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.
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页数：11
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