We extend the research into cross-sectional momentum trading strategies. Our main result is our novel ranking algorithm, the naive Bayes asset ranker (nbar), which we use to select subsets of assets to trade from the S&P 500 index. We perform feature representation transfer from radial basis function networks to a curds and whey (caw) multivariate regression model that takes advantage of the correlations between the response variables to improve predictive accuracy. The nbar ranks this regression output by computing the sequential posterior probability that individual assets will be ranked higher than other portfolio constituents. Unlike the weighted majority algorithm, which deals with nonstationarity by ensuring the weights assigned to each expert never dip below a minimum threshold, our ranking algorithm allows experts who previously performed poorly to have increased weights when they start performing well. Our algorithm outperforms a strategy that would hold the long-only S&P 500 index with hindsight, despite the index appreciating by 205% during the test period. It also outperforms a regress-then-rank baseline, the caw model.
翻译:我们把研究扩展至跨部门动力交易战略。 我们的主要结果就是我们的新排名算法,天天的贝耶斯资产排名(nbar),我们用它从S & P 500指数中选择资产子集进行交易。我们从半径基函数网络向曲线和螺旋(caw)多变回归模型进行特征代表转换,利用反应变量之间的相互关系来提高预测准确性。 nbar 将这一回归输出排序,方法是计算相继的后继概率,使个别资产排名高于其他组合成分。 与加权多数算法不同,该算法通过确保分配给每位专家的重量从不低于最低阈值,处理非静态问题。 我们的排名算法允许以往表现不佳的专家在表现良好时增加重量。 我们的算法优于一种战略,即将长期的S & P 500指数维持在测试期间205%的指数,尽管指数升值了205 % 。 它也超越了回归时的基线,即CPO模型。