...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied Soft Computing >Analysis of temporal pattern, causal interaction and predictive modeling of financial markets using nonlinear dynamics, econometric models and machine learning algorithms
【24h】

Analysis of temporal pattern, causal interaction and predictive modeling of financial markets using nonlinear dynamics, econometric models and machine learning algorithms

机译：非线性动力学，计量计量模型和机器学习算法金融市场的颞型，因果互动和预测建模分析

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper presents a novel predictive modeling framework for forecasting the future returns of financial markets. The task is very challenging as the movements of the financial markets are volatile, chaotic, and nonlinear in nature. For accomplishing this arduous task, a three-stage approach is proposed. In the first stage, fractal modeling and recurrence analysis are used, and the efficient market hypothesis is tested to comprehend the temporal behavior in order to investigate autoregressive properties. In the second stage, Granger causality tests are applied in a vector auto regression environment to explore the causal interaction structures among the indexes and identify the explanatory variables for predictive analytics. In the final stage, the maximal overlap discrete wavelet transformation is carried out to decompose the stock indexes into linear and nonlinear subcomponents. Seven machine and deep learning algorithms are then applied on the decomposed components to learn the inherent patterns and predicting future movements. For numerical testing, the daily closing prices of four major Asian emerging stock indexes, exhibiting non-stationary behavior, during the period January 2012 to January 2017 are considered. Statistical analyses are performed to ascertain the comparative performance assessment. The obtained results prove the effectiveness of the proposed framework.
机译：本文提出了一种新的预测建模框架，用于预测金融市场未来回报。这项任务非常具有挑战性，因为金融市场的动作是挥发性的，混乱和非线性的。为了实现这种艰苦的任务，提出了一种三阶段方法。在第一阶段，使用分形建模和复发分析，测试有效的市场假设，以理解时间行为，以调查自回归性质。在第二阶段，Granger因果关系测试应用于向量自动回归环境，以探索索引之间的因果交互结构，并确定预测分析的解释变量。在最后阶段，进行最大重叠离散小波变换，以将股票指数分解为线性和非线性子组件。然后应用七种机器和深度学习算法在分解组件上，以学习固有模式并预测未来运动。对于数值测试，考虑了2012年1月至2017年1月期间，展出了四个主要亚洲新兴股指的每日截止价格，展出了非静止行为。进行统计分析以确定比较绩效评估。获得的结果证明了拟议框架的有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号