Short Term Firm-Specific Stock Forecasting with BDI Framework

Ahmed, Mansoor and Sriram, Anirudh and Singh, Sanjay (2021) Short Term Firm-Specific Stock Forecasting with BDI Framework. Computational Economics, 55. pp. 745-778. ISSN 0927-7099

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In today’s information age, a comprehensive stock trading decision support system which aids a stock investor in decision making without relying on random guesses and reading financial news from various sources is the need of the hour. This paper investigates the predictive power of technical, sentiment and stock market analysis coupled with various machine learning and classification tools in predicting stock trends over the short term for a specific company. Large dataset stretching over a dura�tion of ten years has been used to train, test and validate our system. The efficacy of supervised non-shallow and prototyping learning architectures are illustrated by com�parison of results obtained through myriad optimization, classification and clustering algorithms. The results obtained from our system reveals a significant improvement over the efficient market hypothesis for specific companies and thus strongly chal�lenges it. Technical parameters and algorithms used have shown a significant impact on the predictive power of the system. The predictive accuracy obtained is as high as 70–75% using linear vector quantization. It has been found that sentiment analysis has strong correlation with the future market trends. The proposed system provides a comprehensive decision support system which aids in decision making for stock trading. We also present a novel application of the BDI framework to systematically apply the learning and prediction phases

Item Type: Article
Uncontrolled Keywords: Supervised learning · Stock market forecasting · Technical analysis · Sentiment analysis
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 25 Aug 2021 09:04
Last Modified: 25 Aug 2021 09:04

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