Daily Trading of the FTSE Index Using LSTM with Principal Component Analysis

被引：0
|
作者：
Edelman, David [1 ]
Mannion, David [1 ]
机构：
[1] Univ Coll Dublin, Carysft Campus, Blackrock, County Dublin, Ireland
来源：
MATHEMATICAL AND STATISTICAL METHODS FOR ACTUARIAL SCIENCES AND FINANCE, MAF 2022 | 2022年
关键词：
Deep learning; Recurrent networks; Time series; Ensembling;
D O I：
10.1007/978-3-030-99638-3_37
中图分类号：
F8 [财政、金融];
学科分类号：
0202 ;
摘要：
This study comprises a preliminary investigation into the use of Long Short-Term Memory (LSTM) methodology when used in conjunction with Principal Component Analysis (PCA) for producing trading signals for daily returns of the the FTSE100 index. The model is trained on approximately 35 years of daily data and validated on six months of testing data, demonstrating a high degree of risk-adjusted trading efficacy.
引用
收藏
页码：228 / 234
页数：7
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