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.

Money Supply M2 in Sweden increased to 5012153 SEK Million in June from 4991180 SEK Million in May of 2025. This dataset provides - Sweden Money Supply M2- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Explore the Saudi Arabia Money Supply dataset , including Time & savings deposits, Currency outside banks, Demand deposits, and more. Access quarterly and annual data on Saudi M1, M2, and M3 to analyze the country's monetary trends.

Time & savings deposits, Currency outside banks, Other quasi- money deposits, Quarterly, Demand deposits, Annually, Saudi Arabia M1, Saudi M2, Saudi M3, Saudi Arabia Money Supply, Saudi Quasi, Saudi Deposits, SAMA Monthly

Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research..Important notes:Other Quasi-Money Deposits: Comprise residents' foreign currency deposits, marginal deposits for LCs, outstanding remittances, and banks (Repo) transactions with private sector.Money Supply M1 = Currency outside banks + Demand Deposits.Money Supply M2 = Money Supply M1 + time & Savings Deposits.Money Supply M3 = Money Supply M1 + Other Quasi-Money Deposits.

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 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.

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.

[Video] S&P 500: Bull or Bear?

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

Money Supply M2 In the Euro Area decreased to 15710290 EUR Million in June from 15736686 EUR Million in May of 2025. This dataset provides the latest reported value for - Euro Area Money Supply M2 - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

China Money Supply M2 data was reported at 188,941.214 RMB bn in Mar 2019. This records an increase from the previous number of 182,674.422 RMB bn for Dec 2018. China Money Supply M2 data is updated quarterly, averaging 24,375.688 RMB bn from Mar 1990 (Median) to Mar 2019, with 117 observations. The data reached an all-time high of 188,941.214 RMB bn in Mar 2019 and a record low of 1,248.030 RMB bn in Mar 1990. China Money Supply M2 data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under Global Database’s China – Table CN.KA: Money Supply: Quarterly.

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.

Does S&P 500 beat inflation?

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 dataset shows Fortnight wise Sources and Components of Money Stock like Currency with the Public,Demand Deposits with Banks,Time Deposits with Banks,‘Other’ Deposits with Reserve Bank, Net Bank Credit to Government, Bank Credit to Commercial Sector, Net Foreign Exchange Assets of Banking Sector, Government's Currency Liabilities to the Public, Banking Sectors Net Non-Monetary Liabilities:

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.

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

This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

Examples of Annotated Headlines


    Forex Pair
    Headline
    Sentiment
    Explanation




    GBPUSD 
    Diminishing bets for a move to 12400 
    Neutral
    Lack of strong sentiment in either direction


    GBPUSD 
    No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft 
    Positive
    Positive sentiment towards GBPUSD (Cable) in the near term


    GBPUSD 
    When are the UK jobs and how could they affect GBPUSD 
    Neutral
    Poses a question and does not express a clear sentiment


    JPYUSD
    Appropriate to continue monetary easing to achieve 2% inflation target with wage growth 
    Positive
    Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply


    USDJPY
    Dollar rebounds despite US data. Yen gains amid lower yields 
    Neutral
    Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other


    USDJPY
    USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains 
    Negative
    USDJPY is expected to reach a lower value, with the USD losing value against the JPY


    AUDUSD

RBA Governor Lowe’s Testimony High inflation is damaging and corrosive

    Positive
    Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD.

Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.

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.

An In-depth Analysis of the S&P 500 Index: Performance, Composition, and Implications

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

Economic data on forestry and logging, physical and monetary data on supply and use of wood, and employment data. 2. Aggregates include output, intermediate consumption, gross value added, fixed capital consumption, gross fixed capital formation and different measures of income of forestry and logging. The data are in current basic prices and use the concepts and definitions of National Accounts. 3. EU Member States, EFTA countries, the UK and selected candidate countries. Data for France cover only mainland France without the overseas territories and dominions French Guyana, Guadeloupe, Martinique, Réunion or Mayotte. They are collected as part of European Forest Accounts (EFA), which also covers wooded land, timber, output of the forestry industry by type, and labour input in annual work units (AWU). Employment data from Eurostat's Labour Force Survey (LFS) are presented as well, covering estimates of the number of employees in forestry and logging, the manufacture of wood and products of wood and cork, the manufacture of paper and paper products, and the manufacture of furniture. There are two separate tables because of the change in the EU's classification of economic activities from NACE Rev. 1.1 to NACE Rev. 2 in 2008. Sources: questionnaire on European Forest Accounts (EFA) and LFS . The questionnaire and its explanatory notes can be found on our open-access communication platform under the interest group 'Forestry statistics and accounts'.

Money Supply M0 in the United Kingdom increased to 99678 GBP Million in June from 99403 GBP Million in May of 2025. This dataset provides - United Kingdom Money Supply M0 - 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.

Lantern Pharma's (LTRN) Gleaming Future or Fading Light?

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

Money Supply M2 in Turkey increased to 21386412838 TRY Thousand in June from 20657975574 TRY Thousand in May of 2025. This dataset provides the latest reported value for - Turkey Money Supply M2 - 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.

Microsoft Caught Red-Handed: $20 Million Fine for Violating Children's Privacy

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