Stock Market Price Prediction Using Machine Learning

Main Article Content

Saranya K
Vijayashaarathi S
Sasirekha N
Koushikrajaa M
Lohith Raksha S

Keywords

Machine Learning, Linear regression, Long short term memory (LSTM), Neural Networks, ANN, RNN

Abstract

Stock Market value prediction is changing into a well-liked and vital topic in financial and educational studies. From previous couple of decades, there's associate explosive increase in the average person’s interest for stock exchange. The goal of stock market prediction is to foresee the stock price of a specific company over the long term. Accurate prediction of exchange value may be a terribly difficult task due to its unstable and non-linear nature of the stock markets. With the introduction of computer science Artificial Intelligence (AI) and Advanced Machine Learning strategies of prediction have proved to be efficient in predicting the stock costs. In this Paper, Linear Regression and LSTM techniques are used for predicting the longer-term stock value of two corporations of totally different fields of operation. In order to forecast the stock prices over the long run, these machine learning algorithms employ a dataset of stock prices from previous years. These datasets were obtained from Yahoo Finance with values of Date, Open, High, Low, Close, Volume value. Though share market will never be foretold with hundred Percent accuracy, because of its unstable and imprecise nature, this paper aims at proving the potency of LSTM model for predicting the longer-term stock costs with most possible accuracy.

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References

1. Tuarob, S., Wettayakorn, P., Phetchai, P. et al. DAViS, “a unified solution for data collection, analyzation, and visualization in real-time stock market prediction.” Financ Innov 7, 56 – 2021
2. Pang, X., Zhou, Y., Wang, P. et al. “An innovative neural network approach for stock market prediction”. J Supercomput 76, - 2020
3. R. Anand, T. Shanthi, R.S. Sabeenian and S. Veni on a title "Real time noisy dataset implementation of optical character identification using CNN" in International Journal of Intelligent Enterprise – 2020
4. Gopinathan R, Durai S, “Stock market and macroeconomic variables: new evidence from India.” Financ Innov 5:12 – 2019
5. Fischer T, Krauss C, “Deep learning with long short-term memory networks for financial market predictions”. Eur J Oper Res. S0377221717310652 – 2017
6. M Qiu, Y Song and F. Akagi, “Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market[J]”, Chaos Solitons & Fractals, vol. 85, pp. 1-7,- 2016.
7. Khaidem, L., Saha, S., & Dey, S. R. “Predicting the direction of stock market prices using random forest”. arXiv preprint arXiv:1605.00003 – 2016
8. A H Moghaddam, M H Moghaddam and M. Esfandyari, “Stock market index prediction using artificial neural network:[J]”, Journal of Economics Finance & Administrative Science, -2016.
9. Cavalcante RC, Brasileiro RC, Souza VLF, Nobrega JP, Oliveira ALI, “Computational intelligence and financial markets: a survey and future directions.” Expert Syst Appl 55:194–211- 2016
10. M Qiu and Y. Song, “Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model[J]”, PLoS ONE, vol. 11, no. 5,- 2016.
11. Ding X, Zhang Y, Liu T, Duan J (eds) “Deep learning for event-driven stock prediction”. In: International conference on artificial intelligence – 2015
12. X Y Lv, S L Sun and H Liu, “Stock Price Prediction Model Based on BA Neural Network and its Applications[J]”, Advanced Materials Research, vol. 989-994, no. 9, pp. 1646-1651, - 2014.
13. R Anand, R.S Sabeenian, Deepika Gurang, R Kirthika and Shaik Rubeena on a title "AI based Music Recommendation system using Deep Learning Algorithms" in IOP Conference Series: Earth and Environmental Science in 2013.