Survey of stock market prediction using machine learning approach

Ashish Sharma, Dinesh Bhuriya, Upendra Singh
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引用次数: 127

Abstract

Stock market is basically nonlinear in nature and the research on stock market is one of the most important issues in recent years. People invest in stock market based on some prediction. For predict, the stock market prices people search such methods and tools which will increase their profits, while minimize their risks. Prediction plays a very important role in stock market business which is very complicated and challenging process. Employing traditional methods like fundamental and technical analysis may not ensure the reliability of the prediction. To make predictions regression analysis is used mostly. In this paper we survey of well-known efficient regression approach to predict the stock market price from stock market data based. In future the results of multiple regression approach could be improved using more number of variables.
用机器学习方法预测股票市场
股票市场本质上是非线性的,对股票市场的研究是近年来的热点问题之一。人们根据一些预测来投资股票市场。为了预测股票市场价格,人们寻找这样的方法和工具,可以增加他们的利润,同时最小化他们的风险。股票市场预测是一个复杂而富有挑战性的过程,在股票市场预测中起着非常重要的作用。采用传统的方法,如基本面分析和技术分析,可能无法保证预测的可靠性。为了做出预测,回归分析是最常用的。本文综述了基于股票市场数据预测股票市场价格的有效回归方法。在未来,多元回归方法可以使用更多的变量来改善结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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