{"id":6645,"date":"2020-03-19T16:50:17","date_gmt":"2020-03-19T15:50:17","guid":{"rendered":"https:\/\/quantpedia.com\/?p=6645"},"modified":"2025-06-04T14:04:11","modified_gmt":"2025-06-04T12:04:11","slug":"quantpedia-premium-update-19th-march-2020","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-19th-march-2020\/","title":{"rendered":"Quantpedia Premium Update \u2013 19th March 2020"},"content":{"rendered":"

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

#476 – Speculator Spreading Pressure and the Commodity Futures Risk Premium<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>commodities
Instruments used for trading:<\/strong> futures, CFDs
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 2005-2018
Indicative performance:<\/strong> 21.19%
Estimated volatility:<\/strong> 24.42%<\/p>\n\n\n\n

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

Yujing Gong: Speculator Spreading Pressure and the Commodity Futures Risk Premium<\/strong>
https:\/\/editorialexpress.com\/cgi-bin\/conference\/download.cgi?db_name=AFAPS2020&paper_id=302<\/a>
Abstracto:
This paper investigates the impact of speculators\u2019 trading activities on the commodity futures risk premium. In particular, we focus on speculators\u2019 spread positions, and study the asset pricing implications of spreading pressure on the cross-section of commodity futures returns. We document that spreading pressure negatively predicts futures excess returns even after controlling for well-known determinants of futures returns such as basis-momentum. Furthermore, the spreading pressure factor-mimicking portfolio carries a significant risk premium of 21.55% per annum after commodity market financialization. Our single-factor model provides a better cross-sectional fit than the existing 2-factor or 3-factor models in the literature. We interpret these results as spreading pressure reflecting speculators\u2019 expectation on the change in the slope and curvature of futures term structures and our spreading pressure factor linking to innovations in real economic uncertainty.<\/p>\n\n\n\n

#47<\/strong>7 – Generalised Risk-Adjusted Momentum in Commodities<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>commodities
Instruments used for trading:<\/strong> futures, CFDs
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1984-2018
Indicative performance:<\/strong> 10.10%
Estimated volatility:<\/strong> 17.40%<\/p>\n\n\n\n

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

Fan, Minyou and Kearney, Fearghal Joseph and Li, Youwei and Liu, Jiadong: Momentum and the Cross-Section of Stock Volatility<\/strong>
https:\/\/ssrn.com\/abstract=3541766 <\/a>
Abstracto:
Recent literature shows that momentum strategies exhibit significant downside risks over certain periods, or called “momentum crashes.” We find that the high uncertainty of momentum strategies is sourced from the cross-sectional volatility of individual stocks. Stocks with high realised volatility over the formation period tend to lose momentum effect, while stocks with low realised volatility show strong momentum. A new approach, generalised risk-adjusted momentum (GRJMOM), is introduced to mitigate the negative impact of high momentum risks. GRJMOM is proven to be more profitable and less risky than the existing momentum ranking approaches in multiple asset classes, including the UK stock, commodity, global equity index, and fixed income markets. <\/p>\n\n\n\n

#478 – Generalised Risk-Adjusted Momentum in Equity Indexes<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> ETFs, futures
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1970-2018
Indicative performance:<\/strong> 12.70%
Estimated volatility:<\/strong> 19.90%<\/p>\n\n\n\n

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

Fan, Minyou and Kearney, Fearghal Joseph and Li, Youwei and Liu, Jiadong: Momentum and the Cross-Section of Stock Volatility<\/strong>
https:\/\/ssrn.com\/abstract=3541766 <\/a>
Abstracto:
Recent literature shows that momentum strategies exhibit significant downside risks over certain periods, or called “momentum crashes.” We find that the high uncertainty of momentum strategies is sourced from the cross-sectional volatility of individual stocks. Stocks with high realised volatility over the formation period tend to lose momentum effect, while stocks with low realised volatility show strong momentum. A new approach, generalised risk-adjusted momentum (GRJMOM), is introduced to mitigate the negative impact of high momentum risks. GRJMOM is proven to be more profitable and less risky than the existing momentum ranking approaches in multiple asset classes, including the UK stock, commodity, global equity index, and fixed income markets. <\/p>\n\n\n\n

#479 <\/strong>– Generalised Risk-Adjusted Momentum in Stocks<\/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> 1965-2018
Indicative performance:<\/strong> 30.30%
Estimated volatility:<\/strong> 23.00%<\/p>\n\n\n\n

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

Fan, Minyou and Kearney, Fearghal Joseph and Li, Youwei and Liu, Jiadong: Momentum and the Cross-Section of Stock Volatility<\/strong>
https:\/\/ssrn.com\/abstract=3541766 <\/a>
Abstracto:
Recent literature shows that momentum strategies exhibit significant downside risks over certain periods, or called “momentum crashes.” We find that the high uncertainty of momentum strategies is sourced from the cross-sectional volatility of individual stocks. Stocks with high realised volatility over the formation period tend to lose momentum effect, while stocks with low realised volatility show strong momentum. A new approach, generalised risk-adjusted momentum (GRJMOM), is introduced to mitigate the negative impact of high momentum risks. GRJMOM is proven to be more profitable and less risky than the existing momentum ranking approaches in multiple asset classes, including the UK stock, commodity, global equity index, and fixed income markets. <\/p>\n\n\n\n

#480 – Machine Learning-Based Financial Statement Analysis<\/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> 1991-2017
Indicative performance:<\/strong> 47.47%
Estimated volatility:<\/strong> 18.00%<\/p>\n\n\n\n

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

Amel-Zadeh, Amir and Calliess, Jan-Peter and Kaiser, Daniel and Roberts, Stephen: Machine Learning-Based Financial Statement Analysis<\/strong>
https:\/\/ssrn.com\/abstract=3520684<\/a>
Abstracto:
This paper explores the application of machine learning methods to financial statement analysis. We investigate whether a range of models in the machine learning repertoire are capable of forecasting the sign and magnitude of abnormal stock returns around earnings announcements based on financial statement data alone. We find random forests and recurrent neural networks to outperform deep neural networks and linear models such as OLS and Lasso. Using the models’ predictions in an investment strategy we find that random forests dominate all other models and that non-linear methods perform relatively better for predictions of extreme market reactions, while the linear methods are relatively better in predicting moderate market reactions. Analysing the underlying economic drivers of the performance of the random forests, we find that the models select as most important predictors accounting variables commonly used to forecast free cash flows and firm characteristics that are known cross-sectional predictors of stock returns. <\/p>\n\n\n\n

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

#454 – Time Series Momentum Strategies Using Deep Neural Networks<\/a><\/strong><\/p>\n\n\n\n

Zhang, Zohren, Roberts: Deep Reinforcement Learning for Trading
\nhttps:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3519858
\nAbstracto:
\nWe adopt Deep Reinforcement Learning algorithms to design trading strategies for continuous futures contracts. Both discrete and continuous action spaces are considered and volatility scaling is incorporated to create reward functions which scale trade positions based on market volatility. We test our algorithms on the 50 most liquid futures contracts from 2011 to 2019, and investigate how performance varies across different asset classes including commodities, equity indices, fixed income and FX markets. We compare our algorithms against classical time series momentum strategies, and show that our method outperforms such baseline models, delivering positive profits despite heavy transaction costs. The experiments show that the proposed algorithms can follow large market trends without changing positions and can also scale down, or hold, through consolidation periods.<\/p>\n\n\n\n

#117 – Lottery Effect in Stocks<\/a><\/strong>
#268 – Expected Skewness and Momentum in Stocks<\/a><\/strong>
#390 – Lottery Stocks and the 52-Week High <\/a><\/strong><\/p>\n\n\n\n

Chung: Retail Trading and Momentum Profitability<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3486843<\/a>
Abstracto:
Monthly momentum returns increase monotonically across quintile portfolios of stocks sorted by retail trading participation with a top-minus-bottom spread of 1.42% (t-statistics = 3.46). Stocks that are heavily traded by retail investors exhibit lottery-like features such as low prices, high idiosyncratic volatilities\/skewness, and high past maximum returns. Using lottery characteristics to proxy for the extent of retail trading, future momentum profits monotonically increase in the cross-sectional lotteryness of stocks over a 77-year back-testing period for which retail trading data is unavailable. Further analysis shows that lottery-like stocks exhibit stronger comovements that amplify momentum profits. <\/p>\n\n\n\n

#460 \u2013 ESG Factor Investing Strategy<\/a>
#461 – ESG Momentum<\/a><\/strong><\/p>\n\n\n\n

MELAS, NAGY, NISHIKAWA, LEE, GIESE: Foundations of ESG Investing \u2013 Part 1: How ESG Affects Equity Valuation, Risk and Performance<\/strong>
https:\/\/www.msci.com\/www\/research-paper\/foundations-of-esg-investing\/0795306949<\/a>
Abstracto:
Many studies have focused on the relationship between companies with strong ESG characteristics and corporate financial performance. However, these have often struggled to show that positive correlations \u2014 when produced \u2014 can in fact explain the behavior. This paper provides a link between ESG information and the valuation and performance of companies, both through their systematic risk profile (lower costs of capital and higher valuations) and their idiosyncratic risk profile (higher profitability and lower exposures to tail risk). The research suggests that changes in a company\u2019s ESG characteristics may be a useful financial indicator. ESG ratings may also be suitable for integration into policy benchmarks and financial analyses.<\/p>\n\n\n\n

And four interesting free blog post has been published during last 2 weeks:<\/strong><\/p>\n\n\n\n

A Comparison of Global Factor Models<\/a><\/strong><\/p>\n\n\n\n

Mirror, mirror on the wall, what\u2019s the best factor model of them all? We at Quantpedia are probably not the only one asking this question. A lot of competing factor models are described in the academic literature and used in practice. That\u2019s the reason why we consider a new research paper written by Matthias Hanauer really valuable. He compared several commonly employed factor models across non-U.S. developed and emerging market countries and answered the question from the beginning of this paragraph. Which model seems the winner? The six-factor model proposed in Barillas et al. (2019) that substitutes the classic value factor in the Fama and French (2018) six-factor model for a monthly updated value factor \u2026<\/strong><\/strong> <\/p>\n\n\n\n

Autores:<\/strong> Hanauer<\/p>\n\n\n\n

T\u00edtulo:<\/strong> A Comparison of Global Factor Models <\/p>\n\n\n\n

Rational Panic on Markets Because of Coronavirus?<\/a><\/a><\/strong><\/p>\n\n\n\n

Financial markets are in panic mode. \nEverybody is talking about the next bear market and economic \nimplications of spreading coronavirus to the whole world. People are \nsplit into two groups. One group reasons that a new covid-19 virus is \njust a stronger flu. Other are worried and draw parallels to Spanish flu\n pandemic with tens of millions of dead.\n\nWe would like to show you two charts which can explain why the high market volatility can be completely rational.<\/strong><\/strong><\/p>\n\n\n\n

Author:<\/strong> Vojtko<\/p>\n\n\n\n

Bitcoin in a Time of Financial Crisis<\/a><\/a><\/strong><\/p>\n\n\n\n

One of the very often promoted attributes of Bitcoin \nis said to be its \u201csafe heaven\u201d characteristic. Some cryptocurrency \nproponents advocate that Bitcoin can be used as a store of value mainly \nduring the economic and financial crisis. We argue that it\u2019s not so.<\/strong>\nBitcoin (and all cryptocurrencies too) is, in our opinion, \nfundamentally more similar to stocks of small companies from the \ntechnological sector. It is a very speculative bet on blockchain \ntechnology. It may seem unrelated to the broader equity market (like the\n S&P 500 index) during normal times. But when a stressful time \ncomes, investors are more concerned to meet a deadline for the next \nmortgage payment. This is the time when the speculative bets are closed,\n and cash is raised. And this is precisely the time when Bitcoin falls \nas equities do too.<\/strong><\/p>\n\n\n\n

Autores:<\/strong> Vojtko, Cisar<\/p>\n\n\n\n

Modelling the Bottom of the Covid-19 Financial Crisis<\/strong><\/a><\/p>\n\n\n\n

The global pandemic of current \nscope is something that was experienced by only a few living people. We \nhave some historical accounts of how it unfolded in the past, but \notherwise, it is uncharted territory. It is a true Black Swan event \u2013 \nevent that I believe was in nobody\u2019s lineup of stress testing scenarios.\n But we can still try to get some understanding of the scope of the \ncurrent situation.<\/strong>\nThe actual global crisis is a mix of 2 crisis. The first one is the \nhealth-care \/ pandemic crisis, during which millions of people will be \ninfected, and unfortunately, a lot of people will die. The second crisis\n is the economic crisis\/recession, which will follow simultaneously with\n (or soon after) the first one (due to the decrease in worldwide supply \nand demand).\nThe second crisis cannot end before the first one is solved. We \ncannot exactly say when the market bottom will occur, but at least we \ncan try to model the minimum time needed for things to get under control\n during the pandemic.\n<\/strong><\/p>\n\n\n\n

Author:<\/strong> Vojtko<\/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":"

<\/p>\n

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

Three new related research papers have been included into existing strategy reviews. And four short free blog posts<\/strong><\/a> have been published during last few weeks.<\/p>\n

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