Original Research

Does the Baltic Dry Index predict economic activity in South Africa? A review from 1985 to 2016

Kurt Sartorius, Benn Sartorius, Dino Zuccollo
South African Journal of Economic and Management Sciences | Vol 21, No 1 | a1457 | DOI: https://doi.org/10.4102/sajems.v21i1.1457 | © 2018 Kurt Sartorius, Benn Sartorius, Dino Zuccollo | This work is licensed under CC Attribution 4.0
Submitted: 12 August 2015 | Published: 05 April 2018

About the author(s)

Kurt Sartorius, School of Accounting, University of the Witwatersrand, South Africa
Benn Sartorius, School of Public Health, University of KwaZulu-Natal, South Africa
Dino Zuccollo, Westbrooke Alternative Asset Management, Denmark


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Abstract

Background: The ability of the Baltic Dry Index to predict economic activity has been evaluated in a number of developed and developing countries.

 

Aim: Firstly, the article determines the primary factors driving the dynamics of the Baltic Dry Index (BDI) and, secondly, whether the BDI can predict future share price reactions on the Johannesburg Stock Exchange All Share Index (JSE ALSI), South Africa.

 

Setting: This article investigates the dynamics and predictive properties of the BDI in South Africa between 1985 and 2016.

 

Methods: The article uses a review of a wide range of published data and two time-series data sets to adopt a mixed methods approach. An inductive contents analysis is used to answer the first research question and a combination of a unit root test, correlation analysis and a Granger causality model is employed to test the second research question.

 

Results: The results show that the BDI price is primarily driven by four underlying constructs that include the supply and demand for dry bulk shipping, as well as risk, cost and logistics management factors. Secondly, the results indicate a break in the BDI data set in July 2008 that influences a fundamental change in its relationship with the JSE ALSI index. In the pre-break period (1985 to 2008), the BDI is positively correlated with the ALSI (0.837, α = 0.05) before sharply diverging in the second period from August 2008 to 2016. In the first period, the BDI showed an optimal lag period of 6 months as a predictor of the ALSI index, but this predictive ability ceases after July 2008. The article makes a two-part contribution. Firstly, it demonstrates that the BDI is a useful predictor of future economic activity in an African developing country. Secondly, the BDI can be incorporated in government and industry sector planning models as a variable to assess future gross domestic product trends.

 

Conclusion: The study confirms that the BDI is only a reliable indicator of future economic activity when the supply of shipping capacity is well matched with the demand.


Keywords

Baltic Dry Index; ALSI; South Africa

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