Bayesian forecasting of stock returns on the JSE using simultaneous graphical dynamic linear models

Abstract

Cross-series dependencies are crucial in obtaining accurate forecasts when forecasting a multivariate time series. Simultaneous Graphical Dynamic Linear Models (SGDLMs) are Bayesian models that elegantly capture cross-series dependencies. This study forecasts returns of a 40 -dimensional time series of stock data from the Johannesburg Stock Exchange (JSE) using SGDLMs. The SGDLM approach involves constructing a customised dynamic linear model (DLM) for each univariate time series. At each time point, the DLMs are recoupled using importance sampling and decoupled using mean-field variational Bayes. Our findings indicate that the SGDLM forecasts stock data similarly to decoupled DLMs, as evident from values of root mean square errors and mean absolute deviations. The SGDLM forecasts the returns quite accurately as seen from the slightly overestimated prediction interval coverages.