{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:47:01Z","timestamp":1776811621516,"version":"3.51.2"},"reference-count":21,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2022,3,28]]},"abstract":"<jats:p>The stability of the economic market is an important factor for the rapid development of the economy, especially for the listed companies, whose financial and economic stability affects the stability of the financial market. It is helpful for the healthy development of enterprises and financial markets to make an accurate early warning of the financial economy of listed enterprises. This paper briefly introduced the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms in the machine learning method. To make up for the defects of the two algorithms, they were combined and applied to the enterprise financial economics early warning. A simulation experiment was carried out on the single SVM algorithm-based, single BPNN algorithm-based, and SVM algorithm and BPNN algorithm combined model with the MATLAB software. The results show that the SVM algorithm and BP algorithm combined model converges faster and has higher precision and recall rate and larger area under the curve (AUC) than the single SVM algorithm-based model and the single BPNN algorithm-based model.<\/jats:p>","DOI":"10.3233\/jcm-215783","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T12:05:21Z","timestamp":1639483521000},"page":"529-539","source":"Crossref","is-referenced-by-count":2,"title":["Research on enterprise financial economics early warning based on machine learning method"],"prefix":"10.66113","volume":"22","author":[{"given":"Jian","family":"Yi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"11","key":"10.3233\/JCM-215783_ref1","doi-asserted-by":"crossref","first-page":"e0165819","DOI":"10.1371\/journal.pone.0165819","article-title":"Early warning signals of financial crises with multi-scale quantile regressions of log-periodic power law singularities","volume":"11","author":"Zhang","year":"2016","journal-title":"PLoS ONE."},{"key":"10.3233\/JCM-215783_ref2","doi-asserted-by":"crossref","unstructured":"Klibanov MV, Kuzhuget AV. Profitable forecast of prices of stock options on real market data via the solution of an ill-posed problem for the Black-Scholes equation. Papers. 2015; 32(1).","DOI":"10.1088\/0266-5611\/32\/1\/015010"},{"key":"10.3233\/JCM-215783_ref3","doi-asserted-by":"crossref","first-page":"7","DOI":"10.4028\/www.scientific.net\/AMM.764-765.1400","article-title":"Using the event classifier system to forecast the stock price: An empirical study of taiwan financial market","volume":"764-765","author":"Huang","year":"2015","journal-title":"Appl Mech Mater."},{"issue":"7","key":"10.3233\/JCM-215783_ref4","doi-asserted-by":"crossref","first-page":"3132","DOI":"10.1109\/TII.2018.2794389","article-title":"Forward forecast of stock price using sliding-window metaheuristic-optimized machine learning regression","volume":"14","author":"Chou","year":"2018","journal-title":"IEEE T Ind Inform."},{"key":"10.3233\/JCM-215783_ref5","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.jbankfin.2015.03.003","article-title":"Interpreting financial market crashes as earthquakes: A new early warning system for medium term crashes (digest summary)","volume":"56","author":"Cfa","year":"2015","journal-title":"J Bank Financ."},{"issue":"6","key":"10.3233\/JCM-215783_ref6","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1002\/qre.1684","article-title":"Feature selection for support vector machine in the study of financial early warning system","volume":"30","author":"Li","year":"2015","journal-title":"Qual Reliab Eng."},{"issue":"2","key":"10.3233\/JCM-215783_ref7","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.qref.2014.07.004","article-title":"Toward an early warning system of financial crises: What can index futures and options tell us","volume":"55","author":"Li","year":"2015","journal-title":"Quart Rev Econ Finance."},{"key":"10.3233\/JCM-215783_ref8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2014.02.137","article-title":"Evaluation of clustering algorithms for financial risk analysis using MCDM methods","volume":"275","author":"Kou","year":"2014","journal-title":"Inform Sci."},{"key":"10.3233\/JCM-215783_ref9","doi-asserted-by":"crossref","first-page":"113429","DOI":"10.1016\/j.dss.2020.113429","article-title":"Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection","volume":"140","author":"Kou","journal-title":"Decis Support Syst."},{"key":"10.3233\/JCM-215783_ref10","doi-asserted-by":"crossref","first-page":"113366","DOI":"10.1016\/j.dss.2020.113366","article-title":"Three-stage reject inference learning framework for credit scoring using unsupervised transfer learning and three-way decision theory \u2013 ScienceDirect","volume":"137","author":"Shen","year":"2020","journal-title":"Decis Support Syst."},{"issue":"1","key":"10.3233\/JCM-215783_ref11","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s13042-015-0359-0","article-title":"A novel hybrid model using teaching\u00a0\u2013 learning-based optimization and a support vector machine for commodity futures index forecasting","volume":"9","author":"Das","year":"2018","journal-title":"Int J Mach Learn Cyb."},{"key":"10.3233\/JCM-215783_ref12","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.asoc.2016.08.026","article-title":"Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine","volume":"49","author":"Zhang","year":"2016","journal-title":"Appl Soft Comput."},{"issue":"1","key":"10.3233\/JCM-215783_ref13","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.eswa.2014.07.040","article-title":"Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques","volume":"42","author":"Patel","year":"2015","journal-title":"Expert Syst Appl."},{"key":"10.3233\/JCM-215783_ref14","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.asoc.2015.06.005","article-title":"Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks","volume":"35","author":"Laboissiere","year":"2015","journal-title":"Appl Soft Comput."},{"key":"10.3233\/JCM-215783_ref15","doi-asserted-by":"crossref","unstructured":"Nayak SC, Misra BB, Behera HS. Artificial chemical reaction optimization of neural networks for efficient prediction of stock market indices. Ain Shams Eng J. 2015; 8(3).","DOI":"10.1016\/j.asej.2015.07.015"},{"key":"10.3233\/JCM-215783_ref16","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.apenergy.2016.12.134","article-title":"Multi-step-ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm","volume":"190","author":"Wang","year":"2017","journal-title":"Appl Energ."},{"issue":"4","key":"10.3233\/JCM-215783_ref17","doi-asserted-by":"crossref","first-page":"454","DOI":"10.4236\/jfrm.2020.94024","article-title":"Enterprise financial early warning based on lasso regression screening variables","volume":"9","author":"Nie","year":"2020","journal-title":"J Financial Risk Manag."},{"issue":"20","key":"10.3233\/JCM-215783_ref18","doi-asserted-by":"crossref","first-page":"7245","DOI":"10.1016\/j.eswa.2015.04.058","article-title":"Gold price volatility: A forecasting approach using the Artificial Neural Network\u00a0\u2013 GARCH model","volume":"42","author":"Kristjanpoller","year":"2015","journal-title":"Expert Syst Appl."},{"key":"10.3233\/JCM-215783_ref19","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.knosys.2017.05.003","article-title":"Predicting the listing statuses of Chinese-listed companies using decision trees combined with an improved filter feature selection method","volume":"15)","author":"Zhou","year":"2017","journal-title":"Knowl-Based Syst."},{"key":"10.3233\/JCM-215783_ref20","doi-asserted-by":"crossref","unstructured":"Xiao L, Zhang Y, Peng G. Landslide susceptibility assessment using integrated deep learning algorithm along the china-nepal highway. Sensors. 2018; 18(12).","DOI":"10.3390\/s18124436"},{"key":"10.3233\/JCM-215783_ref21","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.eswa.2016.01.016","article-title":"Efficient stock price prediction using a Self Evolving Recurrent Neuro-Fuzzy Inference System optimized through a Modified Differential Harmony Search Technique","volume":"52","author":"Dash","year":"2016","journal-title":"Expert Syst Appl."}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JCM-215783","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:06:45Z","timestamp":1776809205000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JCM-215783"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,28]]},"references-count":21,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/jcm-215783","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,28]]}}}