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Paper Abstract and Keywords
Presentation 2026-03-24 14:20
Stock Price Prediction Integrating Pre-trained Models for Time-Series and Textual Data
Takuma Miwa (NITech), Tohgoroh Matsui (Chubu Univ.), Atsuko Mutou, Koichi Moriyama, Kosuke Shima, Nobuhiro Inuzuka (NITech) IBISML2025-41
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, as interest in investment has grown, stock price prediction has become an important research topic. In particular, against the backdrop of rapid advances in artificial intelligence, various prediction models based on deep learning have been proposed. While conventional approaches utilize stock prices and textual data for prediction, deep learning models typically involve a large number of parameters, making overfitting a significant challenge. The purpose of this study is to address the overfitting problem in stock price prediction models that use both stock price data and textual data. As a proposed approach, we introduce a stock price prediction model that employs a pre-trained time-series model as a stock price feature extractor and a pre-trained natural language processing model as a text feature extractor. Experimental results demonstrate that the proposed method mitigates overfitting observed in conventional methods and improves prediction accuracy on unseen data.
Keyword (in Japanese) (See Japanese page) 
(in English) stock price prediction / pre-trained model / machine learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 425, IBISML2025-41, pp. 1-8, March 2026.
Paper # IBISML2025-41 
Date of Issue 2026-03-17 (IBISML) 
ISSN Online edition: ISSN 2432-6380
Copyright
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reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee PRMU IPSJ-CVIM IBISML ITE-SIP  
Conference Date 2026-03-24 - 2026-03-25 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IBISML 
Conference Code 2026-03-PRMU-CVIM-IBISML-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Stock Price Prediction Integrating Pre-trained Models for Time-Series and Textual Data 
Sub Title (in English)  
Keyword(1) stock price prediction  
Keyword(2) pre-trained model  
Keyword(3) machine learning  
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1st Author's Name Takuma Miwa  
1st Author's Affiliation Nagoya Institute of Technology (NITech)
2nd Author's Name Tohgoroh Matsui  
2nd Author's Affiliation Chubu University (Chubu Univ.)
3rd Author's Name Atsuko Mutou  
3rd Author's Affiliation Nagoya Institute of Technology (NITech)
4th Author's Name Koichi Moriyama  
4th Author's Affiliation Nagoya Institute of Technology (NITech)
5th Author's Name Kosuke Shima  
5th Author's Affiliation Nagoya Institute of Technology (NITech)
6th Author's Name Nobuhiro Inuzuka  
6th Author's Affiliation Nagoya Institute of Technology (NITech)
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Speaker Author-1 
Date Time 2026-03-24 14:20:00 
Presentation Time 20 minutes 
Registration for IBISML 
Paper # IBISML2025-41 
Volume (vol) vol.125 
Number (no) no.425 
Page pp.1-8 
#Pages 8 
Date of Issue 2026-03-17 (IBISML) 

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