Exploring Variants of Extreme Learning Machines for Prediction of Mutual Fund NAV.

Investing money through mutual fund benefits the small investors to access equities of big companies with a small amount of capital. It experiences the fluctuation of price along with the performance of stock, which is a major part in making the fund. Here, in this paper variant of Extreme Learning Machines (ELM) are applied to forecast the end-of-year net asset value (NAV) of mutual fund. Various types of ELM such as basic ELM, evolutionary ELM, online sequential ELM and error minimized ELM are explored and applied to historical data of four mutual funds such as SBI mutual fund, UTI mutual fund, Tata Mutual Fund and Kotak Mahindra Mutual Fund for the prediction of NAV. Along with the different ELM based prediction model, this paper has explored on different types of activation functions and the number of nodes in the hidden layer used in variants of ELM. Examining the simulation result of all the models, along with different activation functions and different number of nodes, it is observed that evolutionary ELM outperforms over the other variants of ELM used in this study. [ABSTRACT FROM AUTHOR]