View monthly updates and historical trends for US M2 Money Supply YoY. from United States. Source: Federal Reserve. Track economic data with YCharts analy…

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 Switzerland increased to 1091429 CHF Million in October from 1082971 CHF Million in September of 2025. This dataset provides - Switzerland Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

View a measure of the most-liquid assets in the U.S. money supply: cash, checking accounts, traveler's checks, demand deposits, and other checkable deposits.

View data of the frequency at which one unit of currency purchases domestically produced goods and services within a given time period.

This dataset provides values for MONEY SUPPLY M3 reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

CNBC Economy Articles Dataset is an invaluable collection of data extracted from CNBC’s economy section, offering deep insights into global and U.S. economic trends, market dynamics, financial policies, and industry developments.

This dataset encompasses a diverse array of economic articles on critical topics like GDP growth, inflation rates, employment statistics, central bank policies, and major global events influencing the market. Designed for researchers, analysts, and businesses, it serves as an essential resource for understanding economic patterns, conducting sentiment analysis, and developing financial forecasting models.

Dataset Highlights

Each record in the dataset is meticulously structured and includes:

• Article Titles
• Publication Dates
• Author Names
• Content Summaries
• URLs to Original Articles

This rich combination of fields ensures seamless integration into data science projects, research papers, and market analyses.

Key Features

• Number of Articles: Hundreds of articles sourced directly from CNBC.
• Data Fields: Includes title, publication date, author, article content, summary, URL, and relevant keywords.
• Topics Covered: U.S. and global economy, GDP trends, inflation, employment, financial markets, and monetary policies.
• Format: Delivered in CSV format for easy integration with research tools and analytical platforms.
• Source: Extracted directly from CNBC’s economy news section, ensuring accuracy and relevance.

Use Cases

• Economic Research: Gain insights into U.S. and global economic policies, market trends, and industry developments.
• Sentiment Analysis: Assess the sentiment of economic articles to gauge market perspectives and investor confidence.
• Financial Modeling: Build forecasting models leveraging key economic indicators discussed in the dataset.
• Content Creation: Develop research-backed reports, articles, and presentations on economic topics.

Who Benefits?

• Researchers & Academics studying macro-economics or financial policy.
  
• Data Scientists building AI models, trend analyzers, or economic forecasting tools.
  
• Economists & Analysts need real-world news data for policy analysis.
  
• Content Strategists who write data-backed articles about economic trends.

Why Choose This Dataset?

• No need to manually scrape CNBC — data is pre-extracted and clean.
  
• High-quality economy news metadata enables detailed filtering (by date, author, topic).
  
• Ready for machine learning, sentiment analysis, or building news-based economic models.
  
• Well-suited for trend tracking, policy analysis, and economic forecasting.

Explore More News Datasets

Interested in additional structured news datasets for your research or analytics needs? Check out our news dataset collection to find datasets tailored for diverse analytical applications.

In 2021, the inflation rate in Ghana amounted to about 9.98 percent compared to the previous year. Ghana’s inflation peaked at almost 17.5 percent in 2016 and is predicted to decrease to 8 percent by 2030. Steady is best for inflationAccording to economists, a steady inflation rate between two and three percent is desirable to achieve a stable economy in a country. Inflation is the increase in the price level of consumer goods and services over a certain time period. A high inflation rate is often caused by excessive money supply and can turn into hyperinflation, i.e. if inflation occurs too quickly and rapidly, it can devalue currency and cause a recession and even economic collapse. This scenario is currently taking place in Venezuela , for example. The opposite of inflation, the decrease in the price level of goods and services below zero percent, is called deflation. While hyperinflation devalues money, deflation usually increases its value. Both events can damage an economy severely. Is Ghana’s economy at risk?Ghana’s economy is considered quite stable and fast-growing, and is rich in oil, diamonds, and gold. After struggling in the years around 2015 due to increased government spending and plummeting oil prices, it is now on an upswing again. This is also reflected in the decreasing inflation rate, and other key indicators like unemployment and rapid GDP growth support this theory. However, Ghana’s government debt is still struggling with the consequences of the 2015 crisis and forecast to keep skyrocketing during the next few years.

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.

(GPRO) GoPro: Capturing Growth or Fading Fast?

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

Graph and download economic data for Nominal Gross Domestic Product for United States (NGDPSAXDCUSQ) from Q1 1950 to Q2 2025 about GDP and USA.

Time Series Forecasting on CUrrency conversion

It was a part of analysis that i have taken as a part of the case study i was into. There are a lot of micro economic factors that impact the Stock.

Economists opine that currency exchange rates are driven by Macro-economic variables like: Trade, GDP growth rate, Industrial activity, money supply etc.

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. I wish you will like this. And give it a thumbs Up!

Key information about Turkey Domestic Credit Growth

• Turkey Domestic Credit increased 35.9 % YoY in Oct 2025, compared with an increase of 34.8 % YoY in the previous month
• Turkey Domestic Credit Growth YoY data is updated monthly
• It averaged 39.6 %, available from Jan 1987 to Oct 2025
• The data reached an all-time high of 153.6 % in Oct 1996 and a record low of 1.0 % in Aug 2019

• In the latest reports, Turkey Domestic Credit reached 628.0 USD bn in Oct 2025
• Money Supply M2 in Turkey increased to 555.9 USD bn YoY in Oct 2025
• Turkey Foreign Exchange Reserves was measured at 72.7 USD bn in Oct 2025
• The Foreign Exchange Reserves equaled 2.3 Months of Import in Oct 2025
• The country's Non Performing Loans Ratio stood at 2.4 % in Oct 2025, compared with the ratio of 2.3 % in the previous month
• Household Debt of Turkey reached 10.2 % in Jun 2025, accounting for 10.2 % of the country's Nominal GDP

In recent decades, economic growth in the Dominican Republic (DR) has been steady. However, growth has not occurred in such a way as to make the benefits widely and evenly available. In fact, although the DR economy grew faster than that of other LAC countries before the Covid-19 pandemic, its poverty rates and social outcomes remain broadly similar to them. This report seeks to explain this conundrum, as well as to expand the knowledge base to improve the effectiveness of ongoing poverty reduction policies in the DR. The Poverty Assessment draws primarily on new analytical work conducted in the DR, structured around four background notes on: (i) trends in monetary poverty and inequality, as well as the key drivers of those changes; (ii) nonmonetary poverty and its spatial dimensions; (iii) social assistance programs and their role in mitigating poverty; and (iv) climate change and its interaction with poverty. By helping to reduce the evidence gap in each of these areas, our analysis hopes to inform government policies and the national dialogue on poverty reduction. In addition, the note integrates existing analytical work and evidence produced inside and outside the Bank, including from its operations in the country.

We investigate how the response of the US economy to monetary policy shocks depends on the state of the business cycle. The effects of monetary policy are less powerful in recessions, especially for durables expenditure and business investment. The asymmetry relates to how fast the economy is growing, rather than to the level of resource utilization. There is some evidence that fiscal policy has counteracted monetary policy in recessions but reinforced it in booms. We also find evidence that contractionary policy shocks are more powerful than expansionary shocks, but contractionary shocks have not been more common in booms. So this asymmetry cannot explain our main finding.

Tokenized Commercial Bank Money Pilots Market Outlook

According to our latest research, the global Tokenized Commercial Bank Money Pilots market size reached USD 1.67 billion in 2024, with a robust year-on-year growth trajectory. The market is currently expanding at a CAGR of 34.8% and is projected to reach USD 21.82 billion by 2033. This remarkable growth is driven by increasing adoption of blockchain and distributed ledger technologies in the banking sector, as well as the growing need for secure, efficient, and transparent payment solutions across both wholesale and retail banking environments.

The primary growth factor for the Tokenized Commercial Bank Money Pilots market is the rapid evolution of digital payment infrastructure worldwide. Financial institutions are increasingly investing in tokenization pilots to streamline cross-border and domestic payments, reduce settlement times, and enhance transaction security. The demand for real-time, programmable money solutions is also spurring innovation, as banks seek to offer customers faster and more efficient services. Furthermore, the integration of tokenized money with existing payment rails is enabling banks to bridge the gap between traditional finance and the emerging digital economy, making tokenized commercial bank money an attractive proposition for both established players and fintech disruptors.

Another significant driver is the regulatory push towards digital currencies and the modernization of payment systems. Central banks and financial regulators in major economies are collaborating with commercial banks to pilot tokenized money solutions, aiming to enhance systemic resilience and reduce reliance on legacy infrastructure. These pilots are not only fostering public-private partnerships but are also creating a regulatory sandbox that encourages experimentation and innovation. The emergence of programmable features, such as smart contracts and conditional payments, is further expanding the scope of tokenized commercial bank money, enabling new business models and use cases in sectors like trade finance, supply chain, and remittances.

Additionally, the growing focus on financial inclusion and the need to serve unbanked and underbanked populations are propelling the adoption of tokenized commercial bank money in emerging markets. By leveraging blockchain and distributed ledger technology, banks can offer low-cost, transparent, and accessible payment solutions to a broader segment of the population. This is particularly relevant in regions where traditional banking infrastructure is limited or inefficient. Moreover, the COVID-19 pandemic has accelerated the shift towards digital payments and highlighted the importance of resilient, scalable, and secure financial systems, further amplifying the demand for tokenized money pilots.

From a regional perspective, North America and Europe are leading the adoption of tokenized commercial bank money pilots, driven by strong regulatory support, advanced digital infrastructure, and a high concentration of global financial institutions. Asia Pacific is emerging as a key growth market, fueled by rapid fintech innovation, rising digital adoption, and proactive government initiatives. Latin America and the Middle East & Africa are also witnessing gradual uptake, as local banks and fintechs explore tokenization to address cross-border payment challenges and foster economic integration. Overall, the global landscape is characterized by a dynamic interplay of technological innovation, regulatory evolution, and shifting consumer preferences, positioning the Tokenized Commercial Bank Money Pilots market for sustained growth in the coming years.

Type Analysis

The Tokenized Commercial Bank Money Pilots market is segmented by type into Wholesale and Retail categories, each catering to distinct user groups and transaction requirements. Wholesale tokenized money primarily targets interbank settlements, large-value transfers, and institutional payments. This segment is witnessing significant interest from major commercial banks and central banks, as it offers the potential to streamline high-value transactions, reduce counterparty risk, and enhance liquidity management. Wholesale pilots often focus on interoperability with existing RTGS (Real-Time Gross Settlement) systems and cross-border payment corridors, leveraging blockchain and distributed ledger technologies to achieve real-time settlement and

This dissertation examines the complex interplay between monetary policy and economic dynamics across three pivotal essays, each focusing on distinct aspects of monetary policy's influence on labor markets, inflationary expectations, and the production sector's extensive margin.

The first chapter analyzes the varied effects of unexpected expansionary monetary policy shocks on high- and low-skilled workers using a New Keynesian DSGE model with asymmetric search and matching frictions. The findings show that unemployment rates for low-skilled workers are more sensitive to these shocks, while high-skilled workers recover faster. This underscores the importance of considering labor skill heterogeneity in devising optimal monetary policies, particularly regarding their effects on consumption, unemployment, and wage dynamics across skill levels.

The second chapter assesses the impact of the Federal Reserve's August 2020 policy framework revision on inflation, employing a representative agent New Keynesian model. Simulations of inflationary shocks under different policy rules indicate that a rule combining asymmetric output growth responses and average inflation targeting initially raises inflation more than the standard Taylor rule but stabilizes it more effectively in the medium term.

The third chapter explores how monetary policy influences the extensive margin of the production sector, specifically how changes in borrowing costs affect firm entry by productivity levels. Using a New Keynesian model that includes Hopenhayn's entry and exit framework, the study finds that while monetary policy reduces borrowing costs and modifies the equity-bond trade-off to facilitate firm entry, it may also inadvertently attract less efficient firms, thereby potentially neutralizing initial output gains.

These chapters collectively contribute to the understanding of the diverse effects of monetary policy on the economy, emphasizing the crucial roles of labor market frictions, inflation targeting, and borrowing costs. This analysis not only advances the existing literature but also provides important insights for policymakers striving to balance economic stability and growth.

We investigate whether the price puzzle, found in previous empirical studies, consists of model misspecification or a feature of the economy. To analyze the anomaly, we estimate the central bank's reaction function through the standard VAR and FAVAR approaches, spanning the period from July 2003 to June 2018. The results suggest that the price puzzle stands out as a feature of the economy only at intervals of activity slowdown. The data consists of the 71 indicators potentially used by the Brazilian Central Bank in formulating monetary policy. Note that “SA” stands for series seasonally adjusted by the source and “*” denotes series seasonally adjusted by the Census X-13 ARIMA methodology (US Census Bureau). The transformation codes are: 1-No transformation; 2-First difference; 3-Logarithm; and 4-First difference of logarithm. The "S/F" stands for “Slow-moving” (S) and “Fast-moving” (F). The sources of the time series used in our study are as follows: the Brazilian Steel Institute (IBS), the State System of Data Analysis, Research and Unemployment Foundation (SEADE), the Center Foundation for Foreign Trade Studies (FUNCEX), the Brazilian Association of Financial and Capital Market Institutions (ANBIMA), the Institute of Applied Economic Research (IPEA), the National Confederation of Industry (CNI), the Brazilian Central Bank (BCB), the Brazilian Institute of Geography and Statistics (IBGE), the Brazilian Ministry of Labor (ML), the Brazilian Foreign Trade Secretary (FTS), the Brazilian stock market exchange (BMF Bovespa), the Getulio Vargas Foundation (FGV, Brazilian economic research institution), the JP Morgan, the Investing (US-based financial investment company), the US Bureau of Labor Statistics (BLS), the Federal Reserve Bank (FED), the Organization for Economic Cooperation and Development (OECD), and the International Monetary Fund (IMF). Importantly, the CDI is the average interest rate, indicative against which a representative group of banks makes unsecured loans to each other in the Brazilian financial market. The swap DI x Fixed Interest Rate is floating for a fixed swap contract . The IPCA, IPC, and INPC are consumer price indexes with different building methodologies. The IPADI is a producer price index. The INCC measures the changes in prices of the construction sector. The IPCA index is composed of free and state-regulated prices. The formers are determined by market supply and demand and comprise the prices of food, beverage, housing, household items, clothing, personal expenses, and education (IPCA Free Prices). The IPCA Free Tradable Prices index is composed of prices of goods that have free prices and are internationally traded. The IPCA Core Prices index is a measure that aims to capture the price trend, excluding the disturbances caused by temporary shocks. The IGPDI and IGPOG are both hybrid price indexes with different methodological approaches.

The Mobile Wallet Transfers market has emerged as a transformative force in the arena of digital payments, seamlessly integrating technology into everyday financial transactions. This innovative solution allows users to store, send, and receive money via their smartphones, making monetary exchanges faster, more acce

Central Banking Systems Market size was valued at USD 7.6 Billion in 2024 and is projected to reach USD 13.6 Billion by 2032, growing at a CAGR of 7.5% during the forecast period 2026 to 2032. Central Banking Systems Market DriversTechnological Advancements and Digitalization:Emergence of Central Bank Digital Currencies (CBDCs): The exploration and potential issuance of CBDCs by central banks worldwide is a major driver. This necessitates significant modernization of existing systems for currency issuance, payments, and financial operations. CBDCs aim to preserve the advantages of central bank money in a digital age and foster competition among private sector intermediaries.Integration of AI, Blockchain, and Big Data: Central banks are increasingly leveraging advanced technologies like Artificial Intelligence (AI), Machine Learning (ML), blockchain, and big data analytics. These technologies enhance efficiency, security, and risk management in financial transactions, improve data analysis for monetary policy, and automate various processes.Modernization of Payment Systems: There's a push for faster, more secure, and more efficient payment systems, including real-time gross settlement (RTGS) and instant payment infrastructures. Central banks are investing in upgrading these systems to support new digital payment methods and cross-border transactions.Cloud Computing Adoption: Cloud-based solutions offer central banks flexibility, scalability, and reduced infrastructure costs, enabling them to expand analytical capabilities and enhance operational efficiency.Increasing Focus on Financial Stability and Risk Management:Post-Crisis Regulatory Reforms: Following global financial crises (e.g., 2008, COVID-19), central banks have expanded their roles in macro- and microprudential regulation and supervision. This drives the need for more robust, integrated systems for monitoring liquidity, credit, and market risks, and ensuring compliance.Crisis Management and Liquidity Provision: Central banks play a vital role as lenders of last resort. Modern systems are crucial for swiftly managing financial crises, providing liquidity to banks, and maintaining confidence in the financial system.Enhanced Data Analytics for Systemic Risk: The need for sophisticated tools to identify, measure, and mitigate systemic risks in the financial system is pushing central banks to invest in advanced data analytics platforms.Globalization and Cross-Border Financial Flows:Management of Foreign Exchange Reserves: Increasing globalization necessitates robust systems for managing cross-border financial flows, influencing exchange rates, and maintaining the stability of national currencies through foreign exchange management.Interoperability and Standardization: As financial markets become more interconnected, central banks need systems that support interoperability with international payment infrastructures and adhere to global standards.

ABSTRACT This short paper discusses the behavior of Brazil’s sovereign risk spread, since the adoption of a floating exchange rate regime in the beginning of 1999. The data presented seem to support the hypothesis of perverse effects of domestic monetary policy on country-risk. On the other hand, fears of a future debt default - sparked by volatile presidential election polls - do not seem to explain a significant part of the risk spread, until very recently. The author is fully aware of the existence of a rich and fast-growing literature on country-risk. This paper is not an attempt to add new pieces of theory or rigorous analytical evidence to that literature; its sole aim is to make a small contribution to the debate, by pointing at some usually neglected factors that may explain Brazil’s sovereign risk spread.