Evaluating active investing with generic trading reactions
We evaluate the performance of rules using past information to generate daily trading signals. Assuming generic trading reactions to buy and sell signals, we derive an analytic excess return that isolates commissions, interests, the impact of trading timing, and that of the benchmark's choice....
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Veröffentlicht in: | International journal of finance and economics 2021-01, Vol.26 (1), p.1018-1036 |
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creator | Zoicas‐Ienciu, Adrian |
description | We evaluate the performance of rules using past information to generate daily trading signals. Assuming generic trading reactions to buy and sell signals, we derive an analytic excess return that isolates commissions, interests, the impact of trading timing, and that of the benchmark's choice. The result is useful in dealing with data snooping through leverage and benchmark tweaking. We illustrate the empirical implications by examining trend‐following performance across Dow Jones Industrial Average (1927–2016) and an international sample of major equity indexes and blue‐chip stocks (1980–2016). The results show substantial, fading, non‐persistent and highly methodology‐sensitive excess returns. |
doi_str_mv | 10.1002/ijfe.1833 |
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Assuming generic trading reactions to buy and sell signals, we derive an analytic excess return that isolates commissions, interests, the impact of trading timing, and that of the benchmark's choice. The result is useful in dealing with data snooping through leverage and benchmark tweaking. We illustrate the empirical implications by examining trend‐following performance across Dow Jones Industrial Average (1927–2016) and an international sample of major equity indexes and blue‐chip stocks (1980–2016). 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Assuming generic trading reactions to buy and sell signals, we derive an analytic excess return that isolates commissions, interests, the impact of trading timing, and that of the benchmark's choice. The result is useful in dealing with data snooping through leverage and benchmark tweaking. We illustrate the empirical implications by examining trend‐following performance across Dow Jones Industrial Average (1927–2016) and an international sample of major equity indexes and blue‐chip stocks (1980–2016). The results show substantial, fading, non‐persistent and highly methodology‐sensitive excess returns.</description><subject>data snooping</subject><subject>market efficiency</subject><subject>Performance evaluation</subject><subject>return predictability</subject><subject>technical trading</subject><subject>trading strategies</subject><issn>1076-9307</issn><issn>1099-1158</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1kE1PAjEQhhujiYge_AebePKwMN1uP9abIaAYEi96bmp3iiW4i-0C4d_TBa-eZuadZz7yEnJPYUQBirFfORxRxdgFGVCoqpxSri77XIq8YiCvyU2MKwAQXMKAPE13Zr01nW-WmbGd32Hmmx3Gk7D33Xe2xAaDt1kXTN2LAXuubeItuXJmHfHuLw7J52z6MXnNF-8v88nzIreMActpaRU6VyOvLP0S6AxzVtRcUqE4GmVYVYJwhUtpanBFU1WWtTCWSSwEG5KH895NaH-36TW9arehSSd1USopS06BJurxTNnQxhjQ6U3wPyYcNAXdW6N7a3RvTWLHZ3bv13j4H9Tzt9n0NHEE9EhmAQ</recordid><startdate>202101</startdate><enddate>202101</enddate><creator>Zoicas‐Ienciu, Adrian</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Periodicals Inc</general><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0001-9049-1373</orcidid></search><sort><creationdate>202101</creationdate><title>Evaluating active investing with generic trading reactions</title><author>Zoicas‐Ienciu, Adrian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3303-14c8effde59c1b6efa3fc6d571685ea8a39406f2fa8afc658106f44d6ac37e263</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>data snooping</topic><topic>market efficiency</topic><topic>Performance evaluation</topic><topic>return predictability</topic><topic>technical trading</topic><topic>trading strategies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zoicas‐Ienciu, Adrian</creatorcontrib><collection>CrossRef</collection><jtitle>International journal of finance and economics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zoicas‐Ienciu, Adrian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluating active investing with generic trading reactions</atitle><jtitle>International journal of finance and economics</jtitle><date>2021-01</date><risdate>2021</risdate><volume>26</volume><issue>1</issue><spage>1018</spage><epage>1036</epage><pages>1018-1036</pages><issn>1076-9307</issn><eissn>1099-1158</eissn><abstract>We evaluate the performance of rules using past information to generate daily trading signals. Assuming generic trading reactions to buy and sell signals, we derive an analytic excess return that isolates commissions, interests, the impact of trading timing, and that of the benchmark's choice. The result is useful in dealing with data snooping through leverage and benchmark tweaking. We illustrate the empirical implications by examining trend‐following performance across Dow Jones Industrial Average (1927–2016) and an international sample of major equity indexes and blue‐chip stocks (1980–2016). The results show substantial, fading, non‐persistent and highly methodology‐sensitive excess returns.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/ijfe.1833</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0001-9049-1373</orcidid></addata></record> |
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subjects | data snooping market efficiency Performance evaluation return predictability technical trading trading strategies |
title | Evaluating active investing with generic trading reactions |
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