The Role of Artificial Intelligence in Investment Decisions and Applications in The Turkish Finance Industry

Öz

Alan Turing was not the first one to ask whether machines can think, but he might have been the closest one with a positive answer. Artificial intelligence (AI) techniques are used for many purposes, such as self-driven cars, manufacturing in dark factories, chatbots, and Robo-advisors. In line with the increasing use of AI in the financial sector, empirical studies also reveal that AI techniques perform better than conventional models. This study aims to shed light on the effects of AI techniques on investors’ decision-making process in the case of the Turkish finance industry. As the first study in the literature, by comparing EU practices, I also review the existing AI policy framework and industry practices in the Turkish finance industry to develop some policy suggestions.

Anahtar Kelimeler

• Artificial intelligence
• neural networks
• genetic algorithms
• Robo-advisor
• investor preferences
• decision making.

Yatırım Kararlarında Yapay Zekanın Rolü ve Türk Finans Sektörü Uygulamaları

Öz

Öz Makinelerin düşünüp düşünemeyeceğini sorgulayan ilk kişi Alan Turing değildi, ancak olumlu bir yanıt veren en yakın kişi olduğu söylenebilir. Yapay zeka (AI) teknikleri, otomatik (kendi kendine) giden arabalar, karanlık fabrikalarda üretim, sohbet robotları ve robo-danışmanlar gibi birçok amaç için kullanılmaktadır. Yapay zekanın finans sektöründe artan kullanımına paralel olarak, ampirik çalışmalar da yapay zeka tekniklerinin geleneksel modellerden daha iyi performans gösterdiğini ortaya koymaktadır. Bu çalışma, Türk finans sektörü örneğinde yapay zeka tekniklerinin yatırımcıların karar verme süreçleri üzerindeki etkilerine ışık tutmayı amaçlamaktadır. Literatürde ilk olarak bu çalışma, bazı politika önerileri geliştirmek için Türkiye ve AB uygulamalarını karşılaştırarak, Türk finans sektöründeki mevcut AI politika çerçevesini ve endüstri uygulamalarını da gözden geçirmektedir.

Anahtar Kelimeler

• Yapay zeka
• sinir ağları
• genetik algoritmalar
• Robo-danışman
• yatırımcı tercihleri
• karar verme.

Kaynakça

1. Afreen, R. (2013). Alan Turing: A Note On His Role As World War II Cryptanalyst. International Journal of Applied Engineering and Technology. Vol. 3 (1) January-March, pp.21-26/Afreen.
2. Ahn, W., Lee, H. S., Ryou, H., & Oh, K. J. (2020). Asset Allocation Model for a Robo-advisor Using the Financial Market Instability Index and Genetic Algorithms. Sustainability, 12(3), 849.
3. Arf, C. (1959). Makine Düşünebilir mi ve Nasıl Düşünebilir? Atatürk Üniversitesi 1958- 1959 Öğretim Yılı Halk Konferansları(1), 91-103. Retrieved 2 March 2022 from: https://l24.im/ROS
4. Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems. Neural Computing and Applications, 19(8), 1165-1195.
5. Bauer, F. L. (2002). Decrypted secrets: methods and maxims of cryptology. Springer Science & Business Media.
6. Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial Intelligence in FinTech: understanding Robo-advisors adoption among customers. Industrial Management & Data Systems, Vol. 119 No. 7, pp. 1411-1430. https://doi.org/10.1108/IMDS-08-2018-036.
7. Chen, B., Zhong, J., & Chen, Y. (2020). A hybrid approach for portfolio selection with higher-order moments: Empirical evidence from Shanghai Stock Exchange. Expert Systems with Applications, 145, 1-27. 113104.
8. Chung, H., & Shin, K. S. (2019). Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction. Neural Computing and Applications, 1-18.

9. Copeland, J. (1993). Symbolic Computation. Artificial Intelligence: A Philosophical Introduction. Blackwell. p. 328.
10. Coskun, Y. and Coskun, E.A. (2022). The Role of Government in Digital Innovations: Case of Turkish Startup Ecosystem. (in, Innovative Finance for Technological Progress: Roles of Fintech, Financial Instruments, and Institutions, Eds.: Taghizadeh-Hesary, F., Aboojafari, R., and Yoshino, N. Routledge) (forthcoming).
11. Diplo Foundation. (2019). Mapping the challenges and opportunities of artificial intelligence for the conduct of diplomacy. January. Retrieved 4 January 2022 from: https://l24.im/A7lo.
12. Elaziz, M. A., Ewees, A. A., & Alameer, Z. (2019). Improving adaptive neuro-fuzzy inference system based on a modified salp swarm algorithm using genetic algorithm to forecast crude oil price. Natural Resources Research, 1-16.
13. Ellis, C., & Willson, P. (2005). Can a neural network property portfolio selection process outperform the property market? Journal of Real Estate Portfolio Management, 11(2), 105–121.
14. Eren, B.A. (2021), "Determinants of customer satisfaction in chatbot use: evidence from a banking application in Turkey", International Journal of Bank Marketing, Vol. 39 No. 2, pp. 294-311. https://doi.org/10.1108/IJBM-02-2020-0056
15. Ersan, O., & Ekinci, C. (2016). Algorithmic and high-frequency trading in Borsa Istanbul. Borsa Istanbul Review, 16(4), 233-248.
16. European Commission. (2021). Digital Economy and Society Index (DESI) 2021. Retrieved 10 December 2021 from: https://digital-strategy.ec.europa.eu/en/policies/desi.
17. Financial Stability Board. (2017). Artificial intelligence and machine learning in financial services. 1 November. Retrieved 2 February 2022 from: https://www.fsb.org/wp-content/uploads/P011117.pdf .
18. Fintech Istanbul. (2019). Fintech Activities In Turkey And Around The World. Retrieved 8 March 2022 from: https://l24.im/JRB4cY.
19. Flavián, C., Pérez-Rueda, A., Belanche, D., & Casaló, L. V. (2021). Intention to use analytical artificial intelligence (AI) in services–the effect of technology readiness and awareness. Journal of Service Management.
20. Han, J. B., Kim, S. H., Jang, M. H., & Ri, K. S. (2019). Using Genetic Algorithm and NARX Neural Network to Forecast Daily Bitcoin Price. Computational Economics, 1-17.
21. Hodges, A. (2003). The Military Use of Alan Turing. In Mathematics and War (pp. 312-325). Birkhäuser, Basel.
22. Hosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert systems with applications, 117, 287-299.
23. Ivanov, O., Snihovyi, O., & Kobets, V. (2018, May). Implementation of Robo-advisors Tools for Different Risk Attitude Investment Decisions. In ICTERI Workshops (pp. 195-206).
24. John, H. (1992). Holland. genetic algorithms. Scientific American, 267(1), 44-50.
25. Kalkınma Bakanlığı. (2018). On Birinci Kalkınma Planı (2019-2023) Finansal Hizmetlerin Geliştirilmesi Özel İhtisas Komisyonu Raporu. Retrieved 12 January 2022 from: https://www.sbb.gov.tr/wp-content/uploads/2020/04/FinansalHizmetlerinGelistirilmesiOzelIhtisasKomisyonuRaporu.pdf.
26. Kumar, M., Husain, M., Upreti, N., & Gupta, D. (2010). Genetic algorithm: Review and application. Available at SSRN 3529843, pp. 451-454.
27. Maedche, A., Legner, C., Benlian, A., Berger, B., Gimpel, H., Hess, T., ... & Söllner, M. (2019). AI-based digital assistants. Business & Information Systems Engineering, 61(4), 535-544.
28. Malkiel, B. G., & Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.
29. Memiş, H., & Geylan, Z. (2021). Creating an AI-based personal assistant: Case study of Is¸ bank ‘Maxi’. Journal of Digital Banking, 5(4), 321-328.
30. Miri, I., Fotros, M. H., & Miri, A. (2020). Comparison of Portfolio Optimization for Investors at Different Levels of Investors' Risk Aversion in Tehran Stock Exchange with Meta-Heuristic Algorithms. Advances in Mathematical Finance and Applications, 5(1), 1-12.
31. Mithen, S. (2007). Creations of Pre-Modern Human Minds: Stone Tool Manufacture and Use by Homo habilis, heidelbergensis, and neanderthalensis. Creations of the Mind: Theories of Artifacts and their Representation, 289-311.
32. OECD. (2017). Robo-Advice for Pensions. Retrieved 2 March 2022 from: https://www.oecd.org/finance/Robo-Advice-for-Pensions-2017.pdf
33. OECD. (2021). Artificial Intelligence, Machine Learning and Big Data in Finance: Opportunities, Challenges, and Implications for Policy Makers. Retrieved 6 March 2022 from:https://www.oecd.org/finance/artificial-intelligence-machine-learningbig-data-in-finance.htm.
34. Park, J. Y., Ryu, J. P., & Shin, H. J. (2017). How to Manage Portfolio by Robo-advisor. International Information Institute (Tokyo). Information, 20(5B), 3463-3470.
35. Participation Banks Association of Turkey (Türkiye Katılım Bankaları Birliği). (2021) Katılım Bankacılığı Dijital Araştırma Raporu 2021. Retrieved 12 February 2022 from: https://tkbb.org.tr/Documents/Tkbbyayinlari/ EY_TKBB_Katilim _Bankacilik_ Dijital_ Arastirma_Raporu_20211227.pdf.
36. Petzold, C. (2008). The annotated Turing: a guided tour through Alan Turing's historic paper on computability and the Turing machine. Wiley Publishing.
37. Phoon, K., & Koh, F. (2017). Robo-advisors and wealth management. The Journal of Alternative Investments, 20(3), 79-94. Presidency of Strategy and Budget. (2019). The Eleventh Development Plan (2019-2023). Retrieved 8 February 2022 from: https://www.sbb.gov.tr/wp-content/uploads/2021/12/Eleventh_Development_Plan_2019-2023.pdf.
38. Presidency of the Republic of Türkiye Digital Transformation Office (PRTDTO) (T.C. Cumhurbaşkanlığı Dijital Dönüşüm Ofisi). (2021). Ulusal Yapay Zeka Stratejisi: 2021-2025. August. Retrieved 4 March 2022 from https://cbddo.gov.tr/SharedFolderServer/Genel/File/TRNationalAIStrategy2021-2025.pdf.
39. Reese, B. (2020). The fourth age: Smart robots, conscious computers, and the future of humanity. Atria Books, pp. 293.
40. Shanmuganathan, M. (2020). Behavioural finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions. Journal of Behavioral and Experimental Finance, 27, 100297.
41. Solin, M. M., Alamsyah, A., Rikumahu, B., & Saputra, M. A. A. (2019, July). Forecasting Portfolio Optimization using Artificial Neural Network and Genetic Algorithm. In 2019 7th International Conference on Information and Communication Technology (ICoICT) (pp. 1-7). IEEE.
42. Tertilt, M., & Scholz, P. (2018). To advise, or not to advise—How Robo-advisors evaluate the risk preferences of private investors. The Journal of Wealth Management, 21(2), 70-84.
43. The Banking Association of Turkey (Türkiye Bankalar Birliği). (2021). Digital, Internet, and Mobile Banking Statistics. December. No: DT22. Retrieved 4 April 2022 from https://www.tbb.org.tr/tr/ bankacilik/banka-ve-sektor-bilgileri/istatistiki-raporlar/59
44. Turing, A. (1948). Intelligent machinery (1948). The Essential Turing, 395-432.
45. Turing, A. M. (1950). Computing machinery and intelligence. The Essential Turing: The Ideas that Gave Birth to the Computer Age. Ed. B. Jack Copeland. (2004). Oxford: Oxford UP, 433-64.
46. Turing, A. M., & Copeland, B. J. (2004). The Essential Turing: Seminal Writings in Computing, Logic, Philosophy. Artificial Intelligence, and Artificial Life plus The Secrets of Enigma. Oxford, USA: Oxford University Press, p. 622.
47. TÜBİSAD (Bilişim Sanayicileri Derneği) and Deloitte. (2020). Bilgi ve İletişim Teknolojileri Sektörü 2019 Pazar Verileri. pp: 1-31. June. Retrieved 2 April 2022 from: https://www.tubisad.org.tr/tr/images/pdf/tubisad-bit-2019.pdf
48. TÜBİSAD. (2021). Turkey’s Digital Transformation Index 2021, pp. 1-61. Retrieved 12 March 2022 from https://www.tubisad.org.tr/en/images/pdf/tubisad_tdti2021_report.pdf
49. Yamamoto, Y., & Zenios, S. A. (1993). Predicting prepayment rates for mortgages using the cascade correlation learning algorithm. Journal of Fixed Income, 2(4), 86–96.
50. Zimmermann, H. J., Neuneier, R., & Grothmann, R. (2001). Active portfolio management based on error correction neural networks, Advances in neural information processing systems, NIPS. Cambridge: The MIT Press, p. 1465-1467.