Stock returns are continuously generated by different data sources and depend on various factors such as financial policies and national economic growths. Stock returns prediction, unlike traditional regression, requires consideration of both the sequential and interdependent nature of financial time-series. This work uses a two-stage approach, using kernel adaptive filtering (KAF) within a stock market interdependence approach to sequentially predict stock returns. Thus, unlike traditional KAF formulations, prediction uses not only their local models but also the individual local models learned from other stocks, enhancing prediction accuracy. The enhanced KAF plus market interdependence framework has been tested on 24 different stocks from major economies. The enhanced approach obtains higher sharpe ratio when compared with KAF-based methods, long short-term memory, and autoregressive-based models.

库存收益是由不同的数据源连续产生的，并且取决于各种因素，例如金融政策和国民经济增长。与传统回归不同，股票收益预测需要同时考虑金融时间序列的顺序性和相互依存性。这项工作采用了两阶段方法，即使用股票市场相互依赖方法中的内核自适应过滤（KAF）顺序预测股票收益。因此，与传统的KAF公式不同，预测不仅使用其局部模型，而且还使用从其他股票中学习的单个局部模型，从而提高了预测准确性。增强的KAF以及市场相互依存关系该框架已针对主要经济体的24种不同股票进行了测试。与基于KAF的方法，较长的短期记忆和基于自回归的模型相比，增强的方法可获得更高的锐化率。