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Financial ratios and stock returns reappraised through a topological data analysis lens
The European Journal of Finance, Volume: 30, Issue: 1, Pages: 53 - 77
Swansea University Authors: Pawel Dlotko , Simon Rudkin
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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...
Published in: | The European Journal of Finance |
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ISSN: | 1351-847X 1466-4364 |
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Informa UK Limited
2024
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URI: | https://cronfa.swan.ac.uk/Record/cronfa59134 |
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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 |
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403ec9c6f5967333948eabebe06a75f5 93f12293ea6ed07ae8162cf25659c5f2 |
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403ec9c6f5967333948eabebe06a75f5_***_Pawel Dlotko 93f12293ea6ed07ae8162cf25659c5f2_***_Simon Rudkin |
author |
Pawel Dlotko Simon Rudkin |
author2 |
Pawel Dlotko W. Qiu Simon Rudkin |
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The European Journal of Finance |
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30 |
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53 |
publishDate |
2024 |
institution |
Swansea University |
issn |
1351-847X 1466-4364 |
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10.1080/1351847x.2021.2009892 |
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Informa UK Limited |
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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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11.035349 |