The Standard & Poor’s (S&P) 500 Index is an index of 500 leading publicly traded companies in the United States. In 2021, the index value closed at ******** points, which was the second highest value on record despite the economic effects of the global coronavirus (COVID-19) pandemic. In 2023, the index values closed at ********, the highest value ever recorded. What is the S&P 500? The S&P 500 was established in 1860 and expanded to its present form of 500 stocks in 1957. It tracks the price of stocks on the major stock exchanges in the United States, distilling their performance down to a single number that investors can use as a snapshot of the economy’s performance at a given moment. This snapshot can be explored further. For example, the index can be examined by industry sector, which gives a more detailed illustration of the economy. Other measures Being a stock market index, the S&P 500 only measures equities performance. In addition to other stock market indices, analysts will look to other indicators such as GDP growth, unemployment rates, and projected inflation. Similarly, since these indicators say something about the economic future, stock market investors will use these indicators to speculate on the stocks in the S&P 500.

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.

Over the last 2 decades, financial systems have been studied and analyzed from the perspective of complex networks, where the nodes and edges in the network represent the various financial components and the strengths of correlations between them. Here, we adopt a similar network-based approach to analyze the daily closing prices of 69 global financial market indices across 65 countries over a period of 2000–2014. We study the correlations among the indices by constructing threshold networks superimposed over minimum spanning trees at different time frames. We investigate the effect of critical events in financial markets (crashes and bubbles) on the interactions among the indices by performing both static and dynamic analyses of the correlations. We compare and contrast the structures of these networks during periods of crashes and bubbles, with respect to the normal periods in the market. In addition, we study the temporal evolution of traditional market indicators, various global network measures, and the recently developed edge-based curvature measures. We show that network-centric measures can be extremely useful in monitoring the fragility in the global financial market indices.

This Stock Market Dataset is designed for predictive analysis and machine learning applications in financial markets. It includes 13647 records of simulated stock trading data with features commonly used in stock price forecasting.

🔹 Key Features Date – Trading day timestamps (business days only) Open, High, Low, Close – Simulated stock prices Volume – Trading volume per day RSI (Relative Strength Index) – Measures market momentum MACD (Moving Average Convergence Divergence) – Trend-following momentum indicator Sentiment Score – Simulated market sentiment from financial news & social media Target – Binary label (1: Price goes up, 0: Price goes down) for next-day prediction This dataset is useful for training hybrid deep learning models such as LSTM, CNN, and Attention-based networks for stock market forecasting. It enables financial analysts, traders, and AI researchers to experiment with market trends, technical analysis, and sentiment-based predictions.

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.

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

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 2024.

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.

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.

Content

It's a time series dataset of SP500.
The S&P 500, or simply the S&P, is a stock market index that measures the stock performance of 500 large companies listed on stock exchanges in the United States. It is one of the most commonly followed equity indices, and many consider it to be one of the best representations of the U.S. stock market.

Date: Date of the Stock Data Close/Last: Last close price of the company's shares on the relevant stock exchange Volume: Number of shares sold, traded over a certain period of time (Usually Daily) Open: Opening price of company's shares High: Highest price at which a stock traded during the trading day Low: Lowest price at which a stock traded during the trading day

Inspiration

Perform time series analysis concept on real world scenario and forecast future stock price on real world data and gain some knowledge.
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Interactive historical chart showing the daily level of the CBOE VIX Volatility Index back to 1990. The VIX index measures the expectation of stock market volatility over the next 30 days implied by S&P 500 index options.

The dataset consists of companies listed in the S&P500, stock market index that measures the stock performance of 500 large companies listed on stock exchanges in the United State.

The S&P 500 stock market index, maintained by S&P Dow Jones Indices, comprises 505 common stocks issued by 500 large-cap companies and traded on American stock exchanges (including the 30 companies that compose the Dow Jones Industrial Average)

The S&P500 or SPX is the most commonly followed equity index, it covers about 80 percent of the American equity market by capitalization.

The index constituents and the constituent weights are updated regularly using rules published by S&P Dow Jones Indices. Although called the S&P 500, the index contains 505 stocks

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.

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.

The industrial domestic output price index measures the average price development of all goods and related services resulting from the activity of the industry sector and sold on the domestic market. The domestic output price index shows the monthly development of transaction prices of economic activities. The domestic market is defined as customers resident in the same national territory as the observation unit. Data are compiled according to the Statistical classification of economic activities in the European Community, (NACE Rev. 2, Eurostat). Industrial producer prices are compiled as a "fixed base year Laspeyres type price-index". The current base year is 2021 (Index 2021 =100). Indexes, as well as both growth rates with respect to the previous month (M/M-1) and with respect to the corresponding month of the previous year (M/M-12) are presented in raw form.

The Consumer Price Index (CPI) measures over time the prices of goods and services in major expenditure categories typically purchased by urban consumers. The expenditure categories include food, housing, apparel, transportation, and medical care. Essentially, the Index measures consumer purchasing power by comparing the cost of a fixed set of goods and services (called a market basket) in a specific month relative to the cost of the same market basket in an earlier reference period, designated as the base period. The CPI is calculated for two population groups: urban wage earners and clerical workers (CPI-W) and all urban consumers (CPI-U). The CPI-W population includes those urban families with clerical workers, sales workers, craft workers, operatives, service workers, or laborers in the family unit and is representative of the prices paid by about 40 percent of the United States population. The CPI-U population consists of all urban households (including professional and salaried workers, part-time workers, the self-employed, the unemployed, and retired persons) and is representative of the prices paid by about 80 percent of the United States population. Both populations specifically exclude persons in the military, in institutions, and all persons living outside of urban areas (such as farm families). National indexes for both populations are available for about 350 consumer items and groups of items. In addition, over 100 of the indexes have been adjusted for seasonality. The indexes are monthly with some beginning in 1913. Area indexes are available for 27 urban places. For each area, indexes are presented for about 65 items and groups. The area indexes are produced monthly for 5 areas, bimonthly for 10 areas, and semiannually for 12 urban areas. Regional indexes are available for four regions with about 95 items and groups per region. Beginning with January 1987, regional indexes are monthly, with some beginning as early as 1966. City-size indexes are available for four size classes with about 95 items and groups per class. Beginning with January 1987, these indexes are monthly and most begin in 1977. Regional and city-size indexes are available cross-classified by region and city-size class. For each of the 13 cross-classifications, about 60 items and groups are available. Beginning with January 1987, these indexes are monthly and most begin in 1977. Each index record includes a series identification code that specifies the sample (either all urban consumers or urban wage earners and clerical workers), seasonality (either seasonally adjusted or unadjusted), periodicity (either semiannual or regular), geographic area, index base period, and item number of the index.

Technical information The house price index measures the price evolution of real estate prices on the market of private property. The index follows price changes of new or existing residential real estate purchased by households, irrespective of their purpose (letting or owner-occupying). Only market prices are taken into account. Houses built by their owners are therefore not included. The price of the building plot is included in the house price. The house price index is based on real estate transaction data from the General Administration of the Patrimonial Documentation of the FPS Finances. The prices used are those included in the deeds of sale. Given the time between the date on which the preliminary sales agreement is signed and the date on which the deed is executed (between three and four months), this index measures the price evolution with a delay compared to the actual date on which the sales price is set. This delay is inherent to the data source. The house price index is calculated by the European Union Member States, Norway and Iceland. Eurostat calculates the index for the Euro area (as well as for the European Union as a whole) using the harmonised indices of the Member States. Given the role of the housing market in the economic and financial crisis of 2008, the house price index was included in the indicators used in the procedure for macroeconomic imbalances procedure of the European Union. The house price index is calculated under the European Regulation 2016/792 on harmonised indices of consumer prices and the house price index and 2023/1470 laying down the methodological and technical specifications as regards the house price index and the owner-occupied housing price index. Data are available from 2005 onward for Belgium as well as for the European Union and the majority of European countries. The house price index can be broken down by new houses and existing houses. The weights of these two items in the overall index are determined by the gross fixed capital formation in houses (for the new houses) and the total value of transactions of the previous year (for the existing houses). Until 2013, the house price index of new houses was roughly estimated based on the output price index in the construction sector. Since 2014, it is also based on real estate transaction data. House price index for existing houses is available per region since 2010. Therefore, data were completely reviewed for the publication of results in the 4th quarter of 2023 in 2024. Since the houses that are put up for sale differ from one quarter to another, the changes in characteristics are processed with hedonic regression models to eliminate price fluctuations due to changes in characteristics of the properties sold. These models aim to estimate the theoretical price based on the characteristics and location of the houses sold. This theoretical price is then compared to the actual price. Two indices are calculated, one with the actually observed transaction prices and the other with the prices estimated by the regression models. The final index is obtained by calculating the ratio of the index obtained with the actual transaction prices compared to the index obtained with the estimated prices. Therefore, the house price index may be evolving differently from the observed average prices.

This report analyses the NZX 50 index. The NZX 50 index measures the performance of the 50 largest companies by market capitalisation listed on the New Zealand Exchange. The NZX 50 index is a float adjusted, market capitalisation weighted, price index. The data for this report is sourced from investing.com and presented as an average of the daily index points at close over each financial year.

Graph and download economic data for Nominal Broad U.S. Dollar Index (DTWEXBGS) from 2006-01-02 to 2025-07-11 about trade-weighted, broad, exchange rate, currency, goods, services, rate, indexes, and USA.

Baltic Dry rose to 1,783 Index Points on July 14, 2025, up 7.22% from the previous day. Over the past month, Baltic Dry's price has fallen 9.72%, and is down 10.54% 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.