Novel Approach for Stock Prediction Using Technical Analysis and Sentiment Analysis

Gauravkumarsingh Gaharwar*, Sharnil Pandya

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Stock prediction is not new; people have tried to predict stock price or good quality stocks for ages. Machine learning has opened a new direction for the problem of stock prediction. The critical factor deciding the success or failure of the machine learning model depends on the quality of features computed. Moreover, the concept of ensemble learning can significantly enhance the quality of prediction. The proposed model gathers data price time series data and news articles from open access domain and do necessary pre-processing. The model also calculates technical and sentiment features and uses them for training the ensemble model. Performance is compared to definite matrices and with other similar research.

Original languageEnglish
Title of host publicationFourth Congress on Intelligent Systems
Subtitle of host publicationCIS 2023, Volume 1
EditorsSandeep Kumar, Balachandran K., Joong Hoon Kim, Jagdish Chand Bansal
Place of PublicationSingapore
PublisherSpringer
Pages101-111
Number of pages11
Volume1
ISBN (Electronic)9789819990375
ISBN (Print)9789819990368
DOIs
Publication statusPublished - 18 Mar 2024
Externally publishedYes
Event4th Congress on Intelligent Systems, CIS 2023 - Bengaluru, India
Duration: 4 Sept 20235 Sept 2023

Publication series

NameLecture Notes in Networks and Systems
Volume868
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference4th Congress on Intelligent Systems, CIS 2023
Country/TerritoryIndia
CityBengaluru
Period4/09/235/09/23

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