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Implementation of Robo-Advisor Services for Different Risk Attitude Investment Decisions Using Machine Learning Techniques

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Information and Communication Technologies in Education, Research, and Industrial Applications (ICTERI 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1007))

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Abstract

In this paper we have researched how to use machine learning in the financial industry on the example of robo-advisor; defined the basic functionality of robo-advisor, an implementation of robo-advisor based on analysis of the most popular financial services, such as Betterment, FutureAdvisor, Motif Investing, Schwab Intelligent and Wealthfront. We have also compared their functionality, formulated a list of critical features and described our own high-level architecture design of a general robo-advisor tool for private investors. Our goal is to build three application modules for a single robo-advisor which combines its architecture and modern financial instruments – cryptocurrencies for the first time. The first module is a Long short-term memory (LSTM) neural network, which forecasts cryptocurrencies prices daily. As a result of simulation experiment through the application using real data from open sources, we have found that the combination of criterion can explain 61% of cryptocurrencies prices variation. The second module uses robo-advising approach to build an investment plan for novice cryptocurrencies investors with different risk attitude investment decisions. The third module is ETL (Extract-Transform-Load) for a statistics dataset and neural networks models. Results of the investigation show that investing in cryptocurrencies can give 23.7% per year for risk-averse, 31.8% per year for risk-seeking investors and 16.5% annually for investors of hybrid type.

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References

  1. Kohavi, R., Provost, F.: Glossary of terms. Mach. Learn. 30, 271–274 (1998)

    Article  Google Scholar 

  2. The implications of machine learning in finance. Mode of access. https://www.bloomberg.com/professional/blog/implications-machine-learning-finance/. Accessed 24 Feb 2018

  3. Maedche, A., Morana, S., Schacht, S., Werth, D., Krumeich, J.: Advanced user assistance systems. Bus. Inf. Syst. Eng. 58(5), 367–370 (2016)

    Article  Google Scholar 

  4. Jung, D., Dorrner, V., Glaser, F., Morana, S.: Robo-advisory – digitalization and automation of financial advisory. Bus. Inf. Syst. Eng. 60, 81–86 (2018). https://doi.org/10.1007/s12599-018-0521-9

    Article  Google Scholar 

  5. Firth, N.: Want to make your vote really count? Stick a blockchain on it. 6 September 2017. Mode of access. https://www.newscientist.com/article/mg23531424-500-bitcoin-tech-to-put-political-power-in-the-hands-of-voters/. Accessed 30 June 2018

  6. Galeon, D., Reedy, C.: Blockchain Is Helping Us Feed the World’s Hungriest Families, 21 March 2017, Mode of access. https://futurism.com/blockchain-is-helping-us-feed-the-worlds-hungriest-families/. Accessed 30 June 2018

  7. Kobets, V., Yatsenko, V., Mazur, A., Zubrii, M.: Data analysis of private investment decision making using tools of Robo-advisers in long-run period. In: Proceedings of the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, Kyiv, Ukraine, 14–17 May 2018, vol. 2104, pp. 144–159 (2018)

    Google Scholar 

  8. Cocca, T.: Potential and limitations of virtual advice in wealth management. J. Financ. Transf. 44, 45–57 (2016)

    Google Scholar 

  9. Ivanon, O., Snihovyi, O., Kobets, V.: Implementation of robo-advisors tools for different risk attitude invesment decisions. In: Proceedings of the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, Kyiv, Ukraine, 14–17 May 2018, vol. 2104, pp. 195–206 (2018)

    Google Scholar 

  10. Fein, M.L.: Robo-Advisors: A Closer Look, 30 June 2015. http://dx.doi.org/10.2139/ssrn.2658701. Accessed 30 June 2018

  11. Robo-advice poses a substantial risk to wealth management firms and customers alike – EBA warns. 15 November 2018, Mode of access. https://thewealthnet.com/page_fullstory.php?articleid=58571&categoryid=2&interestid=15. Accessed 1 Dec 2018

  12. The Real Truth Behind The Rise of Robo-Advisors. 15 November 2018, Mode of access. https://www.nanalyze.com/2016/01/the-real-truth-behind-the-rise-of-robo-advisors/. Accessed 1 Dec 2018

  13. Reuba, K.: Robo-Advisors: Early Disruptors in Private Wealth Management. 5 December 2017, Mode of access. https://www.allianzgi.com/en/insights/investment-themes-and-strategy/robo-advisors-early-disruptors-in-private-wealth-management. Accessed 1 Dec 2018

  14. Ivanov, O., Snihovyi, O., Kobets, V.: Cryptocurrencies prices forecasting with anaconda tool using machine learning techniques. In: Proceedings of the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, Kyiv, Ukraine, 14–17 May 2018, vol. 2105, pp. 453–456 (2018)

    Google Scholar 

  15. Phillips, R.C., Gorse, D.: Predicting cryptocurrency price bubbles using social media data and epidemic modelling. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, pp. 1–7 (2017)

    Google Scholar 

  16. Colianni, S., Rosales, S.M., Signorotti, M.: Algorithmic Trading of Cryptocurrency Based on Twitter Sentiment Analysis (2015). Mode of access. http://cs229.stanford.edu/proj2015/029_report.pdf

  17. Lamon, C., Nielsen, E., Redondo, E.: Cryptocurrency price prediction using news and social media sentiment. SMU Data Sci. Rev. 1(3), 1–22 (2017)

    Google Scholar 

  18. Kim, Y.B., Kim, J.G., Kim, W., Im, J.H., Kim, T.H., Kang, S.J., et al.: Predicting fluctuations in cryptocurrency transactions based on user comments and replies. PLoS One 11(8), e0161197 (2016). https://doi.org/10.1371/journal.pone.0161197

    Article  Google Scholar 

  19. Faggella, D.: Machine Learning in Finance – Present and Future Applications. Mode of access. https://www.techemergence.com/machine-learning-in-finance/. Accessed 24 Feb 2018

  20. Kashner, E.: Ghosts In The Robo Advisor Machine. Mode of access. http://www.etf.com/sections/blog/22973-ghosts-in-the-robo-advisor-machine.html. Accessed 24 Feb 2018

  21. Markowitz, H.M.: Portfolio selection. J. Finance 7(1), 77–91 (1952). https://doi.org/10.2307/2975974

    Article  Google Scholar 

  22. Black, F., Litterman, R.: Global portfolio optimization. Financ. Anal. J. 51, 133–138 (1995)

    Article  Google Scholar 

  23. Betterment Review 2018. Mode of access. https://www.nerdwallet.com/blog/investing/betterment-review/. Accessed 24 Feb 2018

  24. Betterment Adds Institutional Platform, Inks Deal With Fidelity. Mode of access. https://bankinnovation.net/2014/10/betterment-adds-institutional-platform-inks-deal-with-fidelity/. Accessed 3 Mar 2018

  25. Future Advisor Review 2017. Mode of access. https://www.nerdwallet.com/blog/investing/futureadvisor-review/. Accessed 24 Feb 2018

  26. YC Alum FutureAdvisor Is Now Managing $600 Million In Assets. Mode of access. https://techcrunch.com/2015/06/24/yc-alum-futureadvisor-is-now-managing-600-million/. Accessed 3 Mar 2018

  27. Thangavelu, P.: Motif Investing Broker Review: Easy Thematic Investing. Mode of access. https://www.investopedia.com/articles/active-trading/030415/motif-investing-broker-review-easy-thematic-investing.asp. Accessed 24 Feb 2018

  28. Motif Investment Review 2018. Mode of access. https://www.nerdwallet.com/blog/investing/motif-investing-review-1/. Accessed 24 Feb 2018

  29. Motif Investing Review. Mode of access. http://www.investmentzen.com/motif-investing-review. Accessed 3 Mar 2018

  30. Schwab Intelligent Portfolios Review 2018. Mode of access. https://www.nerdwallet.com/blog/investing/charles-schwab-intelligent-portfolios-review/. Accessed 3 Mar 2018

  31. Schwab Intelligent Portfolios Review 2018 – A Free Robo Advisor? Mode of access. https://investorjunkie.com/39634/schwab-intelligent-portfolios-review/. Accessed 3 Mar 2018

  32. Wealthfront Review 2018. Mode of access. https://www.nerdwallet.com/blog/investing/wealthfront-review/?trk_content=brokerage_compare_module. Accessed 3 Mar 2018

  33. Wealthfront Review. Mode of access. https://www.stockbrokers.com/review/wealthfront. Accessed 3 Mar 2018

  34. Ten years in, nobody has come up with a use for blockchain. https://hackernoon.com/ten-years-in-nobody-has-come-up-with-a-use-case-for-blockchain-ee98c180100. Accessed 28 Jan 2018

  35. Want to Be a Millionaire? Two Main Rules of Bitcoin Investing. https://cointelegraph.com/news/want-to-be-a-millionaire-two-main-rules-of-bitcoin-investing. Accessed 28 Jan 2018

  36. Data set for supply. https://drive.google.com/open?id=1FZowiJQUZokrf98FfJiirrvNnBqVAUri

  37. Data set for mining difficulty. https://drive.google.com/open?id=1fqG37woV4Zqja2W_NK6iVkI1je1UK3hM

  38. Is Trading Volume important? https://steemit.com/cryptocurrency/@cryptopy/is-trading-volume-important. Accessed 28 Jan 2018

  39. Why Do Currencies Fluctuate? http://www.xe.com/moneytransfertips/why-do-currencies-fluctuate.php. Accessed 28 Jan 2018

  40. Why the Dollar Is the Global Currency. https://www.thebalance.com/world-currency-3305931. Accessed 28 Jan 2018

  41. Bitcoin’s dataset used for forecasting. https://www.dropbox.com/s/dswj09nn3wc2crb/bitcoin_dataset.csv?dl=0. Accessed 30 June 2018

  42. Bitcoin’s dataset used for comaprison real trading volume and forecasted. https://www.dropbox.com/s/e1fgz8oyuo1bfq4/bitcoin_trading_volume_predicted_dataset.csv?dl=0. Accessed 30 June 2016

  43. Bitcoin’s dataset used for comparison real price and forecasted. https://www.dropbox.com/s/qzikltpfaud63mb/bitcoin_price_predicted_dataset.csv?dl=0. Accessed 30 June 2017

  44. Bitcoin’s dataset used for comparison real price and forecasted. https://www.dropbox.com/s/qzikltpfaud63mb/bitcoin_price_predicted_dataset.csv?dl=0. Accessed 30 June 2018

  45. Kobets, V., Poltoratskiy, M.: Using an evolutionary algorithm to improve investment strategies for industries in an economic system. In: CEUR Workshop Proceedings, vol. 1614, pp. 485–501 (2016). (Indexed by: Sci Verse Scopus, DBLP, Google Scholar). CEUR-WS.org/Vol-1614/ICTERI-2016-CEUR-WS-Volume.pdf

  46. Kobets, V., Yatsenko, V.: Adjusting business processes by the means of an autoregressive model using BPMN 2.0. In: CEUR Workshop Proceedings, vol. 1614, pp. 518–533 (2016). (Indexed by: Sci Verse Scopus, DBLP, Google Scholar). CEUR-WS.org/Vol-1614/ICTERI-2016-CEUR-WS-Volume.pdf

  47. Markowitz model Python implementation. https://gist.github.com/alekseysink/fca420a5e4e60bb010fbb1d21f628bf. Accessed 1 July 2018

  48. Kobets, V., Yatsenko, V., Poltoratskiy, M.: Dynamic Model of Double Electronic Vickrey Auction. In: CEUR Workshop Proceedings, vol. 1356, pp. 236–251 (2015). (Indexed by: Sci Verse Scopus, DBLP, Google Scholar). CEUR-WS.org/Vol-1356

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Author information

Authors and Affiliations

  1. Kherson State University, 27, Universitetska st., Kherson, 73000, Ukraine

    Oleksandr Snihovyi, Vitaliy Kobets & Oleksii Ivanov

Authors
  1. Oleksandr Snihovyi
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  2. Vitaliy Kobets
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  3. Oleksii Ivanov
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Corresponding author

Correspondence to Vitaliy Kobets .

Editor information

Editors and Affiliations

  1. Zaporizhzhya National University, Zaporizhzhia, Ukraine

    Vadim Ermolayev

  2. Universidad Politecnica de Madrid, Boadilla del Monte, Madrid, Spain

    Mari Carmen Suárez-Figueroa

  3. Lviv Polytechnic National University, Lviv, Ukraine

    Vitaliy Yakovyna

  4. Institute of Applied Informatics, Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria

    Heinrich C. Mayr

  5. Department of Theory and Technology of Programming, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine

    Mykola Nikitchenko

  6. Kherson State University, Kherson, Ukraine

    Aleksander Spivakovsky

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Snihovyi, O., Kobets, V., Ivanov, O. (2019). Implementation of Robo-Advisor Services for Different Risk Attitude Investment Decisions Using Machine Learning Techniques. In: Ermolayev, V., Suárez-Figueroa, M., Yakovyna, V., Mayr, H., Nikitchenko, M., Spivakovsky, A. (eds) Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2018. Communications in Computer and Information Science, vol 1007. Springer, Cham. https://doi.org/10.1007/978-3-030-13929-2_15

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  • DOI: https://doi.org/10.1007/978-3-030-13929-2_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13928-5

  • Online ISBN: 978-3-030-13929-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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