Stock Market Prediction using Machine Learning Algorithms

Author:

Dr. K. Velmurugan 1,Meera. T 1,Meenatchi. R 1

Affiliation:

1. Anjalai Ammal Mahalingam Engineering College, Kovilvenni, Tiruvarur, Tamil Nadu, India

Abstract

Machine learning is effectively implemented in forecasting the stock prices. The objective is to predict the stock prices in order to make more informed and accurate investment decisions. The essence of the issue lies in exactly forecasting the future stock price of the reputed firm, based on historical or past prices, using Recurrent Neural Network algorithm, LSTM-LongShort Term Memory, ANN-Artificial Neural Network, CNN - Convolutional Neural Network. We also used regression as a trial and error method and found the limitations in the case of a non-continuous dataset. The findings of the study would lend a hand to a common investor averting huge losses and optimizing the stockholder investing in beneficial stocks. While predicting the actual prices of a stock is an uphill climb , we can build a model that will predict whether the price will go up or down.

Publisher

Naksh Solutions

Subject

General Medicine

Reference22 articles.

1. Adil MOGHAR, Mhamed HAMICHE, "Stock Market Prediction Using LSTM Recurrent Neural Network "International Workshop on Statistical Methods and Artificial intelligence (IWSMAI), April 6-9,2020,warsaw,Poland.

2. Emerson, S., Kennedy, R., O'Shea, L., & O'Brien, J. (2019, May). Trends and Applications of Machine Learning in Quantitative Finance. In 8th International Conference on Economics and Finance Research (ICEFR 2019).

3. VaishnaviGururaj , Shriya V R, Dr.Ashwini K "Stock Market Prediction using Linear Regression and Support Vector Machines" in International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 8 (2019) pp. 1931-1934

4. P. Abinaya, V. S. Kumar, P. Balasubramanian, and V. K. Menon, "Measuring stock price and trading volume causality among nifty50 stocks: The todayamamoto method," in Advances in Computing, Communications, and Informatics (ICACCI), 2016 International Conference on. IEEE, 2016, pp. 1886–1890.

5. Moritz, B., & Zimmermann, T. (2016). Tree-based conditional portfolio sorts: The relation between past and future stock returns. Available at SSRN 2740751.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3