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Financial ratios and stock returns reappraised through a topological data analysis lens

Pawel Dlotko Orcid Logo, Wanling Rudkin, Simon Rudkin Orcid Logo

The European Journal of Finance, Pages: 1 - 25

Swansea University Authors: Pawel Dlotko Orcid Logo, Wanling Rudkin, Simon Rudkin Orcid Logo

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DOI (Published version): 10.1080/1351847x.2021.2009892

Abstract

Firm financials are well-established predictors of stock returns, being the basis for both the traditional econometric, and growing Machine Learning, asset pricing literature. Employing topological data analysis ball mapper (TDABM), we revisit the association between seven of the most commonly studi...

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Published in: The European Journal of Finance
ISSN: 1351-847X 1466-4364
Published: Informa UK Limited 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa59134
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first_indexed 2022-01-17T13:58:29Z
last_indexed 2023-03-16T04:17:56Z
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spelling v2 59134 2022-01-10 Financial ratios and stock returns reappraised through a topological data analysis lens 403ec9c6f5967333948eabebe06a75f5 0000-0001-5352-3102 Pawel Dlotko Pawel Dlotko true false dcbad51452ff28fbfdf7e82d4669c6a4 Wanling Rudkin Wanling Rudkin true false 93f12293ea6ed07ae8162cf25659c5f2 0000-0001-8622-7318 Simon Rudkin Simon Rudkin true false 2022-01-10 SMA Firm financials are well-established predictors of stock returns, being the basis for both the traditional econometric, and growing Machine Learning, asset pricing literature. Employing topological data analysis ball mapper (TDABM), we revisit the association between seven of the most commonly studied financial ratios and stock returns. Upon outlining the methodology to the finance literature, this paper offers three key contributions to the study of asset pricing. Firstly, the characteristic space is visualised to showcase non-monotonic relationships in multiple dimensions that were as yet unseen. Secondly, the means through which neural networks and random forest regressions fit stock returns is also visualised, showing where Machine Learning is contributing to understanding. Finally, an initial application of TDABM for the segmentation of the cross-section is posited, with significant abnormal returns identified. Collectively these three expositions signpost the value of TDABM for financial researchers and practitioners alike. The scope for benefit is limited only by the availability of information to the analyst. Journal Article The European Journal of Finance 0 1 25 Informa UK Limited 1351-847X 1466-4364 Stock returns, anomalies, topological data analysis, data science, mispricing 17 12 2021 2021-12-17 10.1080/1351847x.2021.2009892 COLLEGE NANME Mathematics COLLEGE CODE SMA Swansea University 2023-07-31T11:02:14.3708868 2022-01-10T15:31:40.9837926 Faculty of Humanities and Social Sciences Pawel Dlotko 0000-0001-5352-3102 1 Wanling Rudkin 2 Simon Rudkin 0000-0001-8622-7318 3 59134__22098__cbd61d7f5a8e4e5d964057f49329d72b.pdf AAM.pdf 2022-01-10T15:33:28.4698185 Output 285192 application/pdf Accepted Manuscript true 2023-06-17T00:00:00.0000000 Distributed under the terms of a Creative Commons Attribution Non Commercial 4.0 License (CC BY-NC 4.0). true eng https://creativecommons.org/licenses/by-nc/4.0
title Financial ratios and stock returns reappraised through a topological data analysis lens
spellingShingle Financial ratios and stock returns reappraised through a topological data analysis lens
Pawel Dlotko
Wanling Rudkin
Simon Rudkin
title_short Financial ratios and stock returns reappraised through a topological data analysis lens
title_full Financial ratios and stock returns reappraised through a topological data analysis lens
title_fullStr Financial ratios and stock returns reappraised through a topological data analysis lens
title_full_unstemmed Financial ratios and stock returns reappraised through a topological data analysis lens
title_sort Financial ratios and stock returns reappraised through a topological data analysis lens
author_id_str_mv 403ec9c6f5967333948eabebe06a75f5
dcbad51452ff28fbfdf7e82d4669c6a4
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author_id_fullname_str_mv 403ec9c6f5967333948eabebe06a75f5_***_Pawel Dlotko
dcbad51452ff28fbfdf7e82d4669c6a4_***_Wanling Rudkin
93f12293ea6ed07ae8162cf25659c5f2_***_Simon Rudkin
author Pawel Dlotko
Wanling Rudkin
Simon Rudkin
author2 Pawel Dlotko
Wanling Rudkin
Simon Rudkin
format Journal article
container_title The European Journal of Finance
container_volume 0
container_start_page 1
publishDate 2021
institution Swansea University
issn 1351-847X
1466-4364
doi_str_mv 10.1080/1351847x.2021.2009892
publisher Informa UK Limited
college_str Faculty of Humanities and Social Sciences
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hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
document_store_str 1
active_str 0
description Firm financials are well-established predictors of stock returns, being the basis for both the traditional econometric, and growing Machine Learning, asset pricing literature. Employing topological data analysis ball mapper (TDABM), we revisit the association between seven of the most commonly studied financial ratios and stock returns. Upon outlining the methodology to the finance literature, this paper offers three key contributions to the study of asset pricing. Firstly, the characteristic space is visualised to showcase non-monotonic relationships in multiple dimensions that were as yet unseen. Secondly, the means through which neural networks and random forest regressions fit stock returns is also visualised, showing where Machine Learning is contributing to understanding. Finally, an initial application of TDABM for the segmentation of the cross-section is posited, with significant abnormal returns identified. Collectively these three expositions signpost the value of TDABM for financial researchers and practitioners alike. The scope for benefit is limited only by the availability of information to the analyst.
published_date 2021-12-17T11:02:11Z
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score 10.971005

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