PRESS RELEASE
18 JANUARY 2023

NIKKO ASSET MANAGEMENT CO., LTD.

Nikko AM Quants Analyst Co-authors Published Academic Paper
about Japanese Text Mining in the Financial Domain
Nikko Asset Management Co., Ltd. (Nikko AM), today announces that a paper co-authored by employee
Masahiro Suzuki has been published in Volume 60, Issue 2, of prestigious international academic journal“Information Processing & Management”. This peer-reviewed journal presents ground-breaking research on
information and computing science theory, methods, and applications in a range of domains.
The paper, “Constructing and analyzing domain-specific language model for financial text mining”
highlighted the effectiveness of Bidirectional Encoder Representations from Transformers (BERT). BERT is amachine learning technique for natural language processing that has proven highly effective for sentimentand similarity analysis, but difficult to apply to financial text due highly specialised terminology. The study
explored how the authors researched and built a more precise BERT model for Japanese text that adapts
better to the financial domain.
Suzuki is a quants analyst and member of Nikko AM’s Investment Technology Fund Management Department
and Japan Equity Fund Management Department. The paper’s co-authors were Professor Kiyoshi Izumi,
Project Lecturer Hiroki Sakaji, and Ph.D. student and Technical Assistant Masanori Hirano, all from theUniversity of Tokyo’s Graduate School of Engineering.
Nikko AM has long focused on deep learning research that harnesses Japanese text data and image data fromthe financial sector in order to better analyse portfolio companies, the economy, and capital markets, whilebolstering its quants investment performance and risk management. The firm will continue to enhance itsinvestment management expertise through further study, tapping the findings of its research and ongoingcollaboration with other stakeholders.
About the paper

1. Title

Suzuki, M., Sakaji, H., Hirano, M., & Izumi, K. (2023). Constructing and analyzing domain-specific language model for financial text
mining. Information Processing & Management, 60(2), 103194.
Constructing and analyzing domain-specific language model for financial text mining - ScienceDirect

2. Abstract

The application of natural language processing (NLP) to financial fields is advancing with an increase in the number of available
financial documents. Transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT) havebeen successful in NLP in recent years. These cutting-edge models have been adapted to the financial domain by applying
financial corpora to existing pre-trained models and by pre-training with the financial corpora from scratch. In Japanese, bycontrast, financial terminology cannot be applied from a general vocabulary without further processing. In this study, weconstruct language models suitable for the financial domain. Furthermore, we compare methods for adapting language models
to the financial domain, such as pre-training methods and vocabulary adaptation. We confirm that the adaptation of a pretraining corpus and tokenizer vocabulary based on a corpus of financial text is effective in several downstream financial tasks. No
significant difference is observed between pre-training with the financial corpus and continuous pre-training from the generallanguage model with the financial corpus. We have released our source code and pre-trained models.

3. Information Processing & Management website

Information Processing & Management | Journal | ScienceDirect.com by Elsevier– ENDS –nikkoam.com

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