Graph and download economic data for Money Market Funds; Total Financial Assets, Level (MMMFFAQ027S) from Q4 1945 to Q1 2025 about MMMF, IMA, financial, assets, and USA.

These data are derived from returns submitted by corporations registered as Category D (Money Market Corporations) under the Financial Sector (Collection of Data) Act 2001. Along with Category :Other-C/, these corporations are known collectively as Registered Financial Corporations (RFCs). Category :Other-C/ includes corporations formerly registered as Category F (Finance Companies), G (General Financiers) and E (Pastoral Finance Companies) under the Financial Corporations Act 1974.

In AprilA 2003, responsibility for the collection of financial statistics from Registered Financial Corporations (RFCs) was transferred to APRA. Previously, these data were collected by the RB under the now repealed Financial Corporations Act 1974. The introduction of new reporting forms in April 2003 has led to some significant breaks in series and affected definitions and categories shown for these institutions. There are other breaks from time to time in the historical data due to changes in the number of reporting corporations. Details of data reported by individual corporations are confidential.

Since December 1999, the collections cover RFCs whose assets in Australia (including related corporations) exceed $50A million. Prior to December 1999, this threshold was set at $5A million. This change resulted in breaks in all series covering RFCs.

The collection of statistics from the authorised money market dealers (formerly Category C corporations under the Financial Corporations Act 1974) ceased from August 1996.

From April 2003, the data are derived from RRFA 320.0: Statement of Financial Position collected by APRA. Prior to April 2003, the data were derived from the FC forms: D1, E1, F1 and G1 which were collected by the RBA.

:AFIs-C/ refers to banks, credit unions, building societies, SCCIs, RFCs and the RBA.

From April 2003, :Cash and liquid assets - Cash and balances with AFIs-C/ includes cash and deposits and placements with AFIs. Prior to April 2003, this series includes cash and deposits and placements with banks and RFCs.

From April 2003, :Cash and liquid assets - Other-C/ includes gold bullion and deposits and placements with clearing houses and other (non- AFI) financial institutions. Prior to April 2003, this series includes deposits and placements with all institutions other than banks and RFCs. This series also includes placements with authorised money market dealers prior to August 1996.

:Trading and investment securities - Debt-C/ includes commercial paper and promissory notes, bills of exchange and all other debt securities held by all counterparties.

All series under :Loans and advances-C/ include finance lease receivables.

:Loans and advances - Household-C/ includes housing and other personal loans to households.

From April 2003, :Loans and advances - Business-C/ includes loans to the following counterparties: private trading corporations, private unincorporated businesses, public non-financial corporations, community service organisations and other (non-AFI) financial institutions. Prior to April 2003, this series includes loans to all counterparties other than households and RFCs, and also includes bills of exchange accepted by the reporting corporation.

From April 2003, :Loans and advances - AFIs-C/ includes loans to AFIs. Prior to April 2003, this series only includes loans to RFCs (loans to other AFIs are included in :Loans and advances - Business-C/).

From April 2003, :Borrowings from residents - Borrowings from AFIs-C/ includes deposits and placements due to AFIs and short-term loans from ADIs. Prior to April 2003, this series includes borrowings from banks and related RFCs.

From April 2003, :Borrowings from residents - Deposits and placements-C/ includes deposits and placements due to the following counterparties: private trading corporations, private unincorporated businesses, public non-financial corporations, community service organisations and other (non-AFI) financial institutions.

From April 2003, :Borrowings from residents - Other-C/ includes borrowings by the issue of promissory notes, bills of exchange and other debt securities, short-term loans from non-ADIs and all long-term loans. Prior to April 2003, this series includes borrowings from all counterparties other than banks and related RFCs, and borrowings by the issue of promissory notes, debentures, unsecured notes and bills of exchange accepted by banks.

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 M0 in the United States decreased to 5648600 USD Million in May from 5732900 USD Million in April of 2025. This dataset provides - United States Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

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.

I compare the contemporaneous cash holdings (%), as reported by the CRSP Mutual Fund database, of closed funds to those of open funds in the same “Morningstar Category” (same style) and size quintile. The cash holdings include all money market instruments with maturities of one year or less. The table shows the average values and the average differences. The first column contains the average cash holding of funds that are open and will close next month and that of comparable funds that are open and will remain open next month. The second column contains the average cash holding of funds that are currently closed and the average cash holding of the comparable funds that are currently open. The t-stats are reported in parentheses.

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

This table contains information on the balance sheet of the general government sector. The balance sheet shows stock levels of assets and financial liabilities, as well as net worth of the general government sector. Assets are either financial (e.g. loans) or non-financial (e.g. non-residential buildings). The stock of assets equals the sum of the stock of financial liabilities and net worth. Stocks of assets and liabilities in this table are mostly valued at market value. This is the value of the asset or liability as if it were being acquired or sold on the date to which the balance sheet relates. When there are no observable market prices, estimates are made for the market value. Financial assets and liabilities that are not commonly traded on a market, such as cash, deposits, loans and other accounts receivable/payable are valued at nominal value. The figures in this table are consolidated at the general government level. This means that stocks between units that both belong to the general government sector are eliminated. The terms and definitions used are in accordance with the framework of the Dutch national accounts. National accounts are based on the international definitions of the European System of Accounts (ESA 2010). Small temporary differences with publications of the National Accounts may occur due to the fact that the government finance statistics are sometimes more up to date. Data available from: 1995 Status of the figures: The figures for the period 1995-2022 are final. The figures for 2023 are provisional. Changes as of 23 September 2024: Annual figures for 2023 are available. The financial assets and liabilities and the net saving and capital transfers of general government for 2022 have been revised due to updated information. In the context of the revision policy of the National accounts the annual figures from 1995 of the financial accounts of general government have been revised. The annual figures for 2022 are final. When will new figures be published? New provisional data are published in July or August after the end of the reporting year. The previous provisional figures will become final and previous final figures can be revised at the same time. More information on the revision policy of National Accounts can be found under 'relevant articles' under paragraph 3.

The United States recorded a capital and financial account deficit of 14200 USD Million in April of 2025. This dataset provides the latest reported value for - United States Net Treasury International Capital Flows - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Using all stocks listed in the Tokyo Stock Exchange and macroeconomic data for Japan, the dataset comprises the following series:

1. Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Monthly returns for 20 momentum portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Monthly returns for 25 price-to-cash flow-dividend yield portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
4. Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
5. Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology for all factors, except for RMW, which is determined using the return on assets as sorting variable, as in Hou, Xue and Zhang (2014). (Raw data source: Datastream database)
6. Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Japan. (Raw data source: OECD)
7. Consumer Confidence Index (CCI) for Japan. (Raw data source: OECD)
8. Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD)
9. Gross Domestic Product (GDP) for Japan. (Raw data source: OECD)
10. Consumer Price Index (CPI) growth rate for Japan. (Raw data source: OECD)

We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) price-to-cash flow ratio (PC series), and (vii) dividend yield (DY series). We have used the generic rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. We also exclude stocks with less than twelve observations in the period from July 1992 to June 2018. Accordingly, our sample comprises a total number of 5,312 stocks.

REFERENCES:

Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277. Hou K, Xue C, Zhang L. (2014). Digesting anomalies: An investment approach. Review of Financial Studies, 28, 650-705.

Green credit is changing industrial structure and corporate behavior, but little attention has been paid to the relationship between green credit and corporate cash management behavior. Based on the typical fact that the allocation of traditional bank credit funds is biased towards heavily polluting industries and the exogenous impact event of green credit policy, this paper takes A-share listed companies in China’s capital market from 2008 to 2015 as samples, and uses the DID model to investigate the impact of green credit policy on excess cash holdings of heavily polluting enterprises. The findings indicate that the green credit policy has reduced the excessive cash holdings of heavily polluting enterprises, suggesting that it can correct the issue and align their cash holdings with the requirements of normal production and operations. The mechanism test demonstrates that the green credit policy can alleviate agency conflicts and influence enterprise cash holdings. Moreover, a cross-sectional investigation reveals that the inhibitory effect of the green credit policy on cash holdings is more pronounced in large-scale and state-owned enterprises compared to small-scale and non-state-owned enterprises. Finally, an analysis of the economic consequences reveals that the green credit policy indirectly enhances corporate value by reducing excessive cash holdings. Based on this, banks and financial institutions continue to treat the credit granting of heavily polluting enterprises cautiously, optimize the structure of green financial products, fully consider the different types and nature of customers, and develop differentiated lending conditions and diversified evaluation mechanisms. This paper has enriched the research on the economic consequences of green credit and the influencing factors of corporate cash holdings, and provided policy enlightenment for regulators and listed companies to correctly understand and make full use of green credit policies to keep corporate cash stable through the crisis.

South African policymakers are endeavouring to ensure that the poor have better access to financial services. However, a lack of understanding of the financial needs of poor households impedes a broad strategy to attend to this need. The Financial Diaries study addresses this knowledge gap by examining financial management in rural and urban households. The study is a year-long household survey based on fortnightly interviews in Diepsloot (Gauteng), Langa (Western Cape) and Lugangeni (Eastern Cape). In total, 160 households were involved in this pioneering study which promises to offer important insights into how poor people manage their money as well as the context in which poor people make financial decisions. The study paints a rich picture of the texture of financial markets in townships, highlighting the prevalence of informal financial products, the role of survivalist business and the contribution made by social grants. The Financial Diaries dataset includes highly detailed, daily cash flow data on income, expenditure and financial flows on both a household and individual basis.

Content

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

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

China recorded a capital and financial account deficit of 1656 USD Hundred Million in the first quarter of 2025. This dataset provides - China Capital Flows - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides values for CAPITAL FLOWS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.