EconPapers    
Economics at your fingertips  
 

Spurious Regression and Data Mining in Conditional Asset Pricing Models

Cheng-Few Lee (), Hong-Yi Chen () and John Lee ()
Additional contact information
Cheng-Few Lee: Rutgers University, Department of Finance and Economics, Rutgers Business School
Hong-Yi Chen: National Chengchi University, Department of Finance
John Lee: Center for PBBEF Research

Chapter Chapter 9 in Financial Econometrics, Mathematics and Statistics, 2019, pp 243-275 from Springer

Abstract: Abstract Based upon a pioneering paper entitled, “Spurious Regressions in Econometrics,” by Granger and Newbold (J Econ 4: 111–120, 1974), this chapter investigates how the spurious regression phenomenon can affect asset pricing tests. The measure method used to examine this issue is by data mining methodology. Finally, potential solutions to the problems of spurious regression and data mining are discussed in some detail.

Keywords: Asset allocation; Monte Carlo simulations; Persistent instruments; Predicting stock returns; Time-varying returns (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4939-9429-8_9

Ordering information: This item can be ordered from
http://www.springer.com/9781493994298

DOI: 10.1007/978-1-4939-9429-8_9

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2026-07-28
Handle: RePEc:spr:sprchp:978-1-4939-9429-8_9