{"id":537,"date":"2013-02-16T12:08:31","date_gmt":"2013-02-16T12:08:31","guid":{"rendered":"http:\/\/quantpedia.com\/?p=537"},"modified":"2019-08-22T05:47:27","modified_gmt":"2019-08-22T05:47:27","slug":"quantpedia-update-16th-february-2013","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-update-16th-february-2013\/","title":{"rendered":"Quantpedia Update – 16th February 2013"},"content":{"rendered":"

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

\n\t#234 – Carry Factor within 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> futures
\n\tComplexity:<\/strong> Complex strategy
\n\tBactest period: <\/strong>1991 – 2011
\n\tIndicative performance:<\/strong> 6.90%
\n\tEstimated volatility:<\/strong> 4.90%
\n\tSource paper:<\/strong><\/p>\n

\n\tKoijen, Moskowitz, Pedersen, Vrugt: Carry<\/strong>
\n\thttp:\/\/www2.lse.ac.uk\/fmg\/events\/conferences\/past-conferences\/2012\/PWC-Conference_7-8June\/Papers-and-slides\/Ralph_Koijen_paper.pdf<\/a>
\n\tAbstract:
\n\tA security’s expected return can be decomposed into its “carry” and its expected price appreciation, where carry can be measured in advance without an asset pricing model. We find that carry predicts returns both in the cross section and time series of a variety of different asset classes that include global equities, bonds, currencies, and commodities. This predictability underlies the strong returns to “carry trades” that go long high-carry and short low-carry securities. Decomposing carry returns into static and dynamic components, we investigate the nature of this predictability. We identify “carry downturns”—when carry strategies across asset classes do poorly—and show that these episodes coincide with global recessions and liquidity crises.<\/p>\n

\n\t#235 – Scheduled Economic Announcements Effect in Bonds<\/strong><\/p>\n

\n\tPeriod of rebalancing:<\/strong> Daily
\n\tMarkets traded: <\/strong>bonds
\n\tInstruments used for trading:<\/strong> futures
\n\tComplexity:<\/strong> Simple strategy
\n\tBactest period: <\/strong>1985-2009
\n\tIndicative performance:<\/strong> 7.30%
\n\tEstimated volatility:<\/strong> 4.98%
\n\tSource paper:<\/strong><\/p>\n

\n\tDicke, Hess: Information Content Drives Risk Premium of Macroeconomic News on Bond and Stock Markets<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2114140<\/a>
\n\tAbstract:
\n\tWe show that both stocks and bonds earn substantial excess returns on days when macroeconomic reports are released. However, stocks and bonds award their risk premia on different announcement days. Stock investors earn a risk premium only on days when news about GDP growth is released whereas bond investors are compensated only for bearing the risk of inflation related news. Stock holders earn more than 5% on growth related announcement days. In fact, other trading days do not earn a positive risk premium on stock markets during our sample period 1985 to 2009. On long-term bond markets, over three percent per year are earned on inflation related announcement days, i.e., 45% of the annual risk premium. Our findings have implications for, e.g., asset allocation as they suggest that investors resort to market timing strategies, i.e., they bring forward or delay market entries in order to earn the risk premium.<\/p>\n

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

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

\n\tCaballero, Doyle: Carry Trade and Systemic Risk: Why are FX Options So Cheap?<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2188195<\/a>
\n\tAbstract:
\n\tIn this paper we document first that, in contrast with their widely perceived excess returns, popular carry trade strategies yield low systemic-risk-adjusted returns. In particular, we show that carry trade returns are highly correlated with the return of a VIX rolldown strategy — i.e., the strategy of shorting VIX futures and rolling down its term structure — and that the latter strategy performs at least as well as betaadjusted carry trades, for individual currencies and diversified portfolios. In contrast, hedging the carry with exchange rate options produces large returns that are not a compensation for systemic risk. We show that this result stems from the fact that the corresponding portfolio of exchange rate options provides a cheap form of systemic insurance.<\/p>\n

\n\t#171 – Market Timing Filter Applied to a Classical Stock Anomalies<\/strong><\/p>\n

\n\tGlabadanidis: Market Timing with Moving Averages<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2208441<\/a>
\n\tAbstract:
\n\tI present evidence that a moving average (MA) trading strategy third order stochastically dominates buying and holding the underlying asset in a mean-variance-skewness sense using monthly returns of value-weighted decile portfolios sorted by market size, book-to-market cash-flow-to-price, earnings-to-price, dividend-price, short-term reversal, medium-term momentum, long-term reversal and industry. The abnormal returns are largely insensitive to the four Carhart (1997) factors and produce economically and statistically significant alphas of between 10% and 15% per year after transaction costs. This performance is robust to different lags of the moving average and in subperiods while investor sentiment, liquidity risks, business cycles, up and down markets, and the default spread cannot fully account for its performance. The MA strategy works just as well with randomly generated returns and bootstrapped returns. I also report evidence regarding the profitability of the MA strategy in seven international stock markets. The performance of the MA strategies also holds for more than 18,000 individual stocks from the CRSP database. The substantial market timing ability of the MA strategy appears to be the main driver of the abnormal returns. The returns to the MA strategy resemble the returns of an imperfect at-the-money protective put strategy relative to the underlying portfolio. The lagged signal to switch has substantial predictive power over subsequent decile portfolio returns. Furthermore, combining several MA strategies into a value\/equal-weighted portfolio of MA strategies performs even better and represents a unified framework for security selection and market timing.<\/p>\n

\n\t#212 – Scheduled Economic Announcements Effect in Stocks<\/strong><\/p>\n

\n\tDicke, Hess: Information Content Drives Risk Premium of Macroeconomic News on Bond and Stock Markets<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2114140<\/a>
\n\tAbstract:
\n\tWe show that both stocks and bonds earn substantial excess returns on days when macroeconomic reports are released. However, stocks and bonds award their risk premia on different announcement days. Stock investors earn a risk premium only on days when news about GDP growth is released whereas bond investors are compensated only for bearing the risk of inflation related news. Stock holders earn more than 5% on growth related announcement days. In fact, other trading days do not earn a positive risk premium on stock markets during our sample period 1985 to 2009. On long-term bond markets, over three percent per year are earned on inflation related announcement days, i.e., 45% of the annual risk premium. Our findings have implications for, e.g., asset allocation as they suggest that investors resort to market timing strategies, i.e., they bring forward or delay market entries in order to earn the risk premium.<\/p>","protected":false},"excerpt":{"rendered":"

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

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

\n\t#234 – Carry Factor within Asset Classes<\/strong><\/p>\n

\n\t#235 – Scheduled Economic Announcements Effect in Bonds<\/strong><\/p>\n

\n\tAnd three 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-537","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/537","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=537"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/537\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=537"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=537"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=537"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}