Share:


Support system for speculation by exchange trades funds

Abstract

The paper examines the possibilities of speculating in exchangetraded funds by using artificial intelligence. The main goal of the research is to create a support system for speculative decisionmaking for investors operating in exchange-traded funds market. The research will be based on the theoretical aspects of artificial intelligence and speculation of exchange-traded funds. The support system is developed on the basis of reinforcement learning, the methods of synthesis, concretization and generalization were used to create and detail the system, as well as the methods of mathematical-statistical analysis were used to process them. Successful application of the chosen methodology in the design of the support system has resulted in positive trade results. Successful research broadens the boundaries for usage of deep reinforcement learning, and provides a basis for further development of the support system for exchange-traded funds. The support system put in place will shorten the time between the occurrence of a trading signal and the decision of the investor, which will help to reduce the loss of potential profits.


Article in Lithuanian.


Paramos sistema spekuliavimui biržoje prekiaujamais fondais


Santrauka


Darbe yra nagrinėjamos spekuliavimo biržoje prekiaujamais fondais, naudojant dirbtinį intelektą, galimybės. Pagrindinis mokslinio tyrimo tikslas – remiantis dirbtinio intelekto bei biržoje prekiaujamų fondų spekuliavimo teoriniais aspektais, sukurti spekuliavimo sprendimų priėmimo paramos sistemą investuotojams, veikiantiems biržoje prekiaujamų fondų rinkoje. Paramos sistema yra kuriama remiantis sustiprintuoju mokymusi (angl. reinforcement learning), sistemai sudaryti ir detalizuoti buvo taikyti sintezės, konkretizavimo bei apibendrinimo metodai, taip pat, panaudojus susidarytą sistemą bei gavus rezultatus, jiems apdoroti taikyti matematinės-statistinės analizės metodai. Sėkmingai pritaikius pasirinktą metodologiją, sudarant paramos sistemą, buvo gauti teigiami prekybos rezultatai. Sėkmingas tyrimas išplečia giliojo sustiprintojo mokymosi taikymo suvokimo ribas bei sudaro pagrindą tolesniam biržoje prekiaujamų fondų paramos sistemos vystymui. Sudaryta paramos sistema sutrumpins sugaištamą laiką tarp prekybos signalo atsiradimo ir investuotojo sprendimo priėmimo, o tai padės sumažinti potencialaus pelno praradimą.


Reikšminiai žodžiai: paramos sistema, biržoje prekiaujami fondai, spekuliavimas, dirbtinis intelektas, finansų rinka, sustiprintasis mokymasis, mašininis mokymasis.

Keyword : support system, exchange traded funds, speculation, artificial intelligence, financial market, reinforcement learning, machine learning

How to Cite
Tumaševičius, G., & Maknickienė, N. (2022). Support system for speculation by exchange trades funds. Mokslas – Lietuvos Ateitis / Science – Future of Lithuania, 14. https://doi.org/10.3846/mla.2022.15870
Published in Issue
Mar 14, 2022
Abstract Views
111
PDF Downloads
95
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Ayankoya, K., Calitz, A. P., & Greyling, J. H. (2016). A framework for grain commodities trading decision support for South African maize farmers [Conference presentation]. 10th International Business Conference 2016, Club Mykonos, Langebaan, South Africa.

Badruzaman, J. (2019). Analysis relative strength index and earning per share on stock price. Asian Journal of Economics, Business and Accounting, 12(4), 1–9. https://doi.org/10.9734/ajeba/2019/v12i430157

Gurrib, I. (2018). Performance of the Average Directional Index as a market timing tool for the most actively traded USD based currency pairs. Banks and Bank Systems, 13(3), 58–70. https://doi.org/10.21511/bbs.13(3).2018.06

Huang, S.-C. (2017). A big data analysis system for financial trading [Conference presentation]. World Conference on Business and Management 2017, Bali, Indonesia.

Yang, H., Liu, X. Y., Zhong, S., & Walid, A. (2020). Deep reinforcement learning for automated stock trading: An ensemble strategy. In Proceedings of the First ACM International Conference on AI in Finance (pp. 1–8), New York. https://doi.org/10.1145/3383455.3422540

Klinger, S. J. (1997). Identifying trends with volume analysis. Technical Analysis of Stocks and Commodities Magazine, 15, 68–70.

Kornilov, S. (2020). Assessing organizational efficiency under macroeconomic uncertainty in decision support systems: Ensemble methods in machine learning with two-stage nonparametric efficiency models [Doctoral dissertation, Mykolas Romeris University). https://repository.mruni.eu/bitstream/handle/007/16539/Disertacija_Kornilov.pdf?sequence=1&isAllowed=y

Kratzert, F., Klotz, D., Brenner, C., Schulz, K., & Herrnegger, M. (2018). Rainfall–runoff modelling using long short-term memory (LSTM) networks. Hydrology and Earth System Sciences, 22(11), 6005–6022. https://doi.org/10.5194/hess-22-6005-2018

Kuutti, S., Bowden, R., Joshi, H., de Temple, R., & Fallah, S. (2019, October 27–30). End-to-end reinforcement learning for autonomous longitudinal control using advantage actor critic with temporal context. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 2456–2462). IEEE. https://doi.org/10.1109/ITSC.2019.8917387

Li, R., Wang, C., Zhao, Z., Guo, R., & Zhang, H. (2020). The LSTM-based advantage actor-critic learning for resource management in network slicing with user mobility. IEEE Communications Letters, 24(9), 2005–2009. https://doi.org/10.1109/LCOMM.2020.3001227

Li, S., Bing, S., & Yang, S. (2018). Distributional advantage actor-critic. arXiv:1806.06914.

Li, Y. (2017). Deep reinforcement learning: An overview. arXiv:1701.07274.

Li, Y., Zheng, W., & Zheng, Z. (2019). Deep robust reinforcement learning for practical algorithmic trading. IEEE Access, 7, 108014–108022. https://doi.org/10.1109/ACCESS.2019.2932789

Lietuvos bankas. (2020). Lietuvos gyventojų investavimo įpročiai: vyrauja pensijų fondai ir nekilnojamasis turtas. https://www.lb.lt/lt/naujienos/lietuvos-gyventoju-investavimo-iprociai-vyrauja-pensiju-fondai-ir-nekilnojamasis-turtas

Locklair, V. A. (2018). Giant thinking… minds? The problem of strong artificial intelligence. Concordia Technical Journal, 1(1).

Maknickas, A., & Maknickienė, N. (2019). Support system for trading in exchange market by distributional forecasting model. Informatica, 30(1), 73–90. https://doi.org/10.15388/Informatica.2019.198

Maknickienė, N. (2015). Paramos sistema investuotojui valiutų rinkoje [Daktaro disertacija, Vilniaus Gedimino technikos universitetas). Technika. https://doi.org/10.20334/2310-M

Maknickienė, N., Maknickas, A., & Martinkutė-Kaulienė, R. (2020). Trading support method based on computational intelligence for speculators in the options market. Journal of International Studies, 13(3), 231–247. https://doi.org/10.14254/2071-8330.2020/13-3/15

Mazumder, I. (2014). Investing in exchange traded funds. Applied Finance Letters, 3(2), 16–23. https://doi.org/10.24135/afl.v3i2.23

Merenda, P. F. (1997). A guide to the proper use of factor analysis in the conduct and reporting of research: Pitfalls to avoid. Measurement and Evaluation in Counseling and Development, 30(3), 156–164. https://doi.org/10.1080/07481756.1997.12068936

Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., & Kavukcuoglu, K. (2016, June). Asynchronous methods for deep reinforcement learning. In International Conference on Machine Learning (pp. 1928–1937). PMLR.

Muhtadin, F. (2015). Decision support system for stock trading using fuzzy logic and genetic algorithm. https://informatika.stei.itb.ac.id/~rinaldi.munir/TA/Makalah_TA_Fadhil_Muhtadin.pdf

Naučius, M. (2018). Should fully autonomous artificial intelligence systems be granted legal capacity? Law Review, 17(1), 113–132. https://doi.org/10.7220/2029-4239.17.6

Nilsson, N. J. (2011). The quest for artificial intelligence: A history of ideas and achievements. Cambridge University Press.

Peachavanish, R. (2016, March 16–18). Stock selection and trading based on cluster analysis of trend and momentum indicators. In Proceedings of the International MultiConference of Engineers and Computer Scientists (pp. 1–5), Hong Kong.

Pinto, M. A. F. (2014). Design and implementation of an algorithmic trading system for the Sifox application. https://core.ac.uk/download/pdf/302971773.pdf

Puelz, D., Carvalho, C. M., & Hahn, P. R. (2015). Optimal ETF selection for passive investing. arXiv:1510.03385.

Rashidi, M., Ghodrat, M., Samali, B., & Mohammadi, M. (2018). Decision support systems. In Management of information systems. IntechOpen. https://doi.org/10.5772/intechopen.79390

Russell, S. J., & Norvig, P. (2002). Artificial intelligence: A modern approach (2nd ed.). Prentice Hall.

Soleymani, F., & Paquet, E. (2021). Deep graph convolutional reinforcement learning for financial portfolio management - DeepPocket. arXiv:2105.08664. https://doi.org/10.1016/j.eswa.2021.115127

Stasytytė, V. (2011). Investicijų portfelio sprendimų paramos sistema [Daktaro disertacija, Vilniaus Gedimino technikos universitetas]. Technika.

Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., Hirschberg, J., Kalyanakrishnan, S., Kamar, E., Kraus, S., Leyton-Brown, K., Parkes, D., Press, W., Saxenian, A. L., Shah, J., Tambe, M., & Teller, A. (2016). Artificial intelligence and life in 2030: The one hundred year study on artificial intelligence (Report of the 2015-2016 Study Panel). Stanford University.

Vallat, R. (2018). Pingouin: Statistics in Python. Journal of Open Source Software, 3(31), 1026. https://doi.org/10.21105/joss.01026

Zeebaree, M., & Aqel, M. (2019). A comparison study between intelligent decision support systems and decision support systems. The ISC International Journal of Information Security, 11(3), 187–194.