International Journal of Computational Intelligence Systems

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Volume 14, Issue 1, 2021, Pages 1763 - 1772

An Intelligent Hybrid System for Forecasting Stock and Forex Trading Signals using Optimized Recurrent FLANN and Case-Based Reasoning

Authors
D. K. Bebarta1, T. K. Das2, Chiranji Lal Chowdhary2, *, Xiao-Zhi Gao3
1Department of Information Technology, GVPCEW, Vishakhapatnam, India
2School of Information Technology and Engineering, VIT, Vellore, India
3School of Computing, University of Eastern Finland, Kuopio, Finland
*Corresponding author. Email: chiranji.lal@vit.ac.in
Corresponding Author
Chiranji Lal Chowdhary
Received 3 October 2020, Accepted 6 May 2021, Available Online 7 June 2021.
DOI
10.2991/ijcis.d.210601.001How to use a DOI?
Keywords
Stock forecasting; Dynamic time window; Recurrent FLANN; Firefly algorithm
Abstract

An accurate prediction of future stock market trends is a bit challenging as it requires a profound understanding of stock technical indicators, including market-dominant factors and inherent process mechanism. However, the significance of better trading decisions for a successful trader inspires researchers to conceptualize superior model employing the novel set of techniques. In light of this, an intelligent stock trading system utilizing dynamic time windows with case-based reasoning (CBR), and recurrent function link artificial neural network (FLANN) optimized with Firefly algorithm is designed. The idea of using CBR module is to offer a dynamic window search to assist the recurrent FLANN architecture for superior fine-tuning the trading operations. This integrated stock trading system is intended to pick the buy/sell window of target stock to maximize the profit. To demonstrate the applicability of the projected system, the time-series stock data from IBM, Oracle and in currency Euro to INR and USD to INR exchange data on daily closing stock prices are used for simulation. The performance of the proposed model is assessed using error measures such as mean absolute error and mean absolute percent error. Furthermore, the experimental results obtained with/without using CBR is exhibited for different stock and Forex trading data.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Next Article In Issue>
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
14 - 1
Pages
1763 - 1772
Publication Date
2021/06/07
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.210601.001How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - D. K. Bebarta
AU  - T. K. Das
AU  - Chiranji Lal Chowdhary
AU  - Xiao-Zhi Gao
PY  - 2021
DA  - 2021/06/07
TI  - An Intelligent Hybrid System for Forecasting Stock and Forex Trading Signals using Optimized Recurrent FLANN and Case-Based Reasoning
JO  - International Journal of Computational Intelligence Systems
SP  - 1763
EP  - 1772
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210601.001
DO  - 10.2991/ijcis.d.210601.001
ID  - Bebarta2021
ER  -