{"id":521,"date":"2012-09-07T21:28:03","date_gmt":"2012-09-07T21:28:03","guid":{"rendered":"http:\/\/quantpedia.com\/?p=521"},"modified":"2019-08-22T05:47:23","modified_gmt":"2019-08-22T05:47:23","slug":"quantpedia-update-7th-september-2012","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/quantpedia-update-7th-september-2012\/","title":{"rendered":"Quantpedia Update – 7th September 2012"},"content":{"rendered":"

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

\n\t#208 – Share Issuance Effect<\/strong><\/p>\n

\n\tPeriod of rebalancing:<\/strong> Yearly
\n\tMarkets traded: <\/strong>equities
\n\tInstruments used for trading:<\/strong> stocks
\n\tComplexity:<\/strong> Complex strategy
\n\tBactest period: <\/strong>1990 – 2009
\n\tIndicative performance:<\/strong>  10.56%
\n\tEstimated volatility:<\/strong> 12.25%
\n\tSource paper:<\/strong><\/p>\n

\n\tLancaster, Bornholt: Share Issuance Effects in the Cross-Section of Stock Returns<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2080759<\/a>
\n\tAbstract:
\n\tPrevious research describes the net share issuance anomaly in U.S. stocks as pervasive, both in size-based sorts and in cross-section regressions. As a further test of its pervasiveness, this paper undertakes an in-depth study of share issuance effects in the Australian equity market. The anomaly is observed in all size stocks except micro stocks. For example, equal weighted portfolios of non-issuing big stocks outperform portfolios of high issuing big stocks by an average of 0.84% per month over 1990–2009. This outperformance survives risk adjustment and appears to subsume the asset growth effect in Australian stock returns.<\/p>\n

\n\t <\/p>\n

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

\n\t <\/p>\n

\n\t#22 – Term Structure Effect in Commodities<\/strong><\/p>\n

\n\tKim: Low-High Basis Factor in the Commodity Futures Market<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2139416<\/a>
\n\tAbstract:
\n\tWe consider the profit to the “buy low-basis commodities and sell high-basis commodities” strategy as a pricing factor in the commodity futures market. We call this factor the low-high basis factor, or LHB factor, in short. We first document the significant premium accruing to the LHB factor. We then report a substantial reduction in the pricing errors of factor models. In particular, the zero-intercept hypothesis of factor models is no longer rejected by the data once the LHB factor is included in the model. Finally, we show that the time-variation in the LHB factor return can be predicted, to some extent, by the implied volatility spread. We relate our findings to Keynes’ normal backwardation theory and Kaldor’s theory of storage and convenience yield.<\/p>\n

\n\t <\/p>\n

\n\t#77 – Beta Factor in Stocks<\/strong><\/p>\n

\n\t#78 – Beta Factor in Country Equity Indexes<\/strong><\/p>\n

\n\tBerrada, Messikh, Oderda, Pictet: Beta-Arbitrage Strategies: When Do They Work, and Why?<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2135288<\/a>
\n\tAbstract:
\n\tContrary to what traditional asset pricing would imply, a strategy that bets against beta, i.e. long in low beta stocks and short in high beta stocks, tends to out-perform the market. This puzzling empirical fact can be explained through the concept of relative arbitrage. Considering a market in which diversity is maintained, i.e. no single stock can dominate the entire market, we show that beta-arbitrage strategies out-perform the market portfolio with unit probability in finite time. We use the theoretical decomposition of beta-arbitrage excess return to provide empirical support to our explanation on equity country indices, equity sectors and individual stocks. Finally we show how to construct optimal beta-arbitrage strategies that maximize the expected return relative to a given benchmark.<\/p>\n

\t <\/p>\n

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

\n\tBaltas, Kosowski: Improving Time-Series Momentum Strategies: The Role of Trading Signals and Volatility Estimators<\/strong>
\n\thttp:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2140091<\/a>
\n\tAbstract:
\n\tConstructing a time-series momentum strategy involves the volatility-adjusted aggregation of uni- variate 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 time-series momentum profitability and reach a number of novel findings. 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. 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.<\/p>","protected":false},"excerpt":{"rendered":"

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

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

\n\t#210 – Adaptive Asset Allocation<\/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-521","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/521","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=521"}],"version-history":[{"count":0,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/posts\/521\/revisions"}],"wp:attachment":[{"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/media?parent=521"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/categories?post=521"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/vvv.quantpedia.com\/es\/wp-json\/wp\/v2\/tags?post=521"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}