{"id":8476,"date":"2020-10-18T23:26:59","date_gmt":"2020-10-18T21:26:59","guid":{"rendered":"https:\/\/quantpedia.com\/?p=8476"},"modified":"2025-06-04T14:01:20","modified_gmt":"2025-06-04T12:01:20","slug":"quantpedia-premium-update-18th-october-2020","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-18th-october-2020\/","title":{"rendered":"Quantpedia Premium Update \u2013 18th October 2020"},"content":{"rendered":"

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

#544 \u2013 Impact of intangible assets on B\/M<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Yearly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> stocks
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 1976-2017
Indicative performance:<\/strong> 10.95%
Estimated volatility:<\/strong> 15.22%<\/p>\n\n\n\n

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

Hyuna Park: An Intangible-adjusted Book-to-market Ratio Still Predicts Stock Returns<\/strong>
https:\/\/cfr.pub\/forthcoming\/park2019intangible.pdf
<\/a>Abstracto:
The book-to-market ratio has been widely used to explain the cross-sectional variation in stock returns, but the explanatory power is weaker in recent decades than in the 1970s. I argue that the deterioration is related to the growth of intangible assets unrecorded on balance sheets. An intangible-adjusted ratio, capitalizing prior expenditures to develop intangible assets internally and excluding goodwill, outperforms the original ratio significantly. The average annual return on the intangible-adjusted high-minus-low (iHML) portfolio is 5.9% from July 1976 to December 2017 and 6.2% from July 1997 to December 2017, vs. 3.9% and 3.6% for an equivalent HML portfolio.<\/p>\n\n\n\n

#545 \u2013 Predictability in USD\/CNH FX pair<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded: <\/strong>currencies
Instruments used for trading:<\/strong> futures, CFDs
Complexity:<\/strong> Complex strategy
Backtest period:<\/strong> 2015-2019
Indicative performance:<\/strong> 26.1%
Estimated volatility:<\/strong> 4%<\/p>\n\n\n\n

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

Michael Melvin, Frank Westermann: Chinese Exchange Rate Policy: Lessons for Global Investors<\/strong>
https:\/\/www.cesifo.org\/DocDL\/cesifo1_wp8493.pdf
<\/a>Abstracto:
Chinese currency policy has had a strong impact on the value of investors\u2019 portfolios in recent years. On August 11, 2015, the People\u2019s Bank of China announced a new exchange rate policy where the RMB central parity rate against the USD would be determined each morning by the previous day\u2019s closing rate, market demand and supply, and valuations of other currencies. This new policy suggests an implementable investment strategy for trading the CNH. In this paper we create a forecasting model based on information regarding the central parity rate, implied volatilities and other control variables which correctly predicts the direction of change on about 60 percent of days. The exchange rate forecast is then used to manage the global investor\u2019s problem of mitigating the currency risk inherent in Chinese equity positions. All currency hedging strategies are shown to add value to the equity portfolio. A dynamic currency overlay strategy, where the forecasting model is used as a trading signal to take long and short positions in CNH, performs particularly well.<\/p>\n\n\n\n

#546 \u2013 Implied Volatility Spreads and Expected Market Returns in S&P500<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Daily
Markets traded: <\/strong>equities, bonds
Instruments used for trading:<\/strong> CFDs, ETFs, futures
Complexity: <\/strong>Complex strategy
Backtest period:<\/strong> 1996-2008
Indicative performance:<\/strong> 11.09%
Estimated volatility:<\/strong> 14.43%<\/p>\n\n\n\n

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

Yigit Atilgan, Turan G. Bali, and K. Ozgur Demirtas: Implied Volatility Spreads and Expected Market Returns<\/strong>
http:\/\/research.sabanciuniv.edu\/27774\/1\/Implied_Volatility_Spreads_and_Expected_Market_Returns.pdf
<\/a>Abstracto:
This paper investigates the intertemporal relation between volatility spreads and expected returns on the aggregate stock market. We provide evidence for a significantly negative link between volatility spreads and expected returns at the daily and weekly frequencies. We argue that this link is driven by the information flow from option markets to stock markets. The documented relation is significantly stronger for the periods during which (i) S&P 500 constituent firms announce their earnings; (ii) cash flow and discount rate news are large in magnitude; and (iii) consumer sentiment index takes extreme values. The intertemporal relation remains strongly negative after controlling for conditional volatility, variance risk premium and macroeconomic variables. Moreover, a trading strategy based on the intertemporal relation with volatility spreads has higher portfolio returns compared to a passive strategy of investing in the S&P 500 index, after transaction costs are taken into account.<\/p>\n\n\n\n

#547 \u2013 Return Predictability in Firms with Complex Ownership Network<\/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> 2006-2018
Indicative performance:<\/strong> 13.08%
Estimated volatility:<\/strong> 11.15%<\/p>\n\n\n\n

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

Angelica Gonzalez, Sergei Sarkissian, Jun Tu, Ran Zhang: Return Predictability in Firms with Complex Ownership Network<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3559099
<\/a>Abstracto:
In this study, using global cross-ownership data, we examine all four possible stock return predictability cases in ownership-linked firms (OLFs): parent-subsidiary, subsidiary-parent, subsidiary-subsidiary, and parent-parent. We find that OLF returns predict returns of focal firms in all four cases. A simple long\/short portfolio strategy for firms sorted by the lagged monthly returns of OLFs yields the Fama-French six-factor alpha of 79-113 bps per month. These results are not subsumed by customer-supplier relations, or industry or cross-country return momentums. The return predictability in OLFs is best explained by active internal capital markets \u2013 a specific mechanism unique to firms with complex ownership.<\/p>\n\n\n\n

#548 \u2013 The Intraday Momentum 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> Moderately complex strategy
Backtest period:<\/strong> 1991-2019
Indicative performance:<\/strong> 36.87%
Estimated volatility:<\/strong> 23.5%<\/p>\n\n\n\n

Ya Gaoa, Xing Hanb, Youwei Lic, Xiong Xiongd: Investor Heterogeneity and Momentum-based Trading Strategies in China<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3665443
<\/a>Abstracto:
The conventional momentum strategy performs poorly overall in China, because stock prices behave very differently when markets are open for trading versus when they are closed. Stocks that are past intraday (overnight) winners persistently outperform those that are past intraday (overnight) losers in the subsequent intraday (overnight) periods. However, the same intraday- (overnight-) momentum strategy suffers dramatically in the subsequent overnight (intraday) periods. Further analysis shows that past intraday (overnight) winners tend to be more (less) speculative stocks which are highly demanded during the day (night). Overall, our results are consistent with investor heterogeneity, and this persistent tug of war virtually eliminate the effectiveness of investors pursuing the momentum-based trading strategy in China.<\/p>\n\n\n\n

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

#487 \u2013 Mean-Variance Market Timing in the FX Market<\/a><\/strong><\/p>\n\n\n\n

To, Thuy Duong. (2016). Optimal Factor Strategy in FX Markets.
<\/strong>https:\/\/www.researchgate.net\/profile\/Thuy_Duong_To\/publication\/308259280_Optimal_Factor_Strategy_in_FX_Markets\/links\/59d41d090f7e9b4fd7ffc9e3\/Optimal-Factor-Strategy-in-FX-Markets.pdf
<\/a>Abstracto:
A mean-variance efficient currency trading strategy, which mimics the inverse of the minimum variance stochastic discount factor, earns a remarkable out-of-sample Sharpe ratio of 1.17 before and 0.91 after transaction costs. It outperforms popular currency strategies across performance measures and sub-samples. Crash risk and popular pricing factors do not explain the performance. The strategy is able to time the market, i.e., dynamically adjusts its risk exposure in response to time variations in market prices of risk, and predicts future returns, market volatility and illiquidity. A pricing model with the strategy as a single factor outperforms and subsumes popular pricing factors.<\/p>\n\n\n\n

#546 \u2013 Implied Volatility Spreads and Expected Market Returns in S&P500<\/a><\/strong><\/p>\n\n\n\n

Cao, Charles and Simin, Timothy T. and Xiao, Han, Predicting the Equity Premium with the Implied Volatility Spread<\/strong>
https:\/\/ssrn.com\/abstract=3695262
<\/a>We show that the call-put implied volatility spread (IVS) outperforms many well-known predictors of the U.S. equity premium at return horizons up to six months over the period from 1996:1 to 2017:12. The predictive ability of the IVS is unrelated to the dividend yield and is useful in explaining the cross-section of returns. Decomposing the IVS, we find the longer run predictive ability of the IVS operates primarily through a cash flow channel. We also find the IVS is significantly related to indicators of aggregate market direction and expected market conditions. Our results are consistent with the IVS reflecting market sentiment as well as information about informed trading.<\/p>\n\n\n\n

#199 \u2013 ROA Effect within Stocks<\/a><\/strong>
#224 \u2013 Profitability Factor Combined with Value Factor<\/a><\/strong>
#229 \u2013 Earnings Quality Factor<\/a><\/strong><\/p>\n\n\n\n

Carl Johan Ingvarsson: Quality\u2019s relationship to the idiosyncratic volatility puzzle<\/strong>
http:\/\/lup.lub.lu.se\/luur\/download?func=downloadFile&recordOId=9027212&fileOId=9027213
<\/a>This paper examines the well documented relationship between idiosyncratic volatility and mean returns. By using the recently published quality-minus-junk factor this paper attempts to explain both the abnormal performance of portfolios sorted on idiosyncratic volatility as well as the crosssectional pricing of idiosyncratic volatility. Using data from the U.S. it is shown that the quality factor is able to explain the abnormal performance of the extreme portfolios in the idiosyncratic volatility puzzle, while having no impact on the cross-sectional stock returns. This indicates that the quality-minus-junk factor plays an important role in determining the performance of the portfolios and further research should include it in any model aiming to investigate this puzzle.<\/p>\n\n\n\n

#96 \u2013 Crude Oil Predicts Equity Returns<\/a><\/strong><\/p>\n\n\n\n

Haibo Jiang, Georgios Skoulakis and Jinming Xue: Oil and Equity Index Return Predictability:The Importance of Dissecting Oil Price Changes<\/strong>
http:\/\/wp.lancs.ac.uk\/fofi2018\/files\/2018\/03\/FoFI-2018-0127-Georgios-Skoulakis2.pdf
<\/a>Using data until 2015, we document that oil price changes no longer predict G7 country equity index returns, in contrast to evidence based on earlier samples. Using a structural VAR approach, we decompose oil price changes into oil supply shocks, global demand shocks, and oil-specific demand shocks. The conjecture that oil supply shocks and oil-specific demand shocks (global demand shocks) predict equity returns with a negative (positive) slope is supported by the empirical evidence over the 1986-2015 period. The results are statistically and economically significant and do not appear to be consistent with time-varying risk premia.<\/p>\n\n\n\n

#532 \u2013 Minimum Idiosyncratic Returns in Stocks<\/a><\/strong><\/p>\n\n\n\n

Larry Swedroe: Left Tail Risk and Left Tail Momentum
<\/strong>https:\/\/alphaarchitect.com\/2020\/07\/14\/left-tail-risk-and-left-tail-momentum\/
<\/a>The positive trade-off between risk and expected return is the most fundamental concept in financial economics. Most investors are risk-averse. In order to hold higher-risk securities, they demand higher compensation in the form of higher expected returns. And risk-averse investors are more sensitive to downside risk, the left tail in the distribution of potential outcomes. I covered tail risk mitigation in a previous post titled: Volatility Expectations and Returns. The risk-averse investor theory would suggest that stocks with higher left-tail risk would be expected to have lower prices as compensation for the higher probability and magnitude of large losses associated with them. The result should be higher returns from stocks with higher left-tail risk.<\/p>\n\n\n\n

#237 \u2013 Dispersion Trading<\/a><\/strong><\/p>\n\n\n\n

Lucas Schneider and Johannes St\u00fcbinger: Dispersion Trading Based on the Explanatory Power of S&P 500 Stock Returns
<\/strong>https:\/\/www.mdpi.com\/2227-7390\/8\/9\/1627\/pdf
<\/a>This paper develops a dispersion trading strategy based on a statistical index subsetting procedure and applies it to the S&P 500 constituents from January 2000 to December 2017. In particular, our selection process determines appropriate subset weights by exploiting a principal component analysis to specify the individual index explanatory power of each stock. In the following out-of-sample trading period, we trade the most suitable stocks using a hedged and unhedged approach. Within the large-scale back-testing study, the trading frameworks achieve statistically and economically significant returns of 14.52 and 26.51 percent p.a. after transaction costs, as well as a Sharpe ratio of 0.40 and 0.34, respectively. Furthermore, the trading performance is robust across varying market conditions. By benchmarking our strategies against a naive subsetting scheme and a buy-and-hold approach, we find that our statistical trading systems possess superior risk-return characteristics. Finally, a deep dive analysis shows synchronous developments between the chosen number of principal components and the S&P 500 index.<\/p>\n\n\n\n

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

Quantpedia as an Inspiration<\/a><\/strong><\/p>\n\n\n\n

We have a new video featuring three examples of how to built new strategies on top of ideas from Quantpedia\u2019s database.<\/p>\n\n\n\n

Resurrecting the Value Premium<\/a><\/strong><\/p>\n\n\n\n

Nowadays, the value factor is a hot topic among practitioners and researchers<\/a> as well. It is commonly known that equity factors have a cyclical performance, but many argue that value underperforms for too long. Therefore, many say that the classical HML value factor of Fama and French is dead. On the other hand, there is an emerging amount of research papers that study the value investing <\/a>with an aim to make some alterations. These would result in a profitable factor, as the classic B\/M ratio looks like it’s not a sensible value factor anymore. This branch of literature was recently enriched by novel research of Blitz and Hanauer (2020). By including more value metrics, altering the investment universe and applying basic risk management techniques, value strategy can become profitable in the long term<\/a>. Although the modification is sensible, it stills suffer in a recent period. Only time will tell whether the novel resurrected value factors would emerge again as many times in the past…<\/p>\n\n\n\n

The Daily Volatility of Foreign Exchange Rates and The Time of Day<\/a><\/strong><\/p>\n\n\n\n

The foreign exchange market<\/a> (FOREX<\/a>) is opened 24 hours a day, but traders from different\u00a0parts of the world tend to prefer different trading hours, including\u00a0weekend day trading<\/a>. However, various dominant trading sessions around the globe can lead to time-dependent market characteristics. Novel research by Doman and Doman (2020) has studied how does the daily volatility of FX rates depend on the time of day of calculation. The volatility changes through the day, and the underlying dynamics depend on the time of the estimate. The results can have important implication for practitioners since the volatility differences are large enough so they can influence trading\/risk management decisions.\u00a0\u00a0<\/p>\n\n\n\n

The Knapsack problem implementation in R<\/a><\/strong><\/p>\n\n\n\n

Our own research paper ESG<\/a> Scores<\/a> and Price Momentum Are More Than Compatible<\/a> utilized the Knapsack problem to make the ESG strategies more profitable<\/a> o Momentum strategies<\/a> significantly less risky. The implementation of the Knapsack problem was created in R, using slightly modified Simulated annealing optimization algorithm. Recently, we have been asked about our implementation and the code. The code is commented and probably could be implemented more efficiently (in R or in another programming language). For example, R is more efficient with matrices, but the code would not be that “straightforward”. Lastly, the most important tuning parameter is the temperature decrease (the probability of accepting a new solution is falling with the rising number of iterations). Feel free to contact<\/a> us<\/a> with any questions or new ideas!<\/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

#409 – Trading Volume in Cryptocurrency Markets and Reversals<\/a>
#429 – CAPE Sector Picking Strategy<\/a>
#433 – Computing Power Factor in Cryptocurrencies<\/a>
#434 – Network Size Factor in Cryptocurrencies<\/a>
#538 – Oil Intraday Momentum<\/a>
#543 – Dividend Stocks and Rising or Falling Interest Rates<\/a><\/p>\n\n\n\n

\n\n\n\n

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\u00bfQuieres saber m\u00e1s sobre el servicio Quantpedia Premium? Consulta C\u00f3mo funciona Quantpedia<\/a>, nuestra misi\u00f3n<\/a> y Oferta de precios premium<\/a>.<\/strong><\/p>\n\n\n\n

\u00bfQuieres saber m\u00e1s sobre el servicio Quantpedia Pro? Compru\u00e9balo descripci\u00f3n<\/a>, mirar videos<\/a>, revisar capacidades de generaci\u00f3n de informes<\/a> y visite nuestro oferta de precios<\/a>.<\/strong><\/p>\n\n\n\n

\u00bfBuscas datos hist\u00f3ricos o plataformas de backtesting? Consulta nuestra lista de Descuentos en Algo Trading<\/a><\/strong>.<\/p>\n\n\n\n

\u00bfTe gustar\u00eda tener acceso gratuito a? nuestros servicios<\/a>? Entonces, Abre una cuenta con Lightspeed.<\/a> y disfrute de un a\u00f1o de Quantpedia Premium sin costo alguno.<\/strong><\/p>\n\n\n\n

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O s\u00edguenos en:<\/strong><\/p>\n\n\n\n

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

Six 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. 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-8476","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/8476","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=8476"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/8476\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=8476"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=8476"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=8476"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}