Risk Analysis of Textile Industry Foreign Investment Based on Deep Learning.

Jingyi Liu,Jiaolong Li,Baiyuan Ding

doi:10.1155/2022/3769670

Abstract

With the decline of China's economic growth rate and the uproar of antiglobalization, the textile industry, one of the business cards of China's globalization, is facing a huge impact. When the economic model is undergoing transformation, it is more important to prevent enterprises from falling into financial distress. So, the financial risk early warning is one of the important means to prevent enterprises from falling into financial distress. Aiming at the risk analysis of the textile industry's foreign investment, this paper proposes an analysis method based on deep learning. This method combines residual network (ResNet) and long short-term memory (LSTM) risk prediction model. This method first establishes a risk indicator system for the textile industry and then uses ResNet to complete deep feature extraction, which are further used for LSTM training and testing. The performance of the proposed method is tested based on part of the measured data, and the results show the effectiveness of the proposed method.

Highlights

With the slowdown of China’s economic growth rate and changes in the growth model, the original crude corporate model has become unsustainable [1,2,3,4]
In the traditional convolutional neural networks (CNNs), except for the first layer, the input of each layer is derived from the output of the previous layer. e ResNet adopts a skip structure so that the deep residual network can directly cross the middle layers. e parameters are passed to the subsequent layers, which reduces the complexity of the network, solves the degradation problem of the deep-level network, and promotes the improvement of network performance
Comparing the proposed method with long short-term memory (LSTM) shows that this paper further introduces ResNet for deep feature learning, which further improves the final prediction performance

Summary

Introduction

With the slowdown of China’s economic growth rate and changes in the growth model, the original crude corporate model has become unsustainable [1,2,3,4]. After years of research and development, artificial neural networks and their various improved models still cannot completely get rid of the defect that they tend to fall into local minimums and cannot reflect the timing relationship between samples [16,17,18,19,20,21,22,23,24] This timing relationship is common in the economic field, and predictive analysis is of great help. An indicator system is established for investment risks in the textile industry, and feature vectors are constructed to describe the risk levels in the current state On this basis, ResNet is used to perform further feature learning on the constructed index feature quantity to obtain deep features with stronger descriptive ability. The proposed method is tested and verified with part of the data obtained publicly, and the results showed the effectiveness of the proposed method

Basic Theory

Risk Assessment Method

Experiment and Analysis

Conclusion