{"id":10540,"date":"2021-01-18T10:41:03","date_gmt":"2021-01-18T09:41:03","guid":{"rendered":"https:\/\/quantpedia.com\/?p=10540"},"modified":"2025-06-04T14:01:39","modified_gmt":"2025-06-04T12:01:39","slug":"quantpedia-premium-update-18th-january-2020","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-18th-january-2020\/","title":{"rendered":"Quantpedia Premium Update \u2013 18th January 2021"},"content":{"rendered":"

New strategies:<\/strong><\/h4>\n\n\n\n

#581 \u2013 Equity Index and Risk-free Asset Allocation Using Neural Networks<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Quarterly
Markets traded: <\/strong>equities, bonds
Instruments used for trading:<\/strong> ETFs, futures, bonds
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 1955-2018
Indicative performance:<\/strong> 11.71%
Estimated volatility:<\/strong> 19.34%<\/p>\n\n\n\n

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

Babiak, Mykola and Barunik, Jozef: Deep Learning, Predictability, and Optimal Portfolio Returns<\/strong>
https:\/\/ssrn.com\/abstract=3688577
<\/a>Abstracto:
We study dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. Our results show statistically and economically significant benefits from using deep learning to form optimal portfolios through certainty equivalent returns and Sharpe ratios. Return predictability via deep learning also generates substantially improved portfolio performance across different subsamples, particularly during recessionary periods. These gains are robust to including transaction costs, short-selling and borrowing constraints.<\/p>\n\n\n\n

#582 \u2013 Carbon Risk in the Cross Section of Corporate Bond Returns<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>bonds
Instruments used for trading:<\/strong> bonds
Complexity:<\/strong> Moderately complex strategy
Backtest period:<\/strong> 2006-2019
Indicative performance:<\/strong> 1.81%
Estimated volatility:<\/strong> 1.94%<\/p>\n\n\n\n

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

Tinghua Duan, Frank Weikai Li, Quan Wen: Is Carbon Risk Priced in the Cross Section of Corporate Bond Returns?
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3709572
<\/a>Abstracto:
This paper examines the pricing of a firm’s carbon risk, measured by its carbon emissions intensity, in the cross-section of corporate bond returns. Contrary to the “carbon risk premium” hypothesis, we find bonds of firms with higher carbon emissions intensity earn significantly lower returns. This effect cannot be explained by a comprehensive list of bond characteristics and exposure to known risk factors. Investigating sources of the low carbon premium, we find the underperformance of bonds issued by carbon-intensive firms cannot be fully explained by divestment from institutional investors. Instead, our evidence is most consistent with investor underreaction to carbon risk, as carbon emissions intensity is predictive of lower future cash flow news, deteriorating firm creditworthiness, and more frequent environmental incidents.<\/p>\n\n\n\n

#583 \u2013 Bond Returns Around Italian and German Treasury Auctions<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Intraday
Markets traded: <\/strong>bonds
Instruments used for trading:<\/strong> bonds
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2011-2016
Indicative performance:<\/strong> 2.01%
Estimated volatility:<\/strong> 0.92%<\/p>\n\n\n\n

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

Mario Bellia: Intraday Pricing and Liquidity of Italian and German Treasury Auctions
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3718948
<\/a>Abstracto:
This paper examines how the bond supply influences the price and the liquidity in the secondary market during the primary auction days. The focus is on the intraday behavior of the primary dealers, or market makers, to capture their risk aversion before and after the auction. Using quote data from the Mercato Telematico dei titoli di Stato (MTS), I find evidence of an intraday pronounced inverted V-Shape on the yield difference, which goes up with a maximum at the auction time, and the recovers more than two hours after. This indicates a strong price pressure around the auction time. The analysis of liquidity shows that the bid-ask spread is usually better on the auction days, but rise sharply at the time of the auction. Dealers withdraw their quotes just before the auction and start quoting again from ten to twenty minutes later. A portion of the dealers are very risk-averse and prefers not to expose themselves to the secondary market. The sovereign bond crisis exacerbates the dry-up of liquidity for Italy and the price pressure for Germany. However, the ECB intervention through the Public Sector Purchase Program (PSPP) appears to restore the market makers confidence, especially for Italy.<\/p>\n\n\n\n

#584 \u2013 Return Seasonality and Information Cycle<\/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> 1971-2017
Indicative performance:<\/strong> 12.95%
Estimated volatility:<\/strong> 14.74%<\/p>\n\n\n\n

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

Haoyuan, Liy, Roger K. Lohz : The information cycle aand return seasonability<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3746737
<\/a>Abstracto:
Heston and Sadka (2008) find that cross-sectional stock returns depend on their historical same calendar-month returns. We propose an information-cycle explanation for this seasonality anomaly\u2014that firms’ seasonal information releases lead to higher returns in months with such dissolution of information uncertainty, and lower returns in months with no information releases. Using past earnings announcements and decreases in implied volatility as proxies for scheduled information events, we find indeed that seasonal winners in event months and seasonal losers in non-event months drive the seasonality anomaly. Hence, return seasonality can in fact be consistent with investors’ rational response to information uncertainty.<\/p>\n\n\n\n

#585 \u2013 Trend Factor 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>Complex strategy
Backtest period:<\/strong> 2005-2018
Indicative performance:<\/strong> 15.4%
Estimated volatility:<\/strong> 19.66%<\/p>\n\n\n\n

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

Yang Liu, Guofu Zhou, Yingzi Zhu :Trend Factor in China: The Role of Large Individual Trading
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3402038
<\/a>Abstracto:
We propose a 4-factor model for the Chinese stock market by adding a trend factor into the market, size, and value of Liu, Stambaugh, and Yuan\u2019s (2019) 3-factor model. Because of up to 80% of individual trading, the trend factor captures salient relevant price and volume trends, and earns a monthly Sharpe ratio of 0.48, much greater than that of the market (0.11), size (0.20), and value (0.28). The 4-factor model explains well a number of stylized facts and anomalies of the Chinese stock market. It also explains well mutual fund returns, serving as an analogue of Carhart\u2019s (1997) model in China.<\/p>\n\n\n\n

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

#4 \u2013 Overnight Anomaly<\/a><\/strong><\/p>\n\n\n\n

Knuteson, Bruce, Strikingly Suspicious Overnight and Intraday Returns
<\/strong>https:\/\/ssrn.com\/abstract=3705017
<\/a>Abstracto:
The world’s stock markets display a strikingly suspicious pattern of overnight and intraday returns. Overnight returns to major stock market indices over the past few decades have been wildly positive, while intraday returns have been disturbingly negative. The cause of these astonishingly consistent return patterns is unknown. We highlight the features of these extraordinary patterns that have hindered the construction of any plausible innocuous explanation. We then use those same features to deduce the only plausible explanation so far advanced for these strikingly suspicious returns.<\/p>\n\n\n\n

#510 \u2013 Factor Momentum<\/a><\/strong>
#534 \u2013 Time Series Factor Momentum<\/a><\/strong><\/p>\n\n\n\n

Rutanen, Jere and Grobys, Klaus, Factor Momentum, Investor Sentiment, and Option-Implied Volatility-Scaling
<\/strong>https:\/\/ssrn.com\/abstract=3595147
<\/a>Abstracto:
Factor momentum produces robust average returns that exhibit a similar economic magnitude as documented for stock price momentum. To the extent that the PEAD factor captures mispricing, winner factors profit from being long on underpriced stocks and short on overpriced stocks. Oppositely, loser factors\u2019 negative exposure to the PEAD factor suggests that loser factors capture mispricing by being long on overpriced stocks and short on underpriced stocks. Option-implied volatility scaling increases both the economic magnitude and statistical significance of factor momentum. Factor momentum is not exposed to the same crashes as stock price momentum and could therefore serve as a hedge for stock price momentum crash risks.<\/p>\n\n\n\n

#525 \u2013 Double-Sorting all Possible Strategies<\/a><\/strong>
#534 \u2013<\/strong> Time Series Factor Momentum<\/a><\/strong><\/p>\n\n\n\n

Bouzida, Farah: Is Factors Timing Overrated
<\/strong>https:\/\/ssrn.com\/abstract=3697314
<\/a>Abstracto:
In this paper we investigate two factor rotation approaches, performed directly on the MSCI smart beta indices that are Value, Quality, Momentum, Low Volatility and Size, over US and European Markets. Both approaches use the same indicators built on a macroeconomic signal (PMI), a market sentiment signal based on (VIX, credit spreads), and a momentum signal (time-series, cross-sectional). While the first approach is rule-based and mostly inspired by already known factor rotation frameworks, our work explores those by using our own specifications and it also seeks to check whether a style rotation works at the indices level. Our results show that our framework outperforms a simple equal-weight factor exposure in spite of application of transaction costs. On a stand-alone basis the PMI based rotation fol- lowed by the time-series momentum exhibit the strongest returns. Then we explore if machine learning techniques (tree-based) outperform equal-weight and the rule based strategies particularily after counting for transaction costs.<\/p>\n\n\n\n

#453 \u2013 Machine Learning Adaptive Portfolio Asset Allocation<\/a><\/strong><\/p>\n\n\n\n

Abouseir, Amine and Le Manach, Arthur and El Mennaoui, Mohamed and Zheng, Ban: Integration of Macroeconomic Data into Multi-Asset Allocation with Machine Learning Techniques
<\/strong>https:\/\/ssrn.com\/abstract=3586040 
<\/a>Abstracto:
In this paper, we propose a new way to predict market returns for multi-assets (equity, fixed-income and commodity) by extracting features from macroeconomic data and performing machine learning algorithms for both regression and classification. Our approach aims to select robust models to build alternative risk premia portfolio. We apply machine learning algorithms to our investment universe and then apply different portfolio allocation methods. We discover the importance of integrating macroeconomic data to build portfolio, especially with classification techniques which enhance the Sharpe ratios of strategies.<\/p>\n\n\n\n

#137 \u2013 Trend-following in Futures Markets<\/a><\/strong>
#118 \u2013<\/strong> Time Series Momentum Effect<\/strong><\/a><\/p>\n\n\n\n

Kestner, Lars N., Replicating CTA Positioning: An Improved Method
<\/strong>https:\/\/ssrn.com\/abstract=3674828
<\/a>Abstracto:
Analysis of systematic strategies is a current topic of focus, centering on the impact these strategies have on various financial markets. Risk parity, option overwriting, volatility targeted equity indices, and trend following strategies receive the majority of this attention. In this paper, we focus on the dynamic trading of trend following strategies and detail an improved method for estimating their actions across markets. A simple replication model employed on 16 futures markets explains over 75% of the variation in a trend following benchmark. This replication model is able to estimate trend follower positions without lag. Using estimates of total funds allocated to trend following managers, we can use our replication model to estimate positions by specific market and the expected trading flows when individual markets move.<\/p>\n\n\n\n

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

Fiscal Stimulus Matters to Market<\/a><\/strong><\/p>\n\n\n\n

Fiscal stimulus measures have become a hot topic in the financial markets. However, that is not surprising, since fiscal stimulus is a crucial government method to ease the pandemic crisis’s impacts<\/a>. Therefore, the investors and market are very sensitive to this topic, and they react to the fiscal stimulus and any related news very sharply. While it is intuitive that the withdraw of the stimulus measures will negatively affect the markets and markets will fall, the magnitude of these falls is unknown. Novel research by Chan-Lau and Zhao (2020), quantifies the impacts of withdrawals and it’s effects on the stock markets worldwide. The reactions are especially negative if the fiscal stimulus is withdrawn “too soon”. According to the authors, too soon is when the number of daily COVID cases is high compared to the recent past. <\/p>\n\n\n\n


Crypto Covered Interest Parity Deviations<\/a><\/strong><\/p>\n\n\n\n

Bitcoin<\/a> and other currencies are frequently discussed nowadays<\/a>. The debate has emerged mainly because of the strong uptrend in the Bitcoin price. In this blog post, we will leave the price patters to others. We will instead present interesting novel research connected to the well known theoretical model in the fiat currencies – the Covered Interest Rate Parity (CIP). If the CIP holds, interest rates and both the spot and forward rates of two countries should be in equilibrium. Novel research of Franz and Valentin (2020) examines the CIP in BTC\/USD pair. The CIP theory states that there should be no arbitrage opportunities, but how the CIP holds in such a volatile market, where individual investors\/traders seem to dominate? According to research, there were significant CIP deviations in the past, but it changed with the launch of BTC\/USD futures in CME and high-frequency traders’ market entry. Moreover, the second event was much more successful in the reduction of deviations. <\/p>\n\n\n\n

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

#62 – Shorting Overvalued Stocks<\/a>
#108 – Soccer Clubs\u2019 Stocks Arbitrage<\/a>
#226 – Insiders’ Silence<\/a>
#566 – Multi Asset Pairs Momentum<\/a>
#572 – The Low Volatility Anomaly in Equity Sectors<\/a>
#577 – Surprise in Short Interest<\/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

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-10540","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/10540","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=10540"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/10540\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=10540"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=10540"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=10540"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}