Abstract
Accurate prediction of financial time series or their direction of changes may result in highly profitable returns. There are many approaches to build such models. One of them is to apply machine learning algorithms i.e. Neural Networks or Support Vector Machines. In this paper we would like to propose a modified version of Support Vector Machine classifier, Volume Weighted Support Vector Machine which has the ability to predict short term trends on the stock market. Modification is based on the assumption that incorporating transaction volume into penalty function may lead to better future trends forecasting. Experimental results obtained on the data set composed of daily quotations from 420 stocks from S&P500 Index showed that proposed method gives statistically better results than basic algorithm.
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Żbikowski, K. (2014). Time Series Forecasting with Volume Weighted Support Vector Machines. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures, and Structures. BDAS 2014. Communications in Computer and Information Science, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-319-06932-6_24
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DOI: https://doi.org/10.1007/978-3-319-06932-6_24
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