Nijolė Maknickienė

Department of Financial Engineering, Faculty of Business Management, Vilnius Gediminas Technical University,

Lithuania

nijole.maknickiene@vgtu.lt

ORCID 0000-0003-2785-5183

Trading support method based on computational intelligence for speculators in the options market

Algirdas Maknickas

Faculty of Mechanics, Research Laboratory of Numerical Simulations, Vilnius Gediminas Technical University,

Lithuania

algirdas.maknickas@vgtu.lt

ORCID 0000-0002-8431-2292

Raimonda Martinkutė-Kaulienė

Department of Financial Engineering, Faculty of Business Management, Vilnius Gediminas Technical University,

Lithuania

r.martinkute-kauliene@vgtu.lt

ORCID 0000-0003-4231-5003 

Abstract. The financial world has changed dramatically in recent decades. Electronic data processing, globalisation, and deregulation have changed markets, and the biggest part of these major changes includes derivatives. As financial markets become more interconnected and global, volatility in these markets may increase dramatically in the future. It is natural that the derivatives market is gaining attention and popularity among market participants as an alternative to traditional investment and speculative instruments. The growing number of technology-driven applications and innovations in the financial sector encourages the inclusion of products relating to automated trading and robotic advice in financial decision-making. The aim of this paper is to investigate different option-trading strategies and to evaluate the effect of computational intelligence on trading success in the derivatives markets. The recurrent neural network (RNN) Keras was adopted for forecasting option prices, and the results were compared with the forecasting using the evolution of recurrent systems with optimal linear output algorithms (EVOLINO) for RNNs. This forecasting tool was investigated as a support system for speculators in the options market. The proposed method helps speculators select an appropriate option-trading strategy and increases the probability of profit. The values of trading according to the information from the tools of computational intelligence proved that the proposed method is useful, although trading in options is still very risky.

Received: January, 2020

1st Revision: July, 2020

Accepted: September, 2020

DOI: 10.14254/2071-8330.2020/13-3/15

JEL Classification: G0, G1, C6, O16

Keywords: derivatives, financial engineering, investor, artificial intelligence, deep learning, probability, strategy