• Media type: E-Book
  • Title: In-Sample vs. Out-Of-Sample Analysis of Trading Strategies
  • Contributor: Dujava, Cyril [VerfasserIn]; Vojtko, Radovan [VerfasserIn]
  • imprint: [S.l.]: SSRN, [2023]
  • Extent: 1 Online-Ressource (12 p)
  • Language: English
  • DOI: 10.2139/ssrn.4486423
  • Identifier:
  • Keywords: diversification factor ; allocation factor ; investing ; own-research ; smart beta
  • Origination:
  • Footnote: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments June 20, 2023 erstellt
  • Description: Science has been in a “replication crisis” for more than a decade. Researchers have discovered, over and over, that lots of findings in fields like psychology, sociology, medicine, and economics don’t hold up when other researchers try to replicate them. There are many interesting questions of philosophy of science, for example: Is the problem just that we test for “statistical significance” — the likelihood that similarly strong results could have occurred by chance — in a nuance-free way? Is it that null results (that is when a study finds no detectable effects) are ignored while positive ones make it into journals? Simply said: many published studies cannot be replicated. Piper (2020). But what does it mean to us, investors and traders? We, here at Quantpedia, try to present you academic research in a digestible form for the person that is not used to going rigorously through myriads of papers written in “academese” and often hard to understand true applicable meaning of it. So, is there any “edge” in purely academic-developed trading strategies and investment approaches after publishing, or will they perish shortly after becoming public? After some time, we are about to revisit our own concept and test the out-of-sample decay. But this time, we have hard data – our regularly updated database of replicated quant strategies
  • Access State: Open Access