Implementation of Improved Machine Learning Technique in Stock Analysis and Market Prediction

Neetu Mittal, Ranbir Singh, Kapil Sharma

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

Abstract

The several techniques that are used in stock analysis and market forecasts are nowadays based on machine learning (ML). They rely solely on previous market trends for a particular stock and mainly aim to predict the future pricing by analyzing a certain pre-specified patterns in the dataset. Although this approach provides better and accurate results, it sometimes fails to consider some of the other variables like political events, rumors, public sentiments, and some other psychological events that may eventually affect the market. In this paper, analysis and implementation are performed to predict stock and market analysis. It may use not only in-depth technical analysis but also basic research on a given stock. Variable inputs, which include political issues, ongoing events, etc., are included in the technique that determines the predicted value. This proposed work’s analysis can be used to develop a more accurate ML method that combines mathematical value input and emotion analysis to achieve perfect ally prediction in market trends.

Original languageEnglish
Title of host publicationAdvances in Industrial and Production Engineering - Select Proceedings of FLAME 2022
EditorsRakesh Kumar Phanden, Ravinder Kumar, Pulak Mohan Pandey, Ayon Chakraborty
PublisherSpringer Science and Business Media Deutschland GmbH
Pages191-199
Number of pages9
ISBN (Print)9789819913275
DOIs
StatePublished - 2023
Externally publishedYes
Event3rd Biennial International Conference on Future Learning Aspects of Mechanical Engineering, FLAME 2022 - Noida, India
Duration: 3 Aug 20225 Aug 2022

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Conference

Conference3rd Biennial International Conference on Future Learning Aspects of Mechanical Engineering, FLAME 2022
Country/TerritoryIndia
CityNoida
Period3/08/225/08/22

Keywords

  • Deep learning
  • Fundamental analysis
  • Machine learning
  • Sentiment analysis
  • Stock prediction
  • Technical analysis

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