Application of Support Vector Machine on Algorithmic Trading

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Bibliografiske detaljer
Udgivet i:	Proceedings on the International Conference on Artificial Intelligence (ICAI) (2018), p. 400
Hovedforfatter:	Szklarz, J
Andre forfattere:	Rosillo, R, Alvarez, N, Fernández, I, Garcia, N
Udgivet:	The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)
Fag:	Algorithms Divergence Machine learning Performance indices Trends Support vector machines Decision making Volatility Computer simulation Stock exchanges Artificial intelligence Stock market indexes Program trading
Online adgang:	Citation/Abstract Full Text Full Text - PDF
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Beskrivelse
Resumen:	The following research provides a thoughtful analysis regarding the use of machine learning techniques applied to algorithmic trading using common indexes such as the S&P500 and the Chicago Board Options Exchange Market Volatility Index (VIX). A trading simulation is carried out in order to test the efficiency of the algorithms in up trending and down trending periods. Statistical and economic performance measures are obtained and compared in order to discuss the most effective technique. The inputs used in the analysis are well-known quantitative indicators such as the Relative Strength Index and the Moving Average Convergence-Divergence. The relevance of the results lies in the use of separated training models for each kind of trend.
Fuente:	Advanced Technologies & Aerospace Database