Comparative Analysis Of Various Hybrid Models Over Stock Market Dataset

Comparative Analysis Of Various Hybrid Models Over Stock Market Dataset

Registration ID: IJNRD_216482

Published ID: IJNRD2403521

DOI: Click Here to Get

Rishabh Saxena , Sandeep Kumar

Stock market, forecasting models, predicting model, market closing price, hidden Markov model, ARIMA, MLP, Random Forest, RDA

The world of financial markets faces a formidable challenge when it comes to accurately anticipating stock market movements. Conventional prediction techniques have struggled to contend with the intricate and uncertain nature of market dynamics, often yielding suboptimal outcomes. Inaccurate forecasts can have far-reaching implications, impacting investment strategies, financial choices, and overall economic stability. Consequently, there is an urgent demand for the exploration of fresh and inventive methods that can bolster our capacity to forecast stock prices with increased accuracy and dependability. This study investigates the potential of hybrid Machine-Learning (ML) models as a promising remedy to this persistent issue. This research presents a comparative analysis between multiple hybrid models applied to stock market datasets. These models were assessed using three distinct datasets spanning the years 2022-2023, 2021-2023, and 2018-2023 for five major stocks: RELIANCE, TCS, HDFC, ITC, and INFOSYS. Result dictate that ARIMA-HMM and RDAWA are the two models from the chosen ones that provide good results. Out of the two, ARIMA gives the best perform with an accuracy of 80% and metrics sitting under 0.3 for all datasets. Following that, RDAWA gives a good and robust perform with an accuracy of 70% to 75% with metrics sitting under 0.3 for RELIANCE and TCS.

Published Paper   E-Certificate

"Comparative Analysis Of Various Hybrid Models Over Stock Market Dataset", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 3, page no.f194-f205, March-2024, Available :https://ijnrd.org/papers/IJNRD2403521.pdf

Volume 9 Issue 3, March-2024

Pages : f194-f205

Paper Reg. ID: IJNRD_216482

Published Paper Id: IJNRD2403521

Downloads: 000121132

Research Area: Computer Science & Technology 

Country: Greater Noida, Uttar Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403521.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403521

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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