<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cao, Xinwei</style></author><author><style face="normal" font="default" size="100%">Adam Francis</style></author><author><style face="normal" font="default" size="100%">Xujin Pu</style></author><author><style face="normal" font="default" size="100%">Zenan Zhang</style></author><author><style face="normal" font="default" size="100%">Vasilios Katsikis</style></author><author><style face="normal" font="default" size="100%">Stanimirovic, Predrag</style></author><author><style face="normal" font="default" size="100%">Brajevic, Ivona</style></author><author><style face="normal" font="default" size="100%">Shuai Li</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A novel recurrent neural network based online portfolio analysis for high frequency trading</style></title><secondary-title><style face="normal" font="default" size="100%">Expert Systems with Applications</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Markowitz model</style></keyword><keyword><style  face="normal" font="default" size="100%">Pareto frontier</style></keyword><keyword><style  face="normal" font="default" size="100%">Portfolio analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Recurrent neural network</style></keyword><keyword><style  face="normal" font="default" size="100%">Time-varying problem</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S0957417423014367</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">233</style></volume><pages><style face="normal" font="default" size="100%">120934</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>