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An evolutionary approach to define investment strategies based on macroeconomic indicators and VIX data

Authors:

Oleksandr Yefimochkin,

Rui Neves,

Nuno HortaAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

Pages 1539 - 1540

https://doi.org/10.1145/2330784.2331035

Published: 07 July 2012 Publication History

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Abstract

This paper describes a new evolutionary approach to stock market forecasting. This approach can successfully forecast S&P500 Index's Futures price evolution using mainly Macroeconomic Indicators from different regions (United States of America, European Monetary Union and Germany) and measuring its impact using Index's volatility. In addition to the Macroeconomic data time series, MAs and VIX were used. In order to validate the results, the obtained strategies, based on Macroeconomic Indicators, were compared against the B&H and MA based strategies in the period between 2010/01 and 2011/09 with the S&P500 Index Futures, showing outstanding improvements in performance.

References

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Myszkowski, O., Rachwalski. L. 2009. Trading rule discovery on Warsaw Stock Exchange using revolutionary algorithms. In International Multiconference in Computer Science and Information Technology, 81--88.

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Wen, C., Pan, W. 2009. Construct for Investment Strategy Model through Genetic Programming Planning. In International Joint Conference in Artificial Intelligence, 252--255.

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Index Terms

An evolutionary approach to define investment strategies based on macroeconomic indicators and VIX data
1. Computing methodologies
  1. Artificial intelligence

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GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

July 2012

1586 pages

ISBN:9781450311786

DOI:10.1145/2330784

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2012

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SIGEVO

GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Abstract

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Recommendations

Optimizing investment strategies based on companies earnings using genetic algorithms

Fast Universalization of Investment Strategies

Developing Multi-Time Frame Trading Rules with a Trend Following Strategy, using GA

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