Application of Support Vector Machine on Algorithmic Trading

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Библиографические подробности
Опубликовано в::	Proceedings on the International Conference on Artificial Intelligence (ICAI) (2018), p. 400
Главный автор:	Szklarz, J
Другие авторы:	Rosillo, R, Alvarez, N, Fernández, I, Garcia, N
Опубликовано:	The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)
Предметы:	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-ссылка:	Citation/Abstract Full Text Full Text - PDF
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Описание
Краткий обзор:	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.
Источник:	Advanced Technologies & Aerospace Database