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Support System for Trading in Exchange Market by Distributional Forecasting Model
Volume 30, Issue 1 (2019), pp. 73–90
Pub. online: 1 January 2019      Type: Research Article      Open accessOpen Access
Received
1 February 2018
Accepted
1 September 2018
Published
1 January 2019

Abstract

Integration of algorithms of investment theory and artificial intelligence allows one to create a support system for investors in exchange markets based on the ensemble of long-short-term-memory (LSTM) based recurrent neural networks (RNN). The proposed support system contains five stages: preparation of historical data, prediction by an ensemble of LSTM RNNs, assessment of prediction distributions, investment portfolio formation and verification. The prediction process outputs a multi-modal distribution, which provides useful information for investors. The research compares four different strategies based on a combination of distribution forecasting models. The high-low strategy helps decision-makers in exchange markets to recognize signals of transactions and fix limits for expectations. A combination of high-low-daily-weekly predictions helps investors to make daily transactions with knowing distribution of exchange rates during the week. The shift in time of five hours between London and New York inspired us to create a UK-NY strategy, which allows investors to recognize the signals of the market in a very short time. The joined high-low-UK-NY strategy increases the possibility of recognizing the signals of transactions in a very short time and of fixing the limits for day trading. So, this support system for investors is verified as a profitable tool for speculators in the relatively risky currency market.

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Biographies

Maknickas Algirdas
algirdas.maknickas@vgtu.lt

A. Maknickas was awarded the doctor of technological science degree at Vilnius Gediminas Technical University, in 2009 (Faculty of Fundamental Sciences). From 2011 until 2017 he was an associate professor of Vilnius Gediminas Technical University at the Department of Information Technologies. At present he is a professor of Vilnius Gediminas Technical University at the Department of Mechanical and Material Engineering, senior researcher at Institute of Mechanics and Head at Laboratory of Numerical Simulation. His research interests include the problems of artificial intelligence, computational complexity, continuum mechanics, etc.

Maknickienė Nijole
nijole.maknickiene@vgtu.lt

N. Maknickienė was awarded the doctor of social sciences degree at Vilnius Gediminas Technical University, in 2015. At present she is affiliated in Departments of Finantial Engineering and Economy as an associate professor. Her main research interests include preparation of big data in economical statistics, the applications of artificial intelligence algorithms in financial engineering, risk management, behaviour of investors, etc.


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Open access article under the CC BY license.

Keywords
artificial intelligence EVOLINO finance market forecast distribution

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