Predicting the Unpredictable: An Application of Machine Learning Algorithms in Indian Stock Market

被引：4
作者：
Saini A. [1 ]
Sharma A. [1 ]
机构：
[1] Singhania University, Rajasthan, Pacheri Bari, Jhunjhunu
来源：
Annals of Data Science | 2022年 / 9卷 / 04期
关键词：
Artificial neural network; Long short memory neural network; Machine learning algorithms; Sentiment analysis; Stock market prediction;
D O I：
10.1007/s40745-019-00230-7
中图分类号：
学科分类号：
摘要：
The stock market is a popular investment option for investors because of its expected high returns. Stock market prediction is a complex task to achieve with the help of artificial intelligence. Because stock prices depend on many factors, including trends and news in the market. However, in recent years, many creative techniques and models have been proposed and applied to efficiently and accurately forecast the behaviour of the stock market. This paper presents a comparative study of fundamental and technical analysis based on different parameters. We also discuss a comparative Analysis of various prediction techniques used to predict stock price. These strategies include technical analysis like time series analysis and machine learning algorithms such as the artificial neural network (ANN). Along with them, few researchers focused on the textual analysis of stock prices by continuous analysing the public sentiments from social media and other news sources. Various approaches are compared based on methodologies, datasets, and efficiency with the help of visualisation. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
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页码：791 / 799
页数：8
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