New Evidence on the Information and Predictive Content of the Baltic Dry Index

Apergis, Nicholas; Payne, James E.

doi:10.3390/ijfs1030062

Cited by 29 publications

(22 citation statements)

References 54 publications

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“…In the first period, the hypothesised predictive power of the BDI appears to hold true and the relationship between the BDI and the ALSI exhibits a period of significant convergence. This positive association is supported by other studies that show co-integration, high levels of correlation and significant causality between the BDI and share price indexes in both developed and developing countries (Apergis & Payne 2013;Bakshi et al 2011;Baltyn 2016;Lin & Sim 2013;Oomen 2012). …”

Section: Periods Of Significant Convergence or Divergencesupporting

confidence: 77%

“…The BDI has been used as an economic indicator because it reflects the supply and demand for sea freight that translates into economic activity as reflected by stock market value or growth in gross domestic product (GDP) (Apergis & Payne 2013;Baltyn 2016). The demand, for instance, for raw commodities like iron ore, is intuitively expected to translate into increasing the GDP http://www.sajems.org…”

Section: Introductionmentioning

confidence: 99%

“…Changes therefore in the BDI have been assumed to reflect changes in future global economic activity (Alizadeh & Talley 2011;Blanchflower 2010;Lin & Sim 2013). The BDI's reliability as a predictor has been enhanced by the proposal that its calculation is devoid of speculative activity (Apergis & Payne 2013;Koskinen & Hilmola 2005).…”

Section: Introductionmentioning

confidence: 99%

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

Sartorius

Zuccollo³

2018

SAJEMS

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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.

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Section: Periods Of Significant Convergence or Divergencesupporting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

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

Sartorius

Zuccollo³

2018

SAJEMS

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“…There is recognition that "the BDI growth rate predicts the growth in global economic activity, establishing further BDI's role in revealing a link between the real and financial sectors" (Bakshi et al, 2010). This forecasting applicability "is not only apparent on a short-term basis, but also it is persistent at longer horizons" (Apergis & Payne, 2013). "The documented stock return predictability" adds credence as a predictor and as a result, the BDI can be used in a variety of markets (Bakshi et al, 2010).…”

Section: The Bdi As a Predictormentioning

confidence: 98%

Are Bulk/Container Shipping Companies Improving Compared to the Baltic Dry Index?

Barrows¹

2019

ASS

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This study evaluates the 2000-2017 time frame and assesses the performance of the bulk/container shipping industry before and after the Great Financial Crisis (GFC) in relation to the Baltic Dry Index (BDI) and two other benchmarks in a variety of combinations. This study evaluates two different period portfolios of shipping companies based on their stock price total return performance. Five cases are presented that demonstrate portfolio improvement when comparing performance after the GFC with performance before the GFC in relation to the BDI and the other benchmarks. Included are discussions on shipping industry competition, vessel utilization and freight rates plus the BDI as an economic activity predictor.

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“…Bao et al [14] presented a new BDI forecasting model based on Support Vector Machine combined with Correlation-based Feature Selection and introduced the macroeconomic fundamental indicators of BDI. Apergis and Payne [15] disclosed the importance of the BDI in predicting the future course of the real economy, yielding a link between financial asset markets and the macro economy. Yang et al [16] developed a model which is combination of the wavelet transforms and support vector machine in order to forecast the BPI.…”

Section: Related Workmentioning

confidence: 99%

Forecasting of Dry Freight Index Data by Using Machine Learning Algorithms

Akyol¹

2019

IJISA

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Discovery of meaningful information from the data and design of an expert system are carried out within the frame of machine learning. Supervised learning is used commonly in practical machine learning. It includes basically two stages: a) the training data are sent to as input to the classifier algorithms, b) the performance of pre-learned algorithm is tested on the testing data. And so, knowledge discovery is carried out through the data. In this study, the analysis of Lloyd data is performed by utilizing Gradient Boosted Trees and Multi-Layer Perceptron learning algorithms. Lloyd data consist of the Baltic Dry Index, Capesize Index, Panamax Index and Supramax Index values, updated daily. Accurate prediction of these data is very important in order to eliminate the risks of commercial organization. Eight datasets from Lloyd data are obtained within the frame of two scenarios: a) the last three index values in the freight index datasets; b) the last three index values in both crude oil price and freight index datasets. The results show that the models designed with Gradient Boosted Trees and Multi-Layer Perceptron algorithms are successful for Lloyd data prediction and so proved their applicability.

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New Evidence on the Information and Predictive Content of the Baltic Dry Index

Cited by 29 publications

References 54 publications

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

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

Are Bulk/Container Shipping Companies Improving Compared to the Baltic Dry Index?

Forecasting of Dry Freight Index Data by Using Machine Learning Algorithms

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