{"id":489,"date":"2012-01-19T21:08:48","date_gmt":"2012-01-19T21:08:48","guid":{"rendered":"http:\/\/quantpedia.com\/?p=489"},"modified":"2019-08-22T05:47:16","modified_gmt":"2019-08-22T05:47:16","slug":"quantpedia-update-19th-january-2012","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-update-19th-january-2012\/","title":{"rendered":"Quantpedia Update – 19th January 2012"},"content":{"rendered":"

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

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

\n\tPeriod of rebalancing:<\/strong> Daily
\n\tMarkets traded:<\/strong> equities, currencies, commodities, bonds
\n\tInstruments used for trading:<\/strong> CFDs, futures
\n\tComplexity:<\/strong> Simple strategy
\n\tBactest period: <\/strong>1990-2004
\n\tIndicative performance: <\/strong>21.24%
\n\tEstimated volatility:<\/strong> 24.14%
\n\tSource paper:<\/strong><\/p>\n

\n\tCruset: Analysis of Trend Following Systems<\/strong>
\n\thttp:\/\/www.cruset.com\/systemtrader\/download\/trendfollowing.pdf<\/a>
\n\tAbstract:
\n\tThis assay introduces the reader into system development and presents various successful Trendfollowing systems and simulate them in most popular markets. Since good and reliable data is the basis of correct backtesting results at the beginning we discuss important data issues. Then, we present different trend following concepts and try to point out the inherent risks of over optimizing. To avoid this pitfall we test the presented systems over a broad range of parameters. As another stability test, we run some of our systems on a different set of data, i.e. a completely different portfolio. Finally, we do also look at the impact of money management settings in system results.<\/p>\n

\n\t <\/p>\n

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

\n\t
\n\t#6 –<\/strong> Volatility Effect in Stocks – Long-Short Version<\/strong><\/p>\n

\n\t#7 –<\/strong> Volatility Effect in Stocks – Long-Only Version<\/strong><\/p>\n

\n\tNew related paper:
\n\tBaker, Bradley, Wurgler: Benchmarks as Limits to Arbitrage: Understanding the Low Volatility Anomaly<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=1585031<\/a>
\n\tAbstract:
\n\tOver the past 41 years, high volatility and high beta stocks havesubstantially underperformed low volatility and low beta stocks in U.S.markets. We propose an explanation that combines the average investor'spreference for risk and the typical institutional investor’smandate to maximize the ratio of excess returns and tracking errorrelative to a fixed benchmark (the information ratio) without resortingto leverage. Models of delegated asset management show that suchmandates discourage arbitrage activity in both high alpha, low betastocks and low alpha, high beta stocks. This explanation is consistentwith several aspects of the low volatility anomaly including why it hasstrengthened in recent years even as institutional investors have becomemore dominant.<\/p>\n

\t <\/p>\n

\n\t#11 – Stock Return Reversal within Industries<\/strong><\/p>\n

\n\tNew related paper:
\n\tLiew, Roberts: U.S. Equity Mean-Reversion Examined<\/strong>
\n\thttp:\/\/www.battleofthequants.com\/Research\/US_Equity_Mean_Reversion_Examined_Liew_Robert_2010.pdf<\/a>
\n\tAbstract:
\n\tIn this paper we introduce an intra-sector dynamic trading strategy that captures mean-reversion opportunities across liquid U.S. stocks. Our strategy combines the Avellaneda and Lee (2010)’s methodology (AL) within the Black and Litterman (1991, 1992)’s framework (BL). In particular, we incorporate the s-scores and the conditional mean returns from the Orstein and Uhlenbeck (1930) process into BL. We find that our combined strategy ALBL has generated a 45% increase in Sharpe Ratio when compared to the uncombined AL strategy over the period from January 2, 2001 to May 27, 2010. We believe that the investable hedge fund indices revolution is upon us. These new indices, built to capture dynamic trading strategies, will dominate the replication battle. This paper introduces our first “focused-core” strategy, namely, U.S. Equity Mean-Reversion.
\n\t <\/p>\n

\n\t#118 – Time Series Momentum Effect<\/strong><\/p>\n

\n\tNew related paper:
\n\tBaltas, Kosowski: Trend-following and Momentum Strategies in Futures Markets<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=1968996<\/a>
\n\tAbstract:
\n\tConstructing a time-series momentum strategy involves the volatility-adjusted aggregation of univariate strategies and therefore relies heavily on the efficiency of the volatility estimator and on the quality of the momentum trading signal. Using a dataset with intra-day quotes of 12 futures contracts from November 1999 to October 2009, we investigate these dependencies and their relation to timeseries momentum profitability and reach a number of novel findings. First, momentum trading signals generated by fitting a linear trend on the asset price path maximise the out-of-sample performance while minimizing the portfolio turnover, hence dominating the ordinary momentum trading signal in literature, the sign of past return. Second, the results show strong momentum patterns at the monthly frequency of rebalancing, relatively strong momentum patterns at the weekly frequency and relatively weak momentum patterns at the daily frequency. In fact, significant reversal effects are documented at the very short-term horizon. Finally, regarding the volatility-adjusted aggregation of univariate strategies, the Yang-Zhang range estimator constitutes the optimal choice for volatility estimation in terms of maximizing efficiency and minimizing the bias and the ex-post portfolio turnover.
\n\t <\/p>","protected":false},"excerpt":{"rendered":"

\n\tQuantpedia Update<\/u><\/strong><\/p>\n

\n\tOne new strategy has been added:<\/p>\n

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

\n\tAnd several new related research papers have been included into existing strategy reviews.<\/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-489","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/489","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=489"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/489\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=489"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=489"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=489"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}