Improving the predictability of stock returns with global financial cycle and oil price in oil-exporting African countries

In the face of frequent market turbulences leading to losses for investors, it becomes unarguably essential to determine factors capable of accurately forecasting stock returns. Therefore, this study considers predictors from the global arena by determining the predictive information substance of the global financial cycle (GFCy) and global oil price on the stock returns of African oil exporters. Summarily, our results show that the multi-factor predictive model, which jointly accounts for the GFCy indicator and oil price, offers better forecast performance than the baseline (historical average) and the single-factor predictive models, except in Kenya. Further looking into the importance of oil shocks, forecast performance obviously appears to be sensitive to different oil shocks, predominantly in Kenya and Nigeria. The multi-factor predictive models that include the different oil shocks perform better than the contending models in other countries, while mixed evidence is found for these two exceptional countries. Robustness is provided for the results through various checks, including alternative forecast tests, different sub-samples, and multiple forecast horizons.