• Search Research Projects
  • Search Researchers
  • How to Use
  1. Back to previous page

Financial market modeling integrating language information via deep learning

Research Project

Project/Area Number 21J11781
Research Category

Grant-in-Aid for JSPS Fellows

Allocation TypeSingle-year Grants
Section国内
Review Section Basic Section 61030:Intelligent informatics-related
Research InstitutionThe University of Tokyo

Principal Investigator

DU XIN  東京大学, 工学系研究科, 特別研究員(DC2)

Project Period (FY) 2021-04-28 – 2023-03-31
Project Status Completed (Fiscal Year 2022)
Budget Amount *help
¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2022: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2021: ¥900,000 (Direct Cost: ¥900,000)
Keywords金融 / 自然言語処理 / 深層学習 / 株のベクトル表現 / ポートフォリオモデル / 言語のベクトル表現 / ファットテール / 時系列 / ポートフォリオ
Outline of Research at the Start

人工知能技術の急速な発展と言語処理技術の急速な革新により、大量のテキストの処理と理解が可能になった。この背景で、金融市場や金融活動を分析するための言語処理技術の使用は、近年重要な課題になった。本研究は、英語のニューステキストと米国の株価変動との関係を分析することにより、新しいポートフォリオ構築モデルを提出した。これからは、他の言語に対する有効性を確認し、株式以外の金融資産または高頻度データへの応用を検討する。

Outline of Annual Research Achievements

In recent years, the financial markets have faced significant challenges such as the 2018 bitcoin price crashes and the 2020 US stock market declines. While previous research has largely focused on analyzing price data, this researcher sought to take a novel approach by incorporating natural language data such as news articles. Deep learning techniques were employed to process both price and language data within a single computational framework, establishing a connection between these two complex social systems and ultimately enhancing our understanding of financial markets.
In the past year, the researcher proposed a generalized model for stock portfolio optimization that integrates natural language data. This model represented a stock with a vector obtained from news articles and identified extreme risk correlations between stocks from these articles, effectively diversifying the risks. This work was accepted for publication in "Knowledge-Based Systems."
Additionally, the researcher investigated the limitations of vector representations of stocks in describing complex phenomena like polysemy. To address these limitations, a new representation method using functions instead of vectors was proposed and validated on language data. The method will be further validated on financial markets and has been accepted for publication in "Advances in Neural Information Processing Systems 2022."

Research Progress Status

令和4年度が最終年度であるため、記入しない。

Strategy for Future Research Activity

令和4年度が最終年度であるため、記入しない。

Report

(2 results)
  • 2022 Annual Research Report
  • 2021 Annual Research Report
  • Research Products

    (3 results)

All 2022 Other

All Journal Article (1 results) (of which Peer Reviewed: 1 results) Presentation (1 results) (of which Int'l Joint Research: 1 results) Remarks (1 results)

  • [Journal Article] Stock portfolio selection balancing variance and tail risk via stock vector representation acquired from price data and texts2022

    • Author(s)
      Xin Du and Kumiko Tanaka-Ishii
    • Journal Title

      Knowledge-Based Systems

      Volume: 249 Pages: 108917-108917

    • DOI

      10.1016/j.knosys.2022.108917

    • Related Report
      2022 Annual Research Report 2021 Annual Research Report
    • Peer Reviewed
  • [Presentation] FIRE: Semantic Field of Words Represented as Non-Linear Functions2022

    • Author(s)
      Du Xin、Tanaka-Ishii Kumiko
    • Organizer
      Advances in Neural Information Processing Systems 35 (NeurIPS 2022)
    • Related Report
      2022 Annual Research Report
    • Int'l Joint Research
  • [Remarks] 株ベクトルの実用化の例として、ポートフォリオの自動生成ができるウェブサイト finnewx

    • Related Report
      2021 Annual Research Report

URL: 

Published: 2021-05-27   Modified: 2024-03-26  

Information User Guide FAQ News Terms of Use Attribution of KAKENHI

Powered by NII kakenhi