{"id":4757,"date":"2019-08-30T00:14:21","date_gmt":"2019-08-29T22:14:21","guid":{"rendered":"https:\/\/quantpedia.com\/?p=4757"},"modified":"2025-06-04T14:08:51","modified_gmt":"2025-06-04T12:08:51","slug":"quantpedia-premium-update-29th-august-2019","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-premium-update-29th-august-2019\/","title":{"rendered":"Quantpedia Premium Update – 29th August 2019"},"content":{"rendered":"

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

#443 – Taylor Rule and FX Returns<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>currencies
Instruments used for trading:<\/strong> futures, CFDs
Complexity:<\/strong> Very complex strategy
Backtest period:<\/strong> 1999 – 2017
Indicative performance:<\/strong> 6.16%
Estimated volatility:<\/strong> 7.61%
Source paper:<\/strong><\/p>\n\n\n\n

Filippou, Ilias and Taylor, Mark Peter: Forward-Looking Policy Rules and Currency Premia <\/strong>
https:\/\/ssrn.com\/abstract=3412612<\/a>
Abstracto:
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3428356 – alternative link
We evaluate the cross-sectional predictive ability of a forward-looking monetary policy reaction function, or Taylor rule, in both statistical and economic terms. We find that investors require a premium for holding currency portfolios with high implied interest rates while currency portfolios with low implied rates offer negative currency excess returns. Our forward- looking Taylor rule signals are orthogonal to current nominal interest rates and disconnected from carry trade portfolios and other currency investment strategies. The profitability of the Taylor rule portfolio spread is mainly driven by inflation forecasts rather than the output gap and is robust to data snooping and a wide range of robustness checks. <\/p>\n\n\n\n

#444 – Value Factor After Negative Market Return<\/a><\/strong><\/p>\n\n\n\n

Period of rebalancing:<\/strong> Monthly
Markets traded: <\/strong>equities
Instruments used for trading:<\/strong> ETFs, stocks
Complexity:<\/strong> Simple strategy
Backtest period:<\/strong> 1929-2017
Indicative performance:<\/strong> 3.68%
Estimated volatility:<\/strong> 3.45%
Source paper:<\/strong><\/p>\n\n\n\n

Chibane, Messaoud and Ouzan, Samuel: Value Bubbles<\/strong>
https:\/\/ssrn.com\/abstract=3412459<\/a>
Abstracto:
According to several extended behavioral theories, value profits should mirror momentum profits, and vary over time. We test these theories in the cross section of returns. Value returns depend on market states. From 1926 to 2018, following negative market return, the average so-called value premium is about three time its unconditional counterpart, whereas it appears to vanish following positive market return. Moreover, several short episodes of extreme losses in momentum strategy (momentum crashes) are contemporaneous with extreme value profits (value bubbles). Our results are robust to various time varying risk- based explanations.<\/p>\n\n\n\n

New research paper related to existing strategies:<\/strong><\/p>\n\n\n\n

#137 – Trendfollowing in Futures Markets<\/a><\/strong><\/p>\n\n\n\n

Modest: Some Observations on Trend Following: A Binomial Perspective<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3397783<\/a>
Abstracto:
This paper uses a simple binomial framework to explore trend following. It shows (by counter example) that the existence of positive profits from trend-following strategies, on its own, provides no prima facie evidence on the efficiency or inefficiency of markets. In addition, it explores the most important feature of time series momentum investment strategies: the return shaping impact of trend following through its dynamic positioning. In a stylized efficient market setting (with no transaction costs), the paper shows that the dynamic nature of trend following shapes when profits and losses occur compared to a buy-and-hold strategy. There is, however, a conservation of \u201cmass\u201d in that gains and losses are shuffled across periods such that the unconditional distribution of profits is unaffected. In this sense, trend following, by construction, generates crisis alpha — for crises where large losses occur over extended periods of time. Due to its ability to shape when profit and losses occur, trend following can provide significant portfolio diversification and hedging potential for those investors with strategic risk-on exposures.<\/p>\n\n\n\n

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

The correlation between bonds and stocks is essential information for asset allocation decisions; therefore understanding its macro-economic drivers is very valuable for all investors. Stocks-bonds correlation isn\u2019t stable, as we have experienced in the last 30 years, as the correlation, which was positive until the end of the 1990s, changed sign at the turn of the century. Research paper written by Marcello Pericoli sheds more light on this issue and shows that the correlation is primarily influenced by the uncertainty about inflation and real interest rates as well as by co-movement between inflation, real interest rates and dividend growth.<\/strong><\/p>\n\n\n\n

Pericoli : Macroeconomics Determinants of the Correlation Between Stocks and Bonds<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3429148<\/a>
Abstracto:
We analyze the correlation between the stock and bond markets in Germany and the US. We use a standard no-arbitrage affine model to decompose the correlation between these two assets into its main drivers. The correlation between bond yields and stock returns is a key determinant of asset allocation. Our results show that the correlation is primarily influenced by the uncertainty about inflation and real interest rates as well as by co-movement between inflation, real interest rates and dividend growth. Shocks to inflation, real interest rates and dividend growth can explain the correlation\u2019s temporary deviation from its long-term dynamics. <\/p>\n\n\n\n

The low volatility factor is a well-known example of a stock trading strategy that contradicts the classical CAPM model. A lot of researchers are trying to come up with an explanation for driving forces behind the volatility effect. One such popular explanation is the \u2018attention-grabbing\u2019 hypothesis \u2013 which suggests that low-volatility stocks are \u2018boring\u2019 and therefore require a premium relative to \u2018glittering\u2019 stocks that receive a lot of investor attention. Research paper written by Blitz, Huisman, Swinkels and van Vliet tests this theory and concludes that \u2018attention-grabbing\u2019 hypothesis can\u2019t be used to explain outperformance of low volatility stocks.<\/strong>

Related to: #7 \u2013 Low Volatility Factor Effect in Stocks<\/a><\/strong> <\/p>\n\n\n\n

Blitz, Huisman, Swinkels, van Vliet: Media Attention and the Volatility Effect<\/strong>
https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3403466<\/a>
Abstracto:
Stocks with low return volatility have high risk-adjusted returns, which might be driven by low media attention for such stocks. Using news coverage data we formally test whether the \u2018attention-grabbing\u2019 hypothesis can explain the volatility effect for a sample of international stocks over the period 2001 to 2018. Among stocks with a similar amount of media attention, a low-volatility effect is still present. Among stocks with similar volatility, the amount of media attention is not associated with significantly different risk-adjusted returns. Based on these findings, we reject the hypothesis that media attention is the driving force behind the volatility effect. <\/p>\n\n\n\n

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\n\tTwo new strategies have been added:
\n\tOne new related research paper has been included into existing strategy reviews. And two short free blog posts about interesting related research papers have been published during last few weeks.<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4757","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/4757","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\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=4757"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/4757\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=4757"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=4757"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=4757"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}