{"id":14514,"date":"2021-08-17T23:39:57","date_gmt":"2021-08-17T21:39:57","guid":{"rendered":"https:\/\/quantpedia.com\/?p=14514"},"modified":"2025-06-04T14:00:14","modified_gmt":"2025-06-04T12:00:14","slug":"quantpedia-premium-update-17th-august-2021","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-17th-august-2021\/","title":{"rendered":"Quantpedia Premium Update \u2013 17th August 2021"},"content":{"rendered":"

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

#651 \u2013 Predicting Informed Trading with Machine Learning<\/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> 1993-2019
Indicative performance:<\/strong> 4.65%
Estimated volatility:<\/strong> 5.96%<\/p>\n\n\n\n

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

Bogousslavsky, V., Fos, V., & Murayev Dmitriy: Informed Trading Intensity
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3865990
<\/a>Abstracto:
We train a state-of-the-art machine-learning method (ML) on a class of informed trades to develop a new measure of informed trading, the Informed Trading Intensity (\u2018ITI\u2019). Though the measure is trained on a particular class of informed trades, it predicts various informational events, including stock price reactions to earnings surprises, M&A announcements, and unscheduled news releases. The measure also increases on days with opportunistic insider trades and large changes in short interest. Returns on days with high ITI reverse less than returns on other days. In the cross-section, higher ITI is associated with higher returns next month. Our main insight is that learning from data on informed trades can generate an effective measure of informed trading.<\/p>\n\n\n\n

#652 \u2013 Machine Learning Stock Analyst<\/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> 2001-2016
Indicative performance:<\/strong> 10.92%
Estimated volatility:<\/strong> 14.38%<\/p>\n\n\n\n

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

Cao, S. S., Jiang, W., Wang, J. L., & Yang, B.: From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3840538
<\/a>Abstracto:
An AI analyst we build to digest corporate financial information, qualitative disclosure, and macroeconomic indicators is able to beat the majority of human analysts in stock price forecasts and generate excess returns compared to following human analysts. In the contest of \u201cman vs machine,\u201d the relative advantage of the AI Analyst is stronger when the firm is complex, and when information is high-dimensional, transparent and voluminous. Human analysts remain competitive when critical information requires institutional knowledge (such as the nature of intangible assets). The edge of the AI over human analysts declines over time when analysts gain access to alternative data and to in-house AI resources. Combining AI\u2019s computational power and the human art of understanding soft information produces the highest potential in generating accurate forecasts. Our paper portraits a future of \u201cmachine plus human\u201d (instead of human displacement) in high-skill professions.<\/p>\n\n\n\n

#653 \u2013 Similar Stock Short-term 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> Complex strategy
Backtest period:<\/strong> 1963-2019
Indicative performance:<\/strong> 13.08%
Estimated volatility:<\/strong> 18.86%<\/p>\n\n\n\n

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

Wei He, Yuehan Wang, Jianfeng Yu: Similar Stocks
<\/strong>https:\/\/ssrn.com\/abstract=3815595
<\/a>Abstracto:
Similarity between two stocks is measured by the distance between their characteristics such as price, size, book-to-market, return on assets, and investmentto-assets. We find that after a stock\u2019s most similar stocks have experienced high (low) returns in the past month, this focal stock tends to earn an abnormally high (low) return in the current month. The long-short portfolio strategy sorted on similar-stocks\u2019 past average return earns a monthly CAPM alpha of 1.25% and a Fama-French six-factor alpha of 0.85%. This similarity effect is robust after controlling for style investing and a wide range of well-known firm-level characteristics that can predict returns in the cross section. Our result is consistent with the increased propensity for investors to buy other stocks with similar characteristics after experiencing positive returns for a currently held stock. We also explore other potential explanations for our findings.<\/p>\n\n\n\n

#654 \u2013 Momentum without the Crash Component<\/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> 1927-2018
Indicative performance:<\/strong> 15.12%
Estimated volatility:<\/strong> 19.99%<\/p>\n\n\n\n

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

B\u00fcsing, Pascal and Mohrschladt, Hannes and Siedhoff, Susanne, Decomposing Momentum: Eliminating its Crash Component
<\/strong>https:\/\/ssrn.com\/abstract=3887512
<\/a>Abstracto:
We propose a purely cross-sectional momentum strategy that avoids crash risk and does not depend on the state of the market. To do so, we simply split up the standard momentum return over months t \u2212 12 to t \u2212 2 at the highest stock price within this formation period. Both resulting momentum return components predict subsequent returns on a stand-alone basis. However, the long-short returns associated with the first component completely avoid negative skewness since momentum crashes are entirely driven by the second component.<\/p>\n\n\n\n

#655\u2013 Price Pressure During Top Dividend Days<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> ETFs, futures, stocks
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 1926-2018
Indicative performance:<\/strong> 2.43%
Estimated volatility:<\/strong> 1.3%<\/p>\n\n\n\n

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

Hartzmark, Samuel M. and Solomon, David H.: Predictable Price Pressure
<\/strong>https:\/\/ssrn.com\/abstract=3853096
<\/a>Abstracto:
We present evidence that stock returns, both at the market level and the individual stock level, can be predicted by the timing of uninformed inflows and outflows of cash that are known in advance. Aggregate dividend payments to investors predict higher value-weighted market returns on the day of payment and the day afterwards, by 13 b.p. for the top five days per year, and 5 b.p. for the top fifty days. This effect holds in the US and internationally. Effects are weaker in months when mutual funds pay out dividends to investors (and so are less likely to reinvest). Industries with greater past exposure to dividend price pressure significantly underperform those with less exposure, consistent with an eventual partial reversal. Predictable selling pressure leads to significantly lower returns after earnings announcements for firms with higher stock compensation. Back of the envelope calculations suggest price multipliers of each dollar invested in the aggregate market ranging from 1.5 to 2.3. These results suggest that predictable price pressure is a widespread result of money flows, rather than an anomaly.<\/p>\n\n\n\n

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

#294 \u2013 Seasonality Within Trend-Following Strategy in Commodities<\/a><\/strong><\/p>\n\n\n\n

Ewald, Christian-Oliver and Haugom, Erik and Lien, Gudbrand and St\u00f8rdal, St\u00e5le and Wu, Yuexiang: Trading Time Seasonality in Commodity Futures: An Opportunity for Arbitrage in the Natural Gas and Crude Oil Markets?
<\/strong>https:\/\/ssrn.com\/abstract=3792028
<\/a>Abstracto:
For fixed maturity, under the no-arbitrage assumption, futures prices should follow a martingale with respect to the trading time, at least under the pricing measure. Therefore, a prominent display of trading time seasonality under the physical measure raises warning signs and can only occur by means of strong seasonality in the pricing kernel. We show that for natural gas and crude oil, trading time seasonality is present to an extent where it may violate the no-arbitrage assumption. We provide three layers of evidence. The first layer is descriptive only, the second involves the Kruskal–Wicksell test for establishing trading time seasonality, and the third is in the form of a trading strategy, which exploits trading date seasonality. This strategy can produce statistically significant positive alphas in the CAPM context, thereby indicating the possibility of an arbitrage.<\/p>\n\n\n\n

#442 \u2013 Intraday Momentum in Crude Oil ETF<\/a><\/strong><\/p>\n\n\n\n

Indriawan, Ivan and Lien, Donald and Wen, Zhuzhu and Xu, Yahua: Intraday Return Predictability in the Crude Oil Market: The Role of EIA Inventory Announcements
<\/strong>https:\/\/ssrn.com\/abstract=3822093
<\/a>Abstracto:
We study the impact of the announcements released by the US Energy Information Administration (EIA) crude oil storage every Wednesday at 10:30 ET (the beginning of the third half-hour interval) on intraday return predictability, that is, intraday momentum. Our results indicate that returns on both the first half-hour and third half-hour on EIA announcement days can significantly and positively predict the returns in the last half-hour, whereas, on non-EIA announcement days, only returns in the first half-hour have significant predictability. The dominant source of prediction in the first half-hour return also differs between EIA announcement and non-announcement groups, the market open and overnight component, respectively. EIA announcements contribute to intraday momentum because they attract more informed traders and because the period surrounding their release is often associated with a reduction in liquidity. Substantial economic gains can be made by using efficient intraday predictors as trading signals.<\/p>\n\n\n\n

#117 \u2013 Lottery Effect in Stocks<\/a><\/strong><\/p>\n\n\n\n

Khasawneh, Maher and McMillan, David G. and Kambouroudis, Dimos S: Lottery Stocks in the UK: Evidence, Characteristics and Cause
<\/strong>https:\/\/ssrn.com\/abstract=3835209
<\/a>Abstracto:
Research across international markets identifies lottery-like stocks that contradict the standard positive risk-return trade-off paradigm. This paper, consistent with those results, reports under-performance for lottery-like stocks in the UK market. Moreover, while the under-performance appears stronger in crisis periods, it persists across all periods even when controlling for other return predictors such as size, momentum and downsize risk. However, the cause of the under-performance remains a source of debate. Our results show that a left-tail measure subsumes the UK lottery effect. This suggests under-reaction and continuation behaviour to bad news and is combined with limits to arbitrage. In addition, our findings indicate that poor lottery stock performance is partially explained by the anchoring effect and is more prevalent with greater optimism and sentiment. This contrasts with US results where reversion behaviour is reported for lottery-like stocks and supports the need for market specific research.<\/p>\n\n\n\n

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

Gueant, Olivier and Peladan, Jean-Guillaume and Robert-Dautun, Alain and Tankov, Peter: Environmental transition alignment and portfolio performance
<\/strong>https:\/\/ssrn.com\/abstract=3876731
<\/a>Abstracto:
We contribute to the debate on whether using ESG\/SRI criteria in investment decisions improves portfolio performance. The choice of a specific ESG metric being crucial, we focus on the Net Environmental Contribution, a robust open-source measure of environmental transition alignment. From a universe of 752 European stocks, we select subsets of stocks with high and low NEC scores, and compare the performance of equal-weighted and capitalization-weighted portfolios constructed from these subsets over the 2015-2020 period. The high-NEC portfolios outperform the low-NEC ones consistently throughout the period, and particularly during 18 months starting mid-2019, both before and during the COVID crisis.<\/p>\n\n\n\n

#522 \u2013 ESG, Price Momentum and Stochastic Optimization<\/a><\/strong><\/p>\n\n\n\n

Coqueret, Guillaume and Stiernegrip, Sascha and Morgenstern, Christian and Kelly, James and Frey-Sk\u00f6tt, Johannes and \u00d6sterberg, Bj\u00f6rn: Boosting ESG-Based Optimization With Asset Pricing Characteristics
<\/strong>https:\/\/ssrn.com\/abstract=3877242
<\/a>Abstracto:
This article investigates the usefulness of combining traditional factors with ESG data when building optimal equity portfolios. Our contribution departs from the traditional literature by focusing on allocations designed to adjust benchmark policies. We allow compositions to be embedded in a general factor framework in which firm characteristics are the main drivers of the portfolio weights. In line with much of the literature, our results suggest that it is feasible to improve the ESG score of a portfolio without it being detrimental to its out-of-sample performance. However, pure sustainable attributes alone do not allow to fulfil this objective: they need to be boosted by non-ESG predictors to deliver their full potential.<\/p>\n\n\n\n

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

How Olympic Games Impact Stocks?<\/a><\/strong><\/p>\n\n\n\n

Summer Olympics are a major event that attracts attention<\/a> from the moment the host country is announced. However, that\u2019s not shocking. The Olympics require a lot of planning, infrastructure building and investments. Still, countries battle for the opportunity to host these events. Undoubtedly, hosting the Olympics is prestigious, helps tourism, and many even argue that it also helps the domestic economy despite the costs of hosting. Therefore, it is natural to expect that the Tokyo Olympics should impact the domestic stock market.<\/p>\n\n\n\n

<\/a>Analysis of Systematic Crypto Trading Strategies in 2021<\/a><\/strong><\/p>\n\n\n\n

We started to systematically search for systematic\u00a0cryptocurrency trading strategies<\/a><\/a>\u00a0in academic research approximately two years ago. This article is a short analysis of the performance of systematic crypto strategies<\/a> in 2021. We conclude that non-price predictors offered advantages over price-based only trading strategies in the previous year-to-date and 12-months periods.<\/p>\n\n\n\n

<\/a>New Machine Learning Model for CEOs Facial Expressions<\/a><\/strong><\/p>\n\n\n\n

Nowadays, it is a standard that fillings such as 10-Ks and 10-Qs<\/a> are analyzed with machine learning models<\/a>. ML models can extract sentiment, similarity<\/a> metrics and many more. However, words are not everything, and we humans also communicate in other forms. For example, we show our emotions through facial expressions, but the research on this topic in finance is scarce. Novel research by Banker et al. (2021) fills the gap and examines the CEOs facial expressions during CNBC\u2019s video interviews about corporate earnings.<\/p>\n\n\n\n

An Important Analysis of Stock Momentum and Reversal Factors<\/strong><\/a><\/p>\n\n\n\n

Can we explain stock momentum<\/a> by industry, sector or factor momentum? Moreover, a similar question could be raised about the short-term reversal. The novel research by Li and Turkington (2021) uses a robust regression model to divide momentum<\/a> y reversal<\/a> returns into the main drivers. The individual momentum anomaly that broader market groups do not fully explain exists in the whole sample but is statistically weak. On the other hand, the reversal anomaly is highly significant. Secondly, the traditional 12-months momentum can be better explained by the factor momentum<\/a> than the industry<\/a> o sector momentum<\/a>. Still, the industries, industry groups, sectors, and even factors have distinct drivers, and the anomalies seem different.<\/p>\n\n\n\n

Community Alpha of QuantConnect \u2013 Part 2: Social Trading Factor Strategies<\/strong><\/a><\/p>\n\n\n\n

This blog post is the continuation of series about Quantconnect\u2019s Alpha market strategies.\u00a0\u00a0Part 1 can be found here.<\/a>\u00a0This part is related to the\u00a0factor strategies<\/a>\u00a0notoriously known from the majority of asset classes.
Overall, the factors on alpha strategies provide insightful results that could be utilized. The results particularly point to excluding the most extreme strategies based on various past distribution\u2019s characteristics.
Stay tuned for the 3rd<\/a> y 4th<\/a> part of this series, where we will explore factor meta-strategies built on top of the QuantConnect\u2019s Alpha Market<\/a>.<\/p>\n\n\n\n

The Best Systematic Trading Strategies in 2021: Part 1<\/strong><\/a><\/p>\n\n\n\n

As of the first half of August, the year 2021 seems to be a phenomenal year for equities. World equities have earned more than +16%, and US equities, even more, topping +20% gains. Is there even any better strategy this year than just holding US equities? Well, yes, there are actually several of them. Are they all tied to US equities? Many of them are, but many of them are not. Some of them are not even tied to equities at all.
Note: This blog is Part 1 of a series. Part 2<\/a> y Part 3<\/a> are now available.<\/em><\/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

#401 – Carry On \u2013 Enhanced Carry Strategy<\/a>
#627 – Hedging Portfolio<\/a>
#644 – Cross-asset Time-series Momentum (Equities and Crude Oil)<\/a>
#648 – Mean Absolute Daily Return in Cryptos<\/a>
#649 – Scaled Volume in Cryptos<\/a>
#650 – Volatility Effect in Cryptos<\/a><\/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":"

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

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

Five new related research papers have been included into existing strategy reviews and seven 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>\n

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