Stock forecasting using evolutionary neural network

被引：0

作者：

Gao, W. ^{[1
]}

机构：

[1] Wuhan Polytech Univ, Wuhan 430023, Peoples R China

来源：

Proceedings of the Fourth International Conference on Information and Management Sciences | 2005年 / 4卷

关键词：

stock market; prediction; evolutionary neural; network;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Stock market is complicated dynamic system affected by many factors. To forecast it, many methods have been proposed. But those methods cannot solve this problem very well. In this paper, from analyses the mathematic description of stock market system, a new forecasting method based on new evolutionary neural network is proposed here. In this new evolutionary neural network, the traditional BP algorithm and a new bionics algorithm, immunized evolutionary programming proposed by author is combined. In order to verify this new prediction method, the stock market data of Shanghai market in 1996 is used. The results show that, our new method is very good to real practice.

引用

页码：316 / 320

页数：5

共 50 条

[41] Advantages of direct input-to-output connections in neural networks: The Elman network for stock index forecasting [J].

Wang, Yaoli ;

Wang, Lipo ;

Yang, Fangjun ;

Di, Wenxia ;

Chang, Qing .

INFORMATION SCIENCES, 2021, 547 :1066-1079

[42] Time Series Forecasting of the Austrian Traded Index (ATX) Using Artificial Neural Network Model [J].

Martinovic, Marko ;

Hunjet, Anica ;

Turcin, Ioan .

TEHNICKI VJESNIK-TECHNICAL GAZETTE, 2020, 27 (06) :2053-2061

[43] Stock Prediction using Functional Link Artificial Neural Network (FLANN) [J].

Gupta, Abhinandan ;

Chaudhary, Dev Kumar ;

Choudhury, Tanupriya .

2017 3RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND NETWORKS (CINE), 2017, :10-16

[44] Estimation for Up/Down Fluctuation of Stock Prices by Using Neural Network [J].

Watanabe, Toyohide ;

Iwata, Kenji .

BEST PRACTICES FOR THE KNOWLEDGE SOCIETY: KNOWLEDGE, LEARNING, DEVELOPMENT AND TECHNOLOGY FOR ALL, 2009, 49 :171-178

[45] Forecasting the solar cycle 25 using a multistep Bayesian neural network [J].

Bizzarri, I ;

Barghini, D. ;

Mancuso, S. ;

Alessio, S. ;

Rubinetti, S. ;

Taricco, C. .

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, 2022, 515 (04) :5062-5070

[46] Wind Power Forecasting Using Neural Network Ensembles With Feature Selection [J].

Li, Song ;

Wang, Peng ;

Goel, Lalit .

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2015, 6 (04) :1447-1456

[47] Flow data forecasting for the junction flow using artificial neural network [J].

Sahin, Besir ;

Canpolat, Cetin ;

Bilgili, Mehmet .

FLOW MEASUREMENT AND INSTRUMENTATION, 2024, 100

[48] Using an artificial neural network model for natural gas compositions forecasting [J].

Szoplik, Jolanta ;

Muchel, Paulina .

ENERGY, 2023, 263

[49] Forecasting Portfolio Optimization using Artificial Neural Network and Genetic Algorithm [J].

Solin, Mohammad Maholi ;

Alamsyah, Andry ;

Rikumahu, Brady ;

Saputra, Muhammad Apriandito Arya .

2019 7TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY (ICOICT), 2019, :496-502

[50] Intelligent Forecasting System Using Grey Model Combined with Neural Network [J].

Yang, Shih-Hung ;

Chen, Yon-Ping .

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, 2011, 13 (01) :8-15

← 1 2 3 4 5 →