An approach for Baltic Dry Index analysis based on empirical mode decomposition

Zeng, Qingcheng; Qu, Chenrui

doi:10.1080/03088839.2013.839512

Cited by 28 publications

(21 citation statements)

References 17 publications

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“…Lin et al [69] implemented EMD and the least squares-SVR method to estimate three foreign exchange rates and better performance was obtained. EMD was also combined with the ANN model to predict the volatility of the Baltic dry index (BDI) [70]; the forecasting results were found to be reasonably better than pure SVR and ANN models. Table 5 shows a brief comparison of stock prediction by EMD.…”

Section: Empirical Mode Decomposition-based Modelsmentioning

confidence: 99%

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

Shi

Zhuang

2019

Axioms

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Financial time-series are well known for their non-linearity and non-stationarity nature. The application of conventional econometric models in prediction can incur significant errors. The fast advancement of soft computing techniques provides an alternative approach for estimating and forecasting volatile stock prices. Soft computing approaches exploit tolerance for imprecision, uncertainty, and partial truth to progressively and adaptively solve practical problems. In this study, a comprehensive review of latest soft computing tools is given. Then, examples incorporating a series of machine learning models, including both single and hybrid models, to predict prices of two representative indexes and one stock in Hong Kong’s market are undertaken. The prediction performances of different models are evaluated and compared. The effects of the training sample size and stock patterns (viz. momentum and mean reversion) on model prediction are also investigated. Results indicate that artificial neural network (ANN)-based models yield the highest prediction accuracy. It was also found that the determination of optimal training sample size should take the pattern and volatility of stocks into consideration. Large prediction errors could be incurred when stocks exhibit a transition between mean reversion and momentum trend.

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Section: Empirical Mode Decomposition-based Modelsmentioning

confidence: 99%

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

Shi

Zhuang

2019

Axioms

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“…The shipping markets mirror the financial markets especially with regards to volatility (Qingcheng & Chenrui, 2014). Demand factors include economic outlook, raw materials, finished goods, transportation costs, and political issues, whereas supply factors include size of the commercial feet, its renewal rate, and operational performance (Grigorut, 2012).…”

Section: Shipping Industry Competitionmentioning

confidence: 99%

“…Demand factors include economic outlook, raw materials, finished goods, transportation costs, and political issues, whereas supply factors include size of the commercial feet, its renewal rate, and operational performance (Grigorut, 2012). In summary, the shipping markets can be said to be "seasonal, cyclical, and highly volatile" (Qingcheng & Chenrui, 2014).…”

Section: Shipping Industry Competitionmentioning

confidence: 99%

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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“…The bulk shipping market is seasonal, cyclical and highly volatile (Zeng & Qu 2014). Because of the nonstationary and non-linear nature of price series and the complexity of influencing factors, it is difficult to analyse the fluctuations in the bulk shipping market.…”

Section: Global Commodity Demand Versus Shipping Supplymentioning

confidence: 99%

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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An approach for Baltic Dry Index analysis based on empirical mode decomposition

Cited by 28 publications

References 17 publications

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

A Study Concerning Soft Computing Approaches for Stock Price Forecasting

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

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

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