2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 12 , Issue 1 , PP: 28-39, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Vinamra Nayak 1 *
Doi: https://doi.org/10.54216/AJBOR.120103
Stock price prediction is an important component of the financial analysis because the results influence the increase in economic growth and investment. This work aims to develop an ensemble SL technique that consists of mainly PCA, PSO, and SVM to achieve better prediction. Hence, through PCA, large numbers of stocked data dimensions are compressed without compromising on the crucial feature of data set. The problem of parameter selection for non-linear datasets is handled by using a bio-inspired optimization technique known as PSO in order to optimize the SVM hyperparameters. As the core accurate predictor model, the SVM employs the Radial Basis Function to provide the substantial regression capacity for sophisticated financial data sets. The ensemble framework was used with actual stock price data and the information set into training and testing sets. The acknowledgement of probable manifold values indicated that the proposed approach is more accurate than conventional approaches, with an accuracy rate of 95.5 %, when benchmarked using RMSE or MAE. In particular, the forecasts of stock prices by integrating PCA for feature reduction and PSO for parameter tuning with SVM regression is a notable improvement. The proposed methodology can be easily applied to scale for financial analytics since it manages to solve for the issues of noisy and non-linear high dimensional data.
PCA , PSO , SVM , Stock Price Prediction , Financial Data Analysis , Regression Models. Machine Learning in Finance , Stock Market Trends , Dimensionality Reduction
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