Stock Market Prediction using Machine Learning
| International Journal of Communication and Media Science |
| © 2020 by SSRG - IJCMS Journal |
| Volume 7 Issue 2 |
| Year of Publication : 2020 |
| Authors : Rohit B R, Rajeeva Shreedhara Bhat, Abhishek Manohar, Mamatha K R |
10.14445/2349641X/IJCMS-V7I2P102
How to Cite?
Rohit B R, Rajeeva Shreedhara Bhat, Abhishek Manohar, Mamatha K R, "Stock Market Prediction using Machine Learning," SSRG International Journal of Communication and Media Science, vol. 7, no. 2, pp. 6-9, 2020. Crossref, https://doi.org/10.14445/2349641X/IJCMS-V7I2P102
Abstract:
A stock (also known as equity) is a security that represents the ownership of a fraction of a corporation. This entitles the owner of the stock to a proportion of the corporation's assets and profits equal to how much stock they own. Units of stock are called "shares."
In today's finance world stock trading is one of the most significant exercises. Stock market prediction is a demonstration of attempting to decide the future estimation of the stock price and other monetary characteristics related to stock trading. This paper clarifies the forecast of a stock making use of Machine Learning. The technical and central or the time series analysis is utilized by the a large portion of the stockbrokers while making the stock forecasts.
Primarily, only numerical data was being used for stock prediction. A much better and accurate way is to use of fundamental analysis i.e. to factor in the real economy into the predictions. Using news articles to predict the stock movement on a daily basis is much more reliable.
Naive Bayes, Random Forests, Perceptrons and Linear Support Vector Machines are the Machine Learning methods which have been used for prediction. Linear support vector machines are highly regarded as one of the better text classification machine learning techniques.
Keywords:
stock market, machine learning, predictive analysis, NLP
References:
[1] Gabriel Pui Cheong Fung, Jeffry Xu Yu, Hongian Lu. “The Predicting Power Of Textual Information On Financial Markets”.
[2] Chen, Jerry and Aaron Chai, Madhav Goel, Donavan Lieu, Faazilah Mohamed, David Nahm, Bonnie Wu, Predicting Stock Prices From News Articles.
[3] Gidofalvi, Gyozo, “Using news articles to predict stock price movements.” Department of Computer Science and Engineering, University of California, San Diego.
[4] Aase, Kim-Georg, “Text mining of news articles for stock price predictions.” (2011).
[5] Timmons, Ryan and Kari Lee, “Predicting the stock market with news articles.” CS224N Final Report (2007).
[6] Ma, Qicheng, “Stock price prediction using news articles.” CS224N, Final Report (2008).
[7] Joshi Kalyani, H. N. Bharathi and Rao, Jyothi, “Stock trend prediction using news sentiment analysis.” (2016).
[8] Vladimir N. Vapnik. The Nature of Statistical Learning Theory. Springer, New York, 1995.
[9] Text Categorization with Support Vector Machines: Learning with Many Relevant Features, Cornell, Thorsten Joachims, 1998
[10] NASDAQ: Stock Exchange, URL: www.nasdaq.com [Last Accessed: May, 2020]
[11] Google Finance, URL: finance.google.com [Last Accessed: May, 2020]
[12] Zipline: Trading Simulation Library, URL: www.zipline.io [Last Accessed: April, 2020]