{"id":13343,"date":"2021-07-02T00:11:39","date_gmt":"2021-07-01T22:11:39","guid":{"rendered":"https:\/\/quantpedia.com\/?p=13343"},"modified":"2025-06-04T14:22:19","modified_gmt":"2025-06-04T12:22:19","slug":"quantpedia-premium-update-1st-july-2021","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-1st-july-2021\/","title":{"rendered":"Quantpedia Premium Update \u2013 1st July 2021"},"content":{"rendered":"

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

#635 \u2013 Constituent Stock News Predict ETF Returns<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Intraday
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 2000-2020
Indicative performance:<\/strong> 21.12%
Estimated volatility:<\/strong> 9.82%<\/p>\n\n\n\n

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


<\/p>\n\n\n\n

Jiang, Hao and Li, Sophia Zhengzi and Yuan, Peixuan: Predicting High-Frequency Industry Returns: Machine Learners Meet News Watchers
<\/strong>https:\/\/ssrn.com\/abstract=3700466
<\/a>Abstracto:
This paper uses machine learning-based as well as fundamental-driven, news-based approaches to uncover patterns of high-frequency return predictability for sector exchange-traded funds (ETFs). A LASSO predictor that aggregates high-frequency price movements of a broad universe of individual stocks predicts ETF returns out-of-sample. The news-driven return on ETF constituent firms positively predicts ETF returns, but the component of ETF returns orthogonal to the news return negatively predicts them. These different signals contain independent information, and have different strengths, with the LASSO predictor providing continuous flows of information most powerful during trading hours and the news return offering sporadic information particularly useful during market close. A composite signal combining all three signals with Gradient Boosted Regression Trees (GBRT) has very strong power to forecast ETF returns, especially during the COVID-19 pandemic.<\/p>\n\n\n\n

#636 \u2013 Machine Learning and Stock Anomalies in China<\/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> 2010-2019
Indicative performance:<\/strong> 28.62%
Estimated volatility:<\/strong> 14.3%<\/p>\n\n\n\n

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

Chen, D., Tong, G., Wang, J., & Wu, K.: Nonlinearity in the Cross-Section of Stock Returns: Evidence from China
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3757315
<\/a>Abstracto:
We study which characteristics provide incremental predictive infor- mation for the cross-section of expected returns in the Chinese stock market. Our results provide empirical evidence for strong nonlinear relations between expected returns and selected characteristics, especially in the Trading Friction category. While a four-factor model of Liu, Stambaugh, and Yuan (2019) ex- plains a majority of anomalous characteristics-sorted portfolio returns, we find significant alphas when exploring these characteristics jointly using flexible predictive functions. A long-short spread portfolio based on out-of-sample pre- dicted returns by a nonlinear model delivers higher Sharpe ratio than that by a linear model. We document more supportive evidence for the nonlinear model after exploring potential interaction effect with firm size, state dependency of predictors, and various methods of predictive information aggregation, such as forecast combination, principle component regression, and partial least squares.<\/p>\n\n\n\n

#637 \u2013 Classification of Stocks as Anomaly Longs<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Yearly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Moderately complex strategy
Backtest period:<\/strong> 1997-2018
Indicative performance:<\/strong> 11.68%
Estimated volatility:<\/strong> 21.02%<\/p>\n\n\n\n

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

Betermier, et al.: What Do the Portfolios of Individual Investors Reveal About the Cross-Section of Equity Returns? <\/strong>https:\/\/ssrn.com\/abstract=3795690<\/a>
Abstracto:
We construct a parsimonious set of equity factors by sorting stocks according to the sociodemographic characteristics of the individual investors who own them. The analysis uses administrative data on the stockholdings of Norwegian investors in 1997-2018. Consistent with financial theory, a mature-minus-young factor, a high wealth-minus-low wealth factor, and the market factor price stock returns. Our three factors span size, value, investment, profitability, and momentum, and perform well in out-of-sample bootstrap tests. The tilts of investor portfolios toward the new factors are driven by wealth, indebtedness, macroeconomic exposure, age, gender, education, and investment experience. Our results are consistent with hedging and sentiment jointly driving portfolio decisions and equity premia.<\/p>\n\n\n\n

#638\u2013 Short Selling Activity and Momentum<\/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> 2015-2019
Indicative performance:<\/strong> 15.66%
Estimated volatility:<\/strong> 17.8%<\/p>\n\n\n\n

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

Gargano, Antonio, Short Selling Activity and Future Returns: Evidence from FinTech Data
<\/strong>https:\/\/ssrn.com\/abstract=3775338
<\/a>Abstracto:
We use a novel dataset from a leading FinTech company (S3 Partners) to study the ability of short interest to predict the cross-section of U.S. stock returns. We find that short interest (i.e. the quantity of shares shorted expressed as the fraction of shares outstanding) is a bearish indicator, consistent with theoretical predictions and with the intuition that short sellers are informed traders. The hedged portfolio long (short) in the top (bottom) short-interest decile generates an annual 4-Factor Fama-French alfa of -7.6% when weighting stocks equally and of -6.24% when weighting stocks based on market capitalization. Conditioning on past returns improves the predictive accuracy of short interest: the hedged short-interest portfolio that only uses stocks that appreciated the most in the past six months generates an alfa of -17.88%. Multivariate regressions that control for other known drivers of stock returns (e.g. size, value and liquidity) confirm the validity of these findings. In both Fama-MacBeth and Panel regressions we find that a one standard deviation increase in short interest predicts a drop in future adjusted returns of between 4.3% and 9.3%.<\/p>\n\n\n\n

#639 \u2013 Inventory Mispricing Predicts Oil 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> Simple strategy
Backtest period:<\/strong> 2011-2019
Indicative performance:<\/strong> 6.7%
Estimated volatility:<\/strong> 10%<\/p>\n\n\n\n

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

Alturki, Sultan and Kurov, Alexander, Market Inefficiencies Surrounding Energy Announcements
<\/strong>https:\/\/ssrn.com\/abstract=3747330
<\/a>Abstracto:
We use sequential energy inventory announcements to shed new light on the informational efficiency of financial markets. Our findings provide clear evidence of inefficiency in oil futures and stock markets. This inefficiency can be exploited by sophisticated traders. We examine the effect of market conditions, such as liquidity and oil attention, on the efficient incorporation of information in this setting. We also construct a predictor that can predict inventory surprises and pre-announcement returns in-sample and out-of-sample. Finally, we develop a combination forecast that can be used as a proxy for market expectations of oil inventory announcements.<\/p>\n\n\n\n

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

#383 \u2013 Moving Average Strategies for Cryptocurrencies<\/a><\/strong><\/p>\n\n\n\n

Yen, Kuang-Chieh and Lin, Yu-Li and Nie, Wei-Ying: Does Moving-average Indicators Work Well on the Dynamic of Bitcoin Prices
<\/strong>https:\/\/ssrn.com\/abstract=3836454
<\/a>Abstracto:
This study explores whether the technical analysis based on moving average indicator can predict Bitcoin returns during January 2014 and October 2019. First, we find that Bitcoin weekly returns are well predictable by the technical indicator defined as the difference between the log moving averages and log current price in both in-sample and out-of-sample tests. However, the return predictability is not significant in daily frequency. We further show that the term structure of moving-average indicator provides significantly predictive power to Bitcoin weekly returns, especially for the lower correlated moving-average indicators.<\/p>\n\n\n\n

#542 \u2013 Committee Portfolio Selection<\/a><\/strong><\/p>\n\n\n\n

Caldeira, Jo\u00e3o and A. P. Santos, Andre and Torrent, Hudson: Semiparametric Portfolios: Improving Portfolio Performance by Exploiting Non-Linearities in Firm Characteristics
<\/strong>https:\/\/ssrn.com\/abstract=3830435
<\/a>Abstracto:
We present a semiparametric portfolio optimization method in which portfolio weights are parameterized as a non-linear function of firm characteristics. This approach generalizes the traditional linear parametric portfolio policy of Brandt et al. (2009) and can be applied to high-dimensional problems involving hundreds or thousands of assets at a relatively low computational cost. An empirical implementation exploiting the size, value, and momentum anomalies in the universe of CRSP stocks reveals that non-linearities as well as interaction effects are important for portfolio construction. Moreover, an out-of-sample evaluation indicates that the semiparametric strategies perform well in terms of returns, risk, and risk-adjusted returns both in the absence and in the presence of transaction costs. Our evidence suggests that allowing for a more flexible relation between portfolio weights and firm characteristics can provide a more accurate description of the empirical patterns seen in data.<\/p>\n\n\n\n

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

Varmaz, Armin and Fieberg, Christian and Poddig, Thorsten, Portfolio optimization for sustainable investments
<\/strong>https:\/\/ssrn.com\/abstract=3859616
<\/a>Abstracto:
Investments in firms related to environment, social responsibility and corporate governance (ESG) aspects have recently grown, attracting interest from both academic research and investment fund practice. This paper develops a simple new portfolio optimization approach to include ESG in portfolio formation. In addition to technical and practical advantages over a traditional mean–variance approach that incorporates ESG preferences, our approach allows us to follow competing explanations of the relation among risk, return and ESG. An extension of our portfolio optimization approach can even help distinguish competing explanations from the literature, i.e., between the preferences of investors for ESG firm characteristics and exposure to a common ESG risk factor. The proposed portfolio optimization approach is flexible enough to include additional risk factors and\/or characteristics. We demonstrate the application of our approach to empirical data.<\/p>\n\n\n\n

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

Schmidt, Anatoly B. and Zhang, Xu: Optimal ESG Portfolios: Which ESG Ratings to Use?
<\/strong>https:\/\/ssrn.com\/abstract=3859674
<\/a>Abstracto:
The idea behind the optimal ESG portfolio (OESGP) is to expand the mean variance theory by adding the portfolio ESG value (PESGV) multiplied by the ESG strength parameter \u03b3 (which is investor\u2019s choice) to the minimizing objective function (Pederson et al., 2019; Schmidt, 2020). PESGV is assumed to be the sum of portfolio constituents\u2019 weighted ESG ratings that are offered by several providers. In this work we analyze the sensitivity of the OESGP based on the constituents of the Dow Jones Index to the ESG ratings provided by MSCI, S&P Global, and Sustainalytics. We describe discrepancies among various ESG ratings for the same securities and their effects on the OESGP performance. We found that the OESGP diversity decreases with growing \u03b3. The dependence of the ESG tilted Sharpe ratio on \u03b3 may have two maximums. The 1st maximum exists at moderate values of \u03b3 and yields a moderately diversified OESGP. The 2nd maximum at large \u03b3 corresponds to a highly concentrated OESGP. It appears if portfolio has one or two securities with lucky combinations of high returns and high ESG ratings.<\/p>\n\n\n\n

#614 \u2013 Climate sentiment, carbon prices and Emission minus Clean Portfolio<\/a><\/strong>
#632 \u2013 US Climate Policy News and the Cross-section of Stocks<\/a><\/strong><\/p>\n\n\n\n

Bua, Giovanna and Kapp, Daniel and Ramella, Federico and Rognone, Lavinia, Transition Versus Physical Climate Risk Pricing in Euro Area Financial Markets: A Text-Based Approach
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3860234
<\/a>Abstracto:
This study analyses the pricing of climate risk in equity markets. To this end, we first collect authoritative and scientific texts on the topic of physical and transition risk and build two novel vocabularies. Following, we apply the cosine-similarity approach suggested by Engle et al. 2020 to compare both vocabularies with a corpus of European daily news and construct two novel physical and transition climate risk indices covering the period 2005-2021. Finally, these time series are integrated into an asset pricing model to test the sensitivity of daily equity returns to climate shocks, controlling for several climate exposure metrics. Our results suggest that news on physical risk and transition risk carry relevant information which is reflected in asset prices. Firms with poor environmental and Environmental, Social, and Governance (ESG) performances, as well as firms with high Greenhouse Gas (GHG) emissions underperform when transition risk rises. Analogously, excess returns of firms with low environmental and ESG scores decline in the event of physical risk news. While investors appear to penalise high climate risk exposure, no evidence of outperformance of less exposed firms can be found, suggesting negative screening as a predominant investment strategy.<\/p>\n\n\n\n

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

The Knowledge Graphs for Macroeconomic Analysis with Alternative Big Data<\/a><\/strong><\/p>\n\n\n\n

There are many known relationships among macroeconomic variables in economics, while some of them are even presented as \u201claws\u201d\u2014for example, money supply and inflation or benchmark interest rates and inflation. However, the well-known economic models usually utilize only a small amount of variables. Nowadays, with the advances in machine learning<\/a> and big data fields, these established models might be improved. A possible solution is presented in the research paper of Yang et al. (2020). The authors construct knowledge graphs where they connect widely recognized variables such as GDP, inflation, etc., with other more or less known variables based on the massive textual data from financial journals and research reports published by leading think tanks, consulting firms or asset management companies. With the help of advanced natural language processing, it is possible to basically \u201cread \u201call the relevant published research and find the relationships among the macroeconomic variables. While this task could take years for human readers, the machine learning method can go through these texts in a much shorter time.<\/p>\n\n\n\n

Community Alpha of QuantConnect \u2013 Part 1: Following numerous quantitative strategies<\/a><\/strong><\/p>\n\n\n\n

Quantitative based community is represented by the Quantconnect \u2013 Algorithmic Trading Platform<\/a>, where quants can research, backtest and trade their systematic strategies. Additionally, similar to Seeking Alpha, there is a possibility to follow other quants\/analysts through the open free market \u2013 Alpha Market. To our best knowledge, the literature on community\/social media alpha is scarce, and this paper aims to fill this gap. In the first part, we evaluate the benchmark strategy that consists of all strategies in the alpha market that are equally weighted. Moreover, through multidimensional scaling and clustering analysis, we examine how well can significantly lower amount of strategies track the aforementioned benchmark. This could solve the problem of costly and inconvenient following of every strategy in the market. Overall, this approach can lead to a strategy that follows the benchmark with drastically reduced costs, and these strategies can be even more profitable and less volatile. Stay tuned for the 2nd<\/a>, 3rd<\/a> y 4th<\/a> part of this series, where we will step on the gas and explore factor meta-strategies built on top of the QuantConnect\u2019s Alpha Market.<\/p>\n\n\n\n

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

#236 – Spin-off Anomaly<\/a>
#271 – Earnings Announcements Combined with Stock Repurchases<\/a>
#291 – Options Convexity Predicts Consecutive Stock Returns<\/a>
#624 – Reference Prices and Bad News<\/a>
#625 – Global Imbalance Currency Factor<\/a>
#633 – Liquidity Growth Factor<\/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<\/strong><\/a> 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-13343","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/13343","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=13343"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/13343\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=13343"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=13343"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=13343"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}