Predictive stock market analysis for Japanese textile companiesusing ARIMA model

Predicting the stock prices of Japanese textile companies is crucial for several reasons. First, stock price trends provide valuable insight into the financial health and growth potential of these firms, especially in an industry subject to evolving consumer trends, technological advancements, and global competition. Investors rely on stock price predictions to make informed decisions, helping them maximise returns and manage risks effectively. The study aims to predict the stock prices of Japanese Textile Companies using the ARIMA Model by taking the top 10 textile companies listed on the Tokyo Stock Exchange (TSE) for a span of 10 years, i.e. 2014 to 2023. Upon evaluation of models, it was found that most of the corporations had either a negative impact or no impact of past lagged values on their present value, except for Global Style, which had a positive influence, maybe due to its distinct nature of textile products in comparison to other corporations. Similarly, Itochu was 91% affected by its own past lagged values, maybe due to its huge operations in textiles as well as other sectors distinct from textiles. Mostly due to lagged value of past residuals, all the corporations were negatively affected, but Global Style, Kuraray, Itochu and Yagi were positively impacted, and there was a huge impact seen in terms of Itochu.

Authors

Institutions

Publication Details

Journal
Industria Textila
Published
2025-08-28
DOI
https://doi.org/10.35530/it.076.04.2024158
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predictive stock market analysis for Japanese textile companiesusing ARIMA model

Bharat Kumar Meher, Ramona Birău, Virgil Popescu, Vaikunta Pai T et al.
1 links
Industria Textila
Industrial Vision Systems and Defect Detection
article

Predictive stock market analysis for Japanese textile companiesusing ARIMA model

Bharat Kumar Meher, Ramona Birău, Virgil Popescu, Vaikunta Pai T, Abhishek Anand, Aman Shreevastava, Stefan Margaritescu
article en

Abstract

Predicting the stock prices of Japanese textile companies is crucial for several reasons. First, stock price trends provide valuable insight into the financial health and growth potential of these firms, especially in an industry subject to evolving consumer trends, technological advancements, and global competition. Investors rely on stock price predictions to make informed decisions, helping them maximise returns and manage risks effectively. The study aims to predict the stock prices of Japanese Textile Companies using the ARIMA Model by taking the top 10 textile companies listed on the Tokyo Stock Exchange (TSE) for a span of 10 years, i.e. 2014 to 2023. Upon evaluation of models, it was found that most of the corporations had either a negative impact or no impact of past lagged values on their present value, except for Global Style, which had a positive influence, maybe due to its distinct nature of textile products in comparison to other corporations. Similarly, Itochu was 91% affected by its own past lagged values, maybe due to its huge operations in textiles as well as other sectors distinct from textiles. Mostly due to lagged value of past residuals, all the corporations were negatively affected, but Global Style, Kuraray, Itochu and Yagi were positively impacted, and there was a huge impact seen in terms of Itochu.

Industria TextilaVol. 76(04)
Nitte University (IN), Department of Commerce (AU), University of Craiova (RO)
Openalex Percentile: Top 68%
Industrial Vision Systems and Defect Detection
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Predictive stock market analysis for Japanese textile companiesusing ARIMA model — Bharat Kumar Meher, Ramona Birău, et al. · Industria Textila (2025) | TGRS Research Map | TGRS