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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.

Details

Subject
Algorithms;
Divergence;
Machine learning;
Performance indices;
Trends;
Support vector machines;
Decision making;
Volatility;
Computer simulation;
Stock exchanges;
Artificial intelligence;
Stock market indexes;
Program trading
Business indexing term
Identifier / keyword
Support-vector machine; Algorithmic trading; Trade
Title
Application of Support Vector Machine on Algorithmic Trading
Author
Szklarz, J 1 ; Rosillo, R 2 ; Alvarez, N 2 ; Fernández, I 2 ; Garcia, N 2 

1  Programmer, Izertis S.L, Gijón, Asturias, Spain 
2  Business Management Dept., University of Oviedo, Gijón, Asturias, Spain. [email protected] 
Publication title
Proceedings on the International Conference on Artificial Intelligence (ICAI); Athens
Pages
400-406
Publication year
2018
Publication date
2018
Publisher
The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)
Place of publication
Athens
Country of publication
United States
Publication subject
Computers--Artificial Intelligence
Source type
Conference Paper
Language of publication
English
Document type
Conference Proceedings
ProQuest document ID
2136876869
Document URL
https://www.proquest.com/conference-papers-proceedings/application-support-vector-machine-on-algorithmic/docview/2136876869/se-2?accountid=208611
Copyright
Copyright The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) 2018
Last updated
2024-08-27
Database
ProQuest One Academic