Portfolio optimization using local linear regression ensembles in RapidMiner

TL;DR

This paper introduces LOLREC, a local linear regression ensemble method for predicting stock returns, which effectively guides portfolio selection and outperforms benchmark strategies in real-world trading scenarios.

Contribution

It presents a novel ensemble approach for stock return prediction that improves portfolio performance over traditional methods.

Findings

LOLREC outperforms benchmark strategies in annual yields.

The number of selected stocks (parameter m) influences performance.

The method demonstrates practical usefulness in daily trading.

Abstract

In this paper we implement a Local Linear Regression Ensemble Committee (LOLREC) to predict 1-day-ahead returns of 453 assets form the S&P500. The estimates and the historical returns of the committees are used to compute the weights of the portfolio from the 453 stock. The proposed method outperforms benchmark portfolio selection strategies that optimize the growth rate of the capital. We investigate the effect of algorithm parameter m: the number of selected stocks on achieved average annual yields. Results suggest the algorithm's practical usefulness in everyday trading.