Analysis of net asset value prediction using low complexity neural network with various expansion techniques

Net asset value (NAV) prediction is an important area of research as small investors are doing investment in there, Literature survey reveals that very little work has been done in this field. The reported literature mainly used various neural network models for NAV prediction. But the derivative based learning algorithms of these reported models have the problem of trapping into the local solution. Hence in chapter derivative free algorithm, particle swarm optimization is used to update the parameters of radial basis function neural network for prediction of NAV. The positions of particles represent the centers, spreads and weights of the RBF model and the minimum MSE is used as the cost function. The convergence characteristics are obtained to show the performance of the model during training phase. The MAPE and RMSE value are calculated during testing phase to show the performance of the proposed RBF-PSO model. These performance measure exhibits that the proposed model is better model in comparison to MLANN, FLANN and RBFNN models.

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A Novel Hybrid Model Using RBF and PSO for Net Asset Value Prediction

Intelligent Systems ◽

10.4018/978-1-5225-5643-5.ch043 ◽

2018 ◽

pp. 1031-1049 ◽

Cited By ~ 1

Author(s):

C. M. Anish ◽

Babita Majhi ◽

Ritanjali Majhi

Keyword(s):

Neural Network ◽

Network Models ◽

Performance Measure ◽

Neural Network Models ◽

Proposed Model ◽

Derivative Free ◽

Asset Value ◽

Net Asset Value ◽

Small Investors ◽

The Cost

Net asset value (NAV) prediction is an important area of research as small investors are doing investment in there, Literature survey reveals that very little work has been done in this field. The reported literature mainly used various neural network models for NAV prediction. But the derivative based learning algorithms of these reported models have the problem of trapping into the local solution. Hence in chapter derivative free algorithm, particle swarm optimization is used to update the parameters of radial basis function neural network for prediction of NAV. The positions of particles represent the centers, spreads and weights of the RBF model and the minimum MSE is used as the cost function. The convergence characteristics are obtained to show the performance of the model during training phase. The MAPE and RMSE value are calculated during testing phase to show the performance of the proposed RBF-PSO model. These performance measure exhibits that the proposed model is better model in comparison to MLANN, FLANN and RBFNN models.

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A Mutual Fund Investment Method Using Fruit Fly Optimization Algorithm and Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.571-572.318 ◽

2014 ◽

Vol 571-572 ◽

pp. 318-325 ◽

Cited By ~ 3

Author(s):

Tsu Hua Huang ◽

Yung Ho Leu

Keyword(s):

Neural Network ◽

Mutual Funds ◽

Optimization Algorithm ◽

Mutual Fund ◽

Fruit Fly ◽

Fruit Fly Optimization Algorithm ◽

Fruit Fly Optimization ◽

Asset Value ◽

Net Asset Value ◽

Fund Portfolio

This paper presents a method to construct a profitable portfolio of mutual funds for investors. This method comprises two stages. In the first stage, the DEA, Sharpe and Treynor indices of mutual funds and the monthly rates of return (ROR) of mutual funds are used to select a mutual fund portfolio. In the second stage, the linear regression model, the Fruit Fly Optimization Algorithm (FOA) and the General Regression Neural Network (GRNN) are used to construct a prediction model for the net asset values of each of the constituent mutual funds of the portfolio. The trade decision of a selected mutual fund is then made based on the rise or fall of its net asset value. The empirical results showed that, compared to other combinations, the combination of using Sharpe index for portfolio selection and the GRNN optimized with FOA for net asset value prediction offered the best accumulated return rate for the mutual fund portfolio investment.

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