The Fixed Income Pricing Data Software market is experiencing robust growth, driven by increasing regulatory compliance needs, the demand for enhanced risk management capabilities, and the proliferation of complex financial instruments. The market's expansion is further fueled by the shift towards cloud-based solutions, offering scalability and cost-effectiveness to both large enterprises and SMEs. While on-premise solutions continue to hold a significant share, especially among firms with stringent data security requirements, the cloud segment is projected to witness the fastest growth rate over the forecast period (2025-2033). Key players such as Bloomberg Industry Group, Refinitiv, and IHS Markit are leading the market, leveraging their established brand reputation and extensive data networks. However, the emergence of innovative fintech companies like DealVector, BondCliq, and Finsight is intensifying competition, pushing existing players to innovate and offer more advanced functionalities. Geographic analysis reveals a strong presence in North America and Europe, attributed to well-established financial markets and robust regulatory frameworks. However, growth opportunities are also emerging in Asia-Pacific, driven by expanding financial markets and increasing adoption of technology in the region. The market is anticipated to maintain a healthy CAGR, albeit with potential fluctuations influenced by global economic conditions and technological advancements. The restraints to market growth include the high initial investment costs associated with implementing these sophisticated software solutions, the complexities involved in data integration and management, and the ongoing need for skilled professionals to operate and maintain the systems. Furthermore, cybersecurity concerns and data privacy regulations pose significant challenges for both providers and users. To overcome these hurdles, vendors are focusing on developing user-friendly interfaces, enhancing data security features, and providing comprehensive training and support services. The segmentation of the market by application (Large Enterprises and SMEs) and type (Cloud-based and On-Premise) allows for targeted product development and marketing strategies, catering to the specific needs of each user group. This strategic approach, coupled with ongoing innovation, is anticipated to propel the Fixed Income Pricing Data Software market to significant heights over the coming years.

IHS Holding reported 1.74B in Market Capitalization this March of 2025, considering the latest stock price and the number of outstanding shares.Data for IHS Holding | IHS - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last July in 2025.

IHS predictions include continued revenue growth due to strong demand for its products and services, particularly in the healthcare industry. However, the stock may face risks from increased competition, regulatory changes, and economic headwinds, leading to volatility in share price and potential underperformance relative to the broader market.

Credit report of Ihs Markit contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

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.

Is IHS (IHS) Poised for Growth?

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

Oil, gas and water produced, and water used for hydraulic fracturing treatments for wells in and near the Permian Basin during 2000-2019 was estimated using data reported in IHS Markit (TM) (2020). Hydraulic fracturing treatment data from IHS Markit (TM) (2020) may include volumes in a variety of measurement units, and they may include multiple treatments per well. All listed treatments within the study area were converted to gallons and summed on a per-well basis, discounting any treatments for which the specified measurement units were unclear (for example, “sacks”, or “feet”), which were minor. The per-well treatment volumes and oil, gas, and water production were then aggregated via summation to a 1-mile grid using ArcGIS functions. The annual aggregated hydraulic fracturing treatment data were exported as annual GeoTIFF images with a resolution of 1 square mile per pixel and bundled into a archive file. This data is not part of the USGS Aggregated Water Use Database (AWUDS) or the National Water Information System (NWIS).

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.

IHS's (IHS) Shares: Forecast Sees Potential Upside

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

World's largest collection of continuously updated engineering and technical reference documents from over 400 standards developing organizations and publishers.

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.

IHS (IHS) Stock Forecast: Positive Outlook

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

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.

IHS IHS Holding Limited Ordinary Shares

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

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.

IHS Forecasts: Holding's Share Price Seen with Potential for Gains

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

The Renewable Solvent Market size was valued at USD 4.8 USD Billion in 2023 and is projected to reach USD 6.94 USD Billion by 2032, exhibiting a CAGR of 5.4 % during the forecast period. Green solvents are advanced trending solutions in the current society also known as renewable solvents since they are derived from renewable-based sources, unlike conventional solvents from non-renewable resources. These solvents are derived from mostly options like plants, biomass, agricultural waste, or other bio-based materials. Some important types are recognized as bioethanol, bioacetone, and d-limonene and they all provide specific advantages like a lower contribution to the environment and less toxicity. Bio-solvents boast of better sustainability with better still values on the ecological footprint and the use of fossil resources. They are applied in fields such as paint solvent, and detergents, and in certain industries, where they improve the environmental index in areas including Pharma, Coatings, and Agricultural industries. Thus, by incorporating renewable solvents, industries can reduce the adverse effects on the environment and implement some of the green chemistry principles.Stringent government regulations on the use of fossil fuel-based solventsGrowing consumer consciousness regarding the harmful effects of traditional solventsInnovations in bio-based feedstocks and advanced extraction technologiesThe market finds applications in a wide range of industries, including paints and coatings, pharmaceuticals, adhesives, and personal care products. Major players in the industry include Neste, ASTROBIO, Nexant Inc., US Polychem, and IHS Markit. Key drivers for this market are: Increasing Demand for Surfactants to Propel Market Growth. Potential restraints include: Fluctuating Prices of Raw Materials to Hamper Growth.

This data set contains annual data on bulk and containerized exports of distillers dried grains with solubles (DDGS).

Market Overview: The global drilling engineering contracting services market is valued at approximately XXX million in 2023 and is projected to grow at a CAGR of XX% during the forecast period 2023-2033. The market is driven by factors such as rising demand for oil and gas, increasing exploration and production activities, and technological advancements. However, the market is restrained by volatility in oil and gas prices and environmental concerns. Key Market Segments: The drilling engineering contracting services market is segmented by application, type, region, and company. Based on application, the market is divided into onshore drilling and offshore drilling. Based on type, the market is classified into turnkey drilling contracts, day-rate drilling contracts, and integrated drilling services. Geographically, the market is analyzed across North America, South America, Europe, Middle East & Africa, and Asia Pacific. Major companies in the market include Sinopec, Fluor, Saipem, ZPEC, Noble Drilling, Parker Drilling, USWS, Transocean, Petroleos De Venezuela, COSL, Seadrill, Ensco, IHS Markit, Shelf Drilling, and Paragon Offshore.

The COVID_CO2_ferries dataset stems from variables of the THETIS-MRV and IHS Markit datasets. They are described in the manuscript referenced below in "Additional Resources".

Meaning of the variables in the COVID_CO2_ferries dataset:

    number
    name
    meaning
    units


    1
    IMOn
    IMO number (Vessel unique identifier)
    -


    2
    Eber
    Per-ship CO2 emissions at berth
    ton


    3
    Etot
    Per-ship total CO2 emissions
    ton


    4
    Dom
    Sea Basin (NOR, BAL, MED)
    -


    5
    COVID
    dummy variable (true in 2020)
    -


    6
    year
    year of CO2 emissions
    -


    7
    Pme
    Total power of main engines
    0/1


    8
    LOA
    Length over all
    0/1


    9
    nPax
    Passenger carrying capacity
    0/1


    10
    yearB
    year of building
    0/1


    11
    nCalls
    Per-ship number of port calls
    -


    12
    VType
    Vessel type (see defining Eq. below)
    -

The variables with binary values (0/1 in the "units" column) refer to below (0) or above (1) the thresholds defined by:

    \(k\)
    \(\varphi_k\)
    \(\varphi_{k0}\)
    units


    0
    Pme
    21,600
    kW


    1
    nPax
    1,250
    -


    2
    LOA
    174
    m


    3
    yearB
    1999
    -

The VType variable is defined by:

(\texttt{VType} = \sum_{k=0}^{3} \, 2^{k} \cdot H(\varphi_{k} - \varphi_{k0}) )

Additional Resources

The "How COVID-19 affected GHG emissions of ferries in Europe" manuscript by Mannarini et al. (2022) using this dataset is published on Sustainability 2022, 14(9), 5287; https://doi.org/10.3390/su14095287. You may want to cite it.

A jupyter notebook using this dataset is available at https://github.com/hybrs/COVID-CO2-ferries

The subsurface temperature grids are results/outputs from the 3D petroleum systems model. They represent modern subsurface temperatures in degrees Fahrenheit extracted onto the stratigraphic horizons in the model. The temperature values are calibrated using 24 high-resolution static temperature logs provided by the North Dakota Geological Survey and a large proprietary dataset (>1,000) of drill stem test (DST) and bottom hole temperatures (BHT) from boreholes throughout Montana and North Dakota provided by IHS Markit ® (2022). This is a child item of a larger data release titled "Data release for the 3D petroleum systems model of the Williston Basin, USA".

Water used for hydraulic fracturing treatments in and near the Williston Basin during 2000-2015, was estimated using data reported in IHS Markit (TM) (2016). Hydraulic fracturing treatment data from IHS Markit (TM) (2016) may include volumes in a variety of measurement units, and they may include multiple treatments per well. All listed treatments within the study area were converted to gallons and summed on a per-well basis, discounting any treatments for which the specified measurement units were unclear (for example, “sacks”, or “feet”), which were minor. Of 3,734,380 treatments listed within the study area during the timeframe of interest, 0.7% (26,373 records) were not included. For each well, the date listed as the well completion date (typically the date of final preparation of the well for petroleum production) was considered to be the date of the water consumption. Listings for the actual treatment date are incomplete in the IHS Markit (TM) (2016) database, but generally the completion date is within a few days, or at most months, of the actual treatment date. The per-well treatment volumes were then aggregated via summation to a 1-mile grid using ArcGIS functions. The annual aggregated hydraulic fracturing treatment data were exported as annual GeoTIFF images with a resolution of 1 square mile per pixel and bundled into a TAR archive file. This data is not part of the USGS Aggregated Water Use Database (AWUDS) or the National Water Information System (NWIS).