Yield curve forecast combinations based on bond portfolio performance

Abstract

We propose an economically motivated forecast combination strategy in which model weights are related to portfolio returns obtained by a given forecast model. An empirical application based on an optimal mean-variance bond portfolio problem is used to highlight the advantages of the proposed approach with respect to combination methods based on statistical measures of forecast accuracy. We compute average net excess returns, standard deviation, and the Sharpe ratio of bond portfolios obtained with nine alternative yield curve specifications, as well as with 12 different forecast combination strategies. Return-based forecast combination schemes clearly outperformed approaches based on statistical measures of forecast accuracy in terms of economic criteria. Moreover, return-based approaches that dynamically select only the model with highest weight each period and discard all other models delivered even better results, evidencing not only the advantages of trimming forecast combinations but also the ability of the proposed approach to detect best-performing models. To analyze the robustness of our results, different levels of risk aversion and a different dataset are considered.

Design and Analysis of Robust Deep Learning Models for Stock Price Prediction

“…The informationtheoretic approach and the wavelet analysis have also been proposed in stock price prediction [48,49]. Several portfolio optimization methods have also been presented in some works using forecasted stock returns and risks [50][51][52][53][54][55].…”