Title :
Automated stock price prediction and trading framework for Nifty intraday trading
Author :
Bhat, Ajaz Ahmad ; Kamath, S. Sowmya
Author_Institution :
Dept. of Inf. Technol., Nat. Inst. of Technol., Surathkal, India
Abstract :
Research on automated systems for Stock price prediction has gained much momentum in recent years owing to its potential to yield profits. In this paper, we present an automatic trading system for Nifty for deciding the buying and selling calls for intra-day trading that combines various methods to improve the quality and precision of the prediction. Historical data has been used to implement the various technical indicators and also to train the Neural Network that predicts movement for intra-day Nifty. Further, Sentiment Analysis techniques are applied to popular blog articles written by domain experts and to user comments to find sentiment orientation, so that analysis can be further improved and better prediction accuracy can be achieved. The system makes a prediction for every trading day with these methods to forecast if next day will be a positive day or negative. Further, buy and sell calls for intra-day trading are also decided by the system thus achieving full automation in stock trading.
Keywords :
Web sites; electronic trading; learning (artificial intelligence); natural language processing; neural nets; stock markets; Nifty intraday trading; automated stock price prediction; automatic stock trading system; blog articles; buying calls; domain experts; historical data; neural network training; prediction precision; prediction quality improvement; selling calls; sentiment analysis techniques; sentiment orientation; technical indicators; user comments; Accuracy; Biological neural networks; Market research; Neurons; Resistance; Speech; Neural Network; Sentiment analysis; Stock price prediction; Technical analysis;
Conference_Titel :
Computing, Communications and Networking Technologies (ICCCNT),2013 Fourth International Conference on
Conference_Location :
Tiruchengode
Print_ISBN :
978-1-4799-3925-1
DOI :
10.1109/ICCCNT.2013.6726675