Mutual Fund NAV Prediction Using Cascaded SVM Models

被引：0

作者：

Vangara, Akhila ^{[1
]}

Thouseef, Syed ^{[1
]}

Bhat, Shwetha S. ^{[1
]}

Rao, V. V. ^{[2
]}

机构：

[1] PES Univ, Dept Elect & Commun, South Campus, Bangalore 560100, Karnataka, India

[2] IFCPAR, New Delhi, India

来源：

2018 3RD INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT) | 2018年

关键词：

NAV; SVM; stock prediction; mutual funds; Indicators; Overlay; Lag;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The world of finance is often viewed as a gamble because of its unpredictability. Although NAVs are not as volatile as stock prices, the mutual fund market too has its share of uncertainties. The current models for the prediction of NAVs are based on historical NAV data using neural network models or regression models. The paper aims to predict NAV by expanding the focus onto various other parameters as input, including macroeconomic factors. In addition, results have been optimized using SVM in a cascaded form. The experiments were conducted for the Indian market, in specific, the HDFC mid cap opportunities (Growth) mutual fund scheme.

引用

页数：6

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