Minh Tri Phan
Last Name
Phan
First name
Minh Tri
Email
triminh.phan@unisg.ch
3 results
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Item type:Publication, Unveiling themes in 10-K disclosures: A new topic modeling perspective(2025-07); Type:journal articleJournal:International Review of Financial AnalysisScopus© Citations 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Topic Model for 10-K Management Disclosures(2023-08-15); We investigate the topics discussed in the Management's Discussion and Analysis (MD&A) section of 10-K filings from January 1994 to December 2018. In our modeling approach, we elicit the MD&A topics by clustering words around a set of anchor words that broadly define a potential topic. From the topics, we extract two hidden loading series from the MD&As - a measure of topic prevalence and a measure of topic sentiment. The results are three-fold. First, the topics we find are intelligible and distinctive but are potentially multi-modal, which may explain why classical topic models applied to 10-K filings often lack interpretability. Second, topic prevalence and sentiment tend to follow trends which, by and large, can be rationalized historically. Third, sentiment affects topics heterogeneously, i.e., in topic-specific ways. Adding to the extant document-level techniques, our study demonstrates the potential benefits of using a nuanced topic-level approach to analyze the MD&A.Type:working paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, LongFinBERT: A Language Model for Very Long Financial Documents(2023-12-18); This paper introduces LongFinBERT, a modern language model specialized for processing long financial documents. Due to an adaptation in model architecture, LongFinBERT demonstrates substantially lower computational requirements for lengthy documents compared to other state-of-the-art language models. This characteristic enables processing of e.g. an entire annual accounting filing at once, which was previously computationally infeasible for LMs. We apply LongFinBERT to two empirical settings: Firstly, we aim to improve the detection of financial misreporting using text from 10-K filings from 1994 to 2018. Misreporting predictions that utilize text-based features from LongFinBERT outperform those based solely on accounting variables or other textual models, namely Latent Dirichlet Allocation, neural document embeddings, and FinBERT. Lastly, we find that market returns respond to year-over-year alterations of accounting disclosures, measured using LongFinBERT.Type:working paper