Baltic Dry fell to 2,016 Index Points on July 21, 2025, down 1.75% from the previous day. Over the past month, Baltic Dry's price has risen 20.43%, and is up 6.33% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on July of 2025.

As of September 30, 2024, the Baltic Dry Index amounted to 2,065 points. This was higher than in the previous month, and higher than in May 2020, immediately after the outbreak of COVID-19, when the index stood at 504. The Baltic Dry Index is based on the current freight cost on various shipping routes and is considered a bellwether of the general shipping market.

Containerized Freight Index traded flat at 1,646.90 Points on July 22, 2025. Over the past month, Containerized Freight Index's price has fallen 11.91%, and is down 53.51% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Containerized Freight Index.

Dataset of 10 companies in the OMX Baltic 10 index

The dataset contains returns data for Baltic Dry Index and commodity spot prices

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Chemical Tanker Market Size 2025-2029

The chemical tanker market size is forecast to increase by USD 11.58 billion, at a CAGR of 5.8% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing demand for LNG tanker transportation. This trend is a response to the global shift towards cleaner energy sources and the expanding LNG trade routes. Another key factor influencing the market is the advances in propulsion systems for tankers, which are improving operational efficiency and reducing environmental impact. However, the market is not without challenges. The fluctuation in the Baltic Dry Index (BDI) poses a significant obstacle, as it reflects the volatility in freight rates for major dry bulk commodities, including chemicals.
This uncertainty can impact the profitability of chemical tanker operators and may require strategic planning and adaptability to mitigate potential risks. Companies in the market must stay informed of these dynamics to effectively capitalize on opportunities and navigate challenges in the evolving chemical tanker landscape.

What will be the Size of the Chemical Tanker Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, shaped by dynamic market conditions and shifting industry trends. Deadweight tonnage (DWT) and fleet management play a crucial role in optimizing operations and maximizing efficiency for chemical tanker owners and operators. Voyage charter agreements, a significant aspect of tanker operations, are influenced by various factors such as freight rates in the spot market and environmental regulations. Sustainable shipping practices, including the adoption of green shipping technologies, are increasingly prioritized. Inert gas systems, emissions reduction measures, and ballast water management are essential components of eco-friendly tanker design. Navigation systems and crew training are integral to ensuring safe and efficient voyages.

Maritime insurance, a critical aspect of tanker operations, covers various risks, including oil spills and maritime security threats. Tanker recycling is another area of focus, with a growing emphasis on sustainable practices and adherence to international regulations. Fuel efficiency is a continuous concern, with LNG fuel and other alternative energy sources gaining popularity. Cargo management, from handling to insurance, is an essential aspect of tanker operations, requiring advanced cargo pumps and safety equipment. Flag state regulations and port state control play a significant role in ensuring compliance with international maritime standards. Tanker pools and time charters offer flexibility in managing fleet capacity and optimizing revenue.

Anti-piracy measures and fire fighting systems are essential safety features for tanker vessels. Big data and advanced analytics are transforming tanker operations, from voyage planning to maintenance and fleet management. Continuous innovation and adaptation are essential to staying competitive in the ever-evolving the market.

How is this Chemical Tanker Industry segmented?

The chemical tanker industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Organic chemicals
  Vegetable fats and oils
  Inorganic chemicals
  Others


Type

  Inland
  Coastal
  Deep sea


Vessel Orientation

  IMO 3
  IMO 2
  IMO 1


Geography

  North America

    US


  Europe

    France
    Germany
    Spain
    The Netherlands


  APAC

    Australia
    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Product Insights

The organic chemicals segment is estimated to witness significant growth during the forecast period.

The market is characterized by the implementation of advanced technologies and regulations to ensure safe and efficient transportation of chemicals. Voyage planning and navigation systems play a crucial role in optimizing routes and reducing fuel consumption. Inert gas systems and fire fighting systems are essential safety features in chemical tankers, while crew training and maritime security measures ensure the safety of personnel and cargo. IMO regulations mandate double hulls and strict emissions reduction measures, including the use of LNG fuel and ballast water management systems. Cargo management systems help monitor and control the temperature and pressure of chemicals during transportation.

Tank cleaning and anti-piracy measures are also essential to maintain the integrity of the cargo and protect against potential threats. Tanker design and fleet management are key areas of focu

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Baltic Classifieds on the Rise: (BCG) Stock Forecast

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

China Retail Sales Nowcast: YoY: Contribution: Stock Exchange Index: BEISL: Baltic Exchange Clean Tanker Index (BCTI) data was reported at 0.000 % in 12 May 2025. This stayed constant from the previous number of 0.000 % for 05 May 2025. China Retail Sales Nowcast: YoY: Contribution: Stock Exchange Index: BEISL: Baltic Exchange Clean Tanker Index (BCTI) data is updated weekly, averaging 0.000 % from Feb 2021 (Median) to 12 May 2025, with 224 observations. The data reached an all-time high of 4.071 % in 07 Aug 2023 and a record low of 0.000 % in 12 May 2025. China Retail Sales Nowcast: YoY: Contribution: Stock Exchange Index: BEISL: Baltic Exchange Clean Tanker Index (BCTI) data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s China – Table CN.CEIC.NC: CEIC Nowcast: Retail Sales.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Baltic Classifieds (BCG): Expansion Prospects in a Shrinking Market?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

China Retail Sales Nowcast: YoY: Contribution: Stock Exchange Index: BEISL: Baltic Exchange Capesize Index (BCI_2014) data was reported at 0.121 % in 12 May 2025. This records a decrease from the previous number of 0.376 % for 05 May 2025. China Retail Sales Nowcast: YoY: Contribution: Stock Exchange Index: BEISL: Baltic Exchange Capesize Index (BCI_2014) data is updated weekly, averaging 0.000 % from Feb 2021 (Median) to 12 May 2025, with 224 observations. The data reached an all-time high of 0.472 % in 11 Nov 2024 and a record low of 0.000 % in 04 Nov 2024. China Retail Sales Nowcast: YoY: Contribution: Stock Exchange Index: BEISL: Baltic Exchange Capesize Index (BCI_2014) data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s China – Table CN.CEIC.NC: CEIC Nowcast: Retail Sales.

Wave exposure index at the surface in the Baltic Sea and part of the North Sea (Kattegat strait). Produced by Aquabiota as an input layer for the EUSeaMap broad-scale habitat models. Data acquired from Aquabiota fetch based model with a spatial resolution of a 25m, model run for the period 2002-2007.

Detailed information is found in the EMODnet Seabed Habitats technical report: Populus J. et al 2017. EUSeaMap, a European broad-scale seabed habitat map. Ifremer.

http://doi.org/10.13155/49975

Each model structure has been initialized with random start values (20 initiations) to ensure estimates not stuck in local minima. Models were fit to 68 phytoplankton biomass time series (class x station). R-structure is the variance-covariance matrix structure. The AICc and dAICc values are for the lowest of the iterations for a given model structure.

Springtime Chl-a concentration is here used as a proxy for productive surface waters. In the Baltic Sea Impact Index (BSII), areas with high springtime phytoplankton production will be given higher importance, as they are considered important areas for the Baltic Sea food web. In the current map, mean of springtime maximum weekly values (weeks 12-22, years 2003-2011) Chl-a concentration of the surface waters has been used, derived from satellite data (MERIS). Years 2003-2011 have been used, as there is no MERIS data available for years 2012-2016. The data for eastern Baltic Sea is provided by the Finnish Environment Institute (~300m resolution). Outside this high resolution data, MERIS-data downloaded from JRC-database has been used (~4 km resolution, to calculate average of maximum monthly values for April or May for 2003-2011). Both datasets were converted to 1 km x 1 km grid cells.

The r2 is shown for each time series and overall across all time series.

Time series of indicator for size structure of the Eastern Baltic cod (Gadus morhua) stock in 1991-2021. The index represents length (in mm) at the 95th percentile of the length distribution (L95). The index is calculated using data from Baltic International Bottom Trawl survey in the 1st quarter. Raw data are available from International Council for Exploration of the Sea (https://www.ices.dk/data/data-portals/Pages/DATRAS.aspx)..

[ Derived from parent entry - See data hierarchy tab ]

Much of what was summarized about the North Sea dataset (Loewe, 2022) carries over to the Baltic Sea setting. To make the current text a stand-alone resource that summary is reproduced here mutatis mutandis. Despite all equivalence, there is an important difference as to gale classification arising from relocating the analysis grid that is addressed in the following as well.

Sea level pressure is a fundamental weather and climate element and the very basis of everyday weather maps. Daily sea level pressure distributions provide information on the influence of high and low pressure systems, air flow, weather activity, and, hence, synoptic conditions.

Using sea level pressure distributions from the NCEP/NCAR Reanalysis 1 (Kalnay et al., 1996) and a simplified variant of the weather-typing scheme by Jenkinson and Collison (1977) atmospheric circulation over the Baltic Sea has been classified as to pattern and intensity on a daily basis starting in 1948. A full account of the original weather-typing scheme for the North Sea can be found in Loewe et al. (2005), while the variant scheme has been detailed in Loewe et al. (2006). The original 16-point analysis grid devised for the North Sea was shifted 5 degrees to the North and 15 degrees to the East to accommodate the Baltic Sea. Though formally valid at its central point (60°N, 20°E), results are representative of the Baltic Sea region between 55°N-65°N and 15°E-25°E.

The modified scheme allows for six weather types, namely four directional (NE=Northeast, SE, SW, NW) and two rotational types (C=cyclonic and A=anticyclonic). The strength of the atmospheric circulation is classified by way of a peak-over-threshold technique, employing Coriolis-adjusted thresholds for the gale index G* of 29.9, 38.7, and 47.2 hPa for gale (G), severe gale (SG), and very severe gale (VSG), respectively. These thresholds are elevated by the Coriolis frequency ratio f(60N)/f(55N) (i.e. sin60°/sin55°) over those used with the North Sea dataset (Loewe, 2022) to ensure that gales are identified at an identical geostrophic wind and vorticity scale in either region. G* is a composite measure of gradient and Laplacian of the pressure field at each grid’s central point. Coriolis-adjustment accounts for the fact that the strength of geostrophic flow and vorticity of which G* is indicative also depends on latitude according to Coriolis frequency. Note also that previously given exceedance probabilities of 10, 2, and 1/3.65 % apply to the North Sea thresholds for the period 1971-2000, only. For the same period of reference empirical exceedance probabilities for the Baltic Sea are at 6.4, 1.0, and 0.5/3.65 %.

Technically, the set of weather-typing and gale-classification rules is implemented as a lean FORTRAN code (lwtbssim.f), internally known as "Simple Lamb weather-typing scheme for the Baltic Sea v1". The processing run was done on a Linux server under Debian 10 (Buster).

Both, weather types and gale days, form a catalogue of more than 70 annual calendars since 1948 that is presented and continuously updated to the present day at https://www.bsh.de/DE/DATEN/Klima-und-Meer/Wetterlagen-Stuerme/wetterlagen-und-stuerme_node.html. (A corresponding English page is currently being devised at https://www.bsh.de/EN/DATA/Climate-and-Sea/Weather-and-Gales/weather-and-gales_node.html .) This catalogue concisely documents synoptic conditions in the Baltic Sea region. Possible benefits are manifold. Special events and episodes in regional-scale atmospheric circulation are easily looked up and traced. Beyond that, the dataset is well suited for frequency, trend, persistence, transition, and extreme-value statistics.

Stations are ordered from southwest to northeast.

This map shows the distribution and abundance of grey seals across the Baltic Sea.

The map was originally created for HELCOM Red list assessment of the Baltic Sea, using seal expert consultation. For the Baltic Sea Impact Index, the map was modified to represent four abundance classes, based on expert consultation.

The map has been updated from the 1st version of HOLASII, based on expert consultation (HELCOM Seal EG).

Data set represents the HELCOM 1x1 km Baltic Sea grid, that was originally created for the HOLAS II project (State of the Baltic Sea report 2018) and used in the Baltic Sea Impact Index and Shipping density maps produced for HELCOM Maritime Assessment.

SAMBAH targeted the Baltic Sea population of harbour porpoise (Phocoena phocoena). This population is small and has been drastically reduced during the last decades. The species is listed in Annexes II and IV of the EC Habitats Directive as well as in the national red lists of several Member States. When SAMBAH started, the conservation status of the species in combination with a complex of threats necessitated improved methodologies for collecting data on population size and distribution, and fluctuations over time. The overall objective of the project has been to launch a best practice methodology for this purpose and to provide data for a reliable assessment of distribution and preferred habitats of the species. This would make possible an appropriate designation of SCIs for the species within the Natura 2000 network as well as the implementation of other relevant mitigation measures. The project area encompasses waters between 5-80 metres depth in the Baltic Sea, in the south-east approximately south of latitude 55° 50’ N (in the Sound) and east of longitude 12° E (in Fehmarn Belt) in the southeast, and south of latitude 60⁰20’N (Åland and the Archipelago Sea) in the north.

SAMBAH objective 1 has been to estimate densities, produce distribution maps and estimate abundances of harbour porpoises in the project area.

SAMBAH objective 2 has been to identify hotspots, habitat preferences, and areas with higher risk of conflicts with anthropogenic activities for the Baltic Sea harbour porpoise.

SAMBAH objective 3 has been to increase the knowledge about the Baltic Sea harbour porpoise among policymakers, managers, stakeholders, users of the marine environment and the general public, in the EU Member States bordering the Baltic Sea.

SAMBAH objective 4 has been to implement best practice methods for cost efficient, large-scale surveillance of harbour porpoises in a low density area.

Purpose:

SAMBAH - Static Acoustic Monitoring of the Baltic Sea Harbour Porpoise - is an international project involving all EU countries around the Baltic Sea, with the ultimate goal to secure the conservation of the Baltic Sea harbour porpoise. Project duration was 2010-2015.

Static variables used as covariates for the spatial distribution modelling in the SAMBAH project. Containing depth, topographic complexity index, topographic position index, aspect and slope.

Quality Information:

Quality checked data (range checks, consistency checks).