This paper describes a new model for portfolio optimization (PO), using entropy and mutual information instead of variance and covariance as measurements of risk. We also compare the performance in and out of sample of the original Markowitz model against the proposed model and against other state of the art shrinkage methods. It was found that ME (mean-entropy) models do not always outperform their MV (mean-variance) and robust counterparts, although presenting an edge in terms of portfolio diversity measures, especially for portfolio weight entropy. It further shows that when increasing return constraints on portfolio optimization, ME models were more stable overall, showing dampened responses in cumulative returns and Sharpe indexes in comparison to MV and robust methods, but concentrated their portfolios more rapidly as they were more evenly spread initially. Finally, the results suggest that it was also shown that, depending on the market, increasing return constraints may have positive or negative impacts on the out-of-sample performance.

This study investigates the imputation of missing values in mean air temperature time series in the state of Pará, Brazil, by integrating observational data from INMET and the ERA5-Land reanalysis. A multivariate non-linear approach based on the MissForest algorithm was developed and compared with models using only station data and with linear interpolation. The evaluation employed synthetic gaps together with the RMSE, MAE, and MAE in quantile 99 metrics. The results indicate that the inclusion of ERA5-Land reduced RMSE to 0.664–0.744 °C, MAE to 0.509–0.569 °C, and ∆P99 to 0.909–1.013 °C, while increasing model stability and outperforming the other strategies, thus supporting the methodological consistency of the approach for tropical climate time series.