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首页> 外文期刊>International journal of management and decision making >An empirical net asset value forecasting model based on optimised ANN using elephant herding strategy
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An empirical net asset value forecasting model based on optimised ANN using elephant herding strategy

机译:基于大象群策略的优化神经网络的经验净资产价值预测模型

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摘要

Net asset value (NAV) prediction of mutual funds is one of the promising tasks of financial time series data forecasting. It enables the investors to choose the desired mutual fund for investing. Artificial neural network (ANN) is well suited for NAV prediction as the NAV data are nonlinear in nature. This paper proposes the ANN model hybridised with elephant herding optimisation (EHO) algorithm to predict the NAV of different interval days ahead for two of the Indian mutual funds. The prediction performance of ANN-EHO model is compared with ANN, ANN-GA, ANN-PSO and ANN-DE. The results implicate that ANN-EHO model is superior to other four models.
机译:共同基金的资产净值(NAV)预测是金融时间序列数据预测的有希望的任务之一。它使投资者能够选择所需的共同基金进行投资。人工神经网络(ANN)非常适合NAV预测,因为NAV数据本质上是非线性的。本文提出了一种与大象群优化(EHO)算法混合的神经网络模型,以预测两个印度共同基金未来不同间隔日的资产净值。将ANN-EHO模型的预测性能与ANN,ANN-GA,ANN-PSO和ANN-DE进行了比较。结果表明,ANN-EHO模型优于其他四个模型。

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