{"id":8369,"date":"2020-10-01T16:58:54","date_gmt":"2020-10-01T14:58:54","guid":{"rendered":"https:\/\/quantpedia.com\/?p=8369"},"modified":"2025-06-04T14:00:11","modified_gmt":"2025-06-04T12:00:11","slug":"quantpedia-premium-update-30th-september-2020","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-30th-september-2020\/","title":{"rendered":"Quantpedia Premium Update \u2013 30th September 2020"},"content":{"rendered":"

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

#539 \u2013 Historical and Implied Volatility in FX Options<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>currencies
Instruments used for trading:<\/strong> options
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 1999-2011
Indicative performance:<\/strong> 19.08%
Estimated volatility:<\/strong> 24.14%<\/p>\n\n\n\n

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

Lamya Kermiche and Philippe Dupuy: Understanding Foreign Exchange Option Returns: The Information Content Of Volatility<\/strong>
https:\/\/clutejournals.com\/index.php\/JABR\/article\/download\/9587\/9682\/
<\/a>Abstracto:
According to general asset pricing theory, options should reward their holders for the systematic risk they are bearing. In this paper, we study the returns of foreign exchange options. We find that, by sorting options according to the distance of their implied volatility from the historical volatility, we obtain portfolios with positive average monthly returns. These returns are not explained by standard aggregate risk factors, which suggest either that additional risk factors should be accounted for, or that investors behavior differs from the traditional paradigm of rational agents.<\/p>\n\n\n\n

#540 \u2013 Semivariance and Time Series Momentum in Futures<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded: <\/strong>commodities
Instruments used for trading:<\/strong> futures
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 2007-2018
Indicative performance:<\/strong> 19.11%
Estimated volatility:<\/strong> 10.8%<\/p>\n\n\n\n

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

Liu, Zhenya and Lu, Shanglin and Li, Bo and Wang, Shixuan: Time Series Momentum and Reversal: Intraday Information from Realized Semivariance<\/strong>
https:\/\/ssrn.com\/abstract=3584014
<\/a>Abstracto:
The presence of time series momentum effect has been widely documented in the financial markets across asset classes and countries. We find a predictable pattern of the realized semi-variance to the future individual asset return, especially during the stressed states of time series momentum reversals. A rule-based decision function designed upon these insights aims to capture the life-cycle of the time series momentum. Its application on the Chinese commodity futures markets documents higher Sharpe ratio and Sortino ratio compared to the original one over different looking back windows.<\/p>\n\n\n\n

#541 \u2013 News and Non-news Returns in Stocks<\/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> 2000-2012
Indicative performance:<\/strong> 20.98%
Estimated volatility:<\/strong> 16.18%<\/p>\n\n\n\n

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

Hao Jiang, Sophia Zhengzi Li, Hao Wang: Pervasive underreaction: Evidence from high-frequency data<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2679614
<\/a>We propose a novel high-frequency decomposition of daily stock returns into news- and non-news-driven components, and uncover evidence of pervasive stock market underreaction to firm news. Prices tend to drift in the same direction as the initial market response for several days after the news arrival without reversals. A trading strategy exploiting the return drift generates high abnormal returns and remains profitable after transaction costs. To understand the economic mechanism, we find that the return drift is stronger when investors are distracted. Analysts’ slow adjustments of market expectations following firm news also contribute to the market underreaction.<\/p>\n\n\n\n

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

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities, REITs, bonds
Instruments used for trading:<\/strong> ETFs
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 2002-2019
Indicative performance:<\/strong> 19.6%
Estimated volatility:<\/strong> 8.45%<\/p>\n\n\n\n

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

Tsung-wu Ho: Portfolio Selection \/using Portfolio Comitees
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3653595
<\/a>Abstracto:
Instead of data-mining methods, the author proposes a portfolio committee approach to portfolio selection. Because each optimal portfolio is a combination of three basic elements: strategy, covariance matrix, and risk type; therefore, the author first augments the combination to 250 optimal portfolios at each estimation period, and then the author defines a score to select the best portfolio to hold in the next period. Survival of the fittest, the superior performance of the combination portfolio justifies the committee approach to portfolio selection is not only effective, but also easy to implement.<\/p>\n\n\n\n

#543 \u2013 Dividend Stocks and Rising or Falling Interest Rates<\/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> 1963-2016
Indicative performance:<\/strong> 7.34%
Estimated volatility:<\/strong> 22.23%<\/p>\n\n\n\n

Kent Daniel, Lorenzo Garlappi, Kairong Xiao: Monetary Policy and Reaching for Income<\/strong>
http:\/\/www.kentdaniel.net\/papers\/published\/DGX_paper.pdf
<\/a>Abstracto:
Using data on individual portfolio holdings and on mutual fund flows, we find that low interest rates lead to a significantly higher demand for income-generating assets such as high-dividend stocks and high-yield bonds. We argue that this \u201creaching for income\u201d phenomenon is driven by investors who follow the rule-of-thumb of \u201cliving off income.\u201d Our empirical analysis shows that this preference for current income affects both household portfolio choices and the prices of income-generating assets. In addition, we explore the implications of reaching for income for capital allocation and the effectiveness of monetary policy.<\/p>\n\n\n\n

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

#521 \u2013 Global Dollar Risk Strategy<\/a><\/strong><\/p>\n\n\n\n

Verdelhan, Adrien, The Share of Systematic Variation in Bilateral Exchange Rates<\/strong>
https:\/\/ssrn.com\/abstract=1930516
<\/a>Abstracto:
Sorting countries by their dollar currency betas produces a novel cross-section of average currency excess returns. A slope factor (long in high beta currencies and short in low beta currencies) accounts for this cross-section of currency risk premia. This slope factor is orthogonal to the high-minus-low carry trade factor built from portfolios of countries sorted by their interest rates. The two high-minus-low risk factors account for 18% to 80% of the monthly exchange rate movements. The two risk factors suggest that stochastic discount factors in complete markets’ models should feature at least two global shocks to describe exchange rates.<\/p>\n\n\n\n

#521 \u2013 Global Dollar Risk Strategy<\/a><\/strong><\/p>\n\n\n\n

Boudoukh, Jacob and Richardson, Matthew P. and Thapar, Ashwin K and Wang, Franklin, Is There a Dollar Risk Factor?<\/strong>
https:\/\/ssrn.com\/abstract=3265394
<\/a>Abstracto:
Verdelhan (2018) argues that the dollar HML factor (long high dollar beta currencies and short low dollar beta currencies) is a priced global risk factor beyond carry. In contrast, we document that the dollar HML factor does not explain the cross section of currency risk premia, is conditionally correlated to the carry factor and does not have unconditional alpha over carry. Moreover, the dollar factor does not account for significant variation in exchange rate movements in non-dollar bilateral exchange rates.<\/p>\n\n\n\n

#12 \u2013 Pairs Trading with Stocks<\/a><\/strong><\/strong>
#55 \u2013 Pairs Trading with Country ETFs<\/a><\/strong><\/strong>
#73 \u2013 Pairs Trading with Commodities<\/a><\/strong><\/strong>
#90 \u2013 Pairs Trading with ADRs<\/a><\/strong><\/strong>
#175 \u2013 Pairs Trading on Intraday Basis<\/a><\/strong><\/strong><\/p>\n\n\n\n

Lee, Donovan and Leung, Tim, On the Efficacy of Optimized Exit Rule for Mean Reversion Trading<\/strong>
https:\/\/ssrn.com\/abstract=3626471
<\/a>Abstracto:
We investigate the effect of using an optimized exit rule on pairs trading. For every asset pair, we optimize the positions so that resulting intraday portfolio value is best fitted to an Ornstein-Uhlenbeck (OU) process through maximum likelihood estimation. Using eight asset pairs, we examine the risks and returns of pairs trading strategies with and without an optimize exit rule. We provide empirical evidence that using an optimized exit rule improves the profitability of the trades and reduces turnovers.<\/p>\n\n\n\n

#21 \u2013 Momentum Effect in Commodities<\/a><\/strong><\/strong>
#22 \u2013 Term Structure Effect in Commodities<\/a><\/strong><\/strong>
#23 \u2013 Momentum Effect Combined with Term Structure in Commodities<\/a><\/strong><\/strong><\/p>\n\n\n\n

Hossein Rad, Rand Low, Joelle Miffre, Robert Faf: Does sophistication of the weighting scheme enhance the performance of long-short commodity portfolios?<\/strong>
https:\/\/hal-audencia.archives-ouvertes.fr\/hal-02868473\/document
<\/a>Abstracto:
The article develops a long-short portfolio construction technique that captures the fundamentals of backwardation and contango present in commodity futures markets and simultaneously deviates from the equal-weighting scheme traditionally employed in the literature. The sophisticated weighting schemes based on risk minimization and risk timing are found to dominate the traditional naive allocation and the schemes based on utility maximization. The conclusion applies to both momentum and term structure portfolios and persists after accounting for transaction costs, lack of liquidity, various model specifications, and different sub-periods.<\/p>\n\n\n\n

#536 \u2013 Machine Learning Stock Picking<\/a><\/strong>
#455 \u2013 Nonlinear Support Vector Machines and Stock Picking<\/a><\/strong><\/p>\n\n\n\n

Drobetz, Wolfgang and Otto, Tizian, Empirical Asset Pricing via Machine Learning: Evidence from the European Stock Market<\/strong>
https:\/\/ssrn.com\/abstract=3640631
<\/a>Abstracto:
This paper evaluates the performance of machine learning methods in forecasting stock returns. Compared to a linear benchmark model, interactions and non-linear effects help improve predictive performance. But machine learning models must be adequately trained and tuned to overcome the high dimensionality issue and to avoid over-fitting. Across all machine learning methods, the most important predictors are based on price trends and fundamental signals from valuation ratios. However, the models exhibit disparities in statistical performance that translate into pronounced differences in economic profitability. The return and risk measures of long-only trading strategies indicate that machine learning models produce size-able gains relative to our benchmark. Neural networks perform best, even after adjusting for risk and accounting for transaction costs. However, a classification-based portfolio formation, utilizing a support vector machine that avoids estimating stock-level expected returns, performs even better than the neural network architecture.<\/p>\n\n\n\n

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

<\/a><\/a>Benford\u2019s Law Suggests Bitcoin\u2019s Price Manipulation<\/a><\/strong><\/p>\n\n\n\n

The Bitcoin is a quite controversial topic among the public. Many are interested in the blockchain technology or trade the cryptocurrencies, but the Bitcoin also has many opponents. Most frequently, Bitcoin is criticized for its volatility or a lack of supervision; some even call the Bitcoin a fraud. Yet many argue that blockchain is transparent. Novel research by Peterson examines the price manipulation using Benford\u2019s law and a linkage to the anecdotal evidence of known manipulation. In theory, the distribution of leading digits in numerical data should follow the Benford\u00b4s law and any significant deviations usually signal a fraud. According to the results, in the history of bitcoin prices, several frauds were detected. The results have important implication for the Bitcoin; therefore, this research is probably a must-read for anyone interested in cryptocurrencies.<\/p>\n\n\n\n

<\/a><\/a>First-Half Month Cash-Flow News and Momentum in Stocks<\/a><\/strong><\/p>\n\n\n\n

Stock prices react to the new information that investors continually receive from many sources. There are some major events, which are commonly connected with a new piece of information and subsequent reactions of investors. For example, quarterly earnings-announcements are the cause of the post-earnings-announcement drift or PEAD. According to the PEAD, prices tend to continue to drift up (down) after positive (negative) news. But news related to quarterly announcements is not the only important information. A novel research paper written by the Hong and Yu explores implications of the month-end reporting, analyst revisions and management guidance that are coming to market usually in the first half of each month and are also connected with drifts that offer practitioners profitable opportunities.<\/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

#231 – Comomentum Strategy<\/a>
#436 – A Multi Strategy Approach to Trading Foreign Exchange Futures<\/a>
#441 – Front-Running S&P GSCI Index<\/a>
#464 – Brand Value Asset Pricing Factor<\/a>
#532 – Minimum Idiosyncratic Returns in Stocks<\/a>
#533 – FOMC Cycle and Credit Risk<\/a>
#535 – Idiosyncratic Liquidity in Stocks<\/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 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-8369","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/8369","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=8369"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/8369\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=8369"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=8369"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=8369"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}