Portfolio optimization using local linear regression ensembles in RapidMiner
This is an incremental improvement for financial traders seeking better portfolio returns.
The paper tackles portfolio optimization by implementing a Local Linear Regression Ensemble Committee (LOLREC) to predict 1-day-ahead returns for 453 S&P500 assets and compute portfolio weights, outperforming benchmark strategies in optimizing capital growth rates.
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.