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Intelligent Investment Approaches for Mutual Funds: An Evolutionary Model

Intelligent Investment Approaches for Mutual Funds: An Evolutionary Model

Dipankar Majumdar, Arup Kumar Bhattacharjee, Soumen Mukherjee
Copyright: © 2021 |Pages: 15
ISBN13: 9781799836247|ISBN10: 179983624X|ISBN13 Softcover: 9781799836254|EISBN13: 9781799836261
DOI: 10.4018/978-1-7998-3624-7.ch017
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MLA

Majumdar, Dipankar, et al. "Intelligent Investment Approaches for Mutual Funds: An Evolutionary Model." Machine Learning Applications in Non-Conventional Machining Processes, edited by Goutam Kumar Bose and Pritam Pain, IGI Global, 2021, pp. 267-281. https://doi.org/10.4018/978-1-7998-3624-7.ch017

APA

Majumdar, D., Bhattacharjee, A. K., & Mukherjee, S. (2021). Intelligent Investment Approaches for Mutual Funds: An Evolutionary Model. In G. Bose & P. Pain (Eds.), Machine Learning Applications in Non-Conventional Machining Processes (pp. 267-281). IGI Global. https://doi.org/10.4018/978-1-7998-3624-7.ch017

Chicago

Majumdar, Dipankar, Arup Kumar Bhattacharjee, and Soumen Mukherjee. "Intelligent Investment Approaches for Mutual Funds: An Evolutionary Model." In Machine Learning Applications in Non-Conventional Machining Processes, edited by Goutam Kumar Bose and Pritam Pain, 267-281. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-3624-7.ch017

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Abstract

Investment in the right fund at the right time happens to be the key to success in the stock trading business. Therefore, for strategic investment, the selection of the right opportunity has to be executed crucially so as to reap the maximum returns from the market. Predicting the stock market has always been known to be very critical and needs years of experience as it involves lots of interleaving parameters and constraints. Intelligent investment in mutual funds (MF) can be done when various machine learning tools are used to predict future fund value using the past fund value. In this chapter, an elaborate discussion is presented on the different types of mutual funds and how these data can be used in prediction by machine learning in different literature. In this work, the NAV of a total of 17 different mutual funds have been extracted from the website of AMFI, and thereafter, ANFIS is used to forecast the time series of the NAV of the MF. They have been trained using ANFIS and thereafter tested for prediction with satisfactory results.

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