Financial risk tolerance profiling from text

Xing, F. ORCID: https://orcid.org/0000-0002-5751-3937 (2024) Financial risk tolerance profiling from text. Information Processing & Management, 61 (4). 103704. ISSN 0306-4573 doi: 10.1016/j.ipm.2024.103704

Abstract/Summary

Traditionally, individual financial risk tolerance information is gathered via questionnaires or similar structured psychometric tools. Our abundant digital footprint, as an unstructured alternative, is less investigated. Leveraging such information can potentially support large-scale and cost-efficient financial services. Therefore, I explore the possibility of building a computational model that distills risk tolerance information from user texts in this study, and discuss the design principles discovered from empirical results and their implications. Specifically, a new quaternary classification task is defined for text mining-based risk profiling. Experiments show that pre-trained large language models set a baseline micro-F1 of circa 0.34. Using a convolutional neural network (CNN), the reported system achieves a micro-F1 of circa 0.51, which significantly outperforms the baselines, and is a circa 4% further improvement over the standard CNN configurations (micro-F1 of circa 0.47). Textual feature richness and supervised learning are found to be the key contributors to model performances, while other machine learning strategies suggested by previous research (data augmentation and multi-tasking) are less effective. The findings confirm user texts to be a useful risk profiling resource and provide several insights on this task.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/123577
Identification Number/DOI	10.1016/j.ipm.2024.103704
Refereed	Yes
Divisions	Henley Business School > Digitalisation, Marketing and Entrepreneurship
Publisher	Elsevier
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