Credit spread is the extra risk-reward that an investor is bearing for investing in corporate bonds instead of government bonds. Structural models, which are simple in their framework, fail to explain the occurring credit spread and underestimate the predicted credit spread. Hence, the need for new models and exploration of systematic and idiosyncratic variables arose. The present paper aims to investigate if the predictability of lower-medium investment grade bonds and non-investment grade bonds credit spread can be improved by incorporating systematic and idiosyncratic variables into a fixed effect panel data regression model, and whether the selected variables’ significance has high influence on credit spread or not. Initial results showed that fixed effect panel data regression model underperforms the structural models and under predicts the actual credit spread. The applied model explained 13.5% of the lower-medium investment grade bonds credit spread and 8.5% of non-investment grade bonds. Further, systematic variables have higher influence on lower-medium investment grade bonds and idiosyncratic variables have higher influence on non-investment grade bonds. The predictability of credit spread can be improved by employing correct explanatory variables which are selected based on the characteristics of the sample size.