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Boosting Trading Strategies Performance using VIX Indicator Together with a Dual-Objective Evolutionary Computation Optimizer

Pinto, J. ; Neves, R. ; Horta, N.

Expert Systems with Applications Vol. 42, Nº 19, pp. 6699 - 6716, November, 2015.

ISSN (print): 0957-4174
ISSN (online):

Journal Impact Factor: 2,249 (in 2014)

Digital Object Identifier: 10.1016/j.eswa.2015.04.056

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In this study a Multi-Objective Evolutionary System is used to predict the future tendency of assets price. Therefore, a framework using a Multi-Objective Genetic Algorithm (GA) in its core to optimize a set of Trading or Investment Strategies (TSs) was developed. The investigated framework is used to determine potential buy, sell or hold conditions in stock markets, aiming to yield high returns at a minimal risk. The Volatility Index (VIX), indicators based on the VIX and other Technical Indicators (TI) are optimized to find the best investment strategy. Additionally, fair and established metrics are used to evaluate both the return and the linked risk of the optimized TSs. Furthermore, these strategies are evaluated in several markets using data from the main stock indexes of the most developed economies, such as: NASDAQ, S&P 500, FTSE 100, DAX 30, and also NIKKEI 225. The achieved results clearly outperform both the Buy&Hold and Sell&Hold. Additionally, the Pareto-Fronts obtained with the training data during the experiments clearly show the inherent trade-off between risk and return in financial. In this paper the option of using an adaptive approach was chosen, which led to the development of a framework able to operate continuously and with minimal human intervention. To sum up, the developed framework is able to evolve a set of TSs suitable for the diverse profiles of investors from the most risky to the most careful with interesting results, which suggests great potential in the framework generalization capabilities. The use of the VIX enables the system to increase the stock return compared to traditional Technical Indicators by avoiding losses when the stress in the stock market increases. The GA enables the system to adapt to different types of markets. The algorithm achieves a return of higher than 10% annual for the period of 2006–2014 in the NASDAQ and DAX indexes, in a period that includes the stock market crash of 2008.