[en] One fundamental issue in finance is portfolio selection, which seeks the best strategy for assigning capital among a group of assets. There has been growing interest in online portfolio selection where the investment strategy is frequently readjusted in a short time as new financial market data arrives constantly. Numerous effective algorithms have been extensively examined both in terms of theoretical analysis and empirical
evaluation. Previous online portfolio selection algorithms that incorporate transaction costs are limited by the fact that they often approximate the transaction remainder factor instead of calculating it precisely. This could lead to suboptimal investment performance. To address this issue, we present an innovative method that considers transaction costs and resolves the accurate transaction remainder factor and the optimal portfolio allocation simultaneously for each period. In addition, we take into account the open-end fund, which permits constant cash inflows, and develop a framework for online portfolio selection. We also incorporate the uncertainty set to minimize the impact of the prediction error during the prediction process. Utilizing the
framework presented in this innovative model, we develop a novel algorithm for online portfolio selection that incorporates transaction costs and continuous cash inflows with the objective of maximizing cumulative wealth. Numerical experiments show that the proposed algorithms are able to handle transaction costs and constant cash inflows effectively.
Disciplines :
Physical, chemical, mathematical & earth Sciences: Multidisciplinary, general & others
Author, co-author :
Lyu, Benmeng
WU, Boqian ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) ; University of Twente [NL]
Guo, Sini; Beijing Institute of Technology, China
Gu, Jiawen; Southern University of Science and Technology, China
Ching, WaiKi; The University of Hong Kong, Hong Kong, China
External co-authors :
yes
Language :
English
Title :
Robust online portfolio optimization with cash flows
Publication date :
02 August 2024
Journal title :
Omega: the International Journal of Management Science
Kelly, J.L., A new interpretation of information rate. Bell Syst Tech J 35:4 (1956), 917–926.
Guan, H., An, Z., A local adaptive learning system for online portfolio selection. Knowl-Based Syst, 186, 2019, 104958.
Marques, A.C., Frej, E.A., de Almeida, A.T., Multicriteria decision support for project portfolio selection with the FITradeoff method. Omega, 111, 2022, 102661.
Fereydooni, A., Barak, S., Sajadi, S.M.A., A novel online portfolio selection approach based on pattern matching and ESG factors. Omega, 123, 2024, 102975.
Cover, T.M., Universal portfolios. Math Finance 1:1 (1991), 1–29.
Helmbold, D.P., Schapire, R.E., Singer, Y., Warmuth, M.K., On-line portfolio selection using multiplicative updates. Math Finance 8:4 (1998), 325–347.
Agarwal A, Hazan E, Kale S, Schapire RE. Algorithms for portfolio management based on the newton method. In: Proceedings of the international conference on machine learning. 2006, p. 9–16.
Borodin, A., El-Yaniv, R., Gogan, V., Can we learn to beat the best stock. J Artificial Intelligence Res 21:1 (2004), 579–594.
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 (2013), 1–38.
Li B, Hoi SC. On-Line Portfolio Selection with Moving Average Reversion. In: Proceedings of the international conference on machine learning. 2012, p. 273–80.
Huang, D., Zhou, J., Li, B., Hoi, S.C., Zhou, S., Robust median reversion strategy for online portfolio selection. IEEE Trans Knowl Data Eng 28:9 (2016), 2480–2493.
Das P, Banerjee A. Meta optimization and its application to portfolio selection. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining. 2011, p. 1163–71.
Zhang, Y., Lin, H., Yang, X., Long, W., Combining expert weights for online portfolio selection based on the gradient descent algorithm. Knowl-Based Syst, 234, 2021, 107533.
Blum, A., Kalai, A., Universal portfolios with and without transaction costs. Mach Learn 35:3 (1999), 193–205.
Albeverio, S., Lao, L., Zhao, X., On-line portfolio selection strategy with prediction in the presence of transaction costs. Math Methods Oper Res 54:1 (2001), 133–161.
Das P, Johnson N, Banerjee A. Online lazy updates for portfolio selection with transaction costs. In: Proceedings of the AAAI conference on artificial intelligence. 2013, p. 202–8.
Guo, S., Gu, J.-W., Ching, W.-K., Adaptive online portfolio selection with transaction costs. European J Oper Res 295:3 (2021), 1074–1086.
Jiang, Z., Xu, D., Liang, J., A deep reinforcement learning framework for the financial portfolio management problem. 2017 arXiv preprint arXiv:1706.10059.
Li, B., Wang, J., Huang, D., Hoi, S.C., Transaction cost optimization for online portfolio selection. Quant Finance 18:8 (2018), 1411–1424.
Guo, S., Gu, J.-W., Fok, C.H., Ching, W.-K., Online portfolio selection with state-dependent price estimators and transaction costs. European J Oper Res 311:1 (2023), 333–353.
Dimson, E., Minio-Kozerski, C., Closed-end funds: A survey. Financial Mark Inst Instrum 8:2 (1999), 1–41.
Edelen, R.M., Investor flows and the assessed performance of open-end mutual funds. J Financ Econ 53:3 (1999), 439–466.
Zhang, Y., Zhao, P., Wu, Q., Li, B., Huang, J., Tan, M., Cost-sensitive portfolio selection via deep reinforcement learning. IEEE Trans Knowl Data Eng 34:1 (2022), 236–248.
Bauer, E., Kohavi, R., An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Mach Learn 36:1 (1999), 105–139.
Györfi L, Vajda I. Growth optimal investment with transaction costs. In: Proceedings of the international conference on algorithmic learning theory. 2008, p. 108–22.
Toloo, M., Mensah, E.K., Salahi, M., Robust optimization and its duality in data envelopment analysis. Omega, 108, 2022, 102583.
Georgantas, A., Doumpos, M., Zopounidis, C., Robust optimization approaches for portfolio selection: a comparative analysis. Ann Oper Res 301:1 (2021), 1–17.
Gorissen, B.L., Yanıkoğlu, İ., den Hertog, D., A practical guide to robust optimization. Omega 53 (2015), 124–137.
Peng, X., Xu, D., Kong, L., Chen, D., L1-norm loss based twin support vector machine for data recognition. Inform Sci 340 (2016), 86–103.