Stock price prognosticator using machine learning techniques.

Nishitha, S. Nithya Tanvi; Bano, Shahana; Reddy, G. Greeshmanth; Arja, Pujitha; Niharika, Gorsa Lakshmi

Abstract

Stock market price prediction is one of the favourite research topics under consideration for professionals from various fields like mathematics, statistics, history, finance, computer science engineering etc., as it requires a set of skills to predict variation of price of shares in a very volatile and challenging share market scenario. Share market trading is mostly dependent on sentiments of investors and other factors like economic policies, political changes, natural disasters etc., Many theories were forwarded, mathematical and statistical applications in conjunction with probability, to simplify the complex process. After the advent of computers, it got further simplified but still challenging due to various external influential factors ruling the volatility of the market prices. Thus, AI and ML algorithms were being developed, but for only for next day using Linear Regression procedures.Our project aims to predict the prices of shares more precisely and accurately using special algorithms using RNN by improvising the back propagation, feedback routines to overcome the short-term memory loss involved in RNN thus providing efficiency in LSTM applications.Our project emphasizes how the LSTM applications perform with datasets of extreme, larger and minimal fluctuating data.

Citation

NISHITHA, S.N.T., BANO, S., REDDY, G.G., ARJA, P. and NIHARIKA, G.L. 2020. Stock price prognosticator using machine learning techniques. In Proceedings of the 4th International conference on electronics, communication and aerospace technology (ICECA 2020), 5-7 November 2020, Coimbatore, India. Piscataway: IEEE [online], pages 1636-1642. Available from: https://doi.org/10.1109/ICECA49313.2020.9297644

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 4th International conference on electronics, communication and aerospace technology (ICECA 2020)
• Start Date: Nov 5, 2020
• End Date: Nov 7, 2020
• Acceptance Date: Oct 7, 2020
• Online Publication Date: Dec 28, 2020
• Publication Date: Dec 31, 2020
• Deposit Date: Sep 20, 2023
• Publicly Available Date: Sep 20, 2023
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Pages: 1636-1642
• ISBN: 9781728163888
• DOI: https://doi.org/10.1109/ICECA49313.2020.9297644
• Keywords: Stock markets; Stock predictions; Financial forecasting; Machine learning
• Public URL: https://rgu-repository.worktribe.com/output/2064067