英文标题：
《Discrete Wavelet Transform-Based Prediction of Stock Index: A Study on
National Stock Exchange Fifty Index》
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作者：
Dhanya Jothimani, Ravi Shankar, Surendra S. Yadav
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最新提交年份：
2016
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英文摘要：
Financial Times Series such as stock price and exchange rates are, often, non-linear and non-stationary. Use of decomposition models has been found to improve the accuracy of predictive models. The paper proposes a hybrid approach integrating the advantages of both decomposition model (namely, Maximal Overlap Discrete Wavelet Transform (MODWT)) and machine learning models (ANN and SVR) to predict the National Stock Exchange Fifty Index. In first phase, the data is decomposed into a smaller number of subseries using MODWT. In next phase, each subseries is predicted using machine learning models (i.e., ANN and SVR). The predicted subseries are aggregated to obtain the final forecasts. In final stage, the effectiveness of the proposed approach is evaluated using error measures and statistical test. The proposed methods (MODWT-ANN and MODWT-SVR) are compared with ANN and SVR models and, it was observed that the return on investment obtained based on trading rules using predicted values of MODWT-SVR model was higher than that of Buy-and-hold strategy.
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中文摘要：
《金融时报》的股票价格和汇率等系列通常是非线性和非平稳的。已发现使用分解模型可以提高预测模型的准确性。本文提出了一种融合分解模型（即最大重叠离散小波变换（MODWT））和机器学习模型（ANN和SVR）优点的混合方法来预测全国证券交易所50指数。在第一阶段，使用MODWT将数据分解为数量较少的子序列。在下一阶段，使用机器学习模型（即ANN和SVR）预测每个子序列。对预测的子系列进行聚合，以获得最终预测。在最后阶段，使用误差度量和统计测试来评估所提出方法的有效性。将所提出的方法（MODWT-ANN和MODWT-SVR）与ANN和SVR模型进行比较，发现基于交易规则使用MODWT-SVR模型的预测值获得的投资回报高于买入持有策略。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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