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2021 | OriginalPaper | Chapter

9. An Approach of Safe Stock Prediction Using Genetic Algorithm

Authors : Nilanjana Adhikari, Mahamuda Sultana, Suman Bhattacharya

Published in: Proceedings of International Conference on Innovations in Software Architecture and Computational Systems

Publisher: Springer Singapore

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Abstract

Stock market investments are an admired problem but onerous task. Because it is very unstable in nature for different factors and it is very hard to predict the safe stocks to invest at different circumstances. To guess the safe stocks to invest from a very large no of shares is an attractive research area that needs to be done efficiently because it is the question of profit and loss. In this research work, an optimized search algorithm has been used to predict no of the stocks from a huge scale of shares or stocks in which it will be safe to invest. In this proposed work, NIFTY top 50 shares to which it will be safe to invest for long-term investment has been taken into consideration and has been evaluated the safe stocks in a rank wise manner using an efficient search optimization technique, genetic algorithm (GA). Genetic algorithm is capable to yield better accuracy than other similar models. This is a heuristic search optimization method for searching of a very vast domain of dataset. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation. An insignificant population of individual archetypes can successfully search a large space because they comprehend schemes. Beneficial sub-structures that can be theoretically united to make fittest entities. Fitness is determined by investigating a huge number of distinct fitness cases. This procedure can be very effective if the fitness cases also grow by their individual GAs.

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Metadata
Title
An Approach of Safe Stock Prediction Using Genetic Algorithm
Authors
Nilanjana Adhikari
Mahamuda Sultana
Suman Bhattacharya
Copyright Year
2021
Publisher
Springer Singapore
Book
Proceedings of International Conference on Innovations in Software Architecture and Computational Systems

Print ISBN: 978-981-16-4300-2

Electronic ISBN: 978-981-16-4301-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-16-4301-9_10

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