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This paper investigates the predictability of the asset prices of commodity transport (i.e. dry bulk carriers) by testing the shipping Q index as a leading indicator. We employ a comprehensive back-testing procedure with a broad spectrum of benchmark simulations. The shipping Q index (an adaptation of Tobin's Q index) has been introduced to benchmark models to observe predictive gain and interpret predictability features. This study presents a novel hybrid model to forecast time series data. The forecasting ability of the proposed hybrid algorithm is compared to specific univariate time series models, dynamic models, nonlinear models, and widely used hybrid models in the literature. The findings document that not only the proposed hybrid model performs better than the other competitive models in terms of hold out sample forecasting, but also using the shipping Q index improves the forecast accuracy by remarkably reducing forecasting error.