{"id":639,"date":"2015-11-21T22:03:32","date_gmt":"2015-11-21T22:03:32","guid":{"rendered":"http:\/\/quantpedia.com\/?p=639"},"modified":"2019-08-22T05:47:56","modified_gmt":"2019-08-22T05:47:56","slug":"quantpedia-update-21st-november-2015","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-update-21st-november-2015\/","title":{"rendered":"Quantpedia Update – 21st November 2015"},"content":{"rendered":"

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

\n\t#285 – Spread (Basis) Momentum within Commodities<\/strong><\/p>\n

\n\tPeriod of rebalancing:<\/strong> monthly
\n\tMarkets traded: <\/strong>commodities
\n\tInstruments used for trading:<\/strong> futures, CFDs
\n\tComplexity:<\/strong> Simple strategy
\n\tBactest period:<\/strong> 1960 – 2014
\n\tIndicative performance:<\/strong> 18.38%
\n\tEstimated volatility:<\/strong> 19.98%
\n\tSource paper:<\/strong><\/p>\n

\n\tBoons, Prado: Basis-momentum<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2587784<\/a>
\n\tAbstracto:
\n\tBasis-momentum, the diff erence between momentum signals from a fi rst- and second nearby futures strategy, is key to understanding variation in the term structure of (commodity) futures prices. Basis-momentum strongly outperforms benchmark characteristics, such as basis and momentum, in predicting spot and term premiums in both the time series and cross section. The basis-momentum e ffect is driven by imbalances in supply and demand of futures contracts, which persist because basis-momentum exposes investors to volatility risk. Asset pricing tests for both portfolios and individual commodities show that exposure to a basis-momentum factor is priced and suitably captures cross-sectional variation in returns. These tests also support the interpretation of basis-momentum as a mimicking portfolio of volatility risk.<\/p>\n

\n\t#286 – Spread (Basis) Momentum within Currencies<\/strong><\/p>\n

\n\tPeriod of rebalancing:<\/strong> monthly
\n\tMarkets traded: <\/strong>currencies
\n\tInstruments used for trading:<\/strong> futures, CFDs, swaps
\n\tComplexity:<\/strong> Simple strategy
\n\tBactest period:<\/strong> 1998 – 2015
\n\tIndicative performance:<\/strong> 8.40%
\n\tEstimated volatility:<\/strong> 9.23%
\n\tSource paper:<\/strong><\/p>\n

\n\tBoons, Prado: Basis-momentum<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2587784<\/a>
\n\tAbstracto:
\n\tBasis-momentum, the diff erence between momentum signals from a fi rst- and second nearby futures strategy, is key to understanding variation in the term structure of (commodity) futures prices. Basis-momentum strongly outperforms benchmark characteristics, such as basis and momentum, in predicting spot and term premiums in both the time series and cross section. The basis-momentum e ffect is driven by imbalances in supply and demand of futures contracts, which persist because basis-momentum exposes investors to volatility risk. Asset pricing tests for both portfolios and individual commodities show that exposure to a basis-momentum factor is priced and suitably captures cross-sectional variation in returns. These tests also support the interpretation of basis-momentum as a mimicking portfolio of volatility risk.<\/p>\n

\n\t#287 – The FOMC Cycle Effect<\/strong><\/p>\n

\n\tPeriod of rebalancing:<\/strong> weekly
\n\tMarkets traded: <\/strong>equities
\n\tInstruments used for trading:<\/strong> ETFs, futures, CFDs
\n\tComplexity:<\/strong> Simple strategy
\n\tBactest period:<\/strong> 1994 – 2013
\n\tIndicative performance:<\/strong> 11.58%
\n\tEstimated volatility:<\/strong> 13.92%
\n\tSource paper:<\/strong><\/p>\n

\n\tCieslak, Morse, Vissing-Jorgensen: Stock Returns Over the FOMC Cycle<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2687614<\/a>
\n\tAbstracto:
\n\tWe document that since 1994 the equity premium in the US and in the rest of the world is earned entirely in weeks 0, 2, 4 and 6 in FOMC cycle time, i.e. in time since the last Federal Open Market Committee meeting. This likely reflects a risk premium for news (about monetary policy or the macro economy) coming from the Federal Reserve: (1) The FOMC calendar is quite irregular and changes across sub-periods over which our finding is robust. (2) Even weeks in FOMC cycle time do not line up with important macro releases. (3) Volatility in the federal funds market peaks during even weeks in FOMC cycle time. (4) Information processing\/decision making within the Fed tends to happen bi-weekly in FOMC cycle time: The bi-weekly cycle is driven mainly by even week observations that follow board meetings of the Board of Governors. Furthermore, before 1994, intermeeting target changes were common and disproportionately took place during even weeks in FOMC cycle time. High return weeks do not line up with public information releases from the Federal Reserve or with the frequency of speeches by Fed officials. Systematic informal communication of Federal Reserve officials with the media and the financial sector is a more plausible information transmission mechanism. We discuss the social costs and benefits of this method of communication.<\/p>\n

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

\n\t#117 – Lottery Effect in Stocks<\/strong><\/p>\n

\n\tLin, Liu: Skewness, Lottery-Like Features, and the Cross-Section of Stock Returns<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2676633<\/a>
\n\tAbstracto:
\n\tWe propose a novel perspective which is deeply rooted in individual investor trading behavior to reconcile mixed findings in the negative relation between skewness\/lottery-like features and stock returns. We find a robust negative return predictability of skewness\/lottery-like features when conditioning on stock characteristics preferred by individual investors: low profitability, small size, low price, high idiosyncratic volatility, and low institutional ownership. Our findings suggest that it is individual investors who pay a price in exchange for a small probability to win a large payoff that leads to the negative relation between skewness and return in the cross-section.<\/p>\n

\n\t#175 – Pairs Trading on Intraday Basis<\/strong><\/p>\n

\n\tEnemuwe: Dynamic ETF Pairs Trading System. Evidence From Australia<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2662258<\/a>
\n\tAbstracto:
\n\tThis study evaluates the profitability of dynamic pairs trading strategies using a proposed 3-step pairs selection approach. We extend the pairs trading methodology employed by Miao (2014) to the broad-based exchange traded funds (ETFs) listed on the Australian Securities Exchange (ASX). The 3-step approach incorporates the correlation, cointegration and error correction coefficient as the pre-selection criteria during the formation period. In the subsequent trading period, we employ a daily re-calibration of the parameters using a 252-day rolling window from January 1, 2013 to September 30, 2015. We developed a real-time trading system using the Java programming language and KDB database, and back test the strategies using tick-by-tick historical quotes during the trading period. The back testing of the top five ETF pairs: ISO-SSO, IOZ-VAS, IOZ-STW, STW-VAS and STW-SFY yield cumulative returns of 10.08%, 4.41%, 19.70%, 62.27%, 46.60% and Sharpe ratios of 2.21, 1.00, 9.29, 15.12, 11.17 respectively. The maximum draw down is -32.47% over the trading period.<\/p>\n

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

\n\t#26 – Value (Book-to-Market) Anomaly<\/strong><\/p>\n

\n\tQiao: Conditional Market Exposures of the Value Premium<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2687528<\/a>
\n\tAbstracto:
\n\tValue strategies exhibit a large positive beta if contemporaneous market excess returns are positive, and a small beta if contemporaneous market excess returns are negative. Value also has a large positive beta after bear markets, but a small beta after bull markets. These facts hold for equity-value strategies in 21 countries, and to a lesser extent for three non-equity-value strategies. Betas conditional on contemporaneous market returns are able to capture expected return variation associated with the book-to-market ratio. These betas partially explain the value premium, and are related to a larger cash-flow risk of value strategies.<\/p>\n

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

\n\tKim, Tse, Wald: Time Series Momentum and Volatility Scaling<\/strong>
\n\thttp:\/\/world-finance-conference.com\/papers_wfc2\/468.pdf<\/a>
\n\tAbstracto:
\n\tMoskowitz, Ooi, and Pedersen (2012) show that time series momentum delivers a large and significant alpha for a diversified portfolio of various international futures contracts over the 1985 to 2009 period. Although we confirm these results with similar data,  we find that their results are driven by the volatility-scaled returns (or the so-called risk parity approach to asset allocation) rather than by time series momentum. The alpha of time series momentum monthly returns drops from 1.27% with volatility-scaled weights to 0.41% without volatility scaling, which is significantly lower than the cross-sectional momentum alpha of 0.95%. Using  volatility-scaled positions, the cumulative return of a time series momentum strategy is higher that that of the buy-and-hold  strategy; however, timeseriesmomentuman buy-and-hold offer similar cumulative returns if they are not scaled by volatility. The superior performance of the time series momentum strategy also vanishes in the more recent post-crisis period of 2009 to 2013.<\/p>","protected":false},"excerpt":{"rendered":"

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

\n\t#285 – Spread (Basis) Momentum within Commodities
\n\t#286 – Spread (Basis) Momentum within Currencies
\n\t#287 – The FOMC Cycle Effect<\/strong><\/p>\n

\n\tTwo new related research paper have been included into existing strategy reviews. And two additional related research paper have 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-639","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/639","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=639"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/639\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=639"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=639"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=639"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}