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  • 标题:An Automated Machine Model Analysis for the Stock Market
  • 本地全文:下载
  • 作者:Agon Kokaj ; AliXhan Basha ; Bregor Axhimusa
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2019
  • 卷号:52
  • 期号:25
  • 页码:451-455
  • DOI:10.1016/j.ifacol.2019.12.581
  • 语种:English
  • 出版社:Elsevier
  • 摘要:The stock market is changing rapidly as it is effected by different factors. The total revenue shares and the total costs are important aspects that have had a great impact on the stock exchange. The ability to predict the market and its expected outcome is a problem that shareholders are facing today. An economic model that is efficient and simple will help shareholders to predict the market, enhancing a better understanding to which companies they should invest in order to have a better profit. A coded mathematical model, that uses a machine learning techniques, is introduced and applied to the above mentioned problem. The model is able to increase its deterministic automated value.
  • 关键词:KeywordsStockMarketSharesRevenueMachine Learning
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