100+ datasets found
  1. F

    S&P 500

    • fred.stlouisfed.org
    json
    Updated Jul 11, 2025
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    (2025). S&P 500 [Dataset]. https://fred.stlouisfed.org/series/SP500
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    jsonAvailable download formats
    Dataset updated
    Jul 11, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-pre-approvalhttps://fred.stlouisfed.org/legal/#copyright-pre-approval

    Description

    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.

  2. Annual development Nasdaq 100 Index 1986-2024

    • statista.com
    Updated Feb 28, 2025
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    Statista (2025). Annual development Nasdaq 100 Index 1986-2024 [Dataset]. https://www.statista.com/statistics/261720/annual-development-of-the-sunds-500-index/
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    Dataset updated
    Feb 28, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In 2021, the Nasdaq 100 closed at 16,320.08 points, which was the second highest value on record despite the economic effects of the global coronavirus (COVID-19) pandemic. The index value closed at 21,012.17 points in 2024, an increase of more than 4,000 points compared to its closing value for the previous year. What does the NASDAQ tell us? The Nasdaq 100 index is comprised of 100 largest and most actively traded non-financial companies listed on the Nasdaq stock exchange. Financial firms are represented by the NASDAQ Bank Index. A stock market index is a measurement of average performance of companies forming the index. It gives a snapshot of what investors are thinking at that particular moment. Other indices The Dow Jones Industrial Average gets more attention than the NASDAQ 100, though it only represents 30 companies. It’s best and worst days mark some of the major financial events of the past century. This helps to put more meaning behind events like Black Monday, the Wall Street crash of 1929, or the 2008 Financial Crisis, as well as the speed of their recoveries in financial markets.

  3. Global Equity Indices

    • lseg.com
    Updated Nov 25, 2024
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    LSEG (2024). Global Equity Indices [Dataset]. https://www.lseg.com/en/data-analytics/financial-data/indices/global-equity-indices
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    csv,delimited,gzip,html,json,python,sql,user interface,xml,zip archiveAvailable download formats
    Dataset updated
    Nov 25, 2024
    Dataset provided by
    London Stock Exchange Grouphttp://www.londonstockexchangegroup.com/
    Authors
    LSEG
    License

    https://www.lseg.com/en/policies/website-disclaimerhttps://www.lseg.com/en/policies/website-disclaimer

    Description

    Browse LSEG's Global Equity Indices, discover our range of data, indices & benchmarks. Our Data Catalogue offers unrivaled data and delivery mechanisms.

  4. f

    Numbers of total observed records and respective uninterrupted trends for...

    • plos.figshare.com
    xls
    Updated Jun 17, 2023
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    Héctor Raúl Olivares-Sánchez; Carlos Manuel Rodríguez-Martínez; Héctor Francisco Coronel-Brizio; Enrico Scalas; Thomas Henry Seligman; Alejandro Raúl Hernández-Montoya (2023). Numbers of total observed records and respective uninterrupted trends for all data samples of financial indices studied. [Dataset]. http://doi.org/10.1371/journal.pone.0270492.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 17, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Héctor Raúl Olivares-Sánchez; Carlos Manuel Rodríguez-Martínez; Héctor Francisco Coronel-Brizio; Enrico Scalas; Thomas Henry Seligman; Alejandro Raúl Hernández-Montoya
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The data have been filtered, e.g. by removing null records.

  5. Performance difference between the S&P 500 ESG and S&P 500 indexes 2022-2025...

    • ai-chatbox.pro
    • statista.com
    Updated Jun 3, 2025
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    Statista Research Department (2025). Performance difference between the S&P 500 ESG and S&P 500 indexes 2022-2025 [Dataset]. https://www.ai-chatbox.pro/?_=%2Ftopics%2F7463%2Fesg-and-impact-investing%2F%23XgboD02vawLbpWJjSPEePEUG%2FVFd%2Bik%3D
    Explore at:
    Dataset updated
    Jun 3, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Description

    Until the fourth quarter of 2023, the S&P 500 and the S&P 500 ESG index exhibited similar performance, both indexes were weighted to similar industries as the S&P 500 followed the leading 500 companies in the United States. Throughout 2024, the S&P 500 ESG index steadily outperformed the S&P 500 by three points on average. During the coronavirus pandemic, the technology sector was one of the best-performing sectors in the market. The major differences between the two indexes were the S&P 500 ESG index was skewed towards firms with higher environmental, social, and governance (ESG) scores and had a higher concentration of technology securities than the S&P 500 index. What is a market capitalization index? Both the S&P 500 and the S&P 500 ESG are market capitalization indexes, meaning the individual components (such as stocks and other securities) weighted to the indexes influence the overall value. Market trends such as inflation, interest rates, and international issues like the coronavirus pandemic and the popularity of ESG among professional investors affect the performance of stocks. When weighted components rise in value this causes an increase in the overall value of the index they are weighted too. What trends are driving index performance? Recent economic and social trends have led to higher levels of ESG integration and maintenance among firms worldwide and higher prioritization from investors to include ESG-focused firms in their investment choices. From a global survey group over one-third of the respondents were willing to prioritize ESG benefits over a higher return on their investment. These trends influenced the performance of securities on the market, leading to an increased value of individual weighted stocks, resulting in an overall increase in the index value.

  6. F

    Chicago Fed National Financial Conditions Index

    • fred.stlouisfed.org
    json
    Updated Jul 9, 2025
    + more versions
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    (2025). Chicago Fed National Financial Conditions Index [Dataset]. https://fred.stlouisfed.org/series/NFCI
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 9, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-citation-requiredhttps://fred.stlouisfed.org/legal/#copyright-citation-required

    Area covered
    Chicago
    Description

    Graph and download economic data for Chicago Fed National Financial Conditions Index (NFCI) from 1971-01-08 to 2025-07-04 about financial, indexes, and USA.

  7. f

    Model performance metrics.

    • plos.figshare.com
    xls
    Updated Mar 13, 2024
    + more versions
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    Yuancheng Si; Saralees Nadarajah; Zongxin Zhang; Chunmin Xu (2024). Model performance metrics. [Dataset]. http://doi.org/10.1371/journal.pone.0299164.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Mar 13, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Yuancheng Si; Saralees Nadarajah; Zongxin Zhang; Chunmin Xu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index’s opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model’s proficiency in linear trend analysis and the deep learning models’ capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index’s opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making.

  8. Top Tech Companies Stock Price

    • kaggle.com
    Updated Nov 24, 2020
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    Tomas Mantero (2020). Top Tech Companies Stock Price [Dataset]. https://www.kaggle.com/datasets/tomasmantero/top-tech-companies-stock-price
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 24, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Tomas Mantero
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    In this dataset you can find the Top 100 companies in the technology sector. You can also find 5 of the most important and used indices in the financial market as well as a list of all the companies in the S&P 500 index and in the technology sector.

    The Global Industry Classification Standard also known as GICS is the primary financial industry standard for defining sector classifications. The Global Industry Classification Standard was developed by index providers MSCI and Standard and Poor’s. Its hierarchy begins with 11 sectors which can be further delineated to 24 industry groups, 69 industries, and 158 sub-industries.

    You can read the definition of each sector here.

    The 11 broad GICS sectors commonly used for sector breakdown reporting include the following: Energy, Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, Information Technology, Telecommunication Services, Utilities and Real Estate.

    In this case we will focuse in the Technology Sector. You can see all the sectors and industry groups here.

    To determine which companies, correspond to the technology sector, we use Yahoo Finance, where we rank the companies according to their “Market Cap”. After having the list of the Top 100 best valued companies in the sector, we proceeded to download the historical data of each of the companies using the NASDAQ website.

    Regarding to the indices, we searched various sources to find out which were the most used and determined that the 5 most frequently used indices are: Dow Jones Industrial Average (DJI), S&P 500 (SPX), NASDAQ Composite (IXIC), Wilshire 5000 Total Market Inde (W5000) and to specifically view the technology sector SPDR Select Sector Fund - Technology (XLK). Historical data for these indices was also obtained from the NASDQ website.

    Content

    In total there are 107 files in csv format. They are composed as follows:

    • 100 files contain the historical data of tech companies.
    • 5 files contain the historical data of the most used indices.
    • 1 file contain the list of all the companies in the S&P 500 index.
    • 1 file contain the list of all the companies in the technology sector.

    Column Description

    Every company and index file has the same structure with the same columns:

    Date: It is the date on which the prices were recorded. High: Is the highest price at which a stock traded during the course of the trading day. Low: Is the lowest price at which a stock traded during the course of the trading day. Open: Is the price at which a stock started trading when the opening bell rang. Close: Is the last price at which a stock trades during a regular trading session. Volume: Is the number of shares that changed hands during a given day. Adj Close: The adjusted closing price factors in corporate actions, such as stock splits, dividends, and rights offerings.

    The two other files have different columns names:

    List of S&P 500 companies

    Symbol: Ticker symbol of the company. Name: Name of the company. Sector: The sector to which the company belongs.

    Technology Sector Companies List

    Symbol: Ticker symbol of the company. Name: Name of the company. Price: Current price at which a stock can be purchased or sold. (11/24/20) Change: Net change is the difference between closing prices from one day to the next. % Change: Is the difference between closing prices from one day to the next in percentage. Volume: Is the number of shares that changed hands during a given day. Avg Vol: Is the daily average of the cumulative trading volume during the last three months. Market Cap (Billions): Is the total value of a company’s shares outstanding at a given moment in time. It is calculated by multiplying the number of shares outstanding by the price of a single share. PE Ratio: Is the ratio of a company's share (stock) price to the company's earnings per share. The ratio is used for valuing companies and to find out whether they are overvalued or undervalued.

    Acknowledgements

    SEC EDGAR | Company Filings NASDAQ | Historical Quotes Yahoo Finance | Technology Sector Wikipedia | List of S&P 500 companies S&P Dow Jones Indices | S&P 500 [S&P Dow Jones Indices | DJI](https://www.spglobal.com/spdji/en/i...

  9. T

    United Kingdom Stock Market Index (GB100) Data

    • tradingeconomics.com
    • ko.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jun 13, 2025
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    TRADING ECONOMICS, United Kingdom Stock Market Index (GB100) Data [Dataset]. https://tradingeconomics.com/united-kingdom/stock-market
    Explore at:
    excel, xml, json, csvAvailable download formats
    Dataset updated
    Jun 13, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 3, 1984 - Jul 11, 2025
    Area covered
    United Kingdom
    Description

    United Kingdom's main stock market index, the GB100, fell to 8941 points on July 11, 2025, losing 0.38% from the previous session. Over the past month, the index has climbed 0.63% and is up 8.34% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on July of 2025.

  10. T

    Russia Stock Market Index MOEX CFD Data

    • tradingeconomics.com
    • ko.tradingeconomics.com
    • +12more
    csv, excel, json, xml
    Updated Jul 12, 2025
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    TRADING ECONOMICS (2025). Russia Stock Market Index MOEX CFD Data [Dataset]. https://tradingeconomics.com/russia/stock-market
    Explore at:
    json, csv, excel, xmlAvailable download formats
    Dataset updated
    Jul 12, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Sep 22, 1997 - Jul 11, 2025
    Area covered
    Russia
    Description

    Russia's main stock market index, the MOEX, fell to 2643 points on July 11, 2025, losing 3.27% from the previous session. Over the past month, the index has declined 3.89% and is down 11.17% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Russia. Russia Stock Market Index MOEX CFD - values, historical data, forecasts and news - updated on July of 2025.

  11. f

    Fraction of time, the overall of the studied data trends durations follow a...

    • figshare.com
    xls
    Updated Jun 14, 2023
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    Héctor Raúl Olivares-Sánchez; Carlos Manuel Rodríguez-Martínez; Héctor Francisco Coronel-Brizio; Enrico Scalas; Thomas Henry Seligman; Alejandro Raúl Hernández-Montoya (2023). Fraction of time, the overall of the studied data trends durations follow a geometric distribution with parameter p = 0.5, and with any p, both cases for a significance level of 5%. [Dataset]. http://doi.org/10.1371/journal.pone.0270492.t012
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 14, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Héctor Raúl Olivares-Sánchez; Carlos Manuel Rodríguez-Martínez; Héctor Francisco Coronel-Brizio; Enrico Scalas; Thomas Henry Seligman; Alejandro Raúl Hernández-Montoya
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Fraction of time, the overall of the studied data trends durations follow a geometric distribution with parameter p = 0.5, and with any p, both cases for a significance level of 5%.

  12. Annual performance of the Dow Jones Composite Index 2000-2024

    • statista.com
    Updated Mar 10, 2025
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    Statista (2025). Annual performance of the Dow Jones Composite Index 2000-2024 [Dataset]. https://www.statista.com/statistics/189758/dow-jones-composite-index-closing-year-end-values-since-2000/
    Explore at:
    Dataset updated
    Mar 10, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    The Dow Jones Composite Index finished the year 2024 at 13,391.71 points, an increase compared to the previous year. Even with the economic effects of the global coronavirus (COVID-19) pandemic, 2021 had the highest point of the index in the past two decades. What is Dow Jones Composite Index? The Dow Jones Composite Index is one of the indices from the Dow Jones index family. It is composed of 65 leading U.S. companies: 30 stocks forming the Dow Jones Industrial Average index, 20 stocks from the Dow Jones Transportation index and 15 stocks from the Dow Jones Utility Average index. Importance of stock indices A stock market index shows an average performance of companies from a given section of the market. It is usually a weighted average, meaning that such factors as price of companies or their market capitalization are taken into consideration when calculating the index value. Stock indices are very useful for the financial market participants, as they instantly show the sentiments prevailing on a given market. They are also commonly used as a benchmark against portfolio performance, showing if a given portfolio has outperformed, or underperformed the market.

  13. Indices, Constituents and Weightings (ICW)

    • lseg.com
    Updated Nov 25, 2024
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    LSEG (2024). Indices, Constituents and Weightings (ICW) [Dataset]. https://www.lseg.com/en/data-analytics/financial-data/indices/equity-indices/indices-constituents-weightings
    Explore at:
    csv,delimited,gzip,html,json,pdf,python,sql,text,user interface,xml,zip archiveAvailable download formats
    Dataset updated
    Nov 25, 2024
    Dataset provided by
    London Stock Exchange Grouphttp://www.londonstockexchangegroup.com/
    Authors
    LSEG
    License

    https://www.lseg.com/en/policies/website-disclaimerhttps://www.lseg.com/en/policies/website-disclaimer

    Description

    Explore LSEG's Indices, Constituents and Weightings (ICW), and find real time content for all major indices from exchange to vendor offerings.

  14. w

    Global Financial Inclusion (Global Findex) Database 2011 - Latvia

    • microdata.worldbank.org
    • catalog.ihsn.org
    • +1more
    Updated Sep 21, 2021
    + more versions
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    Development Research Group, Finance and Private Sector Development Unit (2021). Global Financial Inclusion (Global Findex) Database 2011 - Latvia [Dataset]. https://microdata.worldbank.org/index.php/catalog/1204
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    Dataset updated
    Sep 21, 2021
    Dataset authored and provided by
    Development Research Group, Finance and Private Sector Development Unit
    Time period covered
    2011
    Area covered
    Latvia
    Description

    Abstract

    Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

    The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

    Geographic coverage

    National Coverage.

    Analysis unit

    Individual

    Universe

    The target population is the civilian, non-institutionalized population 15 years and above. The sample is nationally representative.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

    Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

    Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

    The sample size in Latvia was 1,006 individuals.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

    Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

    Sampling error estimates

    Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

  15. T

    Italy Stock Market Index (IT40) Data

    • tradingeconomics.com
    • it.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, Italy Stock Market Index (IT40) Data [Dataset]. https://tradingeconomics.com/italy/stock-market
    Explore at:
    xml, json, excel, csvAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 31, 1997 - Jul 11, 2025
    Area covered
    Italy
    Description

    Italy's main stock market index, the IT40, fell to 39939 points on July 11, 2025, losing 1.45% from the previous session. Over the past month, the index has declined 0.02%, though it remains 15.49% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Italy. Italy Stock Market Index (IT40) - values, historical data, forecasts and news - updated on July of 2025.

  16. United States CSI: Personal: Income Increase Probability: Next Yr: Mean

    • ceicdata.com
    Updated Nov 27, 2021
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    CEICdata.com (2021). United States CSI: Personal: Income Increase Probability: Next Yr: Mean [Dataset]. https://www.ceicdata.com/en/united-states/consumer-sentiment-index-personal-finance/csi-personal-income-increase-probability-next-yr-mean
    Explore at:
    Dataset updated
    Nov 27, 2021
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Apr 1, 2017 - Mar 1, 2018
    Area covered
    United States
    Description

    United States CSI: Personal: Income Increase Probability: Next Yr: Mean data was reported at 51.100 % in May 2018. This records a decrease from the previous number of 53.700 % for Apr 2018. United States CSI: Personal: Income Increase Probability: Next Yr: Mean data is updated monthly, averaging 48.750 % from Jun 2002 (Median) to May 2018, with 192 observations. The data reached an all-time high of 56.000 % in Dec 2005 and a record low of 34.700 % in Sep 2011. United States CSI: Personal: Income Increase Probability: Next Yr: Mean data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H024: Consumer Sentiment Index: Personal Finance. The question was: What do you think is the percent chance that your income in the next twelve months will be higher than your income in the past 12 months?

  17. T

    Baltic Exchange Dry Index - Price Data

    • tradingeconomics.com
    • ru.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated May 26, 2017
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    TRADING ECONOMICS (2017). Baltic Exchange Dry Index - Price Data [Dataset]. https://tradingeconomics.com/commodity/baltic
    Explore at:
    csv, excel, xml, jsonAvailable download formats
    Dataset updated
    May 26, 2017
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 4, 1985 - Jul 11, 2025
    Area covered
    World
    Description

    Baltic Dry rose to 1,663 Index Points on July 11, 2025, up 13.52% from the previous day. Over the past month, Baltic Dry's price has fallen 15.50%, and is down 16.73% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on July of 2025.

  18. S&P Global Dow Jones Indices Data

    • lseg.com
    Updated Nov 25, 2024
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    LSEG (2024). S&P Global Dow Jones Indices Data [Dataset]. https://www.lseg.com/en/data-analytics/financial-data/indices/equity-indices/third-party/sp-dow-jones-indices
    Explore at:
    csv,delimited,gzip,html,json,python,sql,user interface,xml,zip archiveAvailable download formats
    Dataset updated
    Nov 25, 2024
    Dataset provided by
    London Stock Exchange Grouphttp://www.londonstockexchangegroup.com/
    Authors
    LSEG
    License

    https://www.lseg.com/en/policies/website-disclaimerhttps://www.lseg.com/en/policies/website-disclaimer

    Description

    S&P Dow Jones Indices is the largest global resource for essential indices, data and research including the S&P 500 and DJIA. Get access via LSEG.

  19. J

    Volatility of Price Indices for Heterogeneous Goods with Applications to the...

    • journaldata.zbw.eu
    txt
    Updated Dec 7, 2022
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    Fabian Bocart; Christian M. Hafner; Fabian Bocart; Christian M. Hafner (2022). Volatility of Price Indices for Heterogeneous Goods with Applications to the Fine Art Market (replication data) [Dataset]. http://doi.org/10.15456/jae.2022321.0717776287
    Explore at:
    txt(1272)Available download formats
    Dataset updated
    Dec 7, 2022
    Dataset provided by
    ZBW - Leibniz Informationszentrum Wirtschaft
    Authors
    Fabian Bocart; Christian M. Hafner; Fabian Bocart; Christian M. Hafner
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Price indices for heterogeneous goods such as real estate or fine art constitute crucial information for institutional or private investors considering alternative investment decisions in times of financial markets turmoil. Classical mean-variance analysis of alternative investments has been hampered by the lack of a systematic treatment of volatility in these markets. In this paper we propose a hedonic regression framework which explicitly defines an underlying stochastic process for the price index, allowing to treat the volatility parameter as the object of interest. The model can be estimated using maximum likelihood in combination with the Kalman filter. We derive theoretical properties of the volatility estimator and show that it outperforms the standard estimator. We show that extensions to allow for time-varying volatility are straightforward using a local-likelihood approach. In an application to a large data set of international blue chip artists, we show that volatility of the art market, although generally lower than that of financial markets, has risen after the financial crisis of 2008-09, but sharply decreased during the recent debt crisis.

  20. Apartment market debt and equity financing index U.S. 2016-2024, per quarter...

    • statista.com
    Updated Jul 11, 2025
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    Statista (2025). Apartment market debt and equity financing index U.S. 2016-2024, per quarter [Dataset]. https://www.statista.com/statistics/1356627/apartment-debt-and-equity-finance-index-usa/
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    Dataset updated
    Jul 11, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2016 - Jul 2024
    Area covered
    United States
    Description

    Financing conditions in the apartment market in the United States improved in *********, according to the National Multifamily Housing Council's (NMHC) finance index. The index is a standard diffusion index and is based on a quarterly survey among NMHC members. A value over ** indicates improving finance availability, while under **, it shows that financing is becoming harder to obtain. In **********, the debt financing index reached its peak at ** index points, meaning that debt financing conditions improved the most. In *********, the debt index stood at ** index points, which was an improvement from the same quarter in 2023.

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(2025). S&P 500 [Dataset]. https://fred.stlouisfed.org/series/SP500

S&P 500

SP500

Explore at:
82 scholarly articles cite this dataset (View in Google Scholar)
jsonAvailable download formats
Dataset updated
Jul 11, 2025
License

https://fred.stlouisfed.org/legal/#copyright-pre-approvalhttps://fred.stlouisfed.org/legal/#copyright-pre-approval

Description

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.

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