{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T01:01:59Z","timestamp":1787878919683,"version":"build-2784847793"},"reference-count":20,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2022,12,19]]},"abstract":"<jats:p>Quantitative investment can manage enterprise assets better to obtain higher revenues. This paper analyzed quantitative investment prediction using machine learning algorithms. First, the support vector machine (SVM) algorithm was introduced, and stock changes were predicted by the SVM algorithm. Then, the feature factors in stock data were extracted by maximum information coefficient (MIC) as the input of the SVM algorithm. Finally, the performance and backtest results of the SVM algorithm was analyzed. It was found that the SVM algorithm had a good performance, and its F1-score was 0.9884, which was better than C4.5 and random forest algorithms. In terms of backtesting, the portfolio built based on the prediction results of the SVM algorithm obtained a higher annualized return rate when the number of stocks was small; when the number of stocks was 10, the portfolio built based on the SVM algorithm had an annualized return rate of 83.67%, a smaller maximum retracement, and a higher Sharpe ratio than the other algorithms, which balanced the risk and return well. The results demonstrate the reliability of the SVM algorithm in predicting quantitative investment, which is beneficial to achieving the optimization of enterprise asset management.<\/jats:p>","DOI":"10.3233\/jcm-226478","type":"journal-article","created":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T12:25:24Z","timestamp":1667305524000},"page":"2425-2433","source":"Crossref","is-referenced-by-count":2,"title":["Quantitative investment prediction analysis for enterprise asset management using machine learning algorithms"],"prefix":"10.66113","volume":"22","author":[{"given":"Gefei","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"55691","reference":[{"issue":"3","key":"10.3233\/JCM-226478_ref1","doi-asserted-by":"crossref","first-page":"305","DOI":"10.4236\/jss.2021.93020","article-title":"The Research on Influencing Factors of Stock Price Fluctuation of Listed Companies in China Based on PCA-Multiple Regression","volume":"09","author":"Ma","year":"2021","journal-title":"Open Journal of Social Sciences."},{"issue":"3","key":"10.3233\/JCM-226478_ref2","first-page":"2021","article-title":"Techniques using artificial intelligence to solve stock market forecast, sales estimating and market division issues","volume":"27","author":"Sangeetha","year":"2021","journal-title":"Journal of Contemporary Issues in Business and Government."},{"key":"10.3233\/JCM-226478_ref3","doi-asserted-by":"crossref","unstructured":"Chen M, Zhang Z, Shen J, Deng Z, He J, Huang S. A quantitative investment model based on random forest and sentiment analysis. 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