Baltic Dry Index Research Articles

Exploring information demand in the maritime industry: a Google Trends approach

ABSTRACT The fundamental impetus for this research is the documented wide use of Google and the ensuing applications of Google Trends in behavioural economics. Expanding the existing literature, we focus on addressing the question of whether information demand from individual investors can reflect a freight rates sentiment. This is achieved by analysing Google queries referring to investment in maritime stocks and other related markets. These searches comprise essentially a blend of information and intuition. More specifically, we examine whether the most frequently searched term and topic referred to Baltic Dry Index (BDI) can serve as a reliable and valid positive or negative sentiment market proxies. We find a negative impact on freight rates, capital markets, and the number of available dry bulk fleet, providing evidence aligned with the risk-averse investor theory. We unveil bidirectional interactions between our sentiment index and the two out of three examined maritime markets, supporting the rejection of the efficient market hypothesis. Our study also verifies the similar effects of information demand and media-produced sentiment indexes on the corresponding market. We succeed in confirming sentiment’s predictability out-of-sample for BDI. Beyond the academic value, our findings suggest a manner of enhancing the accuracy of freight rates predictions for practitioners.

Futures markets and the baltic dry index: A prediction study based on deep learning

The Baltic Dry Index (BDI), representing the shipping sector, displays a notable sensitivity to Chinese commodity futures markets. Stakeholders must grasp the relationship between BDI and China’s commodity futures markets. However, there is currently a lack of comprehensive evaluation of Chinese futures’ forecasting performance. Therefore, we collected data on 17 major Chinese commodity futures from April 16, 2015, to December 27, 2022, and used CNN, BiLSTM, and AM to assess China’s futures market’s BDI prediction power. The CNN-BiLSTM-AM ensemble model emerged as the most accurate, R² value of 95.3 %. This study highlights the Chinese futures market’s ability to predict the global shipping index BDI and broadens our understanding of the financial market-maritime industry interplay. By monitoring fluctuations in China’s futures market, shipping companies and regulators can make precise BDI predictions, offering a scientific foundation for policy adjustments and decision-making amidst future BDI shifts.

Oil and Stock Markets in Ongoing Flux: Impact of Current Events on Oil Price and Stock Market Performance

Abstract The price of oil and, indirectly, the performance of stock indices have been significantly affected by the events of recent years, with a negative impact on the global economy, including the evolution of the energy market in the context of the transition to renewable energy sources and changes in global energy policy. This paper proposes a comprehensive analysis of the evolution of stock indices, oil market volatility and investors' response to the "black swan" events that have recently caused changes in the global economy. Using advanced econometric and statistical models, the paper presents the complex relationship between the analyzed stock indices, the trends and changes that occurred within them and the relevant influencing factors, such as the price of Brent Oil and the Baltic Dry Index. The results obtained, based on univariate GARCH models (GJR-GARCH) and multivariate DCC-GARCH models are in accordance with specialized studies in the field and show that oil price fluctuations were higher at the beginning of the Covid-19 pandemic compared to the period after the military conflicts (stage in which no significant influence of the oil price on the stock market is observed). Also, using the Chow test, in the analyzed period there are 3 important breaking points that coincide with the dates when the COVID-19 pandemic began, the military conflict between Russia and Ukraine as well as the military conflict between Israel and Gaza, events with a strong impact on the economy. The obtained results also show another important aspect, namely that, during the selected period January 2019-November 2023, the price of oil and the stock market were much more affected by the COVID-19 pandemic than by military conflicts, which cannot be also stated in the case of the price of the Baltic-Dry Index.

Distributed Lag Non-linear Model을 이용한 Baltic Dry Index 예측 및 요인 별 시차 영향도 도출에 관한 연구

The Baltic Dry Index (BDI) is a crucial and representative indicator in the shipping logistics industry sector for evaluating the market conditions, providing valuable information on the movement of global trade and production operations. The BDI, known for its significant volatility, has been the focus of several research initiatives utilizing machine learning and deep learning approaches to improve accuracy. Nevertheless, these approaches frequently encounter difficulties in understanding the results of the predictions. This study provides a framework that utilizes the Distributed Lag Non-linear Model to enhance the predictive accuracy of the BDI. The proposed model has the capability of capturing lag effects based on different factors to improve the prediction results. To evaluate the efficacy of the suggested approach, we carried out comparison experiments utilizing four performance metrics that have been widely utilized in previous studies to compare with different machine learning and deep learning models. The results showed enhanced prediction accuracy in comparison with existing methodologies. The lag effects by factor have been examined using the trained DLNM model, and we verified that the analyzed results are in good agreement with prior research by comparing them with previous research

Asymmetric dynamics between the Baltic Dry Index and financial markets during major global economic events

This research investigates the asymmetric dependence of the Baltic Dry Index on conventional financial markets during major global economic events in the period 1995–2023 using the rolling window wavelet correlation (RWWC) and time-varying parameter vector autoregression (TVP-VAR). The results of the RWWC reveal a considerable relationship between these markets over short- to medium-term investment horizons, whereas the relationship dynamically switches between positive and negative in the long term. The TVP-VAR reveals that asymmetry occurs for upward and downward movement spillover results. The total connectedness index is higher for downward market condition spillover than for the upward market spillover.

Freight rate index forecasting with Prophet model based on multi-dimensional significant events

The Baltic Dry Index (BDI) is an essential index to measure international dry bulk shipping freight, which can reflect global economic changes to a certain extent. Accurate forecasting of BDI supports shipping market participants in grasping risks and making scientific decisions. Based on the Prophet model, this paper considers the impact of multi-dimensional significant events related to the shipping industry and conducts BDI forecasting research. Firstly, we simulate the most momentous events in the world in recent years, namely the 2008 Global Financial Crisis and the 2019 New Crown Epidemic, and utilize the Prophet model to decompose the BDI sequence into three parts: trend, seasonality, and momentous event shocks. Secondly, we extensively collect other multi-dimensional significant event uncertainty indexes to establish a “significant event database”. The Maximal Information Coefficient and Boruta methods were employed to extract the uncertainty index particularly correlated with BDI as an exogenous variable for forecasting. Then, we employ the K-Shape method to cluster exogenous variables and explore the combined sense of clustering. Finally, we utilize the Prophet model to forecast BDI in stages. It discusses the influence of exogenous variables and their cluster combinations on the forecasting effect individually and sequentially. Empirical results show that considering the two momentous events of the Financial Crisis and COVID-19 can remarkably improve the accuracy of BDI forecasting. In addition, the study of exogenous variable significance found that during the Financial Crisis, economic policy uncertainty in Europe and the Americas greatly impacted BDI forecasting. During COVID-19, global and developed economic policy uncertainty was noteworthy in the BDI forecasting. The comparative experimental results show the Prophet model has an exemplary forecasting result and strong robustness. It also performs excellently in model generalization and interpretability. This paper proposes a reliable and advanced algorithm for shipping freight rate index forecasting, which has noteworthy reference value for shipping market participants to make investment decisions and risk avoidance.

Navigating Choppy Waters: Interplay between Financial Stress and Commodity Market Indices

Financial stress can have significant implications for individuals, businesses, asset prices and the economy as a whole. This study examines the nonlinear structure and dynamic changes in the multifractal behavior of cross-correlation between the financial stress index (FSI) and four well-known commodity indices, namely Commodity Research Bureau Index (CRBI), Baltic Dry Index (BDI), London Metal Index (LME) and Brent Oil prices (BROIL), using multifractal detrended cross correlation analysis (MFDCCA). For analysis, we utilized daily values of FSI and commodity index prices from 16 June 2016 to 9 July 2023. The following are the most important empirical findings: (I) All of the chosen commodity market indices show cross correlations with the FSI and have notable multifractal characteristics. (II) The presence of power law cross-correlation implies that a noteworthy shift in FSI is likely to coincide with a considerable shift in the commodity indices. (III) The multifractal cross-correlation is highest between FSI and Brent Oil (BROIL) and lowest with LME. (IV) The rolling windows analysis reveals a varying degree of persistency between FSI and commodity markets. The findings of this study have a number of important implications for commodity market investors and policymakers.

Do shipping freight markets impact commodity markets?

The Baltic Dry Index (BDI) is mostly perceived as the leading indicator of shipping freight market and economic activity. In this study, we investigate the dependence structure and distributional predictability between BDI and nineteen major commodity investable indices to offer insights on the predictive ability of BDI for commodity assets. In addition, we explore the effects of policy uncertainty and geopolitical risk on the dynamic dependence structure between BDI and commodities futures. To this end, we apply time-varying Markov switching copula models and non-parametric causality in quantile approach to daily closed price indices of commodities futures running from 23rd May 2006 to 20th November 2020. We provide for the first-time empirical evidence of time-varying Markov tail dependence structure dynamics between marine activities and commodities futures. The nature of the dependence is positives except for petroleum, all metals, livestock wheat, cattle and softs markets. Furthermore, we show the significant effects of global uncertainties, policy uncertainty and geopolitical risk on the dependence between sea transport and commodity assets. Finally, we note from the causality-in-mean and causality-in-variance analysis that the daily change of BDI is an efficient predictor (informational efficiency) of the daily changes of most of the commodity indices. Finally, we show that markets.

Prediction of Baltic Dry Index Based on GRA-BiLSTM Combined Model

The Baltic dry index (BDI) is not only one of the most important indicators of shipping costs but is also an important barometer of global trade and manufacturing sentiment. The BDI is highly volatile and subject to complex factors, which make it difficult to predict. In this paper, a neural network model-based BDI forecasting system was proposed to effectively forecast the BDI. We used the gray relational degree analysis method to select seven factors with higher correlation from 15 factors affecting the variation of BDI index to be used as input indicators for the bi-directional long short-term memory (BiLSTM) model to forecast BDI. From the experimental results, the prediction model proposed in this paper had an excellent prediction effect on the BDI. The mape value of the prediction result was 9.19%. The accuracy was better than the common machine learning models SVR and REG and the neural network model LSTM. In addition, in order to further optimize the prediction performance of the combined model GRA-BiLSTM, this paper introduced the MIV method to conduct an in-depth analysis of the contribution of each variable to the prediction results. Rice price, Shanghai securities composite index and crude oil price were found to be the three most relevant indicators to the prediction accuracy of the model.

Haftanın Günü Anomalisi ile Dünya Deniz Ticareti Oynaklığının Tahmini: Baltık Kuru Yük Endeksi Uygulaması

The shipment of raw materials in the world by sea and the volatility in the Baltic Dry Index (BDI), which is the subject of this trade, and the day anomaly are two important issues. Although there are studies on BDI volatility in the literature, there are not many studies on the day anomaly related to BDI. The aim of this study is to reveal the volatility of the BDI variable used in determining the volume of world maritime trade and to determine the day of the week that causes this volatility. Data of the study includes the observation values for 262 working days for the period of 01 July 2021 – 01 July 2022. By taking the natural logarithmic differences of BDI data, the rate of return of the BDI index was obtained. Using these rates of return, experiments were conducted with various autoregressive conditional heteroscedasticity models and according to the findings obtained, it was concluded that there was an ARCH effect in the BDI variable, that Tuesday had an effect on increasing the volatility in the index and Friday had a decreasing effect.

A new exploration in Baltic Dry Index forecasting learning: application of a deep ensemble model

A new exploration in Baltic Dry Index forecasting learning: application of a deep ensemble model

Assessing the Effect of the Magnitude of Spillovers on Global Supply Chains Using Quantile Vector Autoregressive and Wavelet Approaches

Overwhelmed by the negative impacts of the COVID-19 pandemic, global supply chains are being restructured and improved worldwide. It then becomes essential to accurately assess their vulnerabilities to external shocks and understand the relationships between key influential factors to obtain the desired results. This study provides a new conceptual econometric framework to examine the relationships between the purchasing managers’ index, service purchasing managers’ index, world equity index, unemployment rate, food and beverage historical prices, Baltic Dry Index, West Texas Intermediate Index, and carbon emissions. A quantile vector autoregressive (QVAR) model is used to assess the dynamic connectedness among Brazil, Russia, India, China, South Africa, and the United States based on such factors. A wavelet method is also utilized to assess the coherence between the time series. The results of the correlation and dynamic connectedness analyses for these countries reveal that the service purchasing managers’ index offers the highest spillover value toward the other factors.

Analysis of influencing factors and prediction of China’s Containerized Freight Index

China, as a major maritime nation, the China Containerized Freight Index (CCFI) serves as an objective reflection of the Chinese shipping market and an important indicator for understanding China’s shipping industry globally. The shipping market is a complex ecosystem influenced by various factors, including vessel supply and demand, cargo supply and demand relationships and prices, fuel prices, and competition from substitute and complementary markets. To analyze and study the state of the Chinese shipping market, we selected the CCFI as an indicator and collected data on six factors that may affect the overall shipping market. These factors include “ the China Coastal Bulk Freight Index(CCBFI)”, “the Baltic Dry Index(BDI)”, “the Yangtze River Container Freight Index”, “Global: Aluminum (minimum purity of 99.5%, London Metal Exchange (LME) spot price): UK landed price”, “Major Ports: Container Throughput”, and “Coal Price: US Central Appalachia: Coal Spot Price Index”. Then, we constructed an analyticaland predictive framework using Deep Neural Network (DNN), CatBoost regression model, and robust regression model to study the CCFI. Based on the R2 results of the three models, it is evident that DNN provides the best analytical and predictive performance for the CCFI, accurately forecasting its changes. Additionally, the robust regression model indicates that “Global: Aluminum (minimum purity of 99.5%, LME spot price): UK landed price” has the greatest impact on the CCFI. Finally, from a business perspective, we provide some suggestions for China’s container shipping industry.

Exploring the Factors Influencing the Impact of the COVID-19 Pandemic on Global Shipping: A Case Study of the Baltic Dry Index

The outbreak of COVID-19 in 2020 resulted in notable disruptions to global shipping and the global economy. As a key indicator influenced by supply and demand conditions in the shipping industry, the Baltic Dry Index (BDI) serves as an early economic indicator for global economic production. Contrary to expectations of decline, the BDI has exhibited a substantial increase. This research paper aims to investigate the impact of the COVID-19 pandemic on global shipping through a comprehensive analysis of the BDI. The study incorporates data spanning from 2019 to 2021, encompassing the pre- and post-pandemic periods. It examines 13 independent variables, including raw material prices (such as iron ore prices), international scrap steel prices, energy prices, stock market indexes, international commodity price volatility (as represented by the Commodity Research Bureau Index), global port calls, and confirmed COVID-19 cases. The primary objective is to explore the factors influencing the BDI and how they were affected by the pandemic. The study employs stepwise regression to select variables and build models before and after the pandemic. The findings of this study elucidate the prominent factors that influence the BDI in different temporal contexts. Before the outbreak, the BDI was notably impacted by variables, including the US Dollar Index (positive relationship), Brent, Port Calls, and CRB Index. However, a discernible shift in the relative significance of these factors has been observed in the post-pandemic period. Specifically, the US Dollar Index now exhibits a negative relationship with the BDI, whereas variables such as Port Calls, Iron Price, Steel Scrap Price, and confirmed COVID-19 cases had attained heightened prominence in shaping the dynamics of the freight index. These findings underscored the dynamic nature of the factors influencing the BDI, particularly in light of the unique circumstances brought about by the COVID-19 pandemic.

Drivers of railway container transports between China and Finland

On the China–Europe route, the sea used to dominate entirely continental transports, but in the last decade the railways started to gain some ground. However, it took number of years that railway volumes grew as significant, and finally coronavirus era (2020–2021) promoted much higher usage. This was the case not only on the main route Poland–China, but also on the more northern and lower-volume routes, such as Finland–China. This research uses regression analysis of the latter route to reveal factors that have an effect on rail container volumes. It is shown that Baltic Dry Index (BDI) and coronavirus related variables have influenced most container volumes on the Finland–China route. Oil price development has also played some sort of role. Interestingly, sea port handling of Finland does not play any significance in the model, nor does foreign trade between countries. Findings could be explained with the low starting ground of service, and dominance of sea transportation.

Are there bubbles in shipping freight during COVID-19?

This study examines the potential bubbles' shipping freight rates during the pandemic through the Generalized Supremum Augmented Dickey-Fuller test. The results explore multiple bubbles in the freight rates during corona disease (COVID-19). Moreover, the result finds that Baltic Dry Index is the most explosive and shows four bubbles, followed by Baltic Clean Tanker Index. The volatile behavior has been more frequent and pronounced since the second quarter of 2021, mainly instigated by the economic recovery. The results detect four bubbles in Baltic Dry Index caused by strong demand for raw materials and uncertainty restrictions. Furthermore, a single bubble in the Baltic Dirty Tanker Index is caused by the increasing oil price, growing demand for floating storage while burst due to new restrictions of the second wave, declining oil prices, and shortages of vessels. The need for oil products, logistical problems and the highest oil prices are the leading determinants of Baltic Clean Tanker index bubbles.

DETERMINING THE CAUSAL RELATIONSHIP BETWEEN BALTIC DRY INDEX (BDI) AND MACROECONOMIC VARIABLES BY TODA-YAMAMOTO ANALYSIS

The Baltic Dry Index (BDI) is the most critical indicator taking the pulse of maritime trade. Hence, it is essential to determine the macroeconomic variables that affect the BDI, which is a solid indicator for global economic activities in maritime transport. This research paper studies the association between macroeconomic indicators and the BDI. For this purpose, the Toda-Yamamoto approach was performed to examine the causal association between macroeconomic variables and the BDI. The Lee-Strazicich (LS) unit root test was applied based on Model C (break) to determine the stationary of the model series. The research's findings shed significant light on the relationship between the BDI and macroeconomic variables. According to the Toda-Yamamoto causality test results: BDI has a two-way relationship with S&P 500 and MSCI World indexes; besides, there is a one-way relationship from €/$ parity to BDI and from BDI to BCOM index.

Forecasting BDI Sea Freight Shipment Cost, VIX Investor Sentiment and MSCI Global Stock Market Indicator Indices: LSTAR-GARCH and LSTAR-APGARCH Models

Prediction of the economy in global markets is of crucial importance for individuals, decisionmakers, and policies. To this end, effectiveness in modeling and forecasting the directions of such leading indicators is of crucial importance. For this purpose, we analyzed the Baltic Dry Index (BDI), Investor Sentiment Index (VIX), and Global Stock Market Indicator (MSCI) for their distributional characteristics leading to proposed econometric methods. Among these, the BDI is an economic indicator based on shipment of dry cargo costs, the VIX is a measure of investor fear, and the MSCI represents an emerging and developed county stock market indicator. By utilizing daily data for a sample covering 1 November 2007–30 May 2022, the BDI, VIX, and MSCI indices are investigated with various methods for nonlinearity, chaos, and regime-switching volatility. The BDS independence test confirmed dependence and nonlinearity in all three series; Lyapunov exponent, Shannon, and Kolmogorov entropy tests suggest that series follow chaotic processes. Smooth transition autoregressive (STAR) type nonlinearity tests favored two-regime GARCH and Asymmetric Power GARCH (APGARCH) nonlinear conditional volatility models where regime changes are governed by smooth logistic transitions. Nonlinear LSTAR-GARCH and LSTAR-APGARCH models, in addition to their single-regime variants, are estimated and evaluated for in-sample and out-of-sample forecasts. The findings determined significant prediction and forecast improvement of LSTAR-APGARCH, closely followed by LSTAR-GARCH models. Overall results confirm the necessity of models integrating nonlinearity and volatility dynamics to utilize the BDI, VIX, and MSCI indices as effective leading economic indicators for investors and policymakers to predict the direction of the global economy.

Shipping Market Valuation and BDI Analysis under COVID-19

Contemporarily, COVID-19 as a major "Black swan" event strong affected the trade and shipping industry. Meanwhile, its continuous spread in the global scope has impacted the international shipping market, and the market value of some domestic shipping companies has also shown a downward trend. This paper adopts dual-line parallel research based on ROE DuPont analysis, CAPM and DDM model to analyze the market value of some shipping companies, and using Eviews regression model to analyze the Baltic Dry Index (BDI). According to analysis, COVID-19 has had a certain negative impact on the valuation of shipping companies. Due to the changes of the epidemic situation, the market valuation of shipping companies is still full of uncertain factors. Moreover, there is a certain degree of negative correlation between COVID-19 and BDI index, which will diminish with the improvement of the epidemic situation. The change of COVID-19 situation has become one of the important factors to judge the trend of BDI index. These results shed light on guiding further exploration of the future market valuation of shipping companies and the judgment of BDI index to some extent.

The impact of geopolitical risk on the behavior of oil prices and freight rates

The impact of geopolitical risk on energy markets has drawn attention to the need for better statistical modeling, especially of the crude oil markets and the shipping industry. In this work, the West Texas Intermediate crude oil price and the Baltic Dry Index behavior under the assumption of geopolitical risks are examined by using monthly data from January 1985 until May 2021. Using fractional integration methods, the results indicate that geopolitical risk and the Baltic Dry Index series will return to their original trends in the event of an exogenous shock, in contrast to the West Texas Intermediate behavior. These results are supported by analyzing the long-term relationship of the time series using the Fractional Cointegration Vector AutoRegressive approach. Finally, we use Bai and Perron (2003) and wavelet transform approaches to detect breaks in the prices paid for the maritime transport and for the crude oil prices caused by geopolitical risks.