使用功能丰富的神经网络增强股票市场趋势逆转预测
1School of Computer Science, Semyung University, 65 Semyung-ro, Jecheon-si, 27136, Chungcheongbuk-do, Republic of Korea.
Heliyon
|February 1, 2024
概括
这项研究引入了一种新的神经网络方法,用于预测股票市场在暴跌期间的趋势逆转. 增强的输入功能提高了对波动性市场条件的预测准确度.
科学领域:
- * 计算金融学
- * 机器学习 * 机器学习
- * 金融市场分析
背景情况:
- * 传统的神经网络股票预测器擅长跳模式.
- * 在暴跌的市场中预测趋势的逆转仍然是一个挑战.
- * 现有的方法在市场急剧下降时缺乏一致的准确性.
研究的目的:
- * 提出一种新的神经网络方法,用于预测股票市场暴跌时的上趋势逆转.
- * 为了提高波动性市场模式的预测一致性.
- * 通过新设计的输入功能来提高股票价格预测器的性能.
主要方法:
- *开发一种基于神经网络的新型股票价格预测器.
- * 整合了新设计的输入功能,以改善对入模式的预测.
- *分析了从历史暴跌市场的预测得分统计数据.
- * 应用发现来预测测试期内的上升趋势逆转.
主要成果:
- * 拟议的方法在预测上升趋势逆转方面表现优异.
- * 增强的输入特征导致对暴跌的股票模式进行更一致的预测.
- *在KOSDAQ上的模拟结果显示了该方法在3年时间内的有效性.
结论:
- *新型神经网络方法有效地预测了市场暴跌的上升趋势逆转.
- * 增强的输入功能显著提高了预测准确性和一致性.
- * 该方法为导航和利用波动的股票市场条件提供了有价值的工具.
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