Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Maknickas, Algirdas1; * | Maknickiene, Nijole2
Affiliations: [1] Laboratory of Numerical Simulation, Vilnius Gediminas Technical University, Basanaviciaus al. 28, Vilnius, Lithuania | [2] Department of Financial Engineering, Vilnius Gediminas Technical University, Sauletekio al. 11, Vilnius, Lithuania. E-mails: [email protected], [email protected]
Correspondence: [*] Corresponding author.
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.
Keywords: artificial intelligence, EVOLINO, finance market, forecast distribution
Journal: Informatica, vol. 30, no. 1, pp. 73-90, 2019
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]