{"id":711,"date":"2016-11-19T13:43:32","date_gmt":"2016-11-19T13:43:32","guid":{"rendered":"http:\/\/quantpedia.com\/?p=711"},"modified":"2019-08-22T05:48:17","modified_gmt":"2019-08-22T05:48:17","slug":"quantpedia-update-19th-november-2016","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-update-19th-november-2016\/","title":{"rendered":"Quantpedia Update – 19th November 2016"},"content":{"rendered":"

\n\tNew strategies:<\/u><\/strong><\/p>\n

\t#325 – Abnormal Turnover Effect in the Stock Market<\/strong><\/p>\n

\n\tPeriod of rebalancing:<\/strong> monthly
\n\tMarkets traded: <\/strong>equities
\n\tInstruments used for trading:<\/strong> stocks
\n\tComplexity:<\/strong> Complex strategy
\n\tBactest period:<\/strong> 1968-2015
\n\tIndicative performance:<\/strong> 10.82%
\n\tEstimated volatility:<\/strong> 10.45%
\n\tSource paper:<\/strong><\/p>\n

\n\tLee, Kim, Kim: Abnormal Trading Volume and the Cross-Section of Stock Returns<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2812010<\/a>
\n\tAbstracto:
\n\tStocks with high trading volume outperform otherwise stocks for one week, but subsequently underperform at the longer horizon. We show that such time-varying predictability of trading volume is attributed to abnormal trading activity, which is not explained by past volume. Specifically, we find that the return forecasting power of abnormal trading activity is strongly positive up to five weeks ahead. In contrast, the predictive power of the expected trading activity is negative, and lasts for longer horizons. We further argue that behavioral biases and investors’ attention induces abnormal trading activity, but its price impact is primarily related to behavioral biases. Overall evidence emphasizes the role of behavioral biases and investors’ attention to explain trading volume.<\/p>\n

\t#326 – Volatility Investing Across Asset Classes<\/strong><\/p>\n

\n\tPeriod of rebalancing:<\/strong> monthly
\n\tMarkets traded: <\/strong>equities, bonds, currencies, commodities
\n\tInstruments used for trading:<\/strong> swaps
\n\tComplexity:<\/strong> Complex strategy
\n\tBactest period:<\/strong> 2005-2015
\n\tIndicative performance:<\/strong> 19.90%
\n\tEstimated volatility:<\/strong> 18.90%
\n\tSource paper:<\/strong><\/p>\n

\n\tZarattini: Volatility Investing Across Asset Classes<\/strong>
\n\thttps:\/\/www.researchgate.net\/publication\/281177615_Volatility_Investing_Across_Asset_Classes<\/a>
\n\tAbtract:
\n\tThe asymmetry between protection demand and protection supply causes the implied volatility embedded in option prices to be on average above future realized volatility in the majority of asset classes. The persistent bias in volatility pricing makes systematic short volatility strategies attractive from a risk-reward perspective (the average cross-sectional Sharpe Ratio is 0.94). Moreover the magnitude of excess returns cannot be explained by common risk factors. The goal of this paper is to assess the performance of portfolios that trade volatility simultaneously across di\u00ef\u00ac\u20acerent asset classes and use signals to dynamically tilt the portfolio towards the most rewarding markets. Timing the variance risk premium and increasing the investable universe produced Sharpe Ratios above 1.90 during the last 25 years. Nevertheless it is worth mentioning that the level of transaction costs, especially in new available volatility indexes, can reduce signi\u00ef\u00ac\u0081cantly the risk-adjusted returns of the portfolios.<\/p>\n

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

\n\t#184 – Timing Carry Trade
\n\t#221 – Timing Carry Trade v2<\/strong><\/p>\n

\n\tBroll: Using Option-Implied Information to Improve Currency Carry Trade Profits<\/strong>
\n\thttps:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2851543<\/a>
\n\tAbstracto:
\n\tThis study investigates an efficient parametric portfolio policy model to improve the return distribution of the well-known currency carry trade investment strategy. This carry trade strategy invests into high-yielding currencies that are subsequently funded by low-yielding currencies. Following this investment procedure has led to significantly excess returns for the investors, at least over the past four decades. However, these returns were subject to a high crash risk, which hit its peak during the US subprime crisis in 2008\/2009 with portfolio losses of up to one third of the investment value. The constructed model overcomes these bad portfolio properties through computing the optimal carry trade portfolio weight for any monthly revolving investment period. This is done by modeling the optimal weight as a function of the carry trade’s risk characteristics. Especially, when using global FX option-implied variance risk, as well as global consumer price inflation and commodity prices as background risk factors, the model delivers extremely-efficient out-of-sample results with annualized mean returns of up to 8.4% over an eight-year period, accompanied with a low standard deviation, positively skewed returns and leading to Sharpe ratios around unity, including transaction costs. These promising statistics are largely maintained when allowing for higher leveraged portfolios.<\/p>\n

\n\tOne additional related research paper has been included into existing free strategy reviews during last 2 week:<\/strong><\/u><\/p>\n

\n\tAn academic paper related to multiple smart beta strategies:<\/strong><\/p>\n

\tRatcliffe, Miranda, Ang: Capacity of Smart Beta Strategies: A Transaction Cost Perspective<\/strong>
\n\thttps:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2861324<\/a>
\n\tAbstracto:
\n\tUsing a transaction cost model, and an assumption for the smart beta premium observed in data, we estimate the capacity of momentum, quality, value, size, minimum volatility, and a multi-factor combination of the first four strategies. Flows into these factor strategies incur transaction costs. For a given trading horizon, we can find the fund size where the associated transaction costs negate the smart beta premium, assuming current rebalancing trends and holding constant other market structure characteristics. With a trading horizon of one day, we find that momentum is the strategy with the smallest assets under management (AUM) capacity of $65 billion, and size is the largest with an AUM capacity of $5 trillion. Extending the trading horizon to five days increases capacity in momentum and size to $320 billion and over $10 trillion, respectively.<\/p>","protected":false},"excerpt":{"rendered":"

\n\tTwo new strategies have been added:<\/p>\n

\t #325 – Abnormal Turnover Effect in the Stock Market
\n\t#326 – Volatility Investing Across Asset Classes<\/strong><\/p>\n

\n\tOne new related research paper has been included into existing strategy reviews. And one additional related research paper has been included into existing free strategy reviews during last 2 weeks.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-711","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/711","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\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=711"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/711\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=711"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=711"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=711"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}