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Title
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Book Chapter
Book Chapter
Faculty Publications- Book Chapter
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Forecasting Stock Market Indexes Through Machine Learning Using Technical Analysis Indicators and DWT
Subject
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Discrete wavelet transform; Machine learning; Nifty 50; S&P 500; Stock market forecasting; SVR; Technical analysis indicators
Description
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In recent years, the stock market prices have become more volatile due to refinement in technology and a rise in trading volume. As these seemingly unpredictable price trends continue, the stock market investors and consumers refer to the security indices to assess these financial markets. To maximise their return on investment, the investors could employ appropriate methods to forecast the stock market trends, taking into account the nonlinearity and nonstationarity of the stock market data. This research aims to assess the predictive capability of supervised machine learning models for the stock market regression analysis. The dataset utilised in this research includes the daily prices and additional technical indicator data of S&P 500 Index of US stock exchange and Nifty50 Index of Indian stock exchange from January 2008 to June 2016; both the indexes are weighted measurements of the top companies listed on respective stock exchanges. The model proposed in this research combines the discrete wavelet transform and support vector regression (SVR) with various kernels such as Linear, Poly and Radial basis function kernel (RBF) of the support vector machine. The results show that using the RBF kernel on Nifty 50 index data, the proposed model achieves the lowest MSE and RMSE error during testing are 0.0019 and 0.0431, respectively, and on S&P 500 index data, it achieves 0.0027 and 0.0523, respectively. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Creator
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Patel S.; Surya B.D.V.; Manjunath C.; Marimuthu B.; Ghosh B.
Source
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Lecture Notes on Data Engineering and Communications Technologies, Vol-111, pp. 625-638.
Publisher
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Springer Science and Business Media Deutschland GmbH
Date
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2022-01-01
Identifier
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<a href="https://doi.org/10.1007/978-981-16-9113-3_46" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-9113-3_46</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133556520&doi=10.1007%2F978-981-16-9113-3_46&partnerID=40&md5=9f116fa2103c56642aa354758affe57b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133556520&doi=10.1007%2f978-981-16-9113-3_46&partnerID=40&md5=9f116fa2103c56642aa354758affe57b</a>
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Restricted Access
Relation
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ISSN: 23674512
Format
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Online
Language
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English
Type
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Book chapter
Coverage
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Patel S., Department of Computer Science and Engineering, CHRIST (Deemed To Be University), Karnataka, Bengaluru, India; Surya B.D.V., Department of Computer Science and Engineering, CHRIST (Deemed To Be University), Karnataka, Bengaluru, India; Manjunath C., Department of Computer Science and Engineering, CHRIST (Deemed To Be University), Karnataka, Bengaluru, India; Marimuthu B., Department of Computer Science and Engineering, CHRIST (Deemed To Be University), Karnataka, Bengaluru, India; Ghosh B., Department of Finance, RV Institute of Management, Karnataka, Bengaluru, India