{"id":43787,"date":"2025-12-27T12:11:10","date_gmt":"2025-12-27T11:11:10","guid":{"rendered":"http:\/\/david.melich"},"modified":"2025-12-27T12:11:10","modified_gmt":"2025-12-27T11:11:10","slug":"quantpedia-update-7th-november-2011-2","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-update-7th-november-2011-2\/","title":{"rendered":"Quantpedia Update – 7th November 2011"},"content":{"rendered":"

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

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

\n\tPeriod of rebalancing:<\/strong> Monthly
\n\tMarkets traded:<\/strong> equities, bonds, commodities, currencies
\n\tInstruments used for trading:<\/strong> futures, CFDs
\n\tComplexity:<\/strong> Moderately complex strategy
\n\tBactest period: <\/strong>1965-2009
\n\tIndicative performance: <\/strong>16.21%
\n\tEstimated volatility:<\/strong> 11.73%
\n\tSource paper:<\/strong><\/p>\n

\n\tMoskowitz, Ooi, Pedersen: Time Series Momentum<\/strong>
\n\thttp:\/\/pages.stern.nyu.edu\/~lpederse\/papers\/TimeSeriesMomentum.pdf<\/a>
\n\tAbstract:
\n\tWe document significant "time series momentum" in equity index, currency, commodity, and bond futures for each of the 58 liquid instruments we consider. We find persistence in returns for 1 to 12 months that partially reverses over longer horizons, consistent with sentiment theories of initial under-reaction and delayed over-reaction. A diversified portfolio of time series momentum strategies across all asset classes delivers substantial abnormal returns with little exposure to standard asset pricing factors, and performs best during extreme markets. We show that the returns to time series momentum are closely linked to the trading activities of speculators and hedgers, where speculators appear to profit from it at the expense of hedgers.<\/p>\n

\n\t#119 – Google Search Effect<\/strong><\/p>\n

\n\tPeriod of rebalancing:<\/strong> Weekly
\n\tMarkets traded:<\/strong> equities
\n\tInstruments used for trading:<\/strong> stocks
\n\tComplexity:<\/strong> Complex strategy
\n\tBactest period: <\/strong>2005-2008
\n\tIndicative performance: <\/strong>9,20%
\n\tEstimated volatility:<\/strong> not stated
\n\tSource paper:<\/strong><\/p>\n

\n\tJoseph, Wintoki, Zhang: Forecasting Abnormal Stock Returns and Trading Volume Using Investor Sentiment: Evidence from Online Search<\/strong>
\n\thttp:\/\/web.ku.edu\/~wintoki\/myssi\/_pdf\/Online_Search_Returns_IJF_preprint.pdf<\/a>
\n\tAbstract:
\n\tWe examine the ability of online ticker searches (e.g. XOM for Exxon Mobil) to forecast abnormal stock returns and trading volumes. Specifically, we argue that online ticker search serves as a valid proxy for investor sentiment – a set of beliefs about cash flows and investments risks that are not necessarily justified by the facts at hand – which is generally associated with less sophisticated, retail investors. Based on prior research on investor sentiment, we expect online search intensity to forecast stock returns and trading volume, and that highly volatile stocks, which are more difficult to arbitrage, will be more sensitive to search intensity than less volatile stocks. In a sample of S&P 500 firms over the period 2005–2008, we find that, over a weekly horizon, online search intensity reliably predicts abnormal stock returns and trading volume, and that the sensitivity of returns to search intensity is positively related to the difficulty with which a stock can be arbitraged. We conclude by offering guidelines for the utilization of online search data in other forecasting applications.<\/p>\n

\n\t <\/p>\n

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

\n\tFollowing links aren't showing new academic research papers. However S&P indexes described there are very closely related to existing strategies in Quantpedia database therefore we believe they are worth to be examined.
\n\t <\/p>\n

\n\t#5 – FX Carry Trade<\/strong><\/p>\n

\n\tNew related link:
\n\tS&P Currency Arbitrage Index<\/strong>
\n\thttp:\/\/www.standardandpoors.com\/indices\/sp-currency-arbitrage-index\/en\/eu\/?indexId=spstrtarbcusd—-p-rgl—<\/a>
\n\tAbstract:
\n\tThe S&P Currency Arbitrage Index seeks to model a carry trade strategy. The index consists of positions in the G10 currencies based on their relative interest rates versus the U.S. dollar. Since high yielding currencies tend not to depreciate to the extent that would offset interest rate differentials with low yielding currencies, it has been a profitable strategy to invest long in high yielding currencies and short in low yielding currencies..
\n\t <\/p>\n

\n\t#57 – Term Spread Premium<\/strong><\/p>\n

\n\tNew related link:
\n\tS&P Forward Interest Rate Arbitrage Index<\/strong>
\n\thttp:\/\/www.standardandpoors.com\/indices\/sp-forward-interest-rate-arbitrage\/en\/eu\/?indexId=sp-forward-interest-rate-arbitrage-index<\/a>
\n\tAbstract:
\n\tThe S&P Forward Rate Arbitrage Indices model the outcome of a forward interest rate arbitrage strategy that seeks to profit from the commonly observed tendency for forward interest rates to be overstated by the spot yield curve. They consist of indices that represent the G10 currencies for which a liquid futures contract on an applicable three-month interest rate exists.<\/p>","protected":false},"excerpt":{"rendered":"

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

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

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

\n\t#119 – Google Search Effect<\/strong><\/p>\n

\n\tAnd several new related links have been included into existing strategy reviews.<\/p>","protected":false},"author":11586,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-43787","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/43787","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\/11586"}],"replies":[{"embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/comments?post=43787"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/43787\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=43787"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=43787"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=43787"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}