In 2023, the S&P 500 Information Technology Index outperformed other sectors, with annual return of **** percent. On the other hand, the S&P 500 Utilities Index recorded the lowest returns, with a loss of *** percent.

High-Tech Companies on NASDAQ

Market Capitalization and Performance Metrics

By [source]

About this dataset

This dataset offers an insightful look into the performance of high-tech companies listed on the NASDAQ exchange in the United States. With information pertaining to over 8,000 companies in the electronics, computers, telecommunications, and biotechnology sectors, this is an incredibly useful source of insight for researchers, traders, investors and data scientists interested in acquiring information about these firms.

The dataset includes detailed variables such as stock symbols and names to provide quick identification of individual companies along with pricing changes and percentages from the previous day’s value as well as sector and industry breakdowns for comprehensive analysis. Other metrics like market capitalization values help to assess a firm’s relative size compared to competitors while share volume data can give a glimpse into how actively traded each company is. Additionally provided numbers include earnings per share breakdowns to gauge profits along with dividend pay date symbols for yield calculation purposes as well as beta values that further inform risk levels associated with investing in particular firms within this high-tech sector. Finally this dataset also collects any potential errors found amongst such extensive scrapes of company performance data giving users valuable reassurance no sensitive areas are missed when assessing various firms on an individual basis or all together as part of an overarching system

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How to use the dataset

This dataset is invaluable for researchers, traders, investors and data scientists who want to obtain the latest information about high-tech companies listed on the NASDAQ exchange in the United States. It contains data on more than 8,000 companies from a wide range of sectors such as electronics, computers, telecommunications, biotechnology and many more. In this guide we will learn how to use this dataset effectively.

Basics: The basics of working with this dataset include understanding various columns like symbol, name, price,pricing_changes, pricing_percentage_changes,sector,industry,market_cap,share_volume,earnings_per_share. Each column is further described below: - Symbol: This column gives you the stock symbol of the company. (String) - Name: This column gives you the name of the company. (String)
- Price: The current price of each stock given by symbol is mentioned here.(Float) - Pricing Changes: This represents change in stock price from previous day.(Float) - Pricing Percentage Changes :This provides percentage change in stock prices from previous day.(Float) - Sector : It give information about sector in which company belongs .(String). - Industry : Describe industry in which company lies.(string). - Market Capitalization : Give market capitalization .(String). - Share Volume : It refers to number share traded last 24 hrs.(Integer). - Earnings Per Share : It refer to earnings per share per Stock yearly divided by Dividend Yield ,Symbol Yield and Beta .It also involves Errors related with Data Set so errors specified here proviedes details regarding same if any errors occured while collecting data set or manipulation on it.. (float/string )

Advanced Use Cases: Now that we understand what each individual feature stands for it's time to delve deeper into optimizing returns using this data set as basis for our decision making processes such as selecting right portfolio formation techniques or selecting stocks wisely contrarian investment style etc. We can do a comparison using multiple factors like Current Price followed by Price Change percentage or Earnings feedback loop which would help us identify Potentially Undervalued investments both Short Term & Long Term ones at same time and We could dive into analysis showing Relationship between Price & Volumne across Sectors and

Research Ideas

• Analyzing stock trends - The dataset enables users to make informed decisions by tracking and analyzing changes in indicators such as price, sector, industry or market capitalization trends over time.
• Exploring correlations between different factors - By exploring the correlation between different factors such as pricing changes, earning per share or beta etc., it enables us to get a better understanding of how these elements influence each other and what implications it may have on our investments

Acknowledgements

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Key Statistics on Business Performance and Operating Characteristics of the Industrial Sector - Table 610-72006 : Principal statistics for all establishments in the manufacturing sector by detailed industry grouping

US Stock Market Data (21st November 2023 – 2nd February 2024)

Overview

This dataset provides detailed historical data on the US stock market, covering the period from 21st November 2023 to 2nd February 2024. It includes daily performance metrics for major stocks and indices, enabling investors, analysts, and researchers to study short-term market trends, fluctuations, and patterns.

Dataset Contents

The dataset contains the following key attributes for each trading day:

Date: The trading date.

Ticker: Stock ticker symbol (e.g., AAPL for Apple, MSFT for Microsoft).

Open Price: The price at which the stock opened for trading.

Close Price: The price at which the stock closed for trading . High Price: The highest price reached during the trading session.

Low Price: The lowest price reached during the trading session.

Adjusted Close Price: The closing price adjusted for splits and dividend payouts.

Trading Volume: The total number of shares traded on that day.

Highlights

Time Period: Covers daily data for over two months of trading activity.

Market Scope: Includes data from a diverse set of stocks, industries, and sectors, reflecting the broader US market trends.

Indices and Major Stocks: Tracks key indices (e.g., S&P 500, NASDAQ) and major stocks across various sectors .

Potential Applications

Analyzing short-term market performance trends. Developing trading strategies or backtesting investment models. Exploring the impact of macroeconomic events on stock performance. Studying sector-wise performance in the US stock market.

Data Source

The data has been sourced from publicly available market records, ensuring reliability and accuracy. Each data point represents an official trading record from the respective exchange.

Usage Notes

The dataset is intended for educational, analytical, and research purposes only. Users should be mindful of potential market anomalies or external factors influencing data during this time frame.

Acknowledgments

Special thanks to the organizations and platforms that make financial market data accessible for analysis and research.

The NYSE U.S. Market Consumer Goods Sector Index tracks the performance of the U.S. domiciled equity components listed on the U.S. stock exchanges that offer goods and services in the consumer goods sector. Between December 2015 and June 2023, the index fluctuated but increased overall, standing at ******** index points as of June 2023.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

The dataset contains All India Quarterly Listed Non-Government Non-Financial Companies Industry Performance in Corporate Sector.

Key Statistics on Business Performance and Operating Characteristics of the Industrial Sector - Table 610-72011 : Acquisition and disposal of fixed assets for all establishments in the manufacturing sector by industry grouping

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 150 companies listed on the Zagreb Stock Exchange (XZAG) in Croatia. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Croatia:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Croatia:

Zagreb Stock Exchange (ZSE) CROBEX Index: The main index that tracks the performance of domestic companies listed on the Zagreb Stock Exchange. Monitoring this index provides insights into the overall trends and performance of the Croatian stock market.

Zagreb Stock Exchange (ZSE) CROBIS Index: The index that tracks the performance of foreign companies listed on the Zagreb Stock Exchange. This index reflects the influence of international companies operating within the Croatian market.

Agrokor Group: A major Croatian conglomerate with interests in various sectors including retail, food production, and agriculture. This company's stock is a key indicator of economic activity within Croatia.

Financial Services Company D: A significant financial services company based in Croatia, offering services such as banking, insurance, or asset management. Monitoring the stock of this company provides insights into the financial sector's performance.

Tourism Company E: A leading company in the Croatian tourism industry, contributing to the country's vital tourism sector. Monitoring the stock of this company reflects trends in the tourism industry's performance.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Croatia, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

Snapshot img

Performance Fabric Market Size 2024-2028

The performance fabric market size is estimated to increase by USD 17.34 billion, at a CAGR of 4.36% between 2023 and 2028. Market expansion hinges on numerous factors, including burgeoning government regulations, surging demand for functional apparel, and strides in textile technology. Challenges abound, with the steep expenses associated with research and development (R&D) standing out, alongside escalating raw material costs and the prevalence of counterfeit goods. These hurdles necessitate innovative approaches to remain competitive in the industry. Despite the obstacles, the market's trajectory is buoyed by the growing awareness of consumers regarding the benefits of performance fabrics and their applications across various sectors. As textile technology continues to evolve, companies are compelled to invest in R&D to stay ahead of the curve, ensuring the development of high-quality products that meet stringent regulatory standards and fulfill the demands of discerning consumers.

What will be the Size of the Market During the Forecast Period?

To learn more about this report, View Report Sample

Market Dynamics and Customer Landscape

The global market is witnessing remarkable growth, driven by a multitude of applications and the increasing emphasis on personal safety and hygiene. Performance fabrics, with their innovative features such as anti microbial segment, waterproofing, odor deflection, stain protection, and flame resistance, cater to diverse sectors including healthcare, defense, mining, and oil and gas industries. These fabrics play a pivotal role in ensuring worker safety, enhancing Personal hygienic practices, and addressing labor conditions. From protective clothing in the oil and gas sector to firefighting gear for public safety, performance fabrics are indispensable. Moreover, their ability to regulate body temperature and support physical fitness activities further expands their relevance in today's dynamic market landscape. The market is driven by cutting edge technologies and growing apparel demand, particularly through e-commerce platforms. Innovations like needle punch and wet laid processes enhance fabric durability, while new regulations around asbestos ensure safer, high-performance materials for diverse applications.

Key Market Driver

Increasing demand for functional apparel is the key factor driving the growth of the market. Consumers are becoming increasingly aware of the benefits of performance clothing, especially in terms of comfort and performance. High-performance materials are an important part of sportswear, providing comfort and functionality. Global interest in sports and fitness activities has increased significantly. Performance fabrics play a key role in achieving these goals by providing properties such as moisture management and breathability.

Additionally, the focus on health and wellness has led to increased participation in activities such as yoga, running, and cycling. People are investing in sportswear made with high-performance materials that enhance their workout experience. Therefore, factors such as comfort and moisture management are expected to increase the demand for high-performance fabrics and drive the growth of the market during the forecast period.

Significant Market Trends

The emergence of smart textiles is the primary trend shaping the growth of the market. Smart textiles integrate technologies such as sensors, conductive yarns, microcontrollers, and wireless communication systems directly into the fabric. Smart textiles are increasingly being used in health and fitness wearables. Smart textiles can connect to smartphones and other devices via Bluetooth or other wireless technologies. This connection allows users to receive notifications, control their devices, and access data directly from their clothing.

Additionally, smart textiles are being used in therapeutic and medical applications, such as smart compression garments that adjust pressure according to the wearer's needs, and textiles with integrated sensors for remote patient monitoring. Thus, the emergence of smart textiles will drive the growth of the market during the forecast period.

Major Market Challenge

The high cost of research and development (R&D) is a challenge that affects the growth of the market. High-performance fabrics are not new. These were developed after many years of scientific analysis. As a result, the textile industry has developed into one of the most important economic sectors in the world, with numerous new technologies and innovative developments. To develop new products, manufacturers invest heavily in research and development.

However, excessive R&D expenses can lead to higher costs (market risk), especially if the product concept is unsellable. The issue of R&D productivity is also very important. Research and development departments do not produce tangible pr

A deep dive into everything you need to know about industry research from what it is, to how to apply it, from the industry research experts at IBISWorld.

As of March 2025, the SSE Composite Index had closed at 3,335.75 points. The index reflects the performance of all stocks traded on the Shanghai Stock Exchange, including both boards, the main board, and the Star market. SSE still number one In the greater Chinese region, the stock exchange in Shanghai was the largest, beating the bourses in Shenzhen, Hong Kong, and Taiwan. In 2023, the Shanghai Stock Exchange recorded a market capitalization of over 6.5 trillion. Not only market capitalization was a unique attribute, but the Shanghai Stock Exchange was also home to the most valuable stock in mainland China, which was the baijiu producer Moutai Kweichow. Limited access Despite its size, the exchange in Shanghai only grants limited access to overseas investors. The bourse listed A-shares and B-shares. While A-shares are denominated in yuan and almost exclusively available for domestic traders, the prices of B-shares are in U.S. dollars and available for overseas investors as well. In addition, the bourse offers access to foreign investors through a trading accreditation which is supervised by the Chinese authorities. However, these tight controls are the reason why Hong Kong, despite its lower relative market capitalization, remains an important gateway to capital for mainland Chinese companies.

Autoparts Industry Performance: Percentage Distribution per Destination: Assemblers data was reported at 63.100 % in 2018. This records an increase from the previous number of 62.800 % for 2017. Autoparts Industry Performance: Percentage Distribution per Destination: Assemblers data is updated yearly, averaging 61.550 % from Dec 1993 (Median) to 2018, with 26 observations. The data reached an all-time high of 70.800 % in 2011 and a record low of 54.900 % in 2002. Autoparts Industry Performance: Percentage Distribution per Destination: Assemblers data remains active status in CEIC and is reported by Brazilian Autoparts Manufacturers Association. The data is categorized under Brazil Premium Database’s Automobile Sector – Table BR.RAT002: Autoparts Industry Performance. ABCD: Refers to the cities of Santo André, São Bernardo do Campo, São Caetano do Sul e Diadema respectively. Metropolitan Area of Sao Paulo: Refers to companies located in cities as Guarulhos, Osasco, Mauá and Mogi das Cruzes, but does not include São Paulo City. Interior of Sao Paulo: Refers to companies located in cities as Campinas, Limeira, Jundiaí, Sorocaba and São José dos Campos. Other States: Refers to companies located in the States of Rio de Janeiro, Minas Gerais, Rio Grande do Sul, Santa Catarina, Paraná, Bahia, Pernambuco, Ceará, Amazonas, Goiás and Espírito Santo.

The market for the halal food and beverage industry sector has experienced rapid growth in recent years, which indicate excellent investment opportunities. This paper examine the effect of Technical Efficiency (TE) on firm value in 5 selected influential countries in halal food and beverage sector based on Global Islamic Economy Report 2020. Two steps estimation was used to run the data, using the Stochastic Frontier Analysis (SFA) model to determine the company’s TE and panel data to test the effect of TE through firm value. The results show that Indonesia has the highest score for TE (62%), followed by Pakistan (59%), South Africa (57%), Malaysia (55%), and Singapore (52%), which means, in general, there is inefficiency in allocating resources over 38% up to 48% and needs to be improved by halal food and beverage companies in. Regarding panel data, all countries sample except Pakistan highlight that TE significantly affect company value. It indicates that the crucial part of managing efficiency can be a sign in stock market performance. The result shows that company managers should set efficiency strategies to their business process for creating sustainability and increase their value in the capital market. As for investors, this TE can be used as an indicator before choosing company stocks; if the company is efficient, then it is worthy of being one of the portfolio assets. Form the government side, the finding can help them to set appropriate policy setting to boost halal food and beverages industry such as giving subsidy or incentive to increase the efficiency ability of halal food and beverage companies and identify the industry’s strength by comparing the result of TE between 5 countries.

The Global Performance Apparel market is projected to grow at a substantial CAGR, during the forecast period, 2020–2026. This growth of Global High Performance Apparel market can be attributed to the changes in the life style of the majority of people today. This factor is majorly driving the overall market growth.



Performance Apparel, simply defined, is the garments that perform or function for some particular purpose. Performance clothing helps athletes and active people to keep cool, comfortable and dry through moisture management and other techniques. High Performance Apparel consist of two sections- Sportswear and Protective Clothing. High Performance Apparel is sold to both, individual consumers as sportswear at retail prices, and as business-to-business protective clothing at wholesale prices. In reality, they have the same characteristics working to meet the requirement of the wearer's circumstances, and to beat the risks of the outside environment. There are several methods to make an apparel perform. The methods include making of garment in specified ways, fabric and trim specification, or fiber and chemical treatments. High Performance Apparel is one of the quickest growing sectors of the global textile industry. Not only functionality, it also needs to be fashionable and stylish. As such, high tech fabrics and apparel that are made for high performance has become a necessity. Apart from representing status and sophistication, today, clothing is about being fit for purpose, clothing that performs. However, sales of sportswear bring a lot of opportunities, the study group recommends the new entrants who just have money but without technical advantage, raw materials advantage and downstream support, do not enter into the sportswear field hastily.

Market Trends, Drivers, Restraints, and Opportunities:

• Active sports such as athletics, running, aerobics, cycling, hiking, swimming, sailing, windsurfing, mountaineering, ballooning, parachuting, snowboarding, and ski-ing are preferred today, over any other recreational activities. The rise in adoption of such activities is expected to drive the overall market growth of the market.
• With the increasing risks in the industries due to the exposure to hazardous materials such as chemicals, polluting wastes etc. and because of increased risky events such as fire, terror attacks etc. High Performance Apparel has become more important. The corporate wear sector is also rising with demands for more performance clothing. These factors are further projected to boost the market growth, over the forecast period.
• Fluctuation in prices of raw materials is expected to be a restraining factor for the market growth.

Scope of the Report

Kenya's Gross Domestic Product (GDP) grew by 4.6 percent in the second quarter of 2024. Among sectors, accommodation and food services had the strongest performance, with quarterly growth of 26.6 percent. Financial and insurance sectors followed, registering a 5.1 percent growth rate. On the other hand, the construction sector had a negative growth rate of -2.9 percent.

The Climate Change Agreements (Administration) Regulations 2012 require the administrator to publish a report setting out the energy efficiency improvements and emissions reductions achieved by participants. Climate Change Agreements (CCAs) are voluntary agreements that set targets on operators to improve their energy efficiency and/or reduce carbon dioxide emissions. The CCA scheme runs from 1 April 2013 to 31 March 2025. Eligibility to participate in the scheme requires operators to carry out one or more listed ‘eligible activities’. Operators that enter into and abide by the terms and conditions of their CCA are entitled to claim a discount on the Climate Change Levy (CCL), a tax added to electricity, gas and solid fuel bills by suppliers. Operators must report their energy consumption, carbon emissions and performance against their target for each of the four biennial target periods between 2013 and 2020. Operators meeting their target at the end of each target period will be certified as eligible to claim a discount on the CCL. Participants in the CCA scheme are split across 53 industrial sectors each of which is represented by a trade association or other body. These trade associations manage the submissions from operators carrying out activities which fall within their sector. Each sector holds an umbrella agreement, setting out the improvement targets for each of the four biennial target periods. Sub-sectors may be set up as a practical means of partitioning target units (operators), which either carry out similar activities or have similar target types. This data set contains the performance results for each of the sectors participating in the Climate Change Agreements (CCA) scheme. The data should be read in combination with the previous biennial reports located on GOV.UK. Data may not be included for some target units where a case for commercial confidentiality has been accepted. Attribution statement: © Environment Agency copyright and/or database right 2017. All rights reserved.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 1003 companies listed on the Euronext Amsterdam (XAMS) in Netherlands. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Netherlands:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Netherlands:

Amsterdam Stock Exchange (AEX) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Amsterdam Stock Exchange. This index provides an overview of the overall market performance in the Netherlands.

Amsterdam Stock Exchange (AEX) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Amsterdam Stock Exchange. This index reflects the performance of international companies operating in the Netherlands.

Company A: A prominent Dutch company with diversified operations across various sectors, such as technology, healthcare, or finance. This company's stock is widely traded on the Amsterdam Stock Exchange.

Company B: A leading financial institution in the Netherlands, offering banking, insurance, or investment services. This company's stock is actively traded on the Amsterdam Stock Exchange.

Company C: A major player in the Dutch energy or consumer goods sector, involved in the production and distribution of related products. This company's stock is listed and actively traded on the Amsterdam Stock Exchange.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Netherlands, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Netherlands ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Netherlands?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Netherlands exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment method...

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 54 companies listed on the Prague Stock Exchange (XPRA) in Czech Republic. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

‍

Top 5 used data fields in the End-of-Day Pricing Dataset for Czech Republic:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Czech Republic:

Prague Stock Exchange (PX) Main Market Index: The main index that tracks the performance of domestic companies listed on the Prague Stock Exchange. Monitoring this index provides insights into the overall trends and performance of the Czech stock market.

Prague Stock Exchange (PX) TOP 10 Index: The index that includes the top 10 largest and most liquid companies listed on the Prague Stock Exchange. This index provides a snapshot of the performance of the largest players in the Czech market.

Banking Group A: A major banking group based in the Czech Republic, offering a range of financial services including retail banking, corporate banking, and investment services. Monitoring the stock of this banking group is a key indicator of the financial sector's health.

Automotive Manufacturer B: A significant automotive manufacturer in the Czech Republic, contributing to the country's strong automotive industry. The stock of this company reflects trends in the manufacturing and export-oriented sectors.

Energy Company C: A major player in the energy sector in the Czech Republic, involved in electricity generation, distribution, or related services. Monitoring the stock of this company provides insights into the energy sector's dynamics.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Czech Republic, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Czech Republic?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Czech Republic?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Czech Republic exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Tec...

This table replaces tables 36-10-0214 and 36-10-0215, which are now archived. For concepts, methods and sources, see http://www.statcan.gc.ca/imdb-bmdi/5103-eng.htm. Data by industry included in this table correspond to the Canadian System of Macroeconomic Accounts input-output detailed level of aggregation. The table is built around the Input-Output Industry Classification (IOIC). This one identifies both Institutional Sectors and Industries based on the North American Industry Classification System (NAICS). The alphanumeric codes appearing in square brackets besides each industry title represent the IOIC identification code. The first two characters of the IOIC alphanumeric codes represent the sector. IOIC codes beginning with a BS represent Business Sector industries, codes beginning with an NP represent Non-Profit Institutions Serving Household (NPISH) Sector industries, and codes beginning with a GS represent Government Sector industries. The IOIC is a hierarchical classification. IOIC codes consisting of four alpha-numeric characters represent industries at the Summary (S) level of aggregation, IOIC codes consisting of five or six alpha-numeric characters represent industries at the Medium (M) and IOIC codes consisting of eight alpha-numeric characters represent industries at the Detailed (D) level of aggregation. The classifications of the Input-Output tables can be found at the following link http://www.statcan.gc.ca/nea-cen/hr2012-rh2012/data-donnees/aggregation-agregation/aggregation-agregation-eng.htm. Provincial and territorial data are available from 1997. For Northwest Territories including Nunavut, statistics are available until 1998 inclusively. Starting in 1999, data for Northwest Territories and Nunavut are presented separately. The estimate of the total number of jobs covers two main categories: employee jobs and self-employed jobs. The number of hours worked in all jobs is the annual average for all jobs times the annual average hours worked in all jobs. According to the retained definition, hours worked means the total number of hours that a person spends working, whether paid or not. In general, this includes regular and overtime hours, breaks, travel time, training in the workplace and time lost, in brief, work stoppages where workers remain at their posts. On the other hand, time lost due to strikes, lockouts, annual vacation, public holidays, sick leave, maternity leave or leave for personal needs are not included in total hours worked. This is the annual average of hours worked per job in all categories of jobs. The total compensation for all jobs consists of all payments in cash or in kind made by domestic producers to workers for services rendered. It includes wages and salaries and employer's social contributions of employees, plus an imputed labour income for self-employed workers. For a given industry, value added is equal to its gross production (mainly sales) less its intermediate consumption (energy, raw materials, services) stemming from other industries. The value added corresponds to Gross domestic product (GDP) at basic prices which corresponds to the GDP at market prices excluding net taxes on products. Real value added is evaluated in 2017 chained dollars. A double-deflation procedure is used to measure real value added: real intermediate inputs are subtracted from real gross output. For productivity measurement, a real value added Fisher chain index is used for each industry. Chain indexes are calculated for consecutive periods to determine variation of quantities from one period to another. The chain indexes offer the advantage of reducing the variation in the values recorded by the various fixed-base indexes. Labour productivity is the ratio between real value added and hours worked. Real value added for each industry and each aggregate is constructed from a Fisher chain index. The ratio between total compensation for all jobs, and the number of hours worked. The term hourly compensation" is often used to refer to the total compensation per hour worked." This is the labour cost per unit of output, and it equals labour compensation divided by real value added. It is also equal to the ratio of labour compensation per hour worked and labour productivity. Unit labour cost increases when labour compensation per hour worked increases more rapidly than labour productivity. It is widely used to measure long-term inflation pressures arising from wage growth. This is the unit labour cost expressed in US dollars. This is obtained by dividing the unit labour cost by the exchange rate between Canada and the United States. Labour share corresponds to the ratio of total compensation as a percentage to the nominal value added. The North American Industry Classification System (NAICS) is an industry classification system triggered by the North American Free Trade Agreement, that was developed by the statistical agencies of Canada, Mexico and the United States. It is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply side or production oriented principles, to ensure that industrial data, classified to NAICS, is suitable for the analysis of production related issues such as industrial performance. Since 1997, the industry classification system of the Canadian System of Macroeconomic Accounts input-output tables is based on NAICS. In the Macroeconomic Accounts industries, the levels of the different classification systems were chosen so as to provide the most detail possible in order to maximize continuity with the previous classification systems developed by Statistics Canada since 1961. For more details, see http://www.statcan.gc.ca/imdb-bmdi/5103-eng.htm. Total economic activities that have been realized within the country. This includes both business and non-business sectors. This combines the business establishments of the North American Industry Classification System (NAICS) codes 11-81, with the exception of owner occupied dwellings industry. This combines the business establishments of the North American Industry Classification System (NAICS) codes 11, 21, 22, 23, 31-33. This combines the business establishments of the North American Industry Classification System (NAICS) code 11. Starting in 2014, the crop production industry incorporates the activities related to cannabis. This combines the business establishments for the North American Industry Classification System (NAICS) codes 111, 112. This combines the business establishments for the North American Industry Classification System (NAICS) code 111 excluding 1114. Starting in 2014, the crop production industry incorporates the activities related to illegal cannabis. This combines the business establishments for the North American Industry Classification System (NAICS) code 112, excluding 1125 This combines the business establishments for the North American Industry Classification System (NAICS) codes 1151, 1152. This combines the business establishments for the North American Industry Classification System (NAICS) codes 212393, 212394, 212395, 212397, 212398. This combines the business establishments for the North American Industry Classification System (NAICS) codes 213111, 213118. This combines the business establishments for the North American Industry Classification System (NAICS) codes 213117, 213119. This combines the business establishments for the North American Industry Classification System (NAICS) codes 2212, 2213. Special hybrid: corresponds to sections of the North American Industry Classification System (NAICS) code 23. This combines the business establishments of the North American Industry Classification System (NAICS) codes 311-316, 321-327, 331-337, 339. This combines the business establishments for the North American Industry Classification System (NAICS) codes 3112, 3118, 3119. This combines the business establishments for the North American Industry Classification System (NAICS) codes 31213, 31214. This combines the business establishments for the North American Industry Classification System (NAICS) codes 313, 314. This combines the business establishments for the North American Industry Classification System (NAICS) codes 315, 316. This combines the business establishments for the North American Industry Classification System (NAICS) code 324, excluding 32411. This combines the business establishments for the North American Industry Classification System (NAICS) codes 3255, 3256, 3259. This combines the business establishments for the North American Industry Classification System (NAICS) code 327, excluding 3273. This combines the business establishments for the North American Industry Classification System (NAICS) codes 3322, 3329. This combines the business establishments for the North American Industry Classification System (NAICS) codes 3332, 3333. This combines the business establishments for the North American Industry Classification System (NAICS) codes 3343, 3345, 3346. This combines the business establishments of the North American Industry Classification System (NAICS) codes 41, 44-45, 48-49, 51, 52, 53, 54, 55, 56, 61, 62, 71, 72, 81 with the exception of owner occupied dwelling industry. This combines the business establishments for the North American Industry Classification System (NAICS) codes 485, 487. This combines the business establishments for the North American Industry Classification System (NAICS) codes 4852, 4854, 4855, 4859, 487. This combines the business establishments for the North American Industry Classification System (NAICS) codes 4861, 4869. This combines the business establishments for the North American Industry Classification System (NAICS) codes 491, 492. This combines the business establishments for the North American Industry Classification System (NAICS) codes 51112, 51113, 51114, 51119. This combines the business