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
相关论文
共 50 条
  • [31] LSTM-based Sales Forecasting Model
    Hong, Jun-Ki
    KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2021, 15 (04): : 1232 - 1245
  • [32] Energy management system optimization based on an LSTM deep learning model using vehicle speed prediction
    Pulvirenti L.
    Rolando L.
    Millo F.
    Transportation Engineering, 2023, 11
  • [33] The Effect of Data Skewness on the LSTM-Based Mooring Load Prediction Model
    Chen, Hangyu
    Bu, Yinglei
    Zong, Kun
    Huang, Limin
    Hao, Wei
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2022, 10 (12)
  • [34] Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting
    Maryam Rahimzad
    Alireza Moghaddam Nia
    Hosam Zolfonoon
    Jaber Soltani
    Ali Danandeh Mehr
    Hyun-Han Kwon
    Water Resources Management, 2021, 35 : 4167 - 4187
  • [35] PROPOSED BAYESIAN OPTIMIZATION BASED LSTM-CNN MODEL FOR STOCK TREND PREDICTION
    Chan, Bey Kun
    Johnson, Olanrewaju Victor
    Chew, Xinying
    Khaw, Khai Wah
    Ha Lee, Ming
    Alnoor, Alhamzah
    COMPUTING AND INFORMATICS, 2024, 43 (02) : 38 - 63
  • [36] Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting
    Rahimzad, Maryam
    Moghaddam Nia, Alireza
    Zolfonoon, Hosam
    Soltani, Jaber
    Danandeh Mehr, Ali
    Kwon, Hyun-Han
    WATER RESOURCES MANAGEMENT, 2021, 35 (12) : 4167 - 4187
  • [37] An LSTM-based mixed-integer model predictive control for irrigation scheduling
    Agyeman, Bernard T. T.
    Sahoo, Soumya R. R.
    Liu, Jinfeng
    Shah, Sirish L. L.
    CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 2023, 101 (06): : 3362 - 3381
  • [38] LSTM-based Deep Learning Models for Answer Ranking
    Li, Zhenzhen
    Huang, Jiuming
    Zhou, Zhongcheng
    Zhang, Haoyu
    Chang, Shoufeng
    Huang, Zhijie
    2016 IEEE FIRST INTERNATIONAL CONFERENCE ON DATA SCIENCE IN CYBERSPACE (DSC 2016), 2016, : 90 - 97
  • [39] Stock Prediction Based on Technical Indicators Using Deep Learning Model
    Agrawal, Manish
    Shukla, Piyush Kumar
    Nair, Rajit
    Nayyar, Anand
    Masud, Mehedi
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 70 (01): : 287 - 304
  • [40] Stock prediction based on technical indicators using deep learning model
    Agrawal, Manish
    Shukla, Piyush Kumar
    Nair, Rajit
    Nayyar, Anand
    Masud, Mehedi
    Computers, Materials and Continua, 2021, 70 (01): : 287 - 304
12345