Baltic Dry rose to 1,663 Index Points on July 11, 2025, up 13.52% from the previous day. Over the past month, Baltic Dry's price has fallen 15.50%, and is down 16.73% 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.

The Baltic Sea Impact Index is an assessment component that describes the potential cumulative burden on the environment in different parts of the Baltic Sea.

UAB "Baltic Index" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

Dataset of 10 companies in the OMX Baltic 10 index

In the mobile connectivity index, which investigates the enablers when adopting mobile internet services, all Baltic states had an overall score exceeded ** in 2023. The highest of the three scored Estonia with over ** points, followed by Lithuania and Latvia, respectively. Consumer readiness was the highest-scoring enabler in Latvia and Lithuania, while mobile network infrastructure ranked the highest in Estonia.

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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

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.

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

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

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.

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

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

Wave exposure (m2/s) was modelled, with a spatial resolution of 25 m, as an index using data on fetch (distance to nearest shore, island or coast), averaged wind speed and wind frequency (estimated as the amount of time that the wind came from one of 16 direction). Data on wind speed and direction were delivered by the Norwegian Meteorological Institute and averaged over a 10-year period (i.e. 1995-2004). The model is run using the program WaveImpact based on the method ÔÇ£Simplified Wave ModelÔÇØ (SWM) developed and described by Is├ªus (2004). The method is a fetch model, where the fetch values are adjusted to simulate refraction and diffraction effects. The estimated fetch values for each of the 16 directions are multiplied with the average wind speed in the given direction.

The model has been run by NIVA for the whole Norwegian coast, and has been used as part of the habitat modelling of the National program for mapping biodiversity ÔÇô coast (Bekkby et al. 2013). The model has also been applied in several research projects in Norway (e.g. Bekkby et al. 2008, 2009, 2014, 2015, Bekkby & Moy 2011, Norderhaug et al. 2012, 2014, Pedersen et al. 2012, Rinde et al. 2014). The model has also been run for Sweden (e.g. Eriksson et al. 2004), Finland (Is├ªus & Rygg 2005), the Danish region of the Skagerrak coast and the Russian, Latvian, Estonian, Lithuanian and German territories of the Baltic Sea (Wijkmark & Is├ªus 2010). The wave exposure values range from Ultra sheltered to Extremely exposed (cf Wijkmark & Is├ªus 2010, similar to the EUNIS system of Davies & Moss 2004).

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)..

Stations are ordered from southwest to northeast.

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

Comprehensive dataset of 63 companies listed on Nasdaq Baltic, including detailed financial information, market data, and corporate filings. This dataset provides real-time updates on trading metrics, company profiles, financial statements, regulatory filings, and market performance indicators. Updated every 30 minutes, it covers key data points such as market capitalization, trading volume, stock prices, company fundamentals, and regulatory compliance information for all listed securities on Nasdaq Baltic.

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).

The best 5 models, created by taking into account the Baltic Exchange indices values in the month the ship was sold.