Zhang, H. orcid.org/0000-0002-8727-4906 and Su, C. (2020) A time-series bootstrapping simulation method to distinguish sell-side analysts’ skill from luck. In: Lee, C.F. and Lee, J.C., (eds.) Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning. World Scientific Publishing , pp. 2011-2052. ISBN 9789811202384
Abstract
Data mining is quite common in econometric modeling when a given dataset is applied multiple times for the purpose of inference; it in turn could bias inference. Given the existence of data mining, it is likely that any reported investment performance is simply due to random chance (luck). This study develops a time-series bootstrapping simulation method to distinguish skill from luck in the investment process. Empirically, we find little evidence showing that investment strategies based on UK analyst recommendation revisions can generate statistically significant abnormal returns. Our rolling window based bootstrapping simulations confirm that the reported insignificant portfolio performance is due to sell-side analysts’ lack of skill in making valuable stock recommendations, rather than their bad luck, irrespective of whether they work for more prestigious brokerage houses or not.
Metadata
Item Type: | Book Section |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | © 2020 World Scientific Publishing Co Pte Ltd. This is an author-produced version of a chapter subsequently published in Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning. Uploaded in accordance with the publisher's permission. |
Keywords: | data mining; time-series bootstrapping simulations; sell-side analysts; analyst recommendation revisions |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Jul 2020 07:16 |
Last Modified: | 02 Sep 2021 15:56 |
Published Version: | https://www.worldscientific.com/worldscibooks/10.1... |
Status: | Published |
Publisher: | World Scientific Publishing |
Refereed: | Yes |
Identification Number: | 10.1142/9789811202391_0055 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163456 |