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.

The H.6 release, published weekly, provides measures of the monetary aggregates (M1 and M2) and their components.M1 and M2 are progressively more inclusive measures of money: M1 is included in M2.M1, the more narrowly defined measure, consists of the most liquid forms of money, namely currency and checkable deposits.The non-M1 components of M2 are primarily household holdings of savings deposits, small time deposits, and retail money market mutual funds.Monthly data are available back to January 1959; for most series, weekly data are available back to January 1975.

Dataset Description: 34-year Daily Stock Data (1990-2024)

Context and Inspiration

This dataset captures historical financial market data and macroeconomic indicators spanning over three decades, from 1990 onwards. It is designed for financial analysis, time series forecasting, and exploring relationships between market volatility, stock indices, and macroeconomic factors. This dataset is particularly relevant for researchers, data scientists, and enthusiasts interested in studying: - Volatility forecasting (VIX) - Stock market trends (S&P 500, DJIA, HSI) - Macroeconomic influences on markets (joblessness, interest rates, etc.) - The effect of geopolitical and economic uncertainty (EPU, GPRD)

Sources

The data has been aggregated from a mix of historical financial records and publicly available macroeconomic datasets: - VIX (Volatility Index): Chicago Board Options Exchange (CBOE). - Stock Indices (S&P 500, DJIA, HSI): Yahoo Finance and historical financial databases. - Volume Data: Extracted from official exchange reports. - Macroeconomic Indicators: Bureau of Economic Analysis (BEA), Federal Reserve, and other public records. - Uncertainty Metrics (EPU, GPRD): Economic Policy Uncertainty Index and Global Policy Uncertainty Database.

Columns

1. dt: Date of observation in YYYY-MM-DD format.
2. vix: VIX (Volatility Index), a measure of expected market volatility.
3. sp500: S&P 500 index value, a benchmark of the U.S. stock market.
4. sp500_volume: Daily trading volume for the S&P 500.
5. djia: Dow Jones Industrial Average (DJIA), another key U.S. market index.
6. djia_volume: Daily trading volume for the DJIA.
7. hsi: Hang Seng Index, representing the Hong Kong stock market.
8. ads: Aruoba-Diebold-Scotti (ADS) Business Conditions Index, reflecting U.S. economic activity.
9. us3m: U.S. Treasury 3-month bond yield, a short-term interest rate proxy.
10. joblessness: U.S. unemployment rate, reported as quartiles (1 represents lowest quartile and so on).
11. epu: Economic Policy Uncertainty Index, quantifying policy-related economic uncertainty.
12. GPRD: Geopolitical Risk Index (Daily), measuring geopolitical risk levels.
13. prev_day: Previous day’s S&P 500 closing value, added for lag-based time series analysis.

Key Features

• Cross-Market Analysis: Compare U.S. markets (S&P 500, DJIA) with international benchmarks like HSI.
• Macroeconomic Insights: Assess how external factors like joblessness, interest rates, and economic uncertainty affect markets.
• Temporal Scope: Longitudinal data facilitates trend analysis and machine learning model training.

Potential Use Cases

• Forecasting market indices using machine learning or statistical models.
• Building volatility trading strategies with VIX Futures.
• Economic research on relationships between policy uncertainty and market behavior.
• Educational material for financial data visualization and analysis tutorials.

Feel free to use this dataset for academic, research, or personal projects.

This data set is a digitized version of “All-Bank Statistics, United States, 1896-1955,” (ABS) which the Board of Governors of the Federal Reserve System published in 1959. That volume contained annual aggregate balance sheet aggregates for all depository institutions by state and class of institution for the years 1896 to 1955. The depository institutions include nationally chartered commercial banks, state chartered commercial banks, and private banks as well as mutual savings bank and building and loan societies. The data comes from the last business day of the year or the closest available data. This digital version of ABS contains all data in the original source and only data from the original source.This data set is similar to ICPSR 2393, “U.S. Historical Data on Bank Market Structure, ICPSR 2393” by Mark Flood. ICPSR 2393 reports data from ABS but excludes subcategories of data useful for analyzing the liquidity of bank balance sheets, the operation of financial markets, the functioning of the financial network, and depository institutions’ contribution to monetary aggregates. ICPSR 2393, for example, reports total cash assets from ABS but does not report the subcomponents of that total: bankers balances, cash in banks’ own vaults, and items in the process of collection. Those data are needed to understand how much liquidity banks kept on hand, how much liquidity banks stored in or hoped to draw from reserve depositories, and how much of the apparent cash in the financial system was double-counted checks in the process of collection, commonly called float. Those data are also needed to understand the contribution of commercial banks to the aggregate money supply since cash in banks’ vaults counts within monetary aggregates while interbank deposits and float do not. While this dataset provides comprehensive and complete data from ABS, ICPSR 2393 contains information from other sources that researchers may find valuable including data from the aggregate income statements of nationally chartered banks and regulatory variables. To facilitate the use of that information, the naming conventions in this data set are consistent with those in ICPSR 2393.

Money Supply M2 in the United States increased to 21942 USD Billion in May from 21862.40 USD Billion in April of 2025. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

An index that can be used to gauge broad financial conditions and assess how these conditions are related to future economic growth. The index is broadly consistent with how the FRB/US model generally relates key financial variables to economic activity. The index aggregates changes in seven financial variables: the federal funds rate, the 10-year Treasury yield, the 30-year fixed mortgage rate, the triple-B corporate bond yield, the Dow Jones total stock market index, the Zillow house price index, and the nominal broad dollar index using weights implied by the FRB/US model and other models in use at the Federal Reserve Board. These models relate households' spending and businesses' investment decisions to changes in short- and long-term interest rates, house and equity prices, and the exchange value of the dollar, among other factors. These financial variables are weighted using impulse response coefficients (dynamic multipliers) that quantify the cumulative effects of unanticipated permanent changes in each financial variable on real gross domestic product (GDP) growth over the subsequent year. The resulting index is named Financial Conditions Impulse on Growth (FCI-G). One appealing feature of the FCI-G is that its movements can be used to measure whether financial conditions have tightened or loosened, to summarize how changes in financial conditions are associated with real GDP growth over the following year, or both.

Money Supply M0 in the United States increased to 5748600 USD Million in June from 5648700 USD Million in May of 2025. This dataset provides - United States Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Securities Exchange Act of 1934 (the Act) authorizes the Board to regulate securities credit extended by brokers, dealers, banks, and other lenders. The FR T-4, FR U-1, and FR G-3 are recordkeeping requirements for brokers and dealers, banks, and other lenders, respectively. The FR G-3 and FR U-1 document the purpose of loans secured by margin stock. For purposes of these forms, margin stock is defined as (1) stocks that are registered on a national securities exchange or any over-the-counter security designated for trading in the National Market System, (2) debt securities (bonds) that are convertible into such stocks, and (3) shares of most mutual funds. The FR T-4 documents the purpose of credit being extended when that credit is not to purchase, carry, or trade in securities and the credit is in excess of that otherwise permitted under Regulation T , Credit by Brokers and Dealers. Lenders that are not brokers, dealers, and banks making loans secured by margin stock must register and deregister with the Federal Reserve using the FR G-1 and FR G-2, respectively, and must file an annual report (FR G-4) while registered. The Federal Reserve uses the data collected by the FR G-1, FR G-2, and FR G-4 to identify lenders subject to the Board’s Regulation U (Credit by Banks or Persons other than Brokers or Dealers for the Purpose of Purchasing or Carrying Margin Stocks) to verify their compliance with the regulation, and to monitor margin credit.

Inspired by:

Modeling and predicting U.S. recessions using machine learning techniques

As variáveis do FRED-MD como preditivas e a USREC como alvo (período de 1979-2019)

Diversos Modelos: probit, logit, LDA, árvores Naive-Bayes Algumas variáveis tiveram que ser transformadas em mensais (interpolação cúbica)

128 varibles. Grupos: Output and Income Labor Market Consumption and Orders Orders and Inventories Money and Credit Interest Rates and Exchange Rates Prices Stock Market

Content

The STLFSI measures the degree of financial stress in the markets and is constructed from 18 weekly data series: seven interest rate series, six yield spreads and five other indicators. Each of these variables captures some aspect of financial stress. Accordingly, as the level of financial stress in the economy changes, the data series are likely to move together.

How to Interpret the Index: The average value of the index, which begins in late 1993, is designed to be zero. Thus, zero is viewed as representing normal financial market conditions. Values below zero suggest below-average financial market stress, while values above zero suggest above-average financial market stress.

More information: For additional information on the STLFSI and its construction, see "Measuring Financial Market Stress" (https://files.stlouisfed.org/research/publications/es/10/ES1002.pdf) and the related appendix (https://files.stlouisfed.org/files/htdocs/publications/net/NETJan2010Appendix.pdf).

See this list (https://www.stlouisfed.org/news-releases/st-louis-fed-financial-stress-index/stlfsi-key) of the components that are used to construct the STLFSI.

As of 07/15/2010 the Vanguard Financial Exchange-Traded Fund series has been replaced with the S&P 500 Financials Index. This change was made to facilitate a more timely and automated updating of the FSI. Switching from the Vanguard series to the S&P series produced no meaningful change in the index.

Copyright, 2016, Federal Reserve Bank of St. Louis.

Context

This is a dataset from the Federal Reserve Bank of St. Louis hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve Bank of St. Louis using Kaggle and all of the data sources available through the St. Louis Fed organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1993-12-31

• Observation End : 2019-11-29

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Laura Lefurgey-Smith on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

United States FCI-G Index: Stock Market data was reported at -0.413 Index in Mar 2025. This records an increase from the previous number of -0.562 Index for Feb 2025. United States FCI-G Index: Stock Market data is updated monthly, averaging -0.372 Index from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 1.287 Index in Feb 2009 and a record low of -1.023 Index in Apr 1998. United States FCI-G Index: Stock Market data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.S021: Financial Conditions Impulse on Growth.

This dataset contains the text from Federal Reserve FOMC (Federal Open Market Committee) meeting minutes and statements, collected by scraping the Federal Reserve's website. The data spans a specific period of time, providing insights into the central bank's monetary policy decisions and discussions.

Content

The dataset consists of the following columns:

• Date: The date of the FOMC meeting or statement release in the format YYYYMMDD.
• Type: Indicator for the type of document. 0 represents a statement, while 1 represents meeting minutes.
• Text: The text content of each paragraph in the meeting minutes or statements.

Acknowledgements

The data is collected from the official Federal Reserve website (https://www.federalreserve.gov) using a custom Python scraper built with BeautifulSoup.

Inspiration

This dataset can be used for various purposes, such as:

1. Analyzing the sentiment and tone of FOMC meeting minutes and statements over time.
2. Identifying key phrases and words that indicate changes in monetary policy.
3. Developing natural language processing models to predict future policy decisions based on historical data.
4. Investigating the relationship between FOMC meeting minutes/statements and financial market reactions.

United States FCI-G Index: 1-Yr Lookback: Stock Market data was reported at -0.084 Index in Mar 2025. This records an increase from the previous number of -0.306 Index for Feb 2025. United States FCI-G Index: 1-Yr Lookback: Stock Market data is updated monthly, averaging -0.212 Index from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 1.267 Index in Feb 2009 and a record low of -0.864 Index in Mar 2021. United States FCI-G Index: 1-Yr Lookback: Stock Market data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.S021: Financial Conditions Impulse on Growth.

Content

The Chicago Fed's National Financial Conditions Index (NFCI) provides a comprehensive weekly update on U.S. financial conditions in money markets, debt and equity markets and the traditional and "shadow" banking systems. Positive values of the NFCI indicate financial conditions that are tighter than average, while negative values indicate financial conditions that are looser than average.

For further information, please visit the Federal Reserve Bank of Chicago (http://www.chicagofed.org/webpages/publications/nfci/index.cfm).

Context

This is a dataset from the Federal Reserve Bank of Chicago hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Chicago Fed using Kaggle and all of the data sources available through the Chicago Fed organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1971-01-08

• Observation End : 2020-03-13

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

The license for this dataset is unknown. Please reach out directly to the Chicago Fed for more information on Commercial/Non-Commercial access.

Cover photo by Karina Carvalho on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

United States Memo: Wkly: Institutional Money Market Funds data was reported at 1,833.700 USD bn in 22 Oct 2018. This records a decrease from the previous number of 1,853.400 USD bn for 15 Oct 2018. United States Memo: Wkly: Institutional Money Market Funds data is updated weekly, averaging 601.200 USD bn from Feb 1980 (Median) to 22 Oct 2018, with 2021 observations. The data reached an all-time high of 2,609.600 USD bn in 19 Jan 2009 and a record low of 11.500 USD bn in 11 Feb 1980. United States Memo: Wkly: Institutional Money Market Funds data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.KA004: Money Stock, Liquid Assets and Debt Measures.

United States Money Supply M2: Wkly: sa: Retail Money Market Funds data was reported at 760.200 USD bn in 16 Jul 2018. This records an increase from the previous number of 755.300 USD bn for 09 Jul 2018. United States Money Supply M2: Wkly: sa: Retail Money Market Funds data is updated weekly, averaging 636.400 USD bn from Feb 1980 (Median) to 16 Jul 2018, with 2007 observations. The data reached an all-time high of 1,050.400 USD bn in 20 Oct 2008 and a record low of 41.900 USD bn in 11 Feb 1980. United States Money Supply M2: Wkly: sa: Retail Money Market Funds data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.KA004: Money Stock, Liquid Assets and Debt Measures.

Commercial paper, in the global financial market, is an unsecured promissory note with a fixed maturity of not more than 270 days.

Commercial paper is a money-market security issued (sold) by large corporations to obtain funds to meet short-term debt obligations (for example, payroll), and is backed only by an issuing bank or company promise to pay the face amount on the maturity date specified on the note. Since it is not backed by collateral, only firms with excellent credit ratings from a recognized credit rating agency will be able to sell their commercial paper at a reasonable price. Commercial paper is usually sold at a discount from face value, and generally carries lower interest repayment rates than bonds due to the shorter maturities of commercial paper. Typically, the longer the maturity on a note, the higher the interest rate the issuing institution pays. Interest rates fluctuate with market conditions, but are typically lower than banks' rates.

Commercial paper – though a short-term obligation – is issued as part of a continuous rolling program, which is either a number of years long (as in Europe), or open-ended (as in the U.S.)

Acknowledgements

This dataset was made available by the Federal Reserve. You can find the original dataset, updated daily, here.

Inspiration

• Based solely on this dataset, when would you say the Great Recession financial crisis started? How does that compare with media reports?

Gold rose to 3,371.09 USD/t.oz on August 22, 2025, up 0.95% from the previous day. Over the past month, Gold's price has fallen 0.49%, but it is still 34.33% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on August of 2025.

This dataset contains all the information utilized in the paper 'foreign exchange swaps: a substitute for the use of international reserves?'.

In the B3 related data, the main contribution is the information gathered by data-scraping the daily information available at the B3 exchange to build a time series of the institutional investor's net open positions in this exchange. Furthermore, the time series of the future BRL/USD obtained at the B3 exchange website is made available.

In the swaps related data, there are different sets of data. The swaps raw dataset was collected by gathering information made available by the Brazilian Central Bank through its monthly open market notes. Furthermore, data scraped from the Braziian Central Bank norms' search engine provide the information on the type of the swaps contracts.

With the datasets, it is possible to reproduce: 1. The descriptive statistics presented on the paper and to reproduce; 2. The logistic model that calculate the log-odds ratio of the spread between the coupon and the libor being larger than the EMBI+ risk-oremium measure be associated with expected or unexpected swaps. 3. The generalized autoregressive conditional heteroskedasticity models presented by the end of the paper. The null hypothesis in these model is that the inclusion of the external regressors has no statistical relationship with the conditional mean of the returns of the BRL/USD future' exchange rate in its first maturity and the alternative hypothesis is that the statistical relationship between the set of external regresors and the dependent variable is different from zero.

All of the control variables are publicly available and were obtained at the FRED (St. Louis Federal Reserve database), at the Brazilian Central Bank database and at the IPEADATA database. They are reproduced here to facilitate the reproduction of the paper.