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

Pawel Dlotko Orcid Logo, W. Qiu, Simon Rudkin Orcid Logo

The European Journal of Finance, Volume: 30, Issue: 1, Pages: 53 - 77

Swansea University Authors: Pawel Dlotko Orcid Logo, 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 2024
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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 93f12293ea6ed07ae8162cf25659c5f2 0000-0001-8622-7318 Simon Rudkin Simon Rudkin true false 2022-01-10 MACS 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 30 1 53 77 Informa UK Limited 1351-847X 1466-4364 Stock returns, anomalies, topological data analysis, data science, mispricing 2 1 2024 2024-01-02 10.1080/1351847x.2021.2009892 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University This work was supported by Max-Planck-Gesellschaft [Dioscuri Program], jointly managed with the National Science Centre (Poland), and mutually funded by the Polish Ministry of Science and Higher Education and the German Federal Ministry of Education and Research. 2024-06-05T16:17:23.6503977 2022-01-10T15:31:40.9837926 Faculty of Humanities and Social Sciences School of Social Sciences - Economics Pawel Dlotko 0000-0001-5352-3102 1 W. Qiu 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
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
93f12293ea6ed07ae8162cf25659c5f2
author_id_fullname_str_mv 403ec9c6f5967333948eabebe06a75f5_***_Pawel Dlotko
93f12293ea6ed07ae8162cf25659c5f2_***_Simon Rudkin
author Pawel Dlotko
Simon Rudkin
author2 Pawel Dlotko
W. Qiu
Simon Rudkin
format Journal article
container_title The European Journal of Finance
container_volume 30
container_issue 1
container_start_page 53
publishDate 2024
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
hierarchytype
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
department_str School of Social Sciences - Economics{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Social Sciences - Economics
document_store_str 1
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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 2024-01-02T16:17:22Z
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score 11.037603

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