论文标题
非组织时间序列中的顺序资产排名
Sequential asset ranking in nonstationary time series
论文作者
论文摘要
我们创建了一种排名算法，即天真的贝叶斯资产排名。我们的算法计算了单个资产的排名高于其他投资组合成分的后验概率。与早期的算法（例如加权多数）不同，我们的算法允许表现不佳的专家在开始表现良好时的体重增加。我们的表现胜过仅长期持有标准普尔500指数和回归的基线。
We create a ranking algorithm, the naive Bayes asset ranker. Our algorithm computes the posterior probability that individual assets will be ranked higher than other portfolio constituents. Unlike earlier algorithms, such as the weighted majority, our algorithm allows poor-performing experts to have increased weight when they start performing well. We outperform the long-only holding of the S&P 500 index and a regress-then-rank baseline.
