End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Graph and download economic data for Share of Corporate Equities and Mutual Fund Shares Held by the Top 1% (99th to 100th Wealth Percentiles) (WFRBST01122) from Q3 1989 to Q4 2024 about mutual funds, wealth, equity, percentile, corporate, and USA.

This paper utilizes data on subjective probabilities to study the impact of the stock market crash of 2008 on households' expectations about the returns on the stock market index. We use data from the Health and Retirement Study that was fielded in February 2008 through February 2009. The effect of the crash is identified from the date of the interview, which is shown to be exogenous to previous stock market expectations. We estimate the effect of the crash on the population average of expected returns, the population average of the uncertainty about returns (subjective standard deviation), and the cross-sectional heterogeneity in expected returns (disagreement). We show estimates from simple reduced-form regressions on probability answers as well as from a more structural model that focuses on the parameters of interest and separates survey noise from relevant heterogeneity. We find a temporary increase in the population average of expectations and uncertainty right after the crash. The effect on cross-sectional heterogeneity is more significant and longer lasting, which implies substantial long-term increase in disagreement. The increase in disagreement is larger among the stockholders, the more informed, and those with higher cognitive capacity, and disagreement co-moves with trading volume and volatility in the market.

The main stock market index in the United States (US500) decreased 173 points or 2.94% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on March of 2025.

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

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

JT Jianpu Technology Inc. American depositary shares

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

TX Ternium S.A. Ternium S.A. American Depositary Shares (each representing ten shares USD1.00 par value)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Corporate Profits in the United States decreased to 3128.50 USD Billion in the third quarter of 2024 from 3141.56 USD Billion in the second quarter of 2024. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Nexus of Dada's American Dream (DADA)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

API Crude Oil Stock Change in the United States decreased to -4.60 BBL/1Million in March 21 from 4.59 BBL/1Million in the previous week. This dataset provides - United States API Crude Oil Stock Change- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Nexxen (NEXN): Navigating the American Market?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-03-27 to 2025-03-26 about stock market, average, industry, and USA.

Graph and download economic data for Capital Stock at Constant National Prices for United States (RKNANPUSA666NRUG) from 1950 to 2019 about stocks, capital, price, and USA.

Access real-time and historical US equity options data included as part of Databento's OPRA data feed. NYSE American Options is an electronic trading platform that's part of a dual market structure providing access to both NYSE American and ARCA. NYSE American offers a blend of customer priority and size pro-rata allocation.

Comprehensive global estimates based on projections, models, analysis and research. Available as global, international or North American company packages.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

BPTS Biophytis SA American Depositary Share

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

PURPOSE: To provide a permanent repository of key data series necessary to build a range-wide American eel stock assessment. DESCRIPTION: This collection presents data associated with the following report: Cairns, D.K. 2020. Landings, abundance indicators, and biological data for a potential range-wide American eel stock assessment. Canadian Data Report of Fisheries and Aquatic Science. No. 1311: v + 180 pp. Much of the data collection is from the Atlantic Provinces of Canada, particularly the Southern Gulf of St. Lawrence. The collection also includes data from elsewhere in the American eel's range in Canada, and also the United States and the Caribbean Basin. Files in the collection are as follows. Cairns2020_AnnexA_ReportTables.xlsx: This Excel file (file size 756 kb) contains all 37 tables in Cairns (2020) exactly as they appear in the report. Cairns2020_AnnexB_EelLengthsAgesEfishingRecords.xlsx: This Excel file (file size 3.1 mb) contains 20,047 records of American eel lengths and other biological data from the Canadian Atlantic Provinces, 1983-2017. Records include weights of 8,915 eels and ages of 2,212 eels. Records of 3,224 electrofishing sessions in the Miramichi River, New Brunswick, 1952-2019, and records of 2,590 electrofishing sessions in the Restigouche River, New Brunswick, 1972-2019 are included. Cairns2020_AnnexC_EelLengthsAgesDataDefinitions.csv: This .csv file (file size 4 kb) gives data definitions in English and French for the table of eel lengths and other biological data that is contained in Cairns2020_AnnexB_EelLengthsAgesEfishingRecords.xlsx and in Cairns2020_AnnexD_EelLengthsAges.csv. Cairns2020_AnnexD_EelLengthsAges.csv: This file (file size 2.0 mb) presents in .csv format the table of eel lengths and other biological data that is also presented in Cairns2020_AnnexB_EelLengthsAgesEfishingRecords.xlsx. Cairns2020_AnnexE_EelEFishingDataDefinitions.csv: This .csv file (file size 2 kb) gives data definitions in English and French for the table of eel electrofishing data that is contained in Cairns2020_AnnexB_EelLengthsAgesEfishingRecords.xlsx and in Cairns2020_AnnexD_EelLengthsAges.csv. Cairns2020_AnnexF_EelEFishing.csv: This file (file size 314 kb) presents in .csv format the table of eel electrofishing data that is also presented in Cairns2020_AnnexB_EelLengthsAgesEfishingRecords.xlsx. Cairns2020_AnnexG_OtolithImageMetadata.csv: This .csv file (file size 2 kb) provides metadata for the collection of eel otolith images. Files with names starting with EelOtos . . . . : These .tif, .jpg, and .bmp image files are in zipped format with a summed size of 5.3 gb. The files give magnified photos of 1,838 eel otoliths that have been prepared for age reading. Samples are from the Atlantic Provinces of Canada. Individual otolith codes in Cairns2020_AnnexB_EelLengthsAgesEfishingRecords.xlsx and in Cairns2020_AnnexC_EelLengthsAgesDataDefinitions.csv match the codes embedded in otolith image filenames. PARAMETERS COLLECTED: American eel landings, number caught, and effort of commercial and research fishing gear. American eel lengths, ages, sex and other biological data and sampling locations. NOTES ON QUALITY CONTROL: All keypunched records of landings, densities, and other data were verified against original sources. Landings and abundance indices were reviewed in a Department of Fisheries and Oceans scientific workshop and corrected as necessary. Length and age data were examined by length-weight and length age plots and implausible records were discarded. PHYSICAL SAMPLE DETAILS: No physical samples SAMPLING METHODS: Landings are from government fisheries agencies. Abundance indices are from commercial fyke, spear, and trap catch per unit effort, and from research ladder counts and electrofishing records. Mean elver lengths are compiled from published literature Sex ratios are compiled from published literature Locations of biological and genetic sampling are compiled from published literature American eel lengths are total length of live specimens. Ages are from otolith annulus readings Electrofishing records are from backpack electrofishing surveys in wadeable waters USE LIMITATION: To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

Graph and download economic data for NASDAQ Composite Index (NASDAQCOM) from 1971-02-05 to 2025-03-24 about NASDAQ, composite, stock market, indexes, and USA.

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME
Population estimates

LAST UPDATED
February 2023

DESCRIPTION
Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCE
California Department of Finance: Population and Housing Estimates - http://www.dof.ca.gov/Forecasting/Demographics/Estimates/
Table E-6: County Population Estimates (1960-1970)
Table E-4: Population Estimates for Counties and State (1970-2021)
Table E-8: Historical Population and Housing Estimates (1990-2010)
Table E-5: Population and Housing Estimates (2010-2021)

Bay Area Jurisdiction Centroids (2020) - https://data.bayareametro.gov/Boundaries/Bay-Area-Jurisdiction-Centroids-2020-/56ar-t6bs
Computed using 2020 US Census TIGER boundaries

U.S. Census Bureau: Decennial Census Population Estimates - http://www.s4.brown.edu/us2010/index.htm- via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University
1970-2020

U.S. Census Bureau: American Community Survey (5-year rolling average; tract) - https://data.census.gov/
2011-2021
Form B01003

Priority Development Areas (Plan Bay Area 2050) - https://opendata.mtc.ca.gov/datasets/MTC::priority-development-areas-plan-bay-area-2050/about

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
All historical data reported for Census geographies (metropolitan areas, county, city and tract) use current legal boundaries and names. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of December 2022.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area tracts and PDAs are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Population estimates for PDAs are allocated from tract-level Census population counts using an area ratio. For example, if a quarter of a Census tract lies with in a PDA, a quarter of its population will be allocated to that PDA. Estimates of population density for PDAs use gross acres as the denominator. Note that the population densities between PDAs reported in previous iterations of Vital Signs are mostly not comparable due to minor differences and an updated set of PDAs (previous iterations reported Plan Bay Area 2040 PDAs, whereas current iterations report Plan Bay Area 2050 PDAs).

The following is a list of cities and towns by geographical area:

Big Three: San Jose, San Francisco, Oakland

Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside

Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville

Unincorporated: all unincorporated towns

Snapshot img

Mutual Funds Market Size 2025-2029

The mutual funds market size is forecast to increase by USD 85.5 trillion at a CAGR of 9.9% between 2024 and 2029.

The market, particularly in developing nations, is experiencing significant growth driven by increasing financial literacy, expanding middle class populations, and favorable regulatory environments. This trend is expected to continue as more individuals seek diversified investment opportunities to secure their financial future. However, this market growth comes with its challenges, primarily transaction risks. These risks, including market volatility, liquidity issues, and fraud, can significantly impact investors' confidence and asset values. To capitalize on this market opportunity, companies must prioritize risk management strategies, such as diversification, transparency, and regulatory compliance. Additionally, leveraging technology to streamline transactions, enhance security, and provide real-time information can help build trust and attract investors. Companies that effectively navigate these challenges and provide value-added services will be well-positioned to succeed in the evolving the market landscape.

What will be the Size of the Mutual Funds Market during the forecast period?

Request Free SampleThe mutual fund industry continues to be a significant player in the global investment landscape, with digital penetration driving growth and accessibility. Systematic investment plans, including mutual funds, have gained popularity among small investors seeking diversified investment opportunities. The mutual fund market encompasses various categories, such as equity funds, money market funds, bond funds, index funds, and hedge funds. Equity strategies dominate the fund portfolio of many investors, reflecting the appeal of stocks for potential capital appreciation. Insurance companies also play a crucial role in the industry, offering investment products to both retail and institutional clients. The investment fund industry has witnessed a in investment, particularly among small fund savers, drawn to the convenience of portfolio management services. Short-term debt funds cater to those seeking lower risk and liquidity. Overall, the mutual fund market is poised for continued expansion, driven by the increasing demand for efficient investment solutions.

How is this Mutual Funds Industry segmented?

The mutual funds industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD trillion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeStock fundsBond fundsMoney market fundsHybrid fundsDistribution ChannelAdvice channelRetirement plan channelInstitutional channelDirect channelSupermarket channelGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalySpainUKAPACAustraliaChinaIndiaSouth AmericaMiddle East and Africa

By Type Insights

The stock funds segment is estimated to witness significant growth during the forecast period.Mutual funds are investment vehicles that pool together funds from various investors to purchase a diversified portfolio of securities, primarily stocks. These funds come in various categories, including equity, income, index, sector, bond, money market, commodity, and fund of funds. Equity funds invest in corporate stocks, with growth funds focusing on high-growth stocks and income funds prioritizing dividend-paying stocks. Index funds mirror a specific market index, while sector funds invest in a particular industry sector. Stock mutual funds can also be categorized based on the size of the companies in which they invest, such as large-cap, mid-cap, and small-cap funds. Institutional and retail investors, including individual investors, financial advisors, and robo-advisors, utilize mutual funds for retirement planning, risk management, and diversification strategies. The mutual fund industry has seen significant growth, driven by digital penetration, systematic investment plans, and the increasing popularity of exchange-traded funds (ETFs) and index funds. The asset base under management (AUM) of the investment fund industry is expected to expand due to the increasing number of demat CDSL and NSDL accounts, SIP accounts, and small town investors. Debt-oriented schemes and sustainable strategy segments, such as ESG Integration Funds, Negative Screening Funds, and Impact Funds, are also gaining popularity. The mutual fund industry is subject to regulatory compliance and tax efficiency, offering investors capital appreciation, liquidity benefits, and professional management. The capital market environment is influenced by factors such as market volatility, equity exposure, fixed income, and long-term returns. Mutual fund providers offer portfolio management services, fair pricing, and various investment plans to cater to different risk tolerances and inve