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Stock Prediction System Based on Key Statistics for S&P 500 With Linear SVC

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Stock Prediction System Based on Key Statistics for S&P 500 With Linear SVC


G. Saminath Krisna | Dr. R. Indra Gandhi


https://doi.org/10.31142/ijtsrd11170


G. Saminath Krisna | Dr. R. Indra Gandhi "Stock Prediction System Based on Key Statistics for S&P 500 With Linear SVC" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3, April 2018, pp.972-977, URL: https://www.ijtsrd.com/papers/ijtsrd11170.pdf

Previous research shows strong evidence that traditional regression- based predictive models face significant challenges in predictability tests due to uncertain models and unstable parameters. Recent studies introduce new, stable strategies to overcome these problems. Support Vector Clustering is a relatively new learning algorithm that has the desirable characteristics of the control of the decision function, the use of the kernel method, and the sparsity of the solution. In this paper, we present a theoretical and empirical framework to apply the Support Vector Machines strategy to predict the stock market. There are many factors like macro and microeconomic events that may influence the stock trend. For predicting the stock performance, Support Vector Machine is used to analyze the relationship between these factors. Our results suggest that support vector clustering is a powerful predictive tool for stock predictions in the financial market.

Stock prediction, predictive models, predictive algorithms and training data


IJTSRD11170
Volume-2 | Issue-3, April 2018
972-977
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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