Each of the five New York City Retirement Systems has its own Board of Trustees which, working with the Bureau of Asset Management and the Board’s consultants, makes decisions on the funds’ asset allocations based on factors including economic risk, return, performance, and beneficiary distributions. Data and further information is also available here: Asset Allocation : Office of the New York City Comptroller (nyc.gov). "1U.S. Fixed Income assets do not include cash. 2Market Value of private market investments are reported on a lagged basis. 3Cash includes Securities Lending, State Street Short Term and BNY‐Mellon CD accounts. 4Totals may not add due to rounding. 5Fiscal Year to Date begins July 1st. Information presented is current as of the date of this posting only. Past performance does not guarantee the future performance of any manager or strategy. The performance results and historical information provided herein may have been adversely or favorably impacted by events and economic conditions that will not prevail in the future. Therefore, these results are not indicative of the future performance of any strategy, index, fund, manager or group of managers."

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

This Stata dofile and the accompanying data files reproduce all figures and tables in "Do Index Funds Monitor?" by Heath, Macciocchi, Michaely, Ringgenberg (RFS 2021). The data used in the paper may not be shared publicly. To protect this private data, we have replaced firm identifiers from all data providers with anonymized firm ids, which are consistent throughout the data.

Historical dataset for the S&P 500 stock market index since 1927. Historical data is inflation-adjusted using the headline CPI and each data point represents the month-end closing value. The current month is updated on an hourly basis with today's latest value.

Context

NIFTY 500 is India’s first broad-based stock market index of the Indian stock market. It contains the top 500 listed companies on the NSE. The NIFTY 500 index represents about 96.1% of free-float market capitalization and 96.5% of the total turnover on the National Stock Exchange (NSE).

NIFTY 500 companies are disaggregated into 72 industry indices. Industry weights in the index reflect industry weights in the market. For example, if the banking sector has a 5% weight in the universe of stocks traded on the NSE, banking stocks in the index would also have an approximate representation of 5% in the index. NIFTY 500 can be used for a variety of purposes such as benchmarking fund portfolios, launching index funds, ETFs, and other structured products.

• Other Notable Indices -
  • NIFTY 50: Top 50 listed companies on the NSE. A diversified 50-stock index accounting for 13 sectors of the Indian economy.
  • NIFTY Next 50: Also called NIFTY Juniors. Represents 50 companies from NIFTY 100 after excluding the NIFTY 50 companies.
  • NIFTY 100: Diversified 100 stock index representing major sectors of the economy. NIFTY 100 represents the top 100 companies based on full market capitalization from NIFTY 500.
  • NIFTY 200: Designed to reflect the behavior and performance of large and mid-market capitalization companies.

Content

The dataset comprises various parameters and features for each of the NIFTY 500 Stocks, including Company Name, Symbol, Industry, Series, Open, High, Low, Previous Close, Last Traded Price, Change, Percentage Change, Share Volume, Value in Indian Rupee, 52 Week High, 52 Week Low, 365 Day Percentage Change, and 30 Day Percentage Change.

Dataset Glossary (Column-Wise)

Company Name: Name of the Company.

Symbol: A stock symbol is a unique series of letters assigned to a security for trading purposes.

Industry: Name of the industry to which the stock belongs.

Series: EQ stands for Equity. In this series intraday trading is possible in addition to delivery and BE stands for Book Entry. Shares falling in the Trade-to-Trade or T-segment are traded in this series and no intraday is allowed. This means trades can only be settled by accepting or giving the delivery of shares.

Open: It is the price at which the financial security opens in the market when trading begins. It may or may not be different from the previous day's closing price. The security may open at a higher price than the closing price due to excess demand for the security.

High: It is the highest price at which a stock is traded during the course of the trading day and is typically higher than the closing or equal to the opening price.

Low: Today's low is a security's intraday low trading price. Today's low is the lowest price at which a stock trades over the course of a trading day.

Previous Close: The previous close almost always refers to the prior day's final price of a security when the market officially closes for the day. It can apply to a stock, bond, commodity, futures or option co-contract, market index, or any other security.

Last Traded Price: The last traded price (LTP) usually differs from the closing price of the day. This is because the closing price of the day on NSE is the weighted average price of the last 30 mins of trading. The last traded price of the day is the actual last traded price.

Change: For a stock or bond quote, change is the difference between the current price and the last trade of the previous day. For interest rates, change is benchmarked against a major market rate (e.g., LIBOR) and may only be updated as infrequently as once a quarter.

Percentage Change: Take the selling price and subtract the initial purchase price. The result is the gain or loss. Take the gain or loss from the investment and divide it by the original amount or purchase price of the investment. Finally, multiply the result by 100 to arrive at the percentage change in the investment.

Share Volume: Volume is an indicator that means the total number of shares that have been bought or sold in a specific period of time or during the trading day. It will also involve the buying and selling of every share during a specific time period.

Value (Indian Rupee): Market value—also known as market cap—is calculated by multiplying a company's outstanding shares by its current market price.

52-Week High: A 52-week high is the highest share price that a stock has traded at during a passing year. Many market aficionados view the 52-week high as an important factor in determining a stock's current value and predicting future price movement. 52-week High prices are adjusted for Bonus, Split & Rights Corporate actions.

52-Week Low: A 52-week low is the lowest ...

Share index and total return index Investment — and property investment funds (average of the month) December 1993 — November 2003 Changed on December 19 2003. Frequency: Discontinued.

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.

Are Index Funds Good Investments?

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

China's main stock market index, the SHANGHAI, rose to 3385 points on June 6, 2025, gaining 0.04% from the previous session. Over the past month, the index has climbed 1.28% and is up 10.95% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on June of 2025.

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.

What is a index fund and how does it work?

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

Context

nifty50.csv The NIFTY 50 index is National Stock Exchange of India's benchmark stock market index for Indian equity market. It is a well diversified 50 stock index accounting for 22 sectors of the economy. It is used for a variety of purposes such as bench-marking fund portfolios, index based derivatives and index funds.

banknifty.csv Bank Nifty represents the 12 most liquid and large capitalized stocks from the banking sector which trade on the National Stock Exchange (NSE). It provides investors and market intermediaries a benchmark that captures the capital market performance of Indian banking sector.

Content

A data frame with 8 variables: index, date, time, open, high, low, close and id. For each year from 2013 to 2016, the number of trading data of each minute of given each date. The currency of the price is Indian Rupee (INR).

• index : market id
• date: numerical value (Ex. 20121203- to be converted to 2012/12/03)
• time: factor (Ex. 09:16)
• open: numeric (opening price)
• high: numeric (high price)
• low: numeric (low price)
• close: numeric (closing price)

Inspiration

Initial raw data sets are very complex and mixed datatypes. These are processed properly using R libraries like dplyr, stringr and other data munging packages. The desired outputs are then converted into a CSV format to use for further analysis.

Context

This study is based on the historical data for some of the indicators on the Egyptian Stock Exchange (EGX), in order to build a prediction model with high accuracy. Data used in this study are purchased from Egypt for Information Dissemination (EGID) which is a Governmental organization that provides data for EGX. The data contain six stock market indices; for example, EGX-30 index local currency is used for interest estimates and denominated in US dollars. It measures top 30 firms in liquidity and activity. The second index used in this study is EGX-30- Capped which is designed to track performance of the most traded companies in accordance with the rules set for mutual funds. The third index is EGX-70 which aims at providing wider tools for investors to monitor market performance. EGX-100 index as a forth dataset evaluates performance of the 100 active firms, including 30 of EGX-30 index as well as 70 of EGX-70 index. NIlE index avoids concentration on one industry and therefore has a good representation of various industries/sectors in the economy, and the index is weighted by market capitalization and adjusted by free float. The last index is EGX-50-EWI which tracks top 50 companies in terms of liquidity and activity. The index is designed to balance the impact of price changes among the constituents of the index as they will have a fixed weight of 2% at each quarterly review.

Acknowledgements

Houssein, Essam; Dirar, Mahmoud ; Hussain , Kashif ; M. Mohamed, Waleed (2020), “Egyptian Stock Exchange (EGX)”, Mendeley Data, V2, doi: 10.17632/7chdr568x7.2

The India Mutual Fund Market report segments the industry into By Asset Class/Scheme Type (Debt-Oriented Schemes, Equity-Oriented Schemes, Money Market, ETFs and FoFs), By Source of Funds (Banks, Insurance Companies, Retail Investors, Indian Institutional Investors, FIIs and FPIs, Other Sources). Get five years of historical data and future forecasts.

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.

Can Strategic Education (STRA) Stock Help Fund Retirement?

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

Historical dataset of the S&P 500 stock market index over the last 10 years. Values shown are daily closing prices. The most recent value is updated on an hourly basis during regular trading hours.

****Dataset Overview**** This dataset contains historical macroeconomic data, featuring key economic indicators in the United States. It includes important metrics such as the Consumer Price Index (CPI), Retail Sales, Unemployment Rate, Industrial Production, Money Supply (M2), and more. The dataset spans from 1993 to the present and includes monthly data on various economic indicators, processed to show their rate of change (either percentage or absolute difference, depending on the indicator).

provenance

The data in this dataset is sourced from the Federal Reserve Economic Data (FRED) database, hosted by the Federal Reserve Bank of St. Louis. FRED provides access to a wide range of economic data, including key macroeconomic indicators for the United States. My work involved calculating the rate of change (ROC) for each indicator and reorganizing the data into a more usable format for analysis. For more information and access to the full database, visit FRED's website.

Purpose and Use for the Kaggle Community:

This dataset is a valuable resource for data scientists, economists, and analysts interested in understanding macroeconomic trends, performing time series analysis, or building predictive models. With the rate of change included, users can quickly assess the growth or contraction in these indicators month-over-month. This dataset can be used for:

• Exploratory Data Analysis (EDA): Understanding historical economic trends. -Time Series Forecasting: Building models to predict future economic conditions. -Macroeconomic Analysis: Analyzing the relationship between various economic indicators. -Machine Learning Projects: Using the data as features to predict financial or economic outcomes. -By utilizing this dataset, users can perform in-depth analysis on the impact of macroeconomic changes, compare the historical performance of various indicators, and experiment with different time series forecasting techniques.

****Column Descriptions****

Year: The year of the observation.

Month: The month of the observation (1-12).

Industrial Production: Monthly data on the total output of US factories, mines, and utilities.

Manufacturers' New Orders: Durable Goods: Measures the value of new orders placed with manufacturers for durable goods, indicating future production activity.

Consumer Price Index (CPIAUCSL): A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.

Unemployment Rate: The percentage of the total labor force that is unemployed but actively seeking employment.

Retail Sales: The total receipts of retail stores, indicating consumer spending and economic activity.

Producer Price Index: Measures the average change over time in the selling prices received by domestic producers for their output.

Personal Consumption Expenditures (PCE): A measure of the prices paid by consumers for goods and services, used in calculating inflation.

National Home Price Index: A measure of changes in residential real estate prices across the country.

All Employees, Total Nonfarm: The number of nonfarm payroll employees, an important indicator of the labor market.

Labor Force Participation Rate: The percentage of the working-age population that is either employed or actively looking for work.

Federal Funds Effective Rate: The interest rate at which depository institutions lend reserve balances to other depository institutions overnight.

Building Permits: The number of building permits issued for residential and non-residential buildings, a leading indicator of construction activity.

Money Supply (M2): The total money supply, including cash, checking deposits, and easily convertible near money.

Personal Income: The total income received by individuals from all sources, including wages, investments, and government transfers.

Trade Balance: The difference between a country's imports and exports, indicating the net trade flow.

Consumer Sentiment: The index reflecting consumer sentiment and expectations for the future economic outlook.

Consumer Confidence: A measure of how optimistic or pessimistic consumers are regarding their expected financial situation and the economy.

Notes on Interest Rates Please note that for the Federal Funds Effective Rate (FEDFUNDS), the dataset includes the absolute change in basis points (bps), not the rate of change. This means that the dataset reflects the direct change in the interest rate rather than the percentage change month-over-month. The change is represented in basis points, where 1 basis point equals 0.01%.

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.

Schwab Total Stock Market Index Fund

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

The house price data are collected from the official website of China's National Bureau of Statistics . We acquired the month-on-month growth data of the house price for 70 large and medium-sized representative cities in China since January 2006, then compiled the composite house price index (Houidx) based on January 2006 as 100. We use the Shanghai stock exchange composite index (SSEI) to measure the stock market price level, and the seasonal adjusted broad money M2 (M2) to proxy for the money supplying, both indexes are collected from the Wind database. The monthly house price shock (hous), stock price change (ssei) or the money supply growth (m2) are calculated as (ln(Idxt) - ln(Idxt-1))×100, where Index are the Houidx, SSEI or M2, correspondingly. 158 observations from February 2006 to March 2019 are obtained.

Context

The S&P 500,[2] or simply the S&P,[4] 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.[5] The average annual total return and compound annual growth rate of the index, including dividends, since inception in 1926 has been approximately 9.8%, or 6% after inflation; however, there were several years where the index declined over 30%.[6][7] The index has posted annual increases 70% of the time.[5] However, the index has only made new highs on 5% of trading days, meaning that on 95% of trading days, the index has closed below its all-time high.[8]

For a list of the components of the index, see List of S&P 500 companies. The components that have increased their dividends in 25 consecutive years are known as the S&P 500 Dividend Aristocrats.[9]:25

The S&P 500 index is a capitalization-weighted index and the 10 largest companies in the index account for 26% of the market capitalization of the index. The 10 largest companies in the index, in order of weighting, are Apple Inc., Microsoft, Amazon.com, Alphabet Inc., Facebook, Johnson & Johnson, Berkshire Hathaway, Visa Inc., Procter & Gamble and JPMorgan Chase, respectively.[2]

Funds that track the index have been recommended as investments by Warren Buffett, Burton Malkiel, and John C. Bogle for investors with long time horizons.[10]

Although the index includes only companies listed in the United States, companies in the index derive on average only 71% of their revenue in the United States.[11]

The index is one of the factors in computation of the Conference Board Leading Economic Index, used to forecast the direction of the economy.[12]

The index is associated with many ticker symbols, including: ^GSPC,[13] INX,[14] and $SPX, depending on market or website.[15] The index value is updated every 15 seconds, or 1,559 times per trading day, with price updates disseminated by Reuters.[16]

The S&P 500 is maintained by S&P Dow Jones Indices, a joint venture majority-owned by S&P Global and its components are selected by a committee.[17][18]

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This dataset has information about the cost of providing General Fund City services per capita of the Full Purpose City population (SD23 measure GTW.A.4). It provides expense information from the annual approved budget document (General Fund Summary and Budget Stabilization Reserve Fund Summary) and population information from the City Demographer's Full Purpose Population numbers. The Consumer Price Index information for Texas is available through the following Key Economic Indicators dataset: https://data.texas.gov/dataset/Key-Economic-Indicators/karz-jr5v. This dataset can be used to help understand the cost of city services over time. View more details and insights related to this dataset on the story page: https://data.austintexas.gov/stories/s/ixex-hibp