Integration Of Technical Indicators With Support Vector Regression Analysis For Improved Stock Market Prediction

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Published: Nov 14, 2023
DOI: https://doi.org/10.53555/jrtdd.v6i10s(2).2668
Keywords:
Support Vector Regression machine learning algorithms accuracy precision and recall

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Syed Abdul Haq
G.Penchala Narasaiah

Abstract

This study employs technical indicators within the Support Vector Regression (SVR) framework to enhance the precision of stock market prediction models. Traditional methodologies for stock market analysis commonly rely on individual technical indicators to prognosticate market movements. However, their efficacy in capturing the nuanced dynamics of financial markets remains restricted. Concurrently, machine learning algorithms, particularly SVR, have showcased adeptness in managing non-linear relationships and intricate data patterns.


Through thorough experimentation and analysis, this study scrutinizes the predictive efficacy of the amalgamated model in comparison to conventional methods and standalone technical indicator analyses. The effectiveness of the proposed approach in foreseeing stock market movements is gauged utilizing performance indicators such as accuracy, precision, and recall. Furthermore, the study evaluates predictive performance metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), while also employing comparison methodologies against baseline models.

Article Details

How to Cite
Syed Abdul Haq, & G.Penchala Narasaiah. (2023). Integration Of Technical Indicators With Support Vector Regression Analysis For Improved Stock Market Prediction. Journal for ReAttach Therapy and Developmental Diversities, 6(10s(2), 2098–2103. https://doi.org/10.53555/jrtdd.v6i10s(2).2668
Issue
Vol. 6 No. 10s(2) (2023)
Section
Articles
Author Biographies

Syed Abdul Haq

Assistant Professor, Department of Computer Science & Engineering – Data Science, Malla Reddy Engineering College (A), Hyderabad

G.Penchala Narasaiah

Assistant Professor, Department of Computer Science & Engineering, Sree Venkateswara College of Engineering, Nellore 

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