49 datasets found
  1. Monthly development S&P 500 Index 2018-2024

    • statista.com
    Updated Feb 28, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Monthly development S&P 500 Index 2018-2024 [Dataset]. https://www.statista.com/statistics/697624/monthly-sandp-500-index-performance/
    Explore at:
    Dataset updated
    Feb 28, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2018 - Dec 2024
    Area covered
    United States
    Description

    The S&P 500, an index of 500 publicly traded companies in the United States, closed at 5,881.63 points on the last trading day of December 2024. What is the S&P 500? The S&P 500 is a stock market index that tracks the evolution of 500 companies. In contrast to the Dow Jones Industrial Index, which measures the performance of thirty large U.S. companies, the S&P 500 shows the sentiments in the broader market. Publicly traded companies Companies on the S&P 500 are publicly traded, meaning that anyone can invest in them. A large share of adults in the United States invest in the stock market, though many of these are through a retirement account or mutual fund. While most people make a modest return, the most successful investors have made billions of U.S. dollars through investing.

  2. Annual development Nasdaq 100 Index 1986-2024

    • statista.com
    Updated Feb 28, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Annual development Nasdaq 100 Index 1986-2024 [Dataset]. https://www.statista.com/statistics/261720/annual-development-of-the-sunds-500-index/
    Explore at:
    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. Broad Based Index Fund Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Dataintelo (2025). Broad Based Index Fund Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/broad-based-index-fund-market
    Explore at:
    csv, pptx, pdfAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Broad Based Index Fund Market Outlook



    The global broad-based index fund market size was valued at USD 5.3 trillion in 2023 and is projected to reach USD 11.2 trillion by 2032, growing at a compound annual growth rate (CAGR) of 8.5% during the forecast period. This substantial growth is driven by increasing investor interest in passive investment strategies, along with the rising emphasis on cost-effective and diversified portfolio management.



    The surge in demand for broad-based index funds can be attributed to several key growth factors. Firstly, the growing awareness and education about the benefits of passive investing over active management have played a significant role. Investors are increasingly leaning towards index funds due to their lower expense ratios, tax efficiency, and the ability to provide broad market exposure with minimal effort. Secondly, technological advancements and the rise of fintech have made these funds more accessible to a wider audience through online platforms and robo-advisors, democratizing investment opportunities for retail investors globally. Lastly, regulatory changes in many regions are encouraging greater transparency and lower fees in the financial services industry, which further bolsters the attractiveness of index funds as a preferred investment vehicle.



    The popularity of broad-based index funds is also bolstered by their performance resilience during market volatility. Historical data indicates that while actively managed funds often struggle to outperform the market consistently, index funds tend to provide more stable returns over the long term. This trend has been particularly noticeable during economic downturns and periods of market uncertainty, where investors seek the relative safety and predictability offered by broad-based diversified portfolios. Additionally, the increased focus on retirement planning and the shift from defined benefit to defined contribution retirement plans have spurred the growth of index funds as they are often the preferred choice in retirement accounts due to their long-term growth potential and lower costs.



    The regional outlook for the broad-based index fund market highlights significant growth potential across various geographies. North America, particularly the United States, remains the largest market for index funds, driven by the deep-rooted culture of investing and a well-established financial infrastructure. Europe follows closely, with growth fueled by regulatory support and increasing investor awareness. The Asia Pacific region is expected to witness the highest growth rate, propelled by the burgeoning middle class, rising disposable incomes, and increasing penetration of financial services. Latin America and the Middle East & Africa are also anticipated to demonstrate steady growth as financial markets in these regions continue to develop and mature.



    Mutual Funds Sales have seen a notable uptick as investors increasingly seek diversified investment options that align with their financial goals. This trend is particularly evident in the context of broad-based index funds, where mutual funds offer a structured approach to investing in a wide array of assets. The appeal of mutual funds lies in their ability to pool resources from multiple investors, enabling access to a diversified portfolio that might otherwise be unattainable for individual investors. This collective investment model not only reduces risk but also provides investors with professional management and oversight. As the financial landscape evolves, mutual funds continue to play a crucial role in facilitating access to index funds, thereby driving sales and expanding their market presence.



    Fund Type Analysis



    Equity index funds represent a significant portion of the broad-based index fund market. These funds track a variety of stock indices, such as the S&P 500, NASDAQ, and MSCI World Index, providing investors with exposure to a wide array of equity markets. The appeal of equity index funds lies in their ability to offer broad market diversification at a low cost. Investors benefit from the lower fees associated with passive management and the reduced risk of individual stock selection. As a result, equity index funds have become a staple in both retail and institutional portfolios, driving robust demand and growth in this segment.



    Bond index funds, though smaller in market share compared to their equity counterparts, are gaining traction as investors seek stable income and risk diversifi

  4. Denmark Index: Copenhagen Stock Exchange: OMX Copenhagen Ex OMXC 20

    • ceicdata.com
    Updated Mar 14, 2018
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CEICdata.com, Denmark Index: Copenhagen Stock Exchange: OMX Copenhagen Ex OMXC 20 [Dataset]. https://www.ceicdata.com/en/denmark/copenhagen-stock-exchange-index/index-copenhagen-stock-exchange-omx-copenhagen-ex-omxc-20
    Explore at:
    Dataset updated
    Mar 14, 2018
    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
    May 1, 2017 - Apr 1, 2018
    Area covered
    Denmark
    Variables measured
    Securities Exchange Index
    Description

    Denmark Index: Copenhagen Stock Exchange: OMX Copenhagen Ex OMXC 20 data was reported at 4,968.520 31Dec1995=100 in Nov 2018. This records a decrease from the previous number of 5,078.930 31Dec1995=100 for Oct 2018. Denmark Index: Copenhagen Stock Exchange: OMX Copenhagen Ex OMXC 20 data is updated monthly, averaging 2,242.556 31Dec1995=100 from Dec 1999 (Median) to Nov 2018, with 228 observations. The data reached an all-time high of 5,648.470 31Dec1995=100 in Aug 2018 and a record low of 893.460 31Dec1995=100 in Sep 2002. Denmark Index: Copenhagen Stock Exchange: OMX Copenhagen Ex OMXC 20 data remains active status in CEIC and is reported by Copenhagen Stock Exchange. The data is categorized under Global Database’s Denmark – Table DK.Z001: Copenhagen Stock Exchange: Index. On May 13, 2013 NASDAQ OMX performed changes to the KFMX indexes. The name was changeed from KFMX to OMX Copenhagen ex OMX Copenhagen 20, and the price algorithm was changed from NEWNX to Last Paid, meaning that the official closing price becomes the latest price regardless of closing best bid and ask prices.

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

    • statista.com
    Updated Mar 10, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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.

  6. f

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

    • plos.figshare.com
    xls
    Updated Jun 17, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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.

  7. f

    Model performance metrics.

    • plos.figshare.com
    xls
    Updated Mar 13, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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. f

    Hyperparameters used in the baseline models.

    • plos.figshare.com
    xls
    Updated May 9, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Shafiqah Azman; Dharini Pathmanathan; Vimala Balakrishnan (2025). Hyperparameters used in the baseline models. [Dataset]. http://doi.org/10.1371/journal.pone.0323015.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 9, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Shafiqah Azman; Dharini Pathmanathan; Vimala Balakrishnan
    License

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

    Description

    The heteroscedastic and volatile characteristics of stock price data have attracted the interest of researchers from various disciplines, particularly in the realm of price forecasting. The stock market’s non-stationary and volatile nature, driven by complex interrelationships among financial assets, economic developments, and market participants, poses significant challenges for accurate forecasting. This research aims to develop a robust forecasting model to improve the accuracy and reliability of stock price predictions using machine learning. A two-stage forecasting model is introduced. First, a random forest subset-based (RFS) feature selection with repeated -fold cross-validation selects the best subset of features from eight predictors: highest price, lowest price, closing price, volume, change, price change ratio, and amplitude. These features are then used as input in a bidirectional gated recurrent unit with an attention mechanism (BiGRU-AM) model to forecast daily opening prices of ten stock indices. The proposed model exhibits superior forecasting performance across ten stock indices when compared to twelve benchmarks, evaluated using root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination, . The improved prediction accuracy enables financial professionals to make more reliable investment decisions, reducing risks and increasing profits.

  9. United States CSI: Savings: Stock Market Increase Probability: Next Yr: Mean...

    • ceicdata.com
    Updated Mar 15, 2018
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CEICdata.com (2018). United States CSI: Savings: Stock Market Increase Probability: Next Yr: Mean [Dataset]. https://www.ceicdata.com/en/united-states/consumer-sentiment-index-savings--retirement/csi-savings-stock-market-increase-probability-next-yr-mean
    Explore at:
    Dataset updated
    Mar 15, 2018
    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: Savings: Stock Market Increase Probability: Next Yr: Mean data was reported at 59.400 % in May 2018. This records a decrease from the previous number of 60.800 % for Apr 2018. United States CSI: Savings: Stock Market Increase Probability: Next Yr: Mean data is updated monthly, averaging 54.500 % from Jun 2002 (Median) to May 2018, with 191 observations. The data reached an all-time high of 66.700 % in Jan 2018 and a record low of 34.000 % in Mar 2009. United States CSI: Savings: Stock Market 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.H026: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the percent change that this one thousand dollar investment will increase in value in the year ahead, so that it is worth more than one thousand dollars one year from now?

  10. f

    Composition of uninterrupted trends observed in the Nasdaq data sample.

    • plos.figshare.com
    xls
    Updated Jun 14, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Héctor Raúl Olivares-Sánchez; Carlos Manuel Rodríguez-Martínez; Héctor Francisco Coronel-Brizio; Enrico Scalas; Thomas Henry Seligman; Alejandro Raúl Hernández-Montoya (2023). Composition of uninterrupted trends observed in the Nasdaq data sample. [Dataset]. http://doi.org/10.1371/journal.pone.0270492.t003
    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

    Composition of uninterrupted trends observed in the Nasdaq data sample.

  11. f

    The proposed model and its benchmark models.

    • figshare.com
    • plos.figshare.com
    xls
    Updated May 9, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Shafiqah Azman; Dharini Pathmanathan; Vimala Balakrishnan (2025). The proposed model and its benchmark models. [Dataset]. http://doi.org/10.1371/journal.pone.0323015.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 9, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Shafiqah Azman; Dharini Pathmanathan; Vimala Balakrishnan
    License

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

    Description

    The heteroscedastic and volatile characteristics of stock price data have attracted the interest of researchers from various disciplines, particularly in the realm of price forecasting. The stock market’s non-stationary and volatile nature, driven by complex interrelationships among financial assets, economic developments, and market participants, poses significant challenges for accurate forecasting. This research aims to develop a robust forecasting model to improve the accuracy and reliability of stock price predictions using machine learning. A two-stage forecasting model is introduced. First, a random forest subset-based (RFS) feature selection with repeated -fold cross-validation selects the best subset of features from eight predictors: highest price, lowest price, closing price, volume, change, price change ratio, and amplitude. These features are then used as input in a bidirectional gated recurrent unit with an attention mechanism (BiGRU-AM) model to forecast daily opening prices of ten stock indices. The proposed model exhibits superior forecasting performance across ten stock indices when compared to twelve benchmarks, evaluated using root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination, . The improved prediction accuracy enables financial professionals to make more reliable investment decisions, reducing risks and increasing profits.

  12. f

    Composition of uninterrupted trends observed in Nikkei index.

    • plos.figshare.com
    xls
    Updated Jun 17, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Héctor Raúl Olivares-Sánchez; Carlos Manuel Rodríguez-Martínez; Héctor Francisco Coronel-Brizio; Enrico Scalas; Thomas Henry Seligman; Alejandro Raúl Hernández-Montoya (2023). Composition of uninterrupted trends observed in Nikkei index. [Dataset]. http://doi.org/10.1371/journal.pone.0270492.t005
    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

    Composition of uninterrupted trends observed in Nikkei index.

  13. J

    How to Identify and Forecast Bull and Bear Markets? (replication data)

    • journaldata.zbw.eu
    • jda-test.zbw.eu
    pdf, txt
    Updated Dec 7, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Erik Kole; Dick van Dijk; Erik Kole; Dick van Dijk (2022). How to Identify and Forecast Bull and Bear Markets? (replication data) [Dataset]. http://doi.org/10.15456/jae.2022326.0701066081
    Explore at:
    txt(2440), txt(299420), txt(20550), txt(31349), txt(28400), pdf(1883158), txt(124319)Available download formats
    Dataset updated
    Dec 7, 2022
    Dataset provided by
    ZBW - Leibniz Informationszentrum Wirtschaft
    Authors
    Erik Kole; Dick van Dijk; Erik Kole; Dick van Dijk
    License

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

    Description

    Because the state of the equity market is latent, several methods have been proposed to identify past and current states of the market and forecast future ones. These methods encompass semi-parametric rule-based methods and parametric Markov switching models. We compare the mean-variance utilities that result when a risk-averse agent uses the predictions of the different methods in an investment decision. Our application of this framework to the S&P 500 shows that rule-based methods are preferable for (in-sample) identification of the state of the market, but Markov switching models for (out-of-sample) forecasting. In-sample, only the mean return of the market index matters, which rule-based methods exactly capture. Because Markov switching models use both the mean and the variance to infer the state, they produce superior forecasts and lead to significantly better out-of-sample performance than rule-based methods. We conclude that the variance is a crucial ingredient for forecasting the market state.

  14. f

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

    • figshare.com
    xls
    Updated Jun 14, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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%.

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

    • ai-chatbox.pro
    • statista.com
    Updated Jun 3, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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.

  16. Denmark Index: Copenhagen Stock Exchange: Gross: OMX Copenhagen Ex OMXC 20

    • ceicdata.com
    Updated Dec 15, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    CEICdata.com (2024). Denmark Index: Copenhagen Stock Exchange: Gross: OMX Copenhagen Ex OMXC 20 [Dataset]. https://www.ceicdata.com/en/denmark/copenhagen-stock-exchange-index/index-copenhagen-stock-exchange-gross-omx-copenhagen-ex-omxc-20
    Explore at:
    Dataset updated
    Dec 15, 2024
    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
    May 1, 2017 - Apr 1, 2018
    Area covered
    Denmark
    Variables measured
    Securities Exchange Index
    Description

    Denmark Index: Copenhagen Stock Exchange: Gross: OMX Copenhagen Ex OMXC 20 data was reported at 8,112.710 31Dec1995=100 in Oct 2018. This records a decrease from the previous number of 8,752.230 31Dec1995=100 for Sep 2018. Denmark Index: Copenhagen Stock Exchange: Gross: OMX Copenhagen Ex OMXC 20 data is updated monthly, averaging 4,294.127 31Dec1995=100 from Nov 2005 (Median) to Oct 2018, with 156 observations. The data reached an all-time high of 8,991.170 31Dec1995=100 in Aug 2018 and a record low of 1,886.816 31Dec1995=100 in Mar 2009. Denmark Index: Copenhagen Stock Exchange: Gross: OMX Copenhagen Ex OMXC 20 data remains active status in CEIC and is reported by Copenhagen Stock Exchange. The data is categorized under Global Database’s Denmark – Table DK.Z001: Copenhagen Stock Exchange: Index. On May 13, 2013 NASDAQ OMX performed changes to the KFMX indexes. The name was changeed from KFMX to OMX Copenhagen ex OMX Copenhagen 20, and the price algorithm was changed from NEWNX to Last Paid, meaning that the official closing price becomes the latest price regardless of closing best bid and ask prices.

  17. m

    Parameters and statistics of models made for selected companies of the...

    • mostwiedzy.pl
    xlsx
    Updated May 10, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Piotr Kasprzak; Kamil Lewandowski (2021). Parameters and statistics of models made for selected companies of the Warsaw Stock Exchange [Dataset]. http://doi.org/10.34808/jdn6-m930
    Explore at:
    xlsx(92278)Available download formats
    Dataset updated
    May 10, 2021
    Authors
    Piotr Kasprzak; Kamil Lewandowski
    License

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

    Description

    For the WIG, WIG20 and mWIG40 indices, no day, week or month statistically differs from the average level of the index, which indicates no anomalies. The situation is different only for the index of small companies. In the case of sWIG80, the mean values on Friday, week 5 and 6, and during January, February and June were statistically different at the level of 1%. The second week of the month also turns out to be statistically different at the significance level of 5%. The study of the mean in the case of sWIG80 indicates the same periods as previously indicated during the preliminary comparison of the means in the first part of the study.

  18. Financial Performance of Companies from S&P500

    • kaggle.com
    Updated Mar 9, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Right Goose (2023). Financial Performance of Companies from S&P500 [Dataset]. https://www.kaggle.com/datasets/ilyaryabov/financial-performance-of-companies-from-sp500/versions/3
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 9, 2023
    Dataset provided by
    Kaggle
    Authors
    Right Goose
    License

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

    Description

    Content

    Company: Ticker Major index membership: Index Market capitalization: Market Cap Income (ttm): Income Revenue (ttm): Sales Book value per share (mrq): Book/sh Cash per share (mrq): Cash/sh Dividend (annual): Dividend Dividend yield (annual): Dividend % Full time employees: Employees Stock has options trading on a market exchange: Optionable Stock available to sell short: Shortable Analysts' mean recommendation (1=Buy 5=Sell): Recom Price-to-Earnings (ttm): P/E Forward Price-to-Earnings (next fiscal year): Forward P/E Price-to-Earnings-to-Growth: PEG Price-to-Sales (ttm): P/S Price-to-Book (mrq): P/B Price to cash per share (mrq): P/C Price to Free Cash Flow (ttm): P/FCF Quick Ratio (mrq): Quick Ratio Current Ratio (mrq): Current Ratio Total Debt to Equity (mrq): Debt/Eq Long Term Debt to Equity (mrq): LT Debt/Eq Distance from 20-Day Simple Moving Average: SMA20 Diluted EPS (ttm): EPS (ttm) EPS estimate for next year: EPS next Y EPS estimate for next quarter: EPS next Q EPS growth this year: EPS this Y EPS growth next year: EPS next Y Long term annual growth estimate (5 years): EPS next 5Y Annual EPS growth past 5 years: EPS past 5Y Annual sales growth past 5 years: Sales past 5Y Quarterly revenue growth (yoy): Sales Q/Q Quarterly earnings growth (yoy): EPS Q/Q Earnings date

    BMO = Before Market Open
    AMC = After Market Close
    : Earnings Distance from 50-Day Simple Moving Average: SMA50 Insider ownership: Insider Own Insider transactions (6-Month change in Insider Ownership): Insider Trans Institutional ownership: Inst Own Institutional transactions (3-Month change in Institutional Ownership): Inst Trans Return on Assets (ttm): ROA Return on Equity (ttm): ROE Return on Investment (ttm): ROI Gross Margin (ttm): Gross Margin Operating Margin (ttm): Oper. Margin Net Profit Margin (ttm): Profit Margin Dividend Payout Ratio (ttm): Payout Distance from 200-Day Simple Moving Average: SMA200 Shares outstanding: Shs Outstand Shares float: Shs Float Short interest share: Short Float Short interest ratio: Short Ratio Analysts' mean target price: Target Price 52-Week trading range: 52W Range Distance from 52-Week High: 52W High Distance from 52-Week Low: 52W Low Relative Strength Index: RSI (14) Relative volume: Rel Volume Average volume (3 month): Avg Volume Volume: Volume Performance (Week): Perf Week Performance (Month): Perf Month Performance (Quarter): Perf Quarter Performance (Half Year): Perf Half Y Performance (Year): Perf Year Performance (Year To Date): Perf YTD Beta: Beta Average True Range (14): ATR Volatility (Week, Month): Volatility Previous close: Prev Close Current stock price: Price Performance (today): Change

  19. Apartment market tightness index U.S. 2016-2024, per quarter

    • statista.com
    Updated Jul 9, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Apartment market tightness index U.S. 2016-2024, per quarter [Dataset]. https://www.statista.com/statistics/1333703/apartment-market-tightness-index-usa/
    Explore at:
    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2016 - Jul 2024
    Area covered
    United States
    Description

    The liquidity in the apartment market in the United States decreased in October 2023, according to the National Multifamily Housing Council's (NMHC) market tightness index. The index is a standard diffusion index and is based on a quarterly survey among NMHC members. A value over ** indicates a tighter market with low liquidity, while under **, it shows that the market is loosening and liquidity increasing. In July 2021, market tightness reached its peak at ** index points, meaning that this liquidity was at its lowest point according to industry experts. In October 2023, the index stood at ** index points, the highest figure observed in the past two years.

  20. f

    Descriptive statistics of data presented in Tables 2–5.

    • plos.figshare.com
    xls
    Updated Jun 14, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Héctor Raúl Olivares-Sánchez; Carlos Manuel Rodríguez-Martínez; Héctor Francisco Coronel-Brizio; Enrico Scalas; Thomas Henry Seligman; Alejandro Raúl Hernández-Montoya (2023). Descriptive statistics of data presented in Tables 2–5. [Dataset]. http://doi.org/10.1371/journal.pone.0270492.t006
    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

    Descriptive statistics of data presented in Tables 2–5.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Statista (2025). Monthly development S&P 500 Index 2018-2024 [Dataset]. https://www.statista.com/statistics/697624/monthly-sandp-500-index-performance/
Organization logo

Monthly development S&P 500 Index 2018-2024

Explore at:
Dataset updated
Feb 28, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
Jan 2018 - Dec 2024
Area covered
United States
Description

The S&P 500, an index of 500 publicly traded companies in the United States, closed at 5,881.63 points on the last trading day of December 2024. What is the S&P 500? The S&P 500 is a stock market index that tracks the evolution of 500 companies. In contrast to the Dow Jones Industrial Index, which measures the performance of thirty large U.S. companies, the S&P 500 shows the sentiments in the broader market. Publicly traded companies Companies on the S&P 500 are publicly traded, meaning that anyone can invest in them. A large share of adults in the United States invest in the stock market, though many of these are through a retirement account or mutual fund. While most people make a modest return, the most successful investors have made billions of U.S. dollars through investing.

Search
Clear search
Close search
Google apps
Main menu