标题： 稳健和自适应的在线投资组合选择算法 显示英文标题

标题： Robust and Adaptive Algorithms for Online Portfolio Selection

摘要： 我们提出一种在线投资组合选择方法。 动机是在算法交易的背景下，这需要在新数据到达时快速且递归地更新投资组合分配。 特别是，我们研究两种在线算法：鲁棒指数加权最小二乘法（R-EWRLS）和正则化在线最小方差算法（O-VAR）。 我们的方法利用了信号处理和统计学中的简单思想，这些思想在实证金融文献中有时被忽视。 这两种方法使用4个真实数据集与基准分配技术进行对比评估。 在计算需求和财务表现方面，我们的方法在这些数据集中优于基准分配技术。 显示英文摘要

摘要： We present an online approach to portfolio selection. The motivation is within the context of algorithmic trading, which demands fast and recursive updates of portfolio allocations, as new data arrives. In particular, we look at two online algorithms: Robust-Exponentially Weighted Least Squares (R-EWRLS) and a regularized Online minimum Variance algorithm (O-VAR). Our methods use simple ideas from signal processing and statistics, which are sometimes overlooked in the empirical financial literature. The two approaches are evaluated against benchmark allocation techniques using 4 real datasets. Our methods outperform the benchmark allocation techniques in these datasets, in terms of both computational demand and financial performance.