The commodity prices displayed in Trading Economics are based on over-the-counter (OTC) and contract for difference (CFD) financial instruments. Our market prices are intended to provide you with a reference only, rather than as a basis for making trading decisions. Trading Economics does not verify any data and disclaims any obligation to do so. This dataset provides a table with prices for several commodities including the latest price for the nearby futures contract, yesterday close, plus weekly, monthly and yearly percentage changes. This dataset provides a table with prices for several commodities including the latest price for the nearby futures contract, yesterday close, plus weekly, monthly and yearly percentage changes.

GSCI rose to 551.39 Index Points on July 11, 2025, up 0.98% from the previous day. Over the past month, GSCI's price has risen 0.10%, but it is still 3.67% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. GSCI Commodity Index - values, historical data, forecasts and news - updated on July of 2025.

Sharp economic volatility, the continued effects of high interest rates and mixed sentiment among investors created an uneven landscape for stock and commodity exchanges. While trading volumes soared in 2020 due to the pandemic and favorable financial conditions, such as zero percent interest rates from the Federal Reserve, the continued effects of high inflation in 2022 and 2023 resulted in a hawkish pivot on interest rates, which curtailed ROIs across major equity markets. Geopolitical volatility amid the Ukraine-Russia and Israel-Hamas wars further exacerbated trade volatility, as many investors pivoted away from traditional equity markets into derivative markets, such as options and futures to better hedge on their investment. Nonetheless, the continued digitalization of trading markets bolstered exchanges, as they were able to facilitate improved client service and stronger market insights for interested investors. Revenue grew an annualized 0.1% to an estimated $20.9 billion over the past five years, including an estimated 1.9% boost in 2025. A core development for exchanges has been the growth of derivative trades, which has facilitated a significant market niche for investors. Heightened options trading and growing attraction to agricultural commodities strengthened service diversification among exchanges. Major companies, such as CME Group Inc., introduced new tradeable food commodities for investors in 2024, further diversifying how clients engage in trades. These trends, coupled with strengthened corporate profit growth, bolstered exchanges’ profit. Despite current uncertainty with interest rates and the pervasive fear over a future recession, the industry is expected to do well during the outlook period. Strong economic conditions will reduce investor uncertainty and increase corporate profit, uplifting investment into the stock market and boosting revenue. Greater levels of research and development will expand the scope of stocks offered because new companies will spring up via IPOs, benefiting exchange demand. Nonetheless, continued threat from substitutes such as electronic communication networks (ECNs) will curtail larger growth, as better technology will enable investors to start trading independently, but effective use of electronic platforms by incumbent exchange giants such as NASDAQ Inc. can help stem this decline by offering faster processing via electronic trade floors and prioritizing client support. Overall, revenue is expected to grow an annualized 3.5% to an estimated $24.8 billion through the end of 2031.

This list will include commodity enrollments by Passport-enrolled vendors.

Master list of names and codes for individual commodities and commodity groups as used by UNHCR, available to other humanitarian actors for applications such as market or needs assessments.

This dataset has been superseded. An updated version of this dataset is available from the ABARES website.

This dataset is the first national compilation of catchment scale commodity data for Australia (CLUMC), current as at September 2017. It has been compiled from vector land use datasets collected as part of state and territory mapping programs through the Australian Collaborative Land Use and Management Program (ACLUMP). It complements the Catchment Scale Land Use of Australia - Update September 2017 dataset (ABARES 2017). Agricultural commodities are assigned to the Australian Land Use and Management (ALUM) Classification version 8 (ABARES 2016) classes based on; perceived intervention to the landscape, growing conditions and management, the intended use of the commodity, consistency with national and international reporting frameworks and standards, such as National Plantation Inventory, industry guidelines, Australian Bureau of Statistics, harmonised trade codes and ABARES commodity reports, where possible.

Commodities data were produced as part of catchment scale land use mapping and primarily uses fine-scale satellite data and information collected in the field (ABARES 2011, 2015). Field validation was critical for mapping commodities. The date of mapping (2003 to 2017) and scale of mapping (1:5 000 to 1:250 000) vary, reflecting the source data, capture date and scale.

Jurisdictions captured commodity data (where possible) for those areas most recently mapped in the Catchment scale land use of Australia - Update September 2017 (ABARES 2017) with a focus on horticultural and intensive animal industries. Other commodities which are tertiary classes of the ALUM classification (such as sugar cane, cotton, rice, olives and grapes) have been mapped by jurisdictions for some time and are included in this dataset. Agricultural commodity level mapping is available for all of the Northern Territory, and is likely to be complete for the following commodities nationally:

• Crops - rice, sugar cane, cotton
• Fruit - bananas (except Southern Queensland), avocados, mangoes, olives, grapes
• Nuts - macadamias
• Livestock - dairy cattle, pigs, poultry, horse studs, aquaculture.

Commodity information is selected from an agreed list of commodity names developed by ACLUMP partners. A commodity may be applied to one or many land use codes. For example the commodity 'wheat' is applied to class 3.3.1, 'Cropping' or 4.4.1, 'Irrigated cropping', while 'cattle' may be applied to any land use where cattle are observed including 2.1.0 'Grazing native vegetation', 3.2.0 'Grazing modified pastures', 4.2.0 'Grazing irrigated modified pastures', 5.2.2 'Feedlots' etc.

The commodity description is intended to add information to the catchment scale land use map which is not otherwise recorded in the ALUM Classification. Where there are several suitable commodity descriptions mappers are encouraged to record the most detailed description. For example when cattle breeds are known to be for milk production mappers would apply the commodity description 'cattle dairy' rather than just 'cattle'.

1315 Active Global Commodities buyers list and Global Commodities importers directory compiled from actual Global import shipments of Commodities.

This dataset has been superseded. An updated version of this dataset is available from the ABARES website. This dataset is the first national compilation of catchment scale commodity data for …Show full descriptionThis dataset has been superseded. An updated version of this dataset is available from the ABARES website. This dataset is the first national compilation of catchment scale commodity data for Australia (CLUMC), current as at September 2017. It has been compiled from vector land use datasets collected as part of state and territory mapping programs through the Australian Collaborative Land Use and Management Program (ACLUMP). It complements the Catchment Scale Land Use of Australia - Update September 2017 dataset (ABARES 2017). Agricultural commodities are assigned to the Australian Land Use and Management (ALUM) Classification version 8 (ABARES 2016) classes based on; perceived intervention to the landscape, growing conditions and management, the intended use of the commodity, consistency with national and international reporting frameworks and standards, such as National Plantation Inventory, industry guidelines, Australian Bureau of Statistics, harmonised trade codes and ABARES commodity reports, where possible. Commodities data were produced as part of catchment scale land use mapping and primarily uses fine-scale satellite data and information collected in the field (ABARES 2011, 2015). Field validation was critical for mapping commodities. The date of mapping (2003 to 2017) and scale of mapping (1:5 000 to 1:250 000) vary, reflecting the source data, capture date and scale. Jurisdictions captured commodity data (where possible) for those areas most recently mapped in the Catchment scale land use of Australia - Update September 2017 (ABARES 2017) with a focus on horticultural and intensive animal industries. Other commodities which are tertiary classes of the ALUM classification (such as sugar cane, cotton, rice, olives and grapes) have been mapped by jurisdictions for some time and are included in this dataset. Agricultural commodity level mapping is available for all of the Northern Territory, and is likely to be complete for the following commodities nationally: Crops - rice, sugar cane, cotton Fruit - bananas (except Southern Queensland), avocados, mangoes, olives, grapes Nuts - macadamias Livestock - dairy cattle, pigs, poultry, horse studs, aquaculture. Commodity information is selected from an agreed list of commodity names developed by ACLUMP partners. A commodity may be applied to one or many land use codes. For example the commodity 'wheat' is applied to class 3.3.1, 'Cropping' or 4.4.1, 'Irrigated cropping', while 'cattle' may be applied to any land use where cattle are observed including 2.1.0 'Grazing native vegetation', 3.2.0 'Grazing modified pastures', 4.2.0 'Grazing irrigated modified pastures', 5.2.2 'Feedlots' etc. The commodity description is intended to add information to the catchment scale land use map which is not otherwise recorded in the ALUM Classification. Where there are several suitable commodity descriptions mappers are encouraged to record the most detailed description. For example when cattle breeds are known to be for milk production mappers would apply the commodity description 'cattle dairy' rather than just 'cattle'.

(StatCan Product) Customization details: This information product has been customized to present information on commodity sector estimates for Alberta and Canada for 2007 (Revised) and 2008 (Preliminary). Other variables include: NAICS Code, Commodity Code, NAICS and Commodity Description, Number of Establishments, Total Revenue, Revenue from Goods Manufactured (Financial Data), Revenue from Goods Manufactured (Commodity Data), Total Expenses, Total Salaries and Wages (Direct and Indirect Labour), Production Workers Wages (Direct Labour), Non-manufacturing Employees Salaries (Indirect Labour), Total Cost of Energy, Water Utility and Vehicle Fuel , Cost of Energy and Water Utility, Cost of Vehicle Fuel, Cost of Materials and Supplies, Total Number of Employees (Direct and Indirect Labour), Number of Production Workers (Direct Labour), Number of Manufacturing Employees (Indirect Labour), Total Opening Inventories, Opening Inventories - Goods or Work in Progress, Opening Inventories - Finished Goods Manufactured, Total Closing Inventories, Closing Inventories - Goods/Work in Progress, Closing Inventories - Finished Goods Manufactured, Manufacturing Value Added. For more information about the industries and commodity codes presented contact OSI.Support@gov.ab.ca. Annual Survey of Manufactures and Logging: The Annual Survey of Manufactures and Logging (ASML) is a survey of the manufacturing and logging industries in Canada. It is intended to cover all establishments primarily engaged in manufacturing and logging activities, as well as the sales offices and warehouses which support these establishments. The details collected include principal industrial statistics (such as revenue, employment, salaries and wages, cost of materials and supplies used, cost of energy and water utility, inventories, etc.), as well as information about the commodities produced and consumed. Data collected by the Annual Survey of Manufactures and Logging are important because they help measure the production of Canada's industrial and primary resource sectors, as well as provide an indication of the well-being of each industry covered by the survey and its contribution to the Canadian economy. Within Statistics Canada, the data are used by the Canadian System of National Accounts, the Monthly Survey of Manufacturing (record number 2101) and Prices programs. The data are also used by the business community, trade associations, federal and provincial departments, as well as international organizations and associations to profile the manufacturing and logging industries, undertake market studies, forecast demand and develop trade and tariff policies.

Graph and download economic data for Producer Price Index by Commodity: Publishing Sales, Excluding Software: Directory, Mailing List, and Related Compilations Publishing Sales (WPU332) from Dec 2008 to Dec 2022 about postal, software, printing, sales, commodities, PPI, inflation, price index, indexes, price, and USA.

The U.S. Exports Commodity Classification CD-ROM is a reference tool that will help you quickly find the 10-digit HS-Based Schedule B numbers for commodities. It contains the complete database of commodity codes and descriptions as well as powerful software for searching the database. It is a Windows application. This CD stores all classification information in HTM files. The user need a browser in order to access information. The CD offers a 'navigation system' in order to access specific in formation from multiple htm type files.Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check out the CDs, subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

This Database was gathered for the book: Erik Radisch, Der Rat für Gegenseitige Wirtschaftshilfe als Konsensimperium (Stuttgart 2022).

In order to ease the reading of this book and facilitate the work of historians with published but not processed data about foreign trade (exports and imports) within CMEA within the period 1946-1966, I have decided to provide the reader with this database. The data was double checked by third persons. The Database might still, however, contain minor errors, although every effort has been made to correct them.

The database is the source publication. Therefore, it includes the raw data without any interpretation or analytical research from the author. The data was collected by the author from Vnešnjaja torgovlja SSSR. statističeskij sbornik 1918-1966 (Moscow, 1967).

The data relates to the six CMEA members (the GDR, Poland, Czechoslovakia, Hungary, Romania and Bulgaria) except Albania. Unfortunately, the source book does not contain any information on Albania’s foreign trade, and I have not been able to locate declassified additional data to remedy this omission. Foreign trade with Albania was, however, by far the lowest.

Foreign trade output presents nine positions of the official commodity nomenclature for foreign trade (see: Vnešnjaja torgovlja SSSR. statističeskij sbornik 1918-1966 (Moscow, 1967), p. XIIIf.). The nomenclature is very extensive. The subcategories which are relevant for the six chosen countries are included in the database (44 subcategories for exports and 43 sub-categories for imports). However, only the first digit of the coding system was included in the database for this work.

The database consists of four tables:

“ExportsCMEA” – Data on exports for nine official over-arching goods categories (with a further breakdown into subcategories) in thousand roubles and in amount of goods (in corresponding units of measure) for six CMEA countries

“ImportsCMEA” - Data on imports for nine official over-arching goods categories (with a further breakdown into subcategories) in thousand roubles and in amount of goods (in corresponding units of measure) for six CMEA countries

“Commodity Codes” – a list of nine over-arching official commodity nomenclature goods categories. The database uses the same coding numbers as was used by official commodity nomenclature. In the database, however, only the first digit of the code was included

“Commodity” – a list of translations and units of quantities of the commodity nomenclature

There are also two queries in the database that present aggregated foreign trade information by code.

The database is in LibreOffice-Database format (odb). Due to long-term accessibility, the tables are also provided as SQL-Scripts.

The data was collected by Erik Radisch for non-commercial purposes. It is free for everyone to use. Therefore, please adhere to the same standards of citation that apply to other products of research like books and articles and refer to the source of your data as proposed by Zenodo.

Graph and download economic data for Producer Price Index by Commodity: Advertising Space and Time Sales: Print Advertising Space Sales in Directories and Mailing Lists (WPU36120101) from Dec 2008 to May 2025 about postal, advertisement, sales, commodities, PPI, inflation, price index, indexes, price, and USA.

Abstract

Commodity Balances (CB) presents a comprehensive picture of the pattern of a country's non-food supply during a specified reference period.

Geographic coverage

All countries of the world – with some minor exceptions – and geographical aggregates according to the United Nations M-49 list.Notes on geographical coverage:(1)Data of Iraq do not include Kurdistan region .(2)Since 2007 France data include French Guiana, Martinique, Guadeloupe, Reunion territories but they exclude French Polynesia and New Caledonia territories.(3)Information provided by the Russian Federation includes statistical data for the Autonomous Republic of Crimea and the city of Sevastopol, Ukraine, temporarily occupied by the Russian Federation and is presented without prejudice to relevant UN General Assembly and UN Security Council resolutions, including UN General Assembly resolution 68/262 of 27 March 2014 and UN Security Council resolution 2202 (2015) of 17 February 2015, which reaffirm the territorial integrity of Ukraine. Information provided by Ukraine excludes statistical data concerning the Autonomous Republic of Crimea, the city of Sevastopol and certain areas of the Donetsk and Luhansk regions. The information is presented without prejudice to relevant UN General Assembly and UN Security Council resolutions, including UN General Assembly resolution 68/262 of 27 March 2014 and UN Security Council resolution 2202 (2015) of 17 February 2015, which reaffirm the territorial integrity of Ukraine.

Mode of data collection

Other

(StatCan Product) Customization details: This information product has been customized to present information on commodity sector estimates for Alberta and Canada for 2008 (Revised) and 2009 (Preliminary). Other variables include: NAICS Code Commodity Code NAICS and Commodity Description Number of Establishments Total Revenue Revenue from Goods Manufactured (Financial Data) Revenue from Goods Manufactured (Commodity Data) Total Expenses Total Salaries and Wages (Direct and Indirect Labour), Production Workers Wages (Direct Labour) Non-manufacturing Employees Salaries (Indirect Labour) Total Cost of Energy, Water Utility and Vehicle Fuel Cost of Energy and Water Utility Cost of Vehicle Fuel Cost of Materials and Supplies Total Number of Employees (Direct and Indirect Labour) Number of Production Workers (Direct Labour) Number of Manufacturing Employees (Indirect Labour) Total Opening Inventories Opening Inventories - Goods or Work in Progress Opening Inventories - Finished Goods Manufactured Total Closing Inventories Closing Inventories - Goods/Work in Progress Closing Inventories - Finished Goods Manufactured Manufacturing Value Added For more information about the industries and commodity codes presented contactOSI.Support@gov.ab.ca Annual Survey of Manufactures and Logging The Annual Survey of Manufactures and Logging (ASML) is a survey of the manufacturing and logging industries in Canada. It is intended to cover all establishments primarily engaged in manufacturing and logging activities, as well as the sales offices and warehouses which support these establishments. The details collected include principal industrial statistics (such as revenue, employment, salaries and wages, cost of materials and supplies used, cost of energy and water utility, inventories, etc.), as well as information about the commodities produced and consumed. Data collected by the Annual Survey of Manufactures and Logging are important because they help measure the production of Canada's industrial and primary resource sectors, as well as provide an indication of the well-being of each industry covered by the survey and its contribution to the Canadian economy. Within Statistics Canada, the data are used by the Canadian System of National Accounts, the Monthly Survey of Manufacturing (record number 2101) and Prices programs. The data are also used by the business community, trade associations, federal and provincial departments, as well as international organizations and associations to profile the manufacturing and logging industries, undertake market studies, forecast demand and develop trade and tariff policies. Product Main Page

United States - Producer Price Index by Commodity: Publishing Sales, Excluding Software: Directory, Mailing List, and Related Compilations Publishing Sales was 135.43200 Index Dec 2008=100 in December of 2022, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Publishing Sales, Excluding Software: Directory, Mailing List, and Related Compilations Publishing Sales reached a record high of 139.91900 in June of 2022 and a record low of 97.70000 in October of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Publishing Sales, Excluding Software: Directory, Mailing List, and Related Compilations Publishing Sales - last updated from the United States Federal Reserve on July of 2025.

Search LSEG's Commodities Data, and find global pricing, exchanges, and fundamentals for energy, agriculture, and metals.

This database provides estimates of agricultural and food commodity flows [in both tons and $US] between the US and China for the year 2017. Pairwise information is provided between US states and Chinese provinces, and US counties and Chinese provinces for 7 Standardized Classification of Transported Goods (SCTG) commodity categories. Additionally, crosswalks are provided to match Harmonized System (HS) codes and China's Multi-Regional Input Output (MRIO) commodity sectors to their corresponding SCTG commodity codes. The included SCTG commodities are: - SCTG 01: Iive animals and fish - SCTG 02: cereal grains - SCTG 03: agricultural products (except for animal feed, cereal grains, and forage products) - SCTG 04: animal feed, eggs, honey, and other products of animal origin - SCTG 05: meat, poultry, fish, seafood, and their preparations - SCTG 06: milled grain products and preparations, and bakery products - SCTG 07: other prepared foodstuffs, fats and oils For additional information, please see the related paper by Pandit et al. (2022) in Environmental Research Letters. ADD DOI WHEN RECEIVED

This data repository provides the Food and Agriculture Biomass Input Output (FABIO) database, a global set of multi-regional physical supply-use and input-output tables covering global agriculture and forestry.

The work is based on mostly freely available data from FAOSTAT, IEA, EIA, and UN Comtrade/BACI. FABIO currently covers 191 countries + RoW, 118 processes and 125 commodities (raw and processed agricultural and food products) for 1986-2013. All R codes and auxilliary data are available on GitHub. For more information please refer to https://fabio.fineprint.global.

The database consists of the following main components, in compressed .rds format:

Z: the inter-commodity input-output matrix, displaying the relationships of intermediate use of each commodity in the production of each commodity, in physical units (tons). The matrix has 24000 rows and columns (125 commodities x 192 regions), and is available in two versions, based on the method to allocate inputs to outputs in production processes: Z_mass (mass allocation) and Z_value (value allocation). Note that the row sums of the Z matrix (= total intermediate use by commodity) are identical in both versions.

Y: the final demand matrix, denoting the consumption of all 24000 commodities by destination country and final use category. There are six final use categories (yielding 192 x 6 = 1152 columns): 1) food use, 2) other use (non-food), 3) losses, 4) stock addition, 5) balancing, and 6) unspecified.

X: the total output vector of all 24000 commodities. Total output is equal to the sum of intermediate and final use by commodity.

L: the Leontief inverse, computed as (I – A)-1, where A is the matrix of input coefficients derived from Z and x. Again, there are two versions, depending on the underlying version of Z (L_mass and L_value).

E: environmental extensions for each of the 24000 commodities, including four resource categories: 1) primary biomass extraction (in tons), 2) land use (in hectares), 3) blue water use (in m3)., and 4) green water use (in m3).

mr_sup_mass/mr_sup_value: For each allocation method (mass/value), the supply table gives the physical supply quantity of each commodity by producing process, with processes in the rows (118 processes x 192 regions = 22656 rows) and commodities in columns (24000 columns).

mr_use: the use table capture the quantities of each commodity (rows) used as an input in each process (columns).

A description of the included countries and commodities (i.e. the rows and columns of the Z matrix) can be found in the auxiliary file io_codes.csv. Separate lists of the country sample (including ISO3 codes and continental grouping) and commodities (including moisture content) are given in the files regions.csv and items.csv, respectively. For information on the individual processes, see auxiliary file su_codes.csv. RDS files can be opened in R. Information on how to read these files can be obtained here: https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/readRDS

Except of X.rds, which contains a matrix, all variables are organized as lists, where each element contains a sparse matrix. Please note that values are always given in physical units, i.e. tonnes or head, as specified in items.csv. The suffixes value and mass only indicate the form of allocation chosen for the construction of the symmetric IO tables (for more details see Bruckner et al. 2019). Product, process and country classifications can be found in the file fabio_classifications.xlsx.

Footprint results are not contained in the database but can be calculated, e.g. by using this script: https://github.com/martinbruckner/fabio_comparison/blob/master/R/fabio_footprints.R

How to cite:

To cite FABIO work please refer to this paper:

Bruckner, M., Wood, R., Moran, D., Kuschnig, N., Wieland, H., Maus, V., Börner, J. 2019. FABIO – The Construction of the Food and Agriculture Input–Output Model. Environmental Science & Technology 53(19), 11302–11312. DOI: 10.1021/acs.est.9b03554

License:

This data repository is distributed under the CC BY-NC-SA 4.0 License. You are free to share and adapt the material for non-commercial purposes using proper citation. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. In case you are interested in a collaboration, I am happy to receive enquiries at martin.bruckner@wu.ac.at.

Known issues:

The underlying FAO data have been manipulated to the minimum extent necessary. Data filling and supply-use balancing, yet, required some adaptations. These are documented in the code and are also reflected in the balancing item in the final demand matrices. For a proper use of the database, I recommend to distribute the balancing item over all other uses proportionally and to do analyses with and without balancing to illustrate uncertainties.

Stock and commodity exchanges can benefit from various sources of revenue, ranging from fees charged through the purchasing and selling of stocks and commodities to the listing of companies on exchanges with IPOs. Yet, this hasn't meant exchanges have been free of challenges, with many companies looking to more attractive overseas markets in countries like the US that embrace stronger growth. The most notable culprits have been ARM and CRH, refusing to put up with the increasingly cheaper valuations offered by UK stock exchanges. Stock and commodity exchange revenue is expected to boom at a compound annual rate of 11.5% over the five years through 2024-25 to £15.4 billion. Boosted by the London Stock Exchange Group's Refinitiv purchase in 2021-22, the growth numbers seem inflated. The industry saw ample consolidations, aided by MiFID II's initiation in 2018. However, M&As have now decreased because of high borrowing costs. New reporting demands have bumped up regulatory costs, resulting in thinner profits. Banks, aligning with Basel IV, are pulling back on investments. Post-COVID market turbulence fuelled trades, but it's slowing down with economic stabilisation. The inflation slowdown pushes investors towards higher-value securities, boosting trade value despite lower volumes. The weak pound has been beneficial for revenue, especially for the LSEG, bolstered by dollar-earning companies in the FTSE 100. Stock and commodity exchange industry revenue is expected to show a moderate increase of 1.3% in 2024-25. Revenue is forecast to climb at a compound annual rate of 4.1% over the five years through 2029-30 to £18.8 billion. The cautious descent of interest rates from the Bank of England will slow down volatility and ensure greater business confidence in the UK. This will bring back up consolidation activity to support revenue growth, reviving the digital information and exchange markets. The most pressing concern for the industry will be potential limitations on access to the EEA for the clearing segment of the industry, which could shatter short-term growth and keep the tap running for companies exiting UK exchanges.