Investor preference analysis: an online optimization approach with missing information. (English) Zbl 1536.91108
Summary: How to derive an investor’s preference is vital for investment advisors and online lending platforms for targeted marketing strategies, e.g., market segmentation and financial product recommendation. However, investor preference analysis usually depends on judgments from human investment experts, which are inherently subjective and costly. Intelligent investment advisors (or Robo-advisors), supported by cutting-edge technologies such as machine learning and artificial intelligence, are established to relieve these pending issues. This paper employs an online optimization framework to obtain investors’ preferences for further financial product recommendations. This proposed method allows us to update the investor’s preference for newly-arriving data sets and tackle the situation where plenty of missing values in investors’ records are present. Unlike the black-box-like machine learning approach, our method can provide more managerial implications regarding why one financial product/service is preferred. Real-world data set from an online financial platform is used to compare the existing approaches and shows the stronger and more stable performance of our method when facing different data-missing types and situations with different missing degrees, followed by a recommendation system application.
Keywords:
decision analysis; online optimization; investor preference derivation; machine learning; fintechReferences:
| [1] | Babaei, G.; Bamdad, S., A multi-objective instance-based decision support system for investment recommendation in peer-to-peer lending, Expert Syst. Appl., 150, Article 113278 pp. (2020) |
| [2] | Bezdek, J. C.; Spillman, B.; Spillman, R., A fuzzy relation space for group decision theory, Fuzzy Sets Syst., 1, 255-268 (1978) · Zbl 0398.90009 |
| [3] | Breffle, W. S.; Morey, E. R.; Thacher, J. A., A joint latent-class model: combining Likert-scale preference statements with choice data to harvest preference heterogeneity, Environ. Resour. Econ., 50, 83-110 (2011) |
| [4] | Chen, L.; Xu, Z., A new fuzzy programming method to derive the priority vector from an interval reciprocal comparison matrix, Inf. Sci., 316, 148-162 (2015) · Zbl 1390.90604 |
| [5] | Dalalyan, A.; Tsybakov, A. B., Aggregation by exponential weighting, sharp pac-bayesian bounds and sparsity, Mach. Learn., 72, 39-61 (2008) · Zbl 1470.62054 |
| [6] | Ding, X.; Chen, L.; Zhou, P.; Xu, Z.; Wen, S.; Lui, J. C.; Jin, H., Dynamic online convex optimization with long-term constraints via virtual queue, Inf. Sci., 577, 140-161 (2021) · Zbl 1529.68327 |
| [7] | Fischer, T. G., Reinforcement learning in financial markets-a survey (2018), Technical Report, FAU Discussion Papers in Economics |
| [8] | Gong, Z. W., Least-square method to priority of the fuzzy preference relations with incomplete information, Int. J. Approx. Reason., 47, 258-264 (2008) · Zbl 1184.91077 |
| [9] | Hahn, E. D., Decision making with uncertain judgments: a stochastic formulation of the analytic hierarchy process, Decis. Sci., 34, 443-466 (2003) |
| [10] | Hart, P., The condensed nearest neighbor rule (corresp.), IEEE Trans. Inf. Theory, 14, 515-516 (1968) |
| [11] | Hazan, E., Introduction to online convex optimization, Found. Trends Optim., 2, 157-325 (2016) |
| [12] | He, H.; Garcia, E. A., Learning from imbalanced data, IEEE Trans. Knowl. Data Eng., 21, 1263-1284 (2009) |
| [13] | Hsueh, M.; Yogeeswaran, K.; Malinen, S., “Leave your comment below”: can biased online comments influence our own prejudicial attitudes and behaviors?, Hum. Commun. Res., 41, 557-576 (2015) |
| [14] | Huang, D.; Yu, S.; Li, B.; Hoi, S. C.; Zhou, S., Combination forecasting reversion strategy for online portfolio selection, ACM Trans. Intell. Syst. Technol. (TIST), 9, 1-22 (2018) |
| [15] | Kalai, A.; Vempala, S., Efficient algorithms for universal portfolios, J. Mach. Learn. Res., 3, 423-440 (2002) · Zbl 1104.91035 |
| [16] | Kou, G.; Ergu, D.; Lin, C.; Chen, Y., Pairwise comparison matrix in multiple criteria decision making, Technol. Econ. Dev. Econ., 22, 738-765 (2016) |
| [17] | Kou, G.; Lin, C., A cosine maximization method for the priority vector derivation in AHP, Eur. J. Oper. Res., 235, 225-232 (2014) · Zbl 1305.91082 |
| [18] | Li, B.; Hoi, S. C.; Zhao, P.; Gopalkrishnan, V., Confidence weighted mean reversion strategy for online portfolio selection, ACM Trans. Knowl. Discov. Data, 7, 1-38 (2013) |
| [19] | Liang, Q.; Zheng, X.; Wang, Y.; Zhu, M., O3ers: an explainable recommendation system with online learning, online recommendation, and online explanation, Inf. Sci., 562, 94-115 (2021) |
| [20] | Lin, J.; Lei, Y.; Zhang, B.; Zhou, D. X., Online pairwise learning algorithms with convex loss functions, Inf. Sci., 406, 57-70 (2017) · Zbl 1429.68230 |
| [21] | Lin, Y.; Liu, S.; Yang, H.; Wu, H.; Jiang, B., Improving stock trading decisions based on pattern recognition using machine learning technology, PLoS ONE, 16, Article e0255558 pp. (2021) |
| [22] | Lovasz, L.; Vempala, S., Fast algorithms for logconcave functions: sampling, rounding, integration and optimization, (IEEE Symposium on Foundations of Computer Science (2006)) |
| [23] | Lovász, L.; Vempala, S., Fast algorithms for logconcave functions: sampling, rounding, integration and optimization, (2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS’06) (2006), IEEE), 57-68 |
| [24] | Ma, X.; Zhao, T.; Guo, Q.; Li, X.; Zhang, C., Fuzzy hypergraph network for recommending top-k profitable stocks, Inf. Sci., 613, 239-255 (2022) |
| [25] | Nair, B. B.; Mohandas, V., An intelligent recommender system for stock trading, Intell. Decis. Technol., 9, 243-269 (2015) |
| [26] | Owsinski, J. W., Fuzzy decision procedures with binary relations: towards a unified theory, Fuzzy Sets Syst., 62, 384-385 (1994) |
| [27] | Pretorius, R.; van Zyl, T., Deep reinforcement learning and convex mean-variance optimisation for portfolio management (2022), arXiv preprint |
| [28] | Ren, K.; Malik, A., Investment recommendation system for low-liquidity online peer to peer lending (p2pl) marketplaces, (Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (2019)), 510-518 |
| [29] | Ren, L.; Zhu, B.; Xu, Z., Data-driven fuzzy preference analysis from an optimization perspective, Fuzzy Sets Syst., 377, 85-101 (2019) · Zbl 1464.91056 |
| [30] | Ren, L.; Zhu, B.; Xu, Z., Robust consumer preference analysis with a social network, Inf. Sci., 566, 379-400 (2021) · Zbl 1530.91302 |
| [31] | Ren, L.; Zhu, B.; Xu, Z., Continuous exp strategy for consumer preference analysis based on online ratings, IEEE Trans. Fuzzy Syst., 30, 2621-2633 (2022) |
| [32] | Roscher, R.; Bohn, B.; Duarte, M. F.; Garcke, J., Explainable machine learning for scientific insights and discoveries, IEEE Access, 8, 42200-42216 (2020) |
| [33] | Rostamizadeh, A.; Agarwal, A.; Bartlett, P., Online and batch learning algorithms for data with missing features (2011), arXiv preprint |
| [34] | Saaty, T. L., The possibility of group choice: pairwise comparisons and merging functions, Soc. Choice Welf., 38, 481-496 (2012) · Zbl 1239.91040 |
| [35] | Shalev-Shwartz, S., Online learning and online convex optimization, Found. Trends Mach. Learn., 4, 107-194 (2012) · Zbl 1253.68190 |
| [36] | Von Neumann, J.; Morgenstern, O., Theory of Games and Economic Behavior (60th Anniversary Commemorative Edition) (2007), Princeton University Press · Zbl 1112.91002 |
| [37] | Wang, C.; Bier, V. M., Expert elicitation of adversary preferences using ordinal judgments, Oper. Res., 61, 372-385 (2013) · Zbl 1268.91059 |
| [38] | Wu, X.; Chen, H.; Wang, J.; Troiano, L.; Loia, V.; Fujita, H., Adaptive stock trading strategies with deep reinforcement learning methods, Inf. Sci., 538, 142-158 (2020) |
| [39] | Xu, S.; Zhang, Q.; Lü, L.; Mariani, M. S., Recommending investors for new startups by integrating network diffusion and investors’ domain preference, Inf. Sci., 515, 103-115 (2020) |
| [40] | Xu, Z. S., Goal programming models for obtaining the priority vector of incomplete fuzzy preference relation, Int. J. Approx. Reason., 36, 261-270 (2004) · Zbl 1088.91015 |
| [41] | Yang, H.; Liu, X. Y.; Wu, Q., A practical machine learning approach for dynamic stock recommendation, (2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/12th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE) (2018), IEEE), 1693-1697 |
| [42] | Yang, H.; Liu, X. Y.; Zhong, S.; Walid, A., Deep reinforcement learning for automated stock trading: an ensemble strategy, (Proceedings of the First ACM International Conference on AI in Finance (2020)), 1-8 |
| [43] | Zhang, H., Group decision making based on multiplicative consistent reciprocal preference relations, Fuzzy Sets Syst., 282, 31-46 (2016) · Zbl 1394.91106 |
| [44] | Zhao, H.; Liu, Q.; Wang, G.; Ge, Y.; Chen, E., Portfolio selections in p2p lending: a multi-objective perspective, (Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)), 2075-2084 |
| [45] | Zhu, B.; Xu, Z., A fuzzy linear programming method for group decision making with additive reciprocal fuzzy preference relations, Fuzzy Sets Syst., 246, 19-33 (2014) · Zbl 1314.91102 |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
