@article {CAO2023120934,
	title = {A novel recurrent neural network based online portfolio analysis for high frequency trading},
	journal = {Expert Systems with Applications},
	volume = {233},
	year = {2023},
	pages = {120934},
	abstract = {The Markowitz model, a Nobel Prize winning model for portfolio analysis, paves the theoretical foundation in finance for modern investment. However, it remains a challenging problem in the high frequency trading (HFT) era to find a more time efficient solution for portfolio analysis, especially when considering circumstances with the dynamic fluctuation of stock prices and the desire to pursue contradictory objectives for less risk but more return. In this paper, we establish a recurrent neural network model to address this challenging problem in runtime. Rigorous theoretical analysis on the convergence and the optimality of portfolio optimization are presented. Numerical experiments are conducted based on real data from Dow Jones Industrial Average (DJIA) components and the results reveal that the proposed solution is superior to DJIA index in terms of higher investment returns and lower risks.},
	keywords = {Markowitz model, Pareto frontier, Portfolio analysis, Recurrent neural network, Time-varying problem},
	issn = {0957-4174},
	doi = {https://doi.org/10.1016/j.eswa.2023.120934},
	url = {https://www.sciencedirect.com/science/article/pii/S0957417423014367},
	author = {Cao, Xinwei and Adam Francis and Xujin Pu and Zenan Zhang and Vasilios Katsikis and Stanimirovic, Predrag and Brajevic, Ivona and Shuai Li}
}
