A Novel Weighted Hybrid Recommendation System using Sharpe Ratio for a Profitable Diversified Investment Portfolio

Identifying where to invest and how much to invest can be very challenging for common people who have limited knowledge in the domain. Portfolio managers are financial professionals who spend a lot of time and effort to help investors in investing funds and implementing investment strategies, but not all can afford to consult them. The study aims to develop a weighted hybrid recommendation system that recommends an optimized investment portfolio based on the investor’s preferences regarding risk and return. Generally, investors usually ask investment for advice from friends or relatives with similar risk preferences or if they are interested in a particular item, the investors ask for the experience of someone who already has invested in the same item. Therefore, the methodology considers the investor’s past behavior and the past behavior of the nearest neighbor investors with similar risk preferences. Using user-based collaborative filtering the number of stocks is recommended using Pearson correlation based on the investor’s income, then using another user-based collaborative filtering the number of stocks is recommended based on the investor’s age. Weights are assigned to the recommended number of stocks generated based on income and age and their weighted average is finally considered. Finally, the feasibility of the proposed system was assessed through various experiments. Based on the received results, the authors conclude that the proposed weighted hybrid approach is robust enough for implementation in the real world. The novelty of the paper lies in the fact that none of the existing approaches make use of more than one type of weighted recommendation algorithm. Additionally, the final results obtained this way have been never further fortified with the highest Sharpe ratio and minimum risk for the investor. This combination of hybrid and Sharpe ratios has never been explored before.

1. Li X., Yu C. An investment portfolio recommendation system for individual e-commerce users. DEStech Transactions on Engineering and Technology Research. 2017:580–585. DOI: 10.12783/dtetr/icpr2017/17674

2. Samundeeswary K., Krishnamurthy V. Comparative study of recommender systems built using various methods of collaborative filtering algorithm. In: Proc. Int. conf. on computational intelligence in data science (ICCIDS-2017). (Chennai, June 02–03, 2017). Piscataway, NJ: IEEE; 2017:1–6. DOI: 10.1109/iccids.2017.8272663

3. Dahiya P., Duhan N. Comparative analysis of various collaborative filtering algorithms. International Journal of Computer Sciences and Engineering. 2019;7(8):347–351. DOI: 10.26438/ijcse/v7i8.347351

4. Burke R. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction. 2002;12(4):331–370. DOI: 10.1023/A:1021240730564

5. Gonzalez-Carrasco I., Colomo-Palacios R., Lopez-Cuadrado J.L., García-Crespo A., Ruiz-Mezcua B. PB-ADVISOR: A private banking multi-investment portfolio advisor. Information Sciences. 2012;206:63–82. DOI: 10.1016/j.ins.2012.04.008

6. Yu K. W., Yang X. Q., Wong H. Portfolio improvement by using the Sharpe rule and value-at-risk. Proceedings in Applied Mathematics and Mechanics. 2007;7(1):2080007–2080008. DOI: 10.1002/pamm.200700221

7. Sharpe W. F. The Sharpe ratio. The Journal of Portfolio Management. 1994;21(1):49–58. DOI: 10.3905/jpm.1994.409501

8. Singh S., Mittal H., Purwar A. Prediction of investment patterns using data mining techniques. International Journal of Computer and Communication Engineering. 2014;3(2):145–148. DOI: 10.7763/ijcce.2014.v3.309

9. Yujun Y., Jianping L., Yimei Y. An efficient stock recommendation model based on big order net inflow. Mathematical Problems in Engineering. 2016;2016:5725143. DOI: 10.1155/2016/5725143

10. Musto C., Semeraro G. Case-based recommender systems for personalized finance advisory. In: Proc. 1st Int. workshop on personalization & recommender systems in financial services (FinREC-2015). (Graz, April 16, 2015). Graz: Graz University of Technology; 2015;1349:35–36. URL: http://ceur-ws.org/Vol-1349/paper06.pdf

11. Li Y., Jiang X.-F., Tian Y., Li S.-P., Zheng B. Portfolio optimization based on network topology. Physica A: Statistical Mechanics and its Applications. 2019;515:671–681. DOI: 10.1016/j.physa.2018.10.014

12. Baser P., Saini J. R. An optimum cluster size identification for k-Means using validity index for stock market data. International Journal of Data Mining and Emerging Technologies. 2014;4(2):107–110. DOI: 10.5958/2249–3220.2014.00008.1

13. Baser P., Saini J. R. k-Means analysis of Nifty companies for an investment perspective. International Journal of Data Mining And Emerging Technologies. 2013;3(1):16–32. DOI: 10.5958/j.2249–3220.3.1.003

14. Baser, P., Saini J. R. An intelligent agent based framework for an efficient portfolio management using stock clustering. International Journal of Information and Computation Technology. 2013;3(2):49–54. URL: https://www.researchgate.net/publication/281965201

15. Baser P., Saini J. R. Agent based stock clustering for efficient portfolio management. International Journal of Computer Applications. 2015;116(3):36–41. DOI: 10.5120/20317–2381

16. Connor G., Li S., Linton O. B. A dynamic semiparametric characteristics-based model for optimal portfolio selection. SSRN Electronic Journal. 2021. DOI: 10.2139/ssrn.3803193

17. Chen J., Li D., Linton O. B. A new semiparametric estimation approach for large dynamic covariance matrices with multiple conditioning variables. Journal of Econometrics. 2019;212(1):155–176. DOI: 10.1016/j.jeconom.2019.04.025

18. Satchell S. E., Williams O. J. On the difficulty of measuring forecasting skill in financial markets. Journal of Forecasting. 2015;34(2):92–113. DOI: 10.1002/for.2320

19. Baddeley M., Burke C., Schultz W., Tobler P. Herding in financial behavior: A behavioral and neuroeconomic analysis of individual differences. Cambridge Working Papers in Economics. 2012;(1225). URL: https://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe1225.pdf

20. Shilov K. D., Zubarev A. V. Evolution of Bitcoin as a financial asset. Finance: Theory and Practice. 2021;25(5):150–171. DOI: 10.26794/2587–5671–2021–25–4–150–171

21. Sapir E. V., Karachev I. A. Challenges of a new investment policy: Investment promotion and protection. Finance: Theory and practice. 2020;24(3):118–131. DOI: 10.26794/2587–5671–2020–24–3–118–131

22. Ezangina I. A., Malovichko A. E. The venture capital market in a pandemic: Realities of time and future prospects. Finance: Theory and Practice. 2021;25(5):92–116. DOI: 10.26794/2587–5671–2021–25–5–92–116

23. Efimova О. V., Volkov М. А., Koroleva D. А. The impact of ESG factors on asset returns: Empirical research. Finance: Theory and Practice. 2021;25(4):82–97. DOI: 10.26794/2587–5671–2021–25–4–82–97

24. Ovcharov А. О., Matveev V. А. Financial fear index in the digital financial assets market. Finance: Theory and Practice. 2021;25(4):136–151. DOI: 10.26794/2587–5671–2021–25–4–136–151

25. Pashtova L. G. The influence of the corporate venture capital on the innovative development of the Russian economy. Finance: Theory and Practice. 2021;25(3):53–65. DOI: 10.26794/2587–5671–2021–25–3–53–65

26. Freitas W. B., Bertini J. R., Jr. Tactical asset allocation through random walk on stock network. In: Britto A., Valdivia Delgado K., eds. Brazilian conference on intelligent systems (BRACIS 2021). Cham: Springer-Verlag; 2021:528–542. (Lecture Notes in Computer Science. Vol. 13073). DOI: 10.1007/978–3–030–91702–9_35

27. Brugière P. Quantitative portfolio management: With applications in Python. Cham: Springer-Verlag; 2020. 220 p. (Springer Texts in Business and Economics). DOI: 10.1007/978–3–030–37740–3

28. Sukharev O. S. The restructuring of the investment portfolio: the risk and effect of the emergence of new combinations. Quantitative Finance and Economics. 2019;3(2):390–411. DOI: 10.3934/qfe.2019.2.390

29. Zhang M., Tang Y., Liua L., Zhou D. Optimal investment portfolio strategies for power enterprises under multipolicy scenarios of renewable energy. Renewable and Sustainable Energy Reviews. 2022;154:111879. DOI: 10.1016/j.rser.2021.111879

30. Carmo B. B.T, Medeiros P. P.M., Gonçalo T. E.E., Correia G. C. Framework to assist investment portfolio generation for financial sector. Exacta — Engenharia de Produção. 2021. DOI: 10.5585/exactaep.2021.18687

31. Eriotis N., Kollias K., Kounadeas T. Has the composition of the Greek banking sector investment portfolio contributed to the Greek economy financial crisis? International Journal of Corporate Finance and Accounting. 2021;8(2):1–11. DOI: 10.4018/ijcfa.2021070101

32. Bulturbayevich M. B., G’ovsiddin N. Formation and management of the investment portfolio of a commercial bank. International Engineering Journal for Research & Development. 2021;17(6):5. DOI: /10.17605/osf.io/tbfdh

33. Deng Y., Xu H., Wu J. Optimization of blockchain investment portfolio under artificial bee colony algorithm. Journal of Computational and Applied Mathematics. 2021;385:113199. DOI: 10.1016/j.cam.2020.113199

34. Park K. T., Yang H., Sohn S. Y. Recommendation of investment portfolio for peer-to-peer lending with additional consideration of bidding period. Annals of Operations Research. 2022;315(2):1083–1105. DOI: 10.1007/s10479–021–04300-z

35. Li M., Wu Y. Dynamic decision model of real estate investment portfolio based on wireless network communication and ant colony algorithm. Wireless Communications and Mobile Computing. 2021;2021:9261312. DOI: 10.1155/2021/9261312