Financial Early-Warning of Information Technology Enterprises Based on Support Vector Machine Algorithm

Shi Yan

2025

Abstract

Support Vector Machines (SVM for short) is a new machine learning method developed on the basis of statistical learning theory. By adopting the principle of structural risk minimization, SVM can better solve the problem of limited sample learning. It has many excellent characteristics, such as using kernel function to avoid local minimum of solution, having sparsity of solution, achieving capacity control or support vector number control through the role of boundary, etc. It shows many unique advantages in solving the problem of limited samples, nonlinear and high-dimensional pattern recognition. This research applies support vector machine (SVM) algorithm to predict the future information technology enterprises in the market. SVM is a supervised learning method for analyzing data and predicting results. In this paper, we focus on financial forecasting using SVM algorithm. Specifically, we use the public historical data of annual revenue and market value of IT companies to develop a model to predict the future annual revenue of IT companies based on the past revenue growth rate. The performance of our prediction model is verified by cross validation analysis.

Paper Citation

in Harvard Style

Yan S. (2025). Financial Early-Warning of Information Technology Enterprises Based on Support Vector Machine Algorithm. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 379-382. DOI: 10.5220/0013543900004664

in Bibtex Style

@conference{incoft25,
author={Shi Yan},
title={Financial Early-Warning of Information Technology Enterprises Based on Support Vector Machine Algorithm},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={379-382},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013543900004664},
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 - Financial Early-Warning of Information Technology Enterprises Based on Support Vector Machine Algorithm
SN - 978-989-758-763-4
AU - Yan S.
PY - 2025
SP - 379
EP - 382
DO - 10.5220/0013543900004664
PB - SciTePress