\n\n\n\n

\u2013 We show the main attribute of most of the risk parity portfolios<\/a> \u2013 lower total returns but significantly lower risk measures.<\/p>\n\n\n\n

Book Value in Modern Era<\/a><\/strong><\/p>\n\n\n\n

Undoubtedly, in the recent past, the value is under scrutiny. Many researchers have aimed to answer questions like is the Value factor dead<\/a>? The recent underperformance of the academic value factor (HML<\/a>) can be tricky to understand, especially when most well-known and influential investors are labelled as \u201cvalue\u201d investors. A novel research paper by Choi et al. (2021) adds to the literature with its valuable insights. The main topic of the paper is the thorough examination of the B\/M ratio in value style investing. Despite the well-known fact of the economy shift towards intangible assets, value investing still seems to be anchored to the B\/M ratio that underestimates the true value. For example, Fama and French\u2019s well-known HML value factor is based on B\/M, value indexes are based on B\/M (such as Russell value indexes) and subsequently, ETFs and benchmarks too.<\/p>\n\n\n\n

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

#539 – Historical and Implied Volatility in FX Options<\/a>
#556 – Long-Term Institutional Trades and the Cross-Section of Returns<\/a>
#579 – Variance Scaled Momentum in Emerging Markets<\/a>
#639 – Inventory Mispricing Predicts Oil Returns<\/a>
#643 – Dynamic Crude Oil Allocation in a Balanced Portfolio<\/a><\/p>\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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Facebook Grupo<\/a>, Facebook P\u00e1gina<\/a>, Gorjeo<\/a>, LinkedIn<\/a>, Medio<\/a> o YouTube<\/a><\/strong><\/p>","protected":false},"excerpt":{"rendered":"

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

Five new related research papers have been included into existing strategy reviews and three short free blog posts<\/strong><\/a> have been published during last few weeks. Plus, five 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-13889","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/13889","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=13889"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/13889\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=13889"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=13889"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=13889"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}