A Synergistic Forecasting Model for Techno-Fundamental Analysis of Gold Market Returns

Abstract

This study presents a novel approach to financial market forecasting based on a synergistic forecasting model, a type of techno-fundamental analysis that combines technical analysis indicators with fundamental variables using the Kalman filter to improve the accuracy of predictions. We used this model to forecast daily market price returns on gold. The obtained results show that our synergistic model can significantly deduct the root-mean-square error (RMSE) of the predictions compared to a sole technical and/or fundamental analysis. Also, 67% of the time, the model significantly and correctly predicted directional changes in prices one day ahead of time, outperforming the benchmark models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.