“Darling, time horizon matters”: applying the CNN-LSTM method for predicting US equity ETFs

IF 1.2 4区经济学 Q3 ECONOMICS Applied Economics Letters Pub Date : 2024-08-30 DOI:10.1080/13504851.2024.2396550

Wenguang Lin

引用次数: 0

Abstract

The paper uses a hybrid model of convolutional neural network and long short-term memory (CNN-LSTM) to examine the impact of the prediction (or input) window length on the prediction accuracy of tr...

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"亲爱的，时间跨度很重要"：应用 CNN-LSTM 方法预测美国股票 ETF

本文使用卷积神经网络和长短期记忆（CNN-LSTM）混合模型，研究了预测（或输入）窗口长度对 Tr...的预测准确性的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Economics Letters ECONOMICS-

CiteScore

2.90

自引率

6.20%

发文量

460

期刊介绍： Applied Economics Letters is a companion journal to Applied Economics and Applied Financial Economics. It publishes short accounts of new original research and encourages discussion of papers previously published in its two companion journals. Letters are reviewed by the Editor, a member of the Editorial Board or another suitable authority. They are generally applied in nature, but may include discussion of method and theoretical formulation. In a change to the format of the Applied Financial Series of journals, from 2009 Applied Financial Economics Letters will be incorporated into its sister journal Applied Economics Letters.