Stock Price Inferencing and Prediction Based on Fama-French and Two-way Clustering Structure

Xuan Peng

Chapter 67 in Economic Management and Big Data Application:Proceedings of the 3rd International Conference, 2024, pp 758-771 from World Scientific Publishing Co. Pte. Ltd.

Abstract: This paper tests the Fama-French model using a new approach to estimate the standard error and verify the significance of different factors. Traditional standard error estimation neglects the correlation between stock return observations. As a result, the standard error will usually be underestimated, and some factors will show ostensible significance due to smaller standard error estimation and larger t-stat. This paper assumes a two-way clustering structure, assumes that stock return is correlated in industry and stock itself in two dimensions, and concludes with more decisive factors. Then this paper utilizes influential factors in stock return prediction and selection with the help of bootstrap simulation, and the result is slightly better than the standard OLS regression.

Keywords: Big Data; Information Management; Economic; Data Applications; Blockchain; E-commerce (search for similar items in EconPapers)
JEL-codes: C63 C8 O14 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.worldscientific.com/doi/pdf/10.1142/9789811270277_0067 (application/pdf)
https://www.worldscientific.com/doi/abs/10.1142/9789811270277_0067 (text/html)
Ebook Access is available upon purchase.

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:wsi:wschap:9789811270277_0067

Ordering information: This item can be ordered from

Access Statistics for this chapter

More chapters in World Scientific Book Chapters from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().