{"id":581,"date":"2015-03-23T21:09:44","date_gmt":"2015-03-23T21:09:44","guid":{"rendered":"http:\/\/quantpedia.com\/?p=581"},"modified":"2019-08-22T05:47:42","modified_gmt":"2019-08-22T05:47:42","slug":"new-related-paper-to-44-paired-switching","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/new-related-paper-to-44-paired-switching\/","title":{"rendered":"New related paper to #44 – Paired Switching"},"content":{"rendered":"

\n\t"This work exploits the relationship between sign predictability and volatility predictability of two risky assets, to present results from meta-data analysis framework of 351 pairs and a rotation methodology. Our results suggest that, on average, rotation trading based on market timing is better off than using the best asset buy-and-hold strategy or the equally weighted portfolio. The suggested rules exhibit better average return than a buy-and- hold strategy, however, the come a cost of higher risk. The rotation strategy which includes relative pricing and relative volatility is to be an overall most robust performer. The success of trading is highly linked to the correlation between the underlying assets only for the models based on relative pricing but is also strongly linked with the differences in means across all models we examined.
\n\t"<\/p>","protected":false},"excerpt":{"rendered":"

\n\t#44 – Paired Switching<\/strong><\/a><\/p>\n

\n\tAuthors: Schizas, Thomakos<\/strong><\/p>\n

\n\tTitle: Market timing using asset rotation on exchange traded funds: a meta-analysis on trading performance<\/strong><\/p>\n

\n\tLink: http:\/\/businessperspectives.org\/journals_free\/imfi\/2013\/imfi_en_2013_02cont_Schizas.pdf<\/a><\/p>\n

\n\tAbstract:<\/p>\n

\tThe ultimate goal of any “paper” investment strategy is to achieve real-life profitability. This paper measures the performance of a trading rule based on the relative pricing and relative volatility of a rotation strategy between two assets, using data from passive ETFs. To avoid problems of pair selection we work with meta-data obtained after the evaluation of a large number of 351 pairs of ETFs. In this way the authors analyze the performance of the proposed strategy on the cross-section of different ETFs. The results show that rotation trading, as applied in this paper, offers advantages even when the simplest model is used in generating trading signals. Furthermore, the authors find that the differences in the actual mean returns (over the evaluation period), the correlation of the pair components and to (a lesser extend) the volatilities of the ETFs can explain the success of the rotation strategies.<\/p>\n

\n\tNotable quotations from the paper:<\/p>\n

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