{"id":13652,"date":"2021-07-23T14:52:25","date_gmt":"2021-07-23T12:52:25","guid":{"rendered":"https:\/\/quantpedia.com\/?p=13652"},"modified":"2025-06-04T14:26:16","modified_gmt":"2025-06-04T12:26:16","slug":"quantpedia-premium-update-22nd-july-2021","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-22nd-july-2021\/","title":{"rendered":"Quantpedia Premium Update \u2013 22nd July 2021"},"content":{"rendered":"

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

#640 \u2013 Climate Beta and Mutual Funds<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> funds
Complexity:<\/strong> Moderately complex strategy
Backtest period:<\/strong> 2010-2018
Indicative performance:<\/strong> 2.55%
Estimated volatility:<\/strong> 2.27%<\/p>\n\n\n\n

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

Thang Ho: Climate sensitivity and mutual fund performance
<\/strong>https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3839888
<\/a>Abstracto:
In the presence of rising concern about climate change that potentially affects risk and return of investors\u2019 portfolio companies, active investors might have dispersed climate risk exposures. We compute mutual fund covariance with market-wide climate change news index and find that high (positive) climate beta funds outperform low (negative) climate beta funds by 0.24% per month on a risk-adjusted basis. High climate beta funds tilt their holdings toward stocks with high potential to hedge against climate change. In the cross section, such stocks yield higher excess returns, which are driven by greater pricing pressure and superior financial performance over our sample period.<\/p>\n\n\n\n

#641 \u2013 Cashflow to Price in Indian Market<\/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> 1994-2018
Indicative performance:<\/strong> 16.49%
Estimated volatility:<\/strong> 28.83%<\/p>\n\n\n\n

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

Goswami, Gautam, Cross-sectional Return Predictability in Indian Stock Market: An Empirical Investigation
<\/strong>https:\/\/ssrn.com\/abstract=3838938
<\/a>Abstracto:
This paper provides a comprehensive analysis of stock return predictability in the Indian stock market by employing both the portfolio and cross-sectional regressions methods using the data from January 1994 and ending in December 2018. We find strong predictive power of size, cash-flow-to-price ratio, momentum and short-term-reversal, and in some cases of book-to-market-ratio, price-earnings-ratio. The total volatility, idiosyncratic volatility, and beta are not consistent stock return predictors in the Indian stock market. In cross-sectional regression analysis, size, short-term reversal, momentum, and cash-flow-to-price ratio predict the future stock returns. Overall, the two variables momentum and cash flow to price ratio demonstrate reliable forecasting power under all methods and both small and large size samples.<\/p>\n\n\n\n

#642 \u2013 The Size Effect in Indian Market<\/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> 1994-2018
Indicative performance:<\/strong> 10.3%
Estimated volatility:<\/strong> 25.64%<\/p>\n\n\n\n

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

Goswami, Gautam, Cross-sectional Return Predictability in Indian Stock Market: An Empirical Investigation
<\/strong><\/strong>https:\/\/ssrn.com\/abstract=3838938
<\/a>Abstracto:
This paper provides a comprehensive analysis of stock return predictability in the Indian stock market by employing both the portfolio and cross-sectional regressions methods using the data from January 1994 and ending in December 2018. We find strong predictive power of size, cash-flow-to-price ratio, momentum and short-term-reversal, and in some cases of book-to-market-ratio, price-earnings-ratio. The total volatility, idiosyncratic volatility, and beta are not consistent stock return predictors in the Indian stock market. In cross-sectional regression analysis, size, short-term reversal, momentum, and cash-flow-to-price ratio predict the future stock returns. Overall, the two variables momentum and cash flow to price ratio demonstrate reliable forecasting power under all methods and both small and large size samples.
<\/p>\n\n\n\n

#643 \u2013 Dynamic Crude Oil Allocation in a Balanced Portfolio<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities, bonds, commodities
Instruments used for trading:<\/strong> futures, ETFs
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 2000-2020
Indicative performance:<\/strong> 10%
Estimated volatility:<\/strong> 10%<\/p>\n\n\n\n

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

Till, Hilary, Commodities, Crude Oil, and Diversified Portfolios
<\/strong>https:\/\/ssrn.com\/abstract=3860231
<\/a>Abstracto:
With concerns on inflation flaring up, there has been renewed interest in potentially including commodities in diversified portfolios. This article builds off prior research in examining which commodities to include and in what size. The article briefly reviews the relevant literature and proposes a novel and uncomplicated portfolio solution, which takes into consideration both historical results and plausible new paradigms. In addition, an investor would be able to implement this portfolio solution through deeply liquid futures markets.<\/p>\n\n\n\n

#644 \u2013 Cross-asset Time-series Momentum (Equities and Crude Oil)<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities, bonds
Instruments used for trading:<\/strong> futures, ETFs, bonds
Complexity:<\/strong> Moderately complex strategy
Backtest period:<\/strong> 2008-2019
Indicative performance:<\/strong> 11.03%
Estimated volatility:<\/strong> 10.88%<\/p>\n\n\n\n

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

Fernandez-Perez, Adrian and Indriawan, Ivan and Tse, Yiuman and Xu, Yahua, Cross-asset Time-series Momentum: Crude Oil Options and Global Stock Markets
<\/strong>https:\/\/ssrn.com\/abstract=3850465
<\/a>Abstracto:
We examine the profitability of a cross-asset time-series momentum strategy (XTSMOM) constructed using past crude oil options and stock market returns as joint predictors. We show that past crude oil options straddle returns negatively predict while past stock returns positively predict future stock market returns globally. The XTSMOM outperforms the single-asset time-series momentum (TSMOM) and buy-and-hold strategies with higher mean returns, lower standard deviations, and higher Sharpe ratios. The XTSMOM is also able to forecast economic cycles. We contribute to the literature on cross-asset momentum spillovers as well as on the impacts of crude oil uncertainty on stock markets.<\/p>\n\n\n\n

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

#58 \u2013 VIX Predicts Stock Index Returns<\/a><\/strong><\/p>\n\n\n\n

Serur, Juan Andr\u00e9s and Dapena, Jos\u00e9 Pablo and Siri, Juli\u00e1n Ricardo: Decomposing the VIX Index into Greed and Fear
<\/strong>https:\/\/ssrn.com\/abstract=3806521
<\/a>Abstracto:
Greed and fear are the main psychological factors driving investment deci-sions, and the VIX Index is regarded as the most important measure of howfearful the market feels about future returns of the main equity index, theS&P 500 Index. However, given that the VIX is calculated by combiningboth upside expected volatility implicit in out-of-the-money calls and down-side expected volatility implicit in the value of out-of-the-money puts, thetaken-for-granted assumption that a rising VIX should be interpreted as asign of growing fear in the equities market can be misleading. In this paperwe formally deconstruct the index into two components, the upside and thedownside expected volatility, in a similar fashion as it is done in statisticswith the semi-variance. We then propose a Greed-Fear index using the dataobtained to provide a better gauge about investors\u2019 sentiment on the market.<\/p>\n\n\n\n

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

Dai, Bochuan and Marshall, Ben R. and Nguyen, Nhut H. and Visaltanachoti, Nuttawat: Lottery Stocks and Stop-loss Rules
<\/strong>https:\/\/ssrn.com\/abstract=3836739
<\/a>Abstracto:
We show that stop-loss rules increase the returns to investment in stocks with lottery features. These stocks, which are popular with individual investors, typically have sporadic big gains and frequent small losses. However, stop-loss rules can reduce losses and allow investors to receive the gains from large price increases. We also highlight the sell signals of popular technical rules are like stop-loss rules and are effective at increasing lottery stock risk-adjusted returns. These rules could help investors avoid instances of major historical drawdowns, are particularly beneficial in declining markets, and are robust to the inclusion of transaction costs.<\/p>\n\n\n\n

#614 \u2013 Climate sentiment, carbon prices and Emission minus Clean Portfolio<\/a><\/strong><\/p>\n\n\n\n

Briere, Marie and Ramelli, Stefano, Green Sentiment, Stock Returns, and Corporate Behavior
<\/strong>https:\/\/ssrn.com\/abstract=3850923
<\/a>Abstracto:
We propose a new method to estimate non-fundamental demand for green financial assets based on the arbitrage activity of exchange traded funds (ETFs). By estimating the monthly abnormal flows into environment-friendly ETFs, we construct a Green Sentiment Index capturing shifts in investors’ appetite for environmental responsibility not yet priced in the value of the underlying assets. Our measure of green sentiment differs significantly from the climate-related news and attention indexes proposed by the extant literature, and it has additional explanatory power on both stock returns and corporate decisions. Over the period 2010-2020, changes in green sentiment anticipate a lasting stock out-performance by more environmentally responsible firms (of approximately 60 basis points over six months for a one-standard-deviation higher green sentiment), as well as an increase in their capital investments and cash holdings.<\/p>\n\n\n\n

#234 \u2013 Carry Factor within Asset Classes<\/a><\/strong><\/p>\n\n\n\n

Du, Wenxin and Schreger, Jesse, CIP Deviations, the Dollar, and Frictions in International Capital Markets
<\/strong>https:\/\/ssrn.com\/abstract=3843204
<\/a>Abstracto:
The covered interest rate parity (CIP) condition is a fundamental arbitrage relationship in international finance. In this chapter, we review its breakdown during the Global Financial Crisis and its continued failure in the subsequent decade. We review how to measure CIP deviations, discuss the drivers of CIP deviations, and the implications of CIP deviations for global financial markets.<\/p>\n\n\n\n

#27 \u2013 Market Timing with Aggregate and Idiosyncratic Stock Volatilities<\/a><\/strong><\/p>\n\n\n\n

Poon, Percy and Yao, Tong and Zhang, Andrew (Jianzhong), The Alphas of Beta and Idiosyncratic Volatility
<\/strong>https:\/\/ssrn.com\/abstract=3706597
<\/a>Abstracto:
We find that the relation between the idiosyncratic volatility (IVOL) anomaly and the beta anomaly is quite different at long horizons than at short horizons. IVOL has a significantly negative relation with subsequent stock returns at the short horizon of up to six months and beta does not predict stock returns at any horizon. However, both IVOL and beta significantly negatively predict stock alphas over horizons from a few months to beyond one year. At short horizons, neither anomaly can fully explain the other. At long horizons of beyond six months, the IVOL-alpha relation is explained by the beta-alpha relation. A measure of idiosyncratic volatility over a long window, popularly used by the investments industry to construct low-volatility portfolios, behaves similarly to beta in predicting returns and alphas at various horizons, and its predictive power is mostly explained by beta. Overall, IVOL and beta each has unique short-term information, but at long horizons the two anomalies appear to be the same. Our findings help reconcile a perceptional gap between academic studies and the investment industry on low volatility investing, and enrich the debate about the relation between the two low-risk anomalies.<\/p>\n\n\n\n

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

Introduction to CPPI \u2013 Constant Proportion Portfolio Insurance<\/a><\/strong><\/p>\n\n\n\n

As we have promised, we present a short article as an introduction into the methodology of the Quantpedia Pro CPPI reports. Quantpedia Pro clients can use the model portfolio built in the Portfolio Manager as a risky asset to test various variants \u2013 Basic CPPI<\/a>, Drawdown Based CPPI<\/a> y Dynamic Multiplier CPPI<\/a>.<\/p>\n\n\n\n

Man vs. Machine: Stock Analysis<\/a><\/strong><\/p>\n\n\n\n

Nowadays, we see an increasing number of machine learning based strategies<\/a> y other related financial analyses<\/a>. But can the machines replace us? Undoubtedly, AI algorithms have greater capacities to “digest” big data, but as always in the markets, everything is not rational. Cao et al. (2021) dives deeper into this topic and examines the stock analysts. Target prices and earnings forecasts are crucial parts of the investing practice and are frequently used by traders and investors (and even ML-based strategies). The novel research examines and compares the abilities of human analysts versus the AI algorithm in forecasting the target price. As a whole, AI-based analysts, on average, outperforms human analysts, but it is not that straightforward. While AI can learn from large datasets, humans do not seem to be replaced soon. There are certain fields where human uniqueness is valuable.<\/a> For example, in illiquid and smaller firms or firms with asset-light business models. Moreover, it seems that rather than competing with each other, AI and human analysts are complementary. The novel technology can be used with great success to help us in areas where we lag, and the combined knowledge and forecasts of AI and humans outperform the AI analyst in each year. <\/p>\n\n\n\n

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

#325 – Abnormal Turnover Effect in the Stock Market<\/a>
#346 – Commodity Option Implied Volatility Strategy<\/a>
#388 – Implied Skewness Strategy in Commodities<\/a>
#634 – Chronological Return Ordering<\/a>
#638 – Short Selling Activity and Momentum<\/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

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, five 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-13652","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/13652","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=13652"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/13652\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=13652"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=13652"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=13652"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}