LSTM-based Deep Learning Model for Stock Prediction and Predictive Optimization Model

被引：19
作者：
Rather, Akhter Mohiuddin [1 ]
机构：
[1] Great Lakes Inst Management, Dept Machine Learning & Data Sci, Gurgaon Campus, New Delhi 122413, India
来源：
EURO JOURNAL ON DECISION PROCESSES | 2021年 / 9卷
关键词：
Artificial Neural Networks; Deep Learning; LSTM; Time Series; Portfolio Model; ARTIFICIAL BEE COLONY; NEURAL-NETWORKS; HYBRID MODEL; PORTFOLIO; VARIANCE; ALGORITHM; TEXT; CLASSIFICATION; SUPPORT; ARIMA;
D O I：
10.1016/j.ejdp.2021.100001
中图分类号：
C93 [管理学];
学科分类号：
12 ; 1201 ; 1202 ; 120202 ;
摘要：
A new method of predicting time-series-based stock prices and a new model of an investment portfolio based on predictions obtained is proposed here. For this purpose, a new regression scheme is implemented on a long-short-term-memory-based deep neural network. The predictions once obtained are used to construct an investment portfolio or more specifically a predicted portfolio. A large set of experiments have been carried on stock data of NIFTY-50 obtained from the National stock exchange of India. The results confirm that the proposed model outperforms various standard predictive models as well as various standard portfolio optimization models.
引用
收藏
页数：11
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