TY - GEN
T1 - Classification and Prediction of Financial Datasets Using Genetic Algorithms
AU - Kanamarlapudi, Arjun
AU - Deshpande, Krutika
AU - Sharma, Chethan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
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U2 - 10.1007/978-981-19-7346-8_25
DO - 10.1007/978-981-19-7346-8_25
M3 - Conference contribution
AN - SCOPUS:85152620111
SN - 9789811973451
T3 - Lecture Notes in Electrical Engineering
SP - 285
EP - 295
BT - Computational Intelligence - Select Proceedings of InCITe 2022
A2 - Shukla, Anupam
A2 - Hasteer, Nitasha
A2 - Murthy, B.K.
A2 - VanBelle, Jean-Paul
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd International Conference on Information Technology, InCITe 2022
Y2 - 3 March 2022 through 4 March 2022
ER -