Stock returns prediction using kernel adaptive filtering within a stock market interdependence approach

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.