Classification and Prediction of Financial Datasets Using Genetic Algorithms

Arjun Kanamarlapudi, Krutika Deshpande, Chethan Sharma

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Finance is the elixir that builds the economy of the world and which has a direct impact in the development and advancement of societies. In the finance domain, it is critical to analyse the data as there are heavy risks involved for industries, governments, and even individuals. Any wrong or untimely decision may amount to huge losses and significantly impact businesses and lives. Whereas, better analysis results in mitigating these risks and help to make better decisions which in turn may help to increase profits abundantly. Machine learning is proving to be very useful to draw insights and make predictions in this domain due the availability and nature of financial data. It is finding its applications in investment banking, algorithmic trading, fraud detection, stock market forecasts, etc. This paper attempts to demonstrate an approach to improve the usefulness of machine learning techniques for classification and prediction in the domain of finance. The approach involves the use of genetic algorithms to improve the accuracy and efficiency of traditional algorithms and achieve optimization.

Original languageEnglish
Title of host publicationComputational Intelligence - Select Proceedings of InCITe 2022
EditorsAnupam Shukla, Nitasha Hasteer, B.K. Murthy, Jean-Paul VanBelle
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9789811973451
Publication statusPublished - 2023
Event2nd International Conference on Information Technology, InCITe 2022 - Noida, India
Duration: 03-03-202204-03-2022

Publication series

NameLecture Notes in Electrical Engineering
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


Conference2nd International Conference on Information Technology, InCITe 2022

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Classification and Prediction of Financial Datasets Using Genetic Algorithms'. Together they form a unique fingerprint.

Cite this