Online portfolio selection is an integral componentof wealth management. The fundamental undertaking is tomaximise returns while minimising risk given investor con-straints. We aim to examine and improve modern strategiesto generate higher returns in a variety of market conditions.By integrating simple data mining, optimisation techniques andmachine learning procedures, we aim to generate aggressive andconsistent high yield portfolios. This leads to a new methodologyof Pattern-Matching that may yield further advances in dynamicand competitive portfolio construction. The resulting strategiesoutperform a variety of benchmarks, when compared using Max-imum Drawdown, Annualised Percentage Yield and AnnualisedSharpe Ratio, that make use of similar approaches. The proposedstrategy returns showcase acceptable risk with high reward thatperforms well in a variety of market conditions. We concludethat our algorithm provides an improvement in searching foroptimal portfolios compared to existing methods.
翻译:在线投资组合选择是财富管理的一个组成部分。 基本任务是在尽量减少风险的同时实现收益最大化,同时尽量减少投资者制约的风险。 我们的目标是审查和改进现代战略,以便在各种市场条件下产生更高的回报。 通过整合简单的数据挖掘、优化技术和机械学习程序,我们的目标是产生积极和一致的高收益组合。 这导致一种新的模式匹配方法,从而有可能在动态和竞争性投资组合建设方面带来进一步的进展。 由此产生的战略在使用最大最低限度的提减、年度化百分率和年度化夏尔普比率时,与采用类似方法相比,实现了各种基准的完善。 拟议的战略回报展示了可接受的风险,其高回报率在各种市场条件下表现良好。 我们的结论是,我们的算法在寻找与现有方法相比的最佳组合方面提供了改进。