Design of an efficient Recurrent Neural Network based VARMA GRU Model for prediction of Inter-day stock movements via augmented indicator analysis

Design of an efficient Recurrent Neural Network based VARMA GRU Model for prediction of Inter-day stock movements via augmented indicator analysis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 23 February 2026 V1 Latest version Share on Design of an efficient Recurrent Neural Network based VARMA GRU Model for prediction of Inter-day stock movements via augmented indicator analysis Authors : Pinky Gangwani , Vikas Bhowate 0009-0007-2475-8309 , Punam Mahakalkar , and Heena Farheen Ansari [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177183566.61930669/v1 107 views 51 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Traditional models for predicting stock value are limited in their ability to handle the complexity and dynamic nature of stock data, making the accurate prediction of intraday stock movements a significant challenge in the financial industry. This paper presents a Recurrent Neural Network (RNN)-based VARMA-GRU model to predict the fluctuations of intraday stock prices. The proposed model integrates an enhanced indicator analysis strategy into a VARMA-GRU model with technical indicators for the extraction of pertinent characteristics of stock data. The datasets & samples show nonlinear dependencies and long-term temporal dynamics, which are modeled by VARMA and GRU, respectively. The proposed model performs better than existing models in terms of accuracy, precision, recall, computational efficiency, and real-time practical use in stock market applications. The use case of this model is to support investors and traders in making informed decisions regarding the stock market by providing them with a very accurate prediction of intraday stock movements for different scenarios. Supplementary Material File (stock prediction research paper.docx) Download 372.73 KB Information & Authors Information Version history V1 Version 1 23 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords augmented indicator analysis gru inter-day stock movements nonlinear dependencies rnn technical indicators varma Authors Affiliations Pinky Gangwani St Vincent Pallotti College of Engineering and Technology View all articles by this author Vikas Bhowate 0009-0007-2475-8309 St Vincent Pallotti College of Engineering and Technology View all articles by this author Punam Mahakalkar St Vincent Pallotti College of Engineering and Technology View all articles by this author Heena Farheen Ansari [email protected] St Vincent Pallotti College of Engineering and Technology View all articles by this author Metrics & Citations Metrics Article Usage 107 views 51 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Pinky Gangwani, Vikas Bhowate, Punam Mahakalkar, et al. Design of an efficient Recurrent Neural Network based VARMA GRU Model for prediction of Inter-day stock movements via augmented indicator analysis. Authorea . 23 February 2026. 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