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

Abstract

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.

Multivariate and Online Prediction of Closing Price Using Kernel Adaptive Filtering

“…Currently, the use of KAF approaches in the stock price prediction is limited [19,20]. In [19], a multiple-kernel learning method was proposed to address KAF's two main issues: kernel size and step size.…”

“…e existing literature focuses on the multiplekernel learning method and solves different issues such as kernel size and step size. We follow the same line of thought and take the existing methods [17,19,20] as the foundation of the proposed work to propose a KAF-based approach for close-price prediction.…”

Bitcoin Prediction using Rain Optimization Algorithm

“…In 2020, Sergio Garcia-Vega et al [1], presented exploits of a two-stage technique, exploiting KAF in a stock market interdependence technique to predict stock returns sequentially. The improved KAF plus market interdependence model has experimented on 24 diverse stocks from the most important economies.…”

Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA

“…Salisu et al ( 2020 ) have applied the GFI (Global Fear Index) to understand the predictability of commodity price levels during COVID. Nabipour et al ( 2020 ) have used machine learning algorithms in predicting the future values of the Tehran stock market while Garcia et al ( 2020 ) have applied the KAF (Kernel Adaptive Index) with a stock market interdependence to systematically predict stock market returns. Xie et al ( 2011 ) have developed support vector machine (SVM) and multivariate discriminate analysis (MDA) to predict Chinese listed companies.…”