Money Supply M2 in the United States increased to 22298.10 USD Billion in October from 22212.50 USD Billion in September of 2025. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Money Supply M2 in China decreased to 335105.40 CNY Billion in October from 335377.10 CNY Billion in September of 2025. This dataset provides - China Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Hungary Money Supply: M3: M2: Index data was reported at 369.630 Dec2001=100 in Oct 2018. This records an increase from the previous number of 364.999 Dec2001=100 for Sep 2018. Hungary Money Supply: M3: M2: Index data is updated monthly, averaging 201.324 Dec2001=100 from Jan 1998 (Median) to Oct 2018, with 250 observations. The data reached an all-time high of 369.630 Dec2001=100 in Oct 2018 and a record low of 0.000 Dec2001=100 in Dec 2001. Hungary Money Supply: M3: M2: Index data remains active status in CEIC and is reported by National Bank of Hungary. The data is categorized under Global Database’s Hungary – Table HU.KA002: Money Supply .

Money Supply M1 in the United States increased to 19004.20 USD Billion in October from 18912.80 USD Billion in September of 2025. This dataset provides - United States Money Supply M1 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

View monthly updates and historical trends for China M2 Money Supply. Source: The People's Bank of China. Track economic data with YCharts analytics.

Hungary Money Supply: M3: Index data was reported at 348.804 Dec2001=100 in Oct 2018. This records an increase from the previous number of 343.983 Dec2001=100 for Sep 2018. Hungary Money Supply: M3: Index data is updated monthly, averaging 204.520 Dec2001=100 from Jan 1998 (Median) to Oct 2018, with 250 observations. The data reached an all-time high of 348.804 Dec2001=100 in Oct 2018 and a record low of 0.000 Dec2001=100 in Dec 2001. Hungary Money Supply: M3: Index data remains active status in CEIC and is reported by National Bank of Hungary. The data is categorized under Global Database’s Hungary – Table HU.KA002: Money Supply .

Money Supply M0 in the United States increased to 53615000 USD Million in October from 5478000 USD Million in September of 2025. This dataset provides - United States Money Supply M0 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Hungary Money Supply: M3: M2: M1: Index data was reported at 678.892 Dec2001=100 in Sep 2018. This records a decrease from the previous number of 682.596 Dec2001=100 for Aug 2018. Hungary Money Supply: M3: M2: M1: Index data is updated monthly, averaging 190.532 Dec2001=100 from Jan 1998 (Median) to Sep 2018, with 249 observations. The data reached an all-time high of 682.596 Dec2001=100 in Aug 2018 and a record low of 0.000 Dec2001=100 in Dec 2001. Hungary Money Supply: M3: M2: M1: Index data remains active status in CEIC and is reported by National Bank of Hungary. The data is categorized under Global Database’s Hungary – Table HU.KA002: Money Supply .

Graph and download economic data for Monetary Base: Total (BOGMBASE) from Jan 1959 to Oct 2025 about monetary base and USA.

Hungary Money Supply: M3: M2: Annualised Growth Index: Trend data was reported at 102.332 Dec2001=100 in Sep 2018. This records a decrease from the previous number of 105.387 Dec2001=100 for Aug 2018. Hungary Money Supply: M3: M2: Annualised Growth Index: Trend data is updated monthly, averaging 111.075 Dec2001=100 from Feb 1998 (Median) to Sep 2018, with 248 observations. The data reached an all-time high of 130.376 Dec2001=100 in Aug 2001 and a record low of 88.761 Dec2001=100 in Jan 2012. Hungary Money Supply: M3: M2: Annualised Growth Index: Trend data remains active status in CEIC and is reported by National Bank of Hungary. The data is categorized under Global Database’s Hungary – Table HU.KA002: Money Supply .

Money Supply: M'4: Government Bonds: Non Financial Institutions: Housing Indexed Unit of Account data was reported at 0.000 BOB th in Jun 2024. This stayed constant from the previous number of 0.000 BOB th for May 2024. Money Supply: M'4: Government Bonds: Non Financial Institutions: Housing Indexed Unit of Account data is updated monthly, averaging 16,949.645 BOB th from Jan 2006 (Median) to Jun 2024, with 222 observations. The data reached an all-time high of 4,091,067.159 BOB th in Dec 2008 and a record low of 0.000 BOB th in Jun 2024. Money Supply: M'4: Government Bonds: Non Financial Institutions: Housing Indexed Unit of Account data remains active status in CEIC and is reported by Central Bank of Bolivia. The data is categorized under Global Database’s Bolivia – Table BO.KA002: Money Supply.

Bolivia Money Supply: M'4: Time Deposits: Housing Indexed Unit of Account data was reported at 7,521.450 BOB th in Jun 2024. This records an increase from the previous number of 7,499.850 BOB th for May 2024. Bolivia Money Supply: M'4: Time Deposits: Housing Indexed Unit of Account data is updated monthly, averaging 31,980.520 BOB th from Apr 2002 (Median) to Jun 2024, with 267 observations. The data reached an all-time high of 2,184,043.263 BOB th in Jul 2008 and a record low of 68.000 BOB th in Nov 2002. Bolivia Money Supply: M'4: Time Deposits: Housing Indexed Unit of Account data remains active status in CEIC and is reported by Central Bank of Bolivia. The data is categorized under Global Database’s Bolivia – Table BO.KA002: Money Supply.

Money Supply M2 in Indonesia decreased to 92328 IDR Billion in January from 9210800 IDR Billion in December of 2024. This dataset provides - Indonesia Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Bolivia Money Supply: M'4: Other Financial Obligations: Housing Indexed Unit of Account data was reported at 0.074 BOB th in Jun 2024. This records an increase from the previous number of 0.074 BOB th for May 2024. Bolivia Money Supply: M'4: Other Financial Obligations: Housing Indexed Unit of Account data is updated monthly, averaging 4.784 BOB th from Feb 2005 (Median) to Jun 2024, with 233 observations. The data reached an all-time high of 104.458 BOB th in Nov 2021 and a record low of 0.052 BOB th in Jun 2012. Bolivia Money Supply: M'4: Other Financial Obligations: Housing Indexed Unit of Account data remains active status in CEIC and is reported by Central Bank of Bolivia. The data is categorized under Global Database’s Bolivia – Table BO.KA002: Money Supply.

Money Supply M2 in India increased to 70702.28 INR Billion in August from 70460.97 INR Billion in July of 2025. This dataset provides - India Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Currency in Circulation (CURRCIR) from Aug 1917 to Oct 2025 about currency and USA.

Graph and download economic data for Money Market Funds; Total Financial Assets, Level (MMMFFAQ027S) from Q4 1945 to Q2 2025 about MMMF, IMA, financial, assets, and USA.

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.

DJ Commodity index set for moderate gains amid global economic shifts

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 study contains aggregate socioeconomic and demographic data for 189 countries in the period 1950-1992. Data are provided in both current and constant dollars for government revenues and expenditures, budget balance, gross national product (GNP), gross domestic product (GDP), private consumption, government consumption, government deficit or surplus, values of exports and imports, terms of trade index, long-term interest payments, net long-term loans, repayments on long-term loans, total external debt, net direct foreign investment, international reserves excluding gold, gold holdings at London market price, net workers remittances, government capital receipts and payments, money supply, consumer price index, gross domestic and national savings, food production per capita, and value-added in agriculture, industry, manufacturing, and services. Demographic and social variables include population characteristics such as the percentage of urban population, life expectancy at birth, infant mortality rate, total fertility rate, school enrollment rates, and the percentage of females enrolled in primary schools, as well as the percentage of the labor force in agriculture, and the percentage of the labor force that was female.

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.

Oil and Gas Index Poised for Steady Growth Amidst Global Demand.

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