A GA-Artificial Neural Network Hybrid System for Financial Time Series Forecasting

被引：0
|
作者：
Nair, Binoy B. [1 ]
Sai, S. Gnana [1 ]
Naveen, A. N. [1 ]
Lakshmi, A. [1 ]
Venkatesh, G. S. [1 ]
Mohandas, V. P. [1 ]
机构：
[1] Amrita Vishwa Vidyapeetham, Coimbatore 641105, Tamil Nadu, India
来源：
INFORMATION TECHNOLOGY AND MOBILE COMMUNICATION | 2011年 / 147卷
关键词：
Genetic algorithm; artificial neural networks; financial; time series; STOCK-MARKET; ACCURACY;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Accurate prediction of financial time series, such as those generated by stock markets, is a highly challenging task due to the highly nonlinear nature of such series. A novel method of predicting the next day's closing value of a stock market is proposed and empirically validated in the present study. The system uses an adaptive artificial neural network based system to predict the next day's closing value of a stock market index. The proposed system adapts itself to the changing market dynamics with the help of genetic algorithm which tunes the parameters of the neural network at the end of each trading session so that best possible accuracy is obtained. The effectiveness of the proposed system is established by testing on five international stock indices using ten different performance measures.
引用
收藏
页码：499 / 506
页数：8
相关论文
共 50 条
  • [41] Time series forecasting using a hybrid ARIMA and neural network model
    Zhang, GP
    NEUROCOMPUTING, 2003, 50 : 159 - 175
  • [42] A novel nonlinear neural network ensemble model for financial time series forecasting
    Lai, Kin Keung
    Yu, Lean
    Wang, Shouyang
    Wei, Huang
    COMPUTATIONAL SCIENCE - ICCS 2006, PT 1, PROCEEDINGS, 2006, 3991 : 790 - 793
  • [43] Financial time series forecasting with multi-modality graph neural network
    Cheng, Dawei
    Yang, Fangzhou
    Xiang, Sheng
    Liu, Jin
    PATTERN RECOGNITION, 2022, 121
  • [44] Comparison of new activation functions in neural network for forecasting financial time series
    Gecynalda S. da S. Gomes
    Teresa B. Ludermir
    Leyla M. M. R. Lima
    Neural Computing and Applications, 2011, 20 : 417 - 439
  • [45] Comparison of new activation functions in neural network for forecasting financial time series
    Gomes, Gecynalda S. da S.
    Ludermir, Teresa B.
    Lima, Leyla M. M. R.
    NEURAL COMPUTING & APPLICATIONS, 2011, 20 (03): : 417 - 439
  • [46] Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand
    Anand, Atul
    Suganthi, L.
    ENERGIES, 2018, 11 (04)
  • [47] A Hybrid Chaotic Oscillatory Neural Network (HCONN) Based Financial Time Series Prediction System
    Qiu, Yifu
    Lee, Raymond S. T.
    2019 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE APPLICATIONS AND TECHNOLOGIES (AIAAT 2019), 2019, 646
  • [48] Forecasting pine sawtimber stumpage prices: A comparison between a time series hybrid model and an artificial neural network
    Lamichhane, Sabhyata
    Mei, Bin
    Siry, Jacek
    FOREST POLICY AND ECONOMICS, 2023, 154
  • [49] A Hybrid Statistical Approach for Stock Market Forecasting Based on Artificial Neural Network and ARIMA Time Series Models
    Ratnayaka, R. M. Kapia Taranga
    Seneviratne, D. M. K. N.
    Wei Jianguo
    Arumawadu, Hasitha Indika
    PROCEEDINGS OF 2015 IEEE INTERNATIONAL CONFERENCE ON BEHAVIORAL, ECONOMIC, SOCIO-CULTURAL COMPUTING (BESC), 2015, : 54 - 60
  • [50] Forecasting of the rice yields time series forecasting using artificial neural network and statistical model
    Shabri, A.
    Samsudin, R.
    Ismail, Z.
    Journal of Applied Sciences, 2009, 9 (23) : 4168 - 4173
12345