Forecasting Stock Market’s Performance Based on Grasshopper Optimized Hybrid Neural Network Method

Authors

  • Sunil Gupta Associate professor, School of Computer Science & System, JAIPUR NAITONAL UNIVERSITY, JAIPUR, India
  • Vikas Tyagi Professor, School of Management & Commerce, Dev Bhoomi Uttarakhand University, Uttarakhand, India
  • Mir Aadil Assistant Professor, Department of Computer Science and IT, Jain(Deemed-to-be University), Bangalore-27, India
  • Neeraj Kumari Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India

Keywords:

Stock market, price prediction, paired t-test, grasshopper optimized integrated deep convolutional feedforward neural network (IDCFNN GOA)

Abstract

Forecasting stock index prices is a key indicator that helps investors and financial analysts make better decisions that maximize profits while minimizing risks. In order to succeed, a robust engine with the capacity to distribute important information is necessary. In this study, a grasshopper-optimized integrated deep convolutional feedforward neural network (IDCFNN+GOA) is employed to increase stock market forecasting accuracy. Using performance indicator and a hypothesis test (paired t-test), the effect of the IDCFNN+GOA model on forecasting the subsequently day's closing price of several stock indices is examined. By combining data from the COVID-19 epidemic, the stock indexes are taken into account. The efficacy of the suggested strategy is evaluated in comparison to current stock market price prediction systems. The simulation results show that the IDCFNN+GOA model may be used to forecast the next day's finishing price.

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Published

11.07.2023

How to Cite

Gupta, S. ., Tyagi, V. ., Aadil, M. ., & Kumari, N. . (2023). Forecasting Stock Market’s Performance Based on Grasshopper Optimized Hybrid Neural Network Method. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 164–170. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3036

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

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