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Paper Abstract and Keywords
Presentation 2026-06-18 16:30
Detecting Precursory Low-Frequency Components of Search Interest Prior to Stock Price Fluctuations: FFT Analysis of Google Trends and Related Events Surrounding Japanese Automobile Manufacturers
Takumi Ishii, Chisa Takano, Yasuhiro Obata (Hiroshima City Univ.), Masaki Aida (Tokyo Metropolitan Univ.), Hikaru Sato (ITOCHU Corporation) NS2026-41
Abstract (in Japanese) (See Japanese page) 
(in English) Online User Dynamics (OUD), as exemplified by social media activity and search behavior, provides an important source of information for capturing changes in social interest and market sentiment.
Conventional analyses often focus on changes observed after a phenomenon has already become apparent, such as a surge in the number of posts or searches, making it difficult to capture subtle early signs before the phenomenon emerges.
In this study, we apply FFT analysis to time-series data of company-related keyword searches obtained from Google Trends. This approach is based on a network oscillation model and on the hypothesis that low-frequency components increase prior to OUD overheating.
As the subjects of analysis, we selected five major Japanese automobile manufacturers that were affected by Trump’s 2025 tariff statements. We then evaluated the relationship between the low-frequency components of search interest and stock price fluctuations using cross-correlation analysis.
The results indicate that, for some companies and keywords, low-frequency components of search interest tended to precede stock price fluctuations.
Keyword (in Japanese) (See Japanese page) 
(in English) User Dynamics / Spectral Analysis / Early Detection / Network Oscillation Model / FinTech / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 75, NS2026-41, pp. 31-36, June 2026.
Paper # NS2026-41 
Date of Issue 2026-06-11 (NS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
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)
Download PDF NS2026-41

Conference Information
Committee OCS NS PN  
Conference Date 2026-06-18 - 2026-06-19 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NS 
Conference Code 2026-06-OCS-NS-PN 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Detecting Precursory Low-Frequency Components of Search Interest Prior to Stock Price Fluctuations: FFT Analysis of Google Trends and Related Events Surrounding Japanese Automobile Manufacturers 
Sub Title (in English)  
Keyword(1) User Dynamics  
Keyword(2) Spectral Analysis  
Keyword(3) Early Detection  
Keyword(4) Network Oscillation Model  
Keyword(5) FinTech  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Takumi Ishii  
1st Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
2nd Author's Name Chisa Takano  
2nd Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
3rd Author's Name Yasuhiro Obata  
3rd Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
4th Author's Name Masaki Aida  
4th Author's Affiliation Tokyo Metropolitan University (Tokyo Metropolitan Univ.)
5th Author's Name Hikaru Sato  
5th Author's Affiliation ITOCHU Corporation (ITOCHU Corporation)
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Speaker Author-1 
Date Time 2026-06-18 16:30:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2026-41 
Volume (vol) vol.126 
Number (no) no.75 
Page pp.31-36 
#Pages 6 
Date of Issue 2026-06-11 (NS) 

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