Financial Early Warning Model for Listed Companies Based on the Smart Sensor Data Network

Abstract

This paper uses intelligent sensors to build a data network, collects information about listed enterprises in all aspects, performs frequency statistics and semantic analysis based on the financial domain lexicon on information related to listed enterprises, and introduces big data variables into a nonlinear support vector machine early warning model of enterprises combined with financial indicators. This paper introduces online reviews as big data indicators based on financial early warning theories and methods. Suitable financial indicators and big data indicators are selected for the financial early warning model to filter the available indicators. The prediction results of only financial indicators are compared with the prediction results of incorporating big data indicators. Quantify the comment information related to enterprises on the platform through sentiment classification and statistics on the number of comment information posted. The cost-sensitive support vector machine is used as the base classifier of the improved AdaBoost algorithm to build a dynamic imbalance warning model. To address the problems of large complexity and computation of unbalanced big data classification, high reliance on a priori knowledge, and classification performance to be improved, the classification and detection method of the intelligent sensor data network is proposed. By introducing migration learning, the problem of knowledge acquisition and training efficiency of high-dimensional complex data feature extraction in a big data environment is effectively solved, and the network performance is optimized by a conjugate gradient descent algorithm. Through simulation experiments, the prediction accuracy of the model for positive class samples is significantly improved after the nonequilibrium processing, the recall rate of the network model reaches 84.15%, and the prediction accuracy of the network model under different time steps reaches more than 90%. The experiment proves that the model can send out financial alert information more quickly and efficiently and accurately when a financial crisis may occur, relative to the traditional financial forecasting methods.

Development and Application of Enterprise Financial Risk Analysis System Based on Data Mining Technology

“…As shown in Formula 1, it is a Logistic regression model, e is a natural logarithm, which is about 2.7182818, and Z is the sum of the product of the input indicator value and the weight variable. [8] For the output result of Logistic regression analysis model, take 0.5 as the benchmark value, when the output value is greater than 0.5, it can be concluded that this indicator will face financial risks, otherwise, this indicator is currently in a normal state.…”

An Improved C4.5 Algorithm Shannon Information Theory-Based to Optimize Financial Early Warning System

“…Finally, a new error prediction formula is obtained. Wang et al [6] used intelligent sensors to build a data network, collected information of listed companies in an all-round way, made frequency statistics and semantic analysis of relevant information of listed companies based on the financial lexicon, and introduced big data variables into the nonlinear support vector machine enterprise early warning model in combination with financial indicators. Although current models can achieve financial warning, the warning effect is not ideal, and the simulation accuracy needs to be improved.…”