Portfolio Algorithm Based on Correlation of Accounting Information Systems

Ruolin Song, Yanhui Zhang, Qingchang Liu

2025

Abstract

This paper analyzes the corporate investment portfolio based on the correlation of accounting information system, and proposes a portfolio algorithm to facilitate the analysis results. In the process of research, this paper gradually implements a system based on various steps such as system architecture, modeling, model optimization and training, and then implements a portfolio algorithm based on the correlation of accounting information system, and puts it into application. Experimental results show that after applying the algorithm, the annual return of the optimized portfolio increases by 10% and the overall volatility decreases, which proves that the method is effective. The final conclusion shows that the portfolio algorithm based on the correlation of accounting information system,It can significantly improve the balance between return and risk, and provide a sufficient and powerful basis for the company to formulate long-term investment strategies.

Paper Citation

in Harvard Style

Zhang Y., Song R. and Liu Q. (2025). Portfolio Algorithm Based on Correlation of Accounting Information Systems. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 289-294. DOI: 10.5220/0013540100004664

in Bibtex Style

@conference{incoft25,
author={Yanhui Zhang and Ruolin Song and Qingchang Liu},
title={Portfolio Algorithm Based on Correlation of Accounting Information Systems},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={289-294},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013540100004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - Portfolio Algorithm Based on Correlation of Accounting Information Systems
SN - 978-989-758-763-4
AU - Zhang Y.
AU - Song R.
AU - Liu Q.
PY - 2025
SP - 289
EP - 294
DO - 10.5220/0013540100004664
PB - SciTePress