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.

This statistic presents the returns of the S&P 500 Real Estate Index in the United States from 2007 to 2023. The real estate sector faced its most challenging year in 2008, with a substantial loss of **** percent in value. In 2023, on the other hand, it reached a **** percent gain, despite recording some losses the previous year.

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.

This statistic presents the returns of the S&P 500 Industrials Index in the United States from 2007 to 2023. The industrial sector had its worst year in 2008, where it lost **** percent of its value. In 2023, it reached a **** percent gain, despite recording some losses the previous year.

The Value Line Investment Survey is one of the oldest, continuously running investment advisory publications. Since 1955, the Survey has been published in multiple formats including print, loose-leaf, microfilm and microfiche. Data from 1997 to present is now available online. The Survey tracks 1700 stocks across 92 industry groups. It provides reported and projected measures of firm performance, proprietary rankings and analysis for each stock on a quarterly basis. DATA AVAILABLE FOR YEARS: 1980-1989 This dataset, a subset of the Survey covering the years 1980-1989 has been digitized from the microfiche collection available at the Dewey Library (FICHE HG 4501.V26). It is only available to MIT students and faculty for academic research. Published weekly, each edition of the Survey has the following three parts: Summary & Index: includes an alphabetical listing of all industries with their relative ranking and the page number for detailed industry analysis. It also includes an alphabetical listing of all stocks in the publication with references to their location in Part 3, Ratings & Reports. Selection & Opinion: contains the latest economic and stock market commentary and advice along with one or more pages of research on interesting stocks or industries, and a variety of pertinent economic and stock market statistics. It also includes three model stock portfolios. Ratings & Reports: This is the core of the Value Line Investment Survey. Preceded by an industry report, each one-page stock report within that industry includes Timeliness, Safety and Technical rankings, 3-to 5-year analyst forecasts for stock prices, income and balance sheet items, up to 17 years of historical data, and Value Line analysts’ commentaries. The report also contains stock price charts, quarterly sales, earnings, and dividend information. Publication Schedule: Each edition of the Survey covers around 130 stocks in seven to eight industries on a preset sequential schedule so that all 1700 stocks are analyzed once every 13 weeks or each quarter. All editions are numbered 1-13 within each quarter. For example, in 1980, reports for Chrysler appear in edition 1 of each quarter on the following dates: January 4, 1980 – page 132 April 4, 1980 – page 133 July 4, 1980 – page 133 October 1, 1980 – page 133 Reports for Coca-Cola were published in edition 10 of each quarter on: March 7, 1980 – page 1514 June 6, 1980 – page 1518 Sept. 5, 1980 – page 1517 Dec. 5, 1980 – page 1548 Any significant news affecting a stock between quarters is covered in the supplementary reports that appear at the end of part 3, Ratings & Reports. File format: Digitized files within this dataset are in PDF format and are arranged by publication date within each compressed annual folder. How to Consult the Value Line Investment Survey: To find reports on a particular stock, consult the alphabetical listing of stocks in the Summary & Index part of the relevant weekly edition. Look for the page number just to the left of the company name and then use the table below to identify the edition where that page number appears. All editions within a given quarter are numbered 1-13 and follow equally sized page ranges for stock reports. The table provides page ranges for stock reports within editions 1-13 of 1980 Q1. It can be used to identify edition and page numbers for any quarter within a given year. Ratings & Reports Edition Pub. Date Pages 1 04-Jan-80 100-242 2 11-Jan-80 250-392 3 18-Jan-80 400-542 4 25-Jan-80 550-692 5 01-Feb-80 700-842 6 08-Feb-80 850-992 7 15-Feb-80 1000-1142 8 22-Feb-80 1150-1292 9 29-Feb-80 1300-1442 10 07-Mar-80 1450-1592 11 14-Mar-80 1600-1742 12 21-Mar-80 1750-1908 13 28-Mar-80 2000-2142 Another way to navigate to the Ratings & Reports part of an edition would be to look around page 50 within the PDF document. Note that the page numbers of the PDF will not match those within the publication.

Bosnia and Herzegovina Banking Sector: Return on Average Assets data was reported at 2.249 % in Dec 2024. This records a decrease from the previous number of 2.408 % for Sep 2024. Bosnia and Herzegovina Banking Sector: Return on Average Assets data is updated quarterly, averaging 1.057 % from Dec 2000 (Median) to Dec 2024, with 90 observations. The data reached an all-time high of 2.459 % in Mar 2024 and a record low of -1.765 % in Dec 2000. Bosnia and Herzegovina Banking Sector: Return on Average Assets data remains active status in CEIC and is reported by Central Bank of Bosnia and Herzegovina. The data is categorized under Global Database’s Bosnia and Herzegovina – Table BA.KB007: Banking Sector: Performance Indicators.

The data is by Vote Function and exclude the Donor Data is in billions of UGX.

Indexes included in the Russell U.S. Index Series Russell 3000®: The Russell 3000 Index measures the performance of the largest 3,000 U.S. companies representing approximately 98% of the investable U.S. equity market. Russell 1000®: The Russell 1000 Index measures the performance of the large-cap segment of the U.S. equity universe. It is a subset of the Russell 3000 Index and includes approximately 1,000 of the largest securities based on a combination of their market cap and current index membership. The Russell 1000 represents approximately 91% of the U.S. market. Russell 2000®: The Russell 2000 Index measures the performance of the small-cap segment of the U.S. equity universe. The Russell 2000 Index is a subset of the Russell 3000 Index representing approximately 9% of the total market capitalization of that index. It includes approximately 2,000 of the smallest securities based on a combination of their market cap and current index membership. Index Inception Dates Russell 1000® Index (1/1979) Russell 1000® Growth Index (1/1979) Russell 1000® Value Index (1/1979) Russell 2000® Index (1/1979) Russell 2000® Growth Index (1/1979) Russell 2000® Value Index (1/1979) Russell 2500™ Index (4/2003) Russell 2500™ Growth Index (4/2003) Russell 2500™ Value Index (4/2003) Russell 3000® Index (1/1979) Russell 3000® Growth Index (1/1979) Russell 3000® Value Index (1/1979) Russell Midcap® Index (1/1986) Russell Midcap® Growth Index (1/1987) Russell Midcap® Value Index (1/1987) Russell Small Cap Completeness Index (4/2003) Russell Small Cap Completeness Growth Index (4/2003) Russell Small Cap Completeness Value Index (4/2003) Russell Top 200® Index (7/1996) Russell Top 200® Growth Index (7/2001) Russell Top 200® Value Index (7/2001) Monthly Files included in the Russell U.S. Index Series Monthly Closing Files – RGS These holdings files reflect the official closing positions for all constituents of the 21 U.S. Russell Indexes at month-end back to December 1986 and at quarter-end from September 1986 back to December 1978. Security level information such as returns, market values, sector and industry classifications, and security weights are included in the file. Files are fixed-width text files and have a naming convention of H_yyyymmdd_RGS.txt. Monthly Closing Files – ICB These holdings files reflect the official closing positions for all constituents of the 21 U.S. Russell Indexes at month-end back to January 2010. Security level information such as returns, market values, sector and industry classifications, and security weights are included in the file. Files are comma delimited text files and have a naming convention of H_yyyymmdd.csv. Monthly Contribution to Return by RGS Files These files provide contribution to return using RGS as of the end of the month for each of the 21 U.S. Russell Indexes back to August 2008. Files are tab delimited text files and have a naming convention of CTR_MONTHLY_RGS_yyyymmdd.txt.. Monthly Contribution to Return by ICB Files These files provide contribution to return using ICB as of the end of the month for each of the 21 U.S. Russell Indexes back to August 2020. Files are comma delimited text files and have a naming convention of CTR_MONTHLY_yyyymmdd.csv. Monthly RGS Sector Weights Files These files provide monthly Russell Global Sector (RGS) weights for all 21 US Indexes at month-end back to November 2009. Files are comma delimited text files and have a naming convention of SWH_RGS_ALL_yyyymmdd.txt. Monthly ICB Sector Weights Files These files provide monthly Industrial Classification Benchmark (ICB) weights for all 21 US Indexes at month-end back to March 2020. Files are comma delimited text files and have a naming convention of SWH_ALL_yyyymmdd.csv. Note: In August 2020 FTSE Russell transitioned to ICB classification from the RGS classification. All data from September, 2020 is only available using ICB Classification. Data is current to April 28, 2023.

Stock market return (%, year-on-year) in Sri Lanka was reported at 57.14 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Sri Lanka - Stock market return (%, year-on-year) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

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

The Financial Asset Investing industry's revenue is largely dictated by the performance of domestic and international financial markets. Volatility in global financial markets due to the pandemic and the Russia-Ukraine conflict has contributed to a decline in confidence. Industry revenue is expected to fall at an annualised 3.2% over the five years through 2023-24, to total $31.1 billion. This trend includes an anticipated uptick of 0.1% in the current year. Industry revenue has been highly volatile, with sharemarket performance remaining relatively weak due to the pandemic. As a result, profitability has trended downwards. Despite this decline, industry participation has jumped, as asset investing becomes more popular among consumers and businesses, including investments in riskier assets like domestic and international equities. To curb inflation, the RBNZ has repeatedly raised its official cash rate, resulting in a 14-year high in cash rates and a surge in interest rates in New Zealand. Rising interest rates have sparked interest in longer term debt securities due to their higher yield, which is more enticing for investors. This has somewhat offset the decline in industry revenue. Industry revenue is forecast to grow moving forwards. A strong economic recovery following the pandemic and easing global concerns are projected to drive revenue growth, as investors are more eager to take on higher risk. Forecast rate cuts are set to stimulate stock market performance. Instead of longer term debt securities like bonds, investors will start gravitating towards equities as yields gradually decline. Other factors set to drive growth include lower revenue volatility and a rise in industry assets contributing to growing investment returns. Technology will continue to play a key role in the Financial Asset Investing industry. Fintech advancements, like chatbots and robo-advisors, are set to enhance profitability in the coming years by automating tasks and reducing reliance on administrative labour. However, firms will likely face stronger competition for funds from superannuation funds and KiwiSaver schemes. Overall, industry revenue is forecast to grow at an annualised 3.3% over the five years through 2028-29, to reach $36.5 billion.

Azerbaijan Banking Sector: Year to Date: Net Interest Profit or Loss: Expenses data was reported at 72.220 AZN mn in Jan 2015. This records a decrease from the previous number of 986.440 AZN mn for Dec 2014. Azerbaijan Banking Sector: Year to Date: Net Interest Profit or Loss: Expenses data is updated monthly, averaging 231.060 AZN mn from Jan 2006 (Median) to Jan 2015, with 109 observations. The data reached an all-time high of 986.440 AZN mn in Dec 2014 and a record low of 6.280 AZN mn in Jan 2006. Azerbaijan Banking Sector: Year to Date: Net Interest Profit or Loss: Expenses data remains active status in CEIC and is reported by Central Bank of the Republic of Azerbaijan. The data is categorized under Global Database’s Azerbaijan – Table AZ.KB004: Banking Sector Performance Indicators.

Azerbaijan Banking Sector: Year to Date: Loan Loss Provisions data was reported at 11.890 AZN mn in Jan 2015. This records a decrease from the previous number of 373.660 AZN mn for Dec 2014. Azerbaijan Banking Sector: Year to Date: Loan Loss Provisions data is updated monthly, averaging 73.670 AZN mn from Jan 2006 (Median) to Jan 2015, with 109 observations. The data reached an all-time high of 447.250 AZN mn in Dec 2011 and a record low of -1.720 AZN mn in Feb 2006. Azerbaijan Banking Sector: Year to Date: Loan Loss Provisions data remains active status in CEIC and is reported by Central Bank of the Republic of Azerbaijan. The data is categorized under Global Database’s Azerbaijan – Table AZ.KB004: Banking Sector Performance Indicators.

Bosnia and Herzegovina Banking Sector: Return on Average Equity data was reported at 16.007 % in Dec 2024. This records a decrease from the previous number of 17.918 % for Sep 2024. Bosnia and Herzegovina Banking Sector: Return on Average Equity data is updated quarterly, averaging 8.018 % from Dec 2000 (Median) to Dec 2024, with 90 observations. The data reached an all-time high of 18.550 % in Mar 2024 and a record low of -7.023 % in Dec 2000. Bosnia and Herzegovina Banking Sector: Return on Average Equity data remains active status in CEIC and is reported by Central Bank of Bosnia and Herzegovina. The data is categorized under Global Database’s Bosnia and Herzegovina – Table BA.KB007: Banking Sector: Performance Indicators.

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.

Penumbra Inc. prepares to reveal its earnings, attracting investor interest with anticipated revenue growth despite a moderating pace compared to last year. Stability in its stock price amidst sector performance challenges adds intrigue.

This paper examines the dynamics of public sector performance across European Union (EU) countries through a comprehensive methodological framework. This study introduces the European Public Sector Performance Index, a novel approach that employs Partial Least Squares (PLS) econometric modelling and cluster analysis to evaluate public sector performance from 2007 to 2021. By assessing performance across governance, social, and economic dimensions, the research captures the multifaceted nature of public sector efficiency in the EU. Our investigation reveals significant determinants of performance, including governance factors like Control of Corruption, Rule of Law, and Government Effectiveness, as well as economic indicators such as Inflation and social factors like Equity of access to healthcare services and Education Spending. These findings underscore the critical role of transparent governance, economic stability, and equitable social policies in enhancing public sector efficiency. Despite its reliance on secondary data and the PLS method, the study provides new methodological insights and empirical evidence on public sector performance, contributing to the literature with a holistic analysis that integrates digitalisation and well-being. This study’s holistic approach offers actionable insights for policymakers and stakeholders, emphasising the need for robust governance and equitable policies to improve public sector performance across the EU. The omission of certain societal components—such as economic conditions, demographic changes, or cultural factors—may result in a skewed representation of how digital transformation and governance interact. These external factors can significantly influence the effectiveness of digital initiatives and the overall performance of public institutions.

Azerbaijan Banking Sector: Year to Date: Net Non Interest Profit or Loss: Income data was reported at 38.230 AZN mn in Jan 2015. This records a decrease from the previous number of 476.450 AZN mn for Dec 2014. Azerbaijan Banking Sector: Year to Date: Net Non Interest Profit or Loss: Income data is updated monthly, averaging 132.840 AZN mn from Jan 2006 (Median) to Jan 2015, with 109 observations. The data reached an all-time high of 476.450 AZN mn in Dec 2014 and a record low of 6.780 AZN mn in Jan 2006. Azerbaijan Banking Sector: Year to Date: Net Non Interest Profit or Loss: Income data remains active status in CEIC and is reported by Central Bank of the Republic of Azerbaijan. The data is categorized under Global Database’s Azerbaijan – Table AZ.KB004: Banking Sector Performance Indicators.

Key Statistics on Business Performance and Operating Characteristics of the Industrial Sector - Table 610-72002 : Principal statistics for all establishments in the manufacturing sector by percentage share of overseas interest