{"id":736,"date":"2017-03-23T22:32:15","date_gmt":"2017-03-23T22:32:15","guid":{"rendered":"http:\/\/quantpedia.com\/?p=736"},"modified":"2019-08-22T05:48:23","modified_gmt":"2019-08-22T05:48:23","slug":"quantpedia-update-23rd-march-2017","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-update-23rd-march-2017\/","title":{"rendered":"Quantpedia Update – 23rd March 2017"},"content":{"rendered":"

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

\n\t#340 – Halloween Effect During the Pre-Election Year<\/strong><\/p>\n

\n\tPeriod of rebalancing:<\/strong> 6 months
\n\tMarkets traded: <\/strong>equities
\n\tInstruments used for trading:<\/strong> futures, funds, ETFs, CFDs
\n\tComplexity:<\/strong> Simple strategy
\n\tBactest period:<\/strong> 1927-2015
\n\tIndicative performance:<\/strong> 11.50%
\n\tEstimated volatility:<\/strong> 24.47%
\n\tSource paper:<\/strong><\/p>\n

\n\tChan, Marsh: No matter the winning Presidential candidate, “buying at Halloween and selling in May” has been attractive for equities in pre-election years, with the opposite for Treasuries <\/strong>
\n\thttps:\/\/papers.ssrn.com\/sol3\/papers2.cfm?abstract_id=2903067<\/a>
\n\tAbstracto:
\n\tThis study shows that since 1927, investors would have earned a statistically significant excess return of nearly two percent per month by investing in the U.S. equity market from November through April in presidential pre-election years. At the same time, Treasury bond returns performed inversely to the equity returns, i.e., they have been significantly higher in summer (May to October) months and in other-than-pre-election-years (especially in midterm election years). Our equity results suggest that the previously documented Halloween and pre-election year effects are intertwined. A combined Halloween–pre-election year effect shows up consistently in sub-periods; in an extended sample period since 1871; and in international stock markets. It appears to be separate from a January anomaly; it is independent of the political party in the White House; and it doesn’t appear to be associated with standard volatility risk measures. In contrast, small (value) stocks outperform large (growth) stocks in the November–to–April period in years other than presidential pre-election years. We show that the winter–pre-election year premiums align with the Baker et al. (2016) measure of economic policy uncertainty, and we propose political uncertainty as a potential explanation for both the equity and bond results.<\/p>\n

\n\t#341 – Opening Range Breakout within Crude Oil<\/strong><\/p>\n

\n\tPeriod of rebalancing:<\/strong> intraday
\n\tMarkets traded: <\/strong>commodities
\n\tInstruments used for trading:<\/strong> futures, CFDs, ETFs
\n\tComplexity:<\/strong> Simple strategy
\n\tBactest period:<\/strong> 2001-2011
\n\tIndicative performance:<\/strong> 9.77%
\n\tEstimated volatility:<\/strong> not stated
\n\tSource paper:<\/strong><\/p>\n

\n\tHolmberg, Lonnbark, Lundstrom: Assessing the profitability of intraday opening range breakout strategies<\/strong>
\n\thttps:\/\/www.researchgate.net\/publication\/254420457_Assessing_the_profitability_of_intraday_opening_range_breakout_strategies<\/a>
\n\tAbstracto:
\n\tIs it possible to beat the market by mechanical trading rules based on historical and publicly known information? Such rules have long been used by investors and in this paper, we test the success rate of trades and profitability of the Open Range Breakout (ORB) strategy. An investor that trades on the ORB strategy seeks to identify large intraday price movements and trades only when the price moves beyond some predetermined threshold. We present an ORB strategy based on normally distributed returns to identify such days and find that our ORB trading strategy result in significantly higher returns than zero as well as an increased success rate in relation to a fair game. The characteristics of such an approach over conventional statistical tests is that it involves the joint distribution of Low, High, Open and Close over a given time horizon.<\/p>\n

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

\n\t#73 – Pairs Trading with Commodities<\/strong><\/p>\n

\n\tUngever: Pairs Trading to the Commodities Futures Market Using Cointegration Method<\/strong>
\n\thttps:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2896370<\/a>
\n\tAbstracto:
\n\tThis paper investigates pairs trading strategy by using the cointegration method among the 10 most popular agricultural future markets. It is found that only in 2 pairs shows trading signal. The pairs trading strategy is performed in two stages that are the formation period and the trading period with daily futures data from 2004 to 2015. After the formation period was constructed, it is assumed that the cointegration error continues to hold the trading period same as it does for the formation period. The pairs trading strategy is created by the long position cotton and the short position coffee and also long position cotton and short position the livecattle. It is found that the profitability of this strategy worked well in both formation period and trading period.<\/p>\n

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

\n\tNice academic paper. Related to simple trendfollowing strategies:<\/strong><\/p>\n

\t Chu: Asymmetry between Uptrend and Downtrend Identification: A Tale of Moving Average Trading Strategy<\/strong>
\n\thttps:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2903855<\/a>
\n\tAbstracto:
\n\tMost market participants are risk adverse and people tend to close their long positions once they perceive a formation of downturn in the market. Large sudden price drops can always be observed near the end of uptrends. On the other hand, people tend to have their own preferences in deciding the market entrance timings and large sudden price changes are relatively less commonly observed near the end of downtrends. Typical Moving Average strategies employ the same approach, using a single pair of time series, to locate the ending points of uptrends and downtrends. This approach does not consider the asymmetry of price changes near the end of uptrend and downtrend distinctively. To cater for the differences, a new approach using distinct pairs of time series for locating uptrends and downtrends is proposed. Performance of the proposed strategy is evaluated using stock market index series from 8 different developed countries including US, UK, Australia, Germany, Canada, Japan, Hong Kong and Singapore under 3 moving average calculation methods. The empirical results indicate that the proposed strategy outperforms the typical strategy and the buy-and-hold strategy. Recommended heuristics for selecting an appropriate MA length will also be addressed in this study.<\/p>\n

\n\tPlus two academic papers related to strategy #5 – FX Carry Trade:<\/strong><\/p>\n

\tMelvin, Shand: When Carry Goes Bad: The Magnitude, Causes, and Duration of Currency Carry Unwinds<\/strong>
\n\thttps:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2897482<\/a>
\n\tAbstracto:
\n\tWe analyze the worst currency carry loss episodes in recent decades, including causes, attribution by currency, timing, and the duration of carry drawdowns. To explore the determinants of the length of carry losses, a model of carry drawdown duration is estimated. We find evidence that drawdown duration varies systematically with expected return from the carry trade at the onset of the drawdown, financial stress indicators and the magnitude of deviations from a fundamental value portfolio of the carry-related portfolio holdings. In an out-of-sample test, we show that these determinants can be used to control carry-related losses and improve investment performance.<\/p>\n

\tAnd<\/p>\n

\tLee, Wang: The Impact of Jumps on Carry Trade Returns<\/strong>
\n\thttps:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2917694<\/a>
\n\tAbstracto:
\n\tThis paper investigates how jump risks are priced in currency markets. We find that currencies whose changes are more sensitive to negative market jumps provide significantly higher expected returns. The positive risk premium constitutes compensation for the extreme losses during periods of market turmoil. Using the empirical findings, we propose a jump modified carry trade strategy, which has approximately 2-percentage-point (per annum) higher returns than the regular carry trade strategy. These findings result from the fact that negative jump betas are significantly related to the riskiness of currencies and business conditions.<\/p>","protected":false},"excerpt":{"rendered":"

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

\t #340 – Halloween Effect During the Pre-Election Year
\n\t#341 – Opening Range Breakout within Crude Oil<\/strong><\/p>\n

\n\tOne new related research paper has been included into existing strategy reviews. And three additional related research papers 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-736","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/736","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=736"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/736\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=736"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=736"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=736"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}