Brazil Broad Money Supply: M3: Operation Committed with Federal Securities data was reported at 113,074.640 BRL mn in Jun 2018. This records an increase from the previous number of 103,266.242 BRL mn for May 2018. Brazil Broad Money Supply: M3: Operation Committed with Federal Securities data is updated monthly, averaging 29,866.033 BRL mn from Jul 1994 (Median) to Jun 2018, with 288 observations. The data reached an all-time high of 218,686.067 BRL mn in Mar 2016 and a record low of 0.000 BRL mn in Jul 1999. Brazil Broad Money Supply: M3: Operation Committed with Federal Securities data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Global Database’s Brazil – Table BR.KAA018: Money Supply. Brazilian Central Bank has made changes in methodology of Financial System Credit Data in February of 2013 after 13 years following the same methodology. These changes are necessary face the expansion of credit, favored by the improvement of the indicators of employment and income, continuous and sharp reduction of the interest rates and by important institutional advances. It is essential the availability of new information, in particular, which allows more detailed monitoring of credit arrangements with targeted resources, especially real estate financing, whose dynamism has contributed to reducing the housing deficit in the country. The main change includes coverage of data on concessions, interest rates, terms and default rates that were extended to the segment of directed credit and also became necessary to further detailing the statistical framework, to enable identification of the terms most relevant as well as reduce the relative share of loans not classified - embedded in 'other receivables'. The Money Supply statistics were revised in August 2018, incorporating methodological updates to increase compliance with international standards and consistency with other sets of macroeconomic statistics. The revision consists the inclusion of cooperatives among the institutions that meke up the money issuing system, resulting in M1 expansion, and the exclusion of non-residents assets, impacting mainly on M4. Replacement series ID: 408100927

This dataset provides a comprehensive collection of key U.S. macroeconomic indicators spanning the past 25 years (approximately 1998–2023). It includes monthly data on:

M2 Money Supply (M2SL): A broad measure of money in circulation, including cash, checking deposits, and easily convertible near money. Federal Funds Effective Rate (FEDFUNDS): The interest rate at which depository institutions trade federal funds with each other overnight. Interest Rates: Various benchmark interest rates relevant to economic analysis. 10-Year Treasury Constant Maturity Rate (GS10): Reflects market expectations for long-term interest rates and economic growth. All data are sourced from the Federal Reserve Economic Data (FRED) database and are seasonally adjusted where applicable.

This dataset is ideal for economic research, financial modeling, market forecasting, and machine learning applications where macroeconomic variables are relevant. The data is cleaned, merged, and formatted for immediate use, with date-stamped entries aligned on a monthly frequency.

Source: Federal Reserve Economic Data (FRED) — https://fred.stlouisfed.org/

License: CC0: Public Domain

Effect of MFS transactions on monetary aggregates during a given month.

This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

There are 20 columns and 343 rows spanning 1990-04 to 2022-10

The columns are:

1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

📂 Dataset Overview - Rows (Entries): 110 - Columns (Features): 6

Columns Description 1. Date - Format: MMM-YYYY (e.g., Jul-2025) - Monthly observations 1. Inflation_YoY (Year-on-Year Inflation %) - Inflation rate in percentage (YoY basis) - Range: 0.3% – 38% - Average: 11.6% - Can be treated as the dependent variable

1. Oil_Price_USD_Barrel
2. Global crude oil price (USD per barrel)
3. Range: 15.18 – 113.77

4. Average: 62.75

5. Exchange_Rate_PKR_USD

• Pakistani Rupee per US Dollar exchange rate
• Range: 104.6 – 304.8
• Average: 185.0

1. Interest_Rate

• State Bank of Pakistan policy rate (%)
• Range: 6.8% – 21.46%
• Average: 11.8%

1. Money_Supply_M2_Billion
2. Broad Money Supply (M2) in billion PKR
3. Range: 12,486 – 41,786
4. Average: 23,124

📊 Statistical Insights

Inflation Trends: High volatility observed between 2019–2023 (peaking at 38%), while in 2025 inflation dropped to ~3–4%.

Oil Price Relation: Fluctuations in crude oil prices appear linked with inflation movements.

Exchange Rate Impact: The depreciation of PKR from ~104 to 300+ significantly impacted inflation and interest rates.

Interest Rate Policy: Mostly ranged between 7–15%, but spiked to ~21% during currency crisis.

Money Supply Growth: Broad money consistently increased, adding long-term inflationary pressure.

📈**Possible Analyses for Kaggle**

1. Trend Analysis

2. Monthly inflation, oil price, exchange rate visualization.

3. Correlation Study

4. Inflation vs Oil Prices

5. Inflation vs Exchange Rate

6. Inflation vs Interest Rate

7. Forecasting Models

8. Time-Series forecasting (ARIMA, Prophet)

9. Regression models using oil prices, exchange rate, and money supply as predictors

10. Economic Insights

• Impact of global oil shocks on Pakistan’s inflation
• Role of monetary policy in inflation control
• Currency depreciation vs domestic inflation

This dataset supports the research exploring the impact of monetary policy instruments on the Colombian economy, focusing on the classical dichotomy and monetary neutrality. The analysis delves into how monetary policy, including instruments such as interest rates and money supply, influences both nominal and real variables in the economy. It also highlights the relationship between monetary policy and economic stability, particularly how central banks manage inflation and economic growth. Key sections explore the separation between nominal and real variables as explained by the classical dichotomy, and the principle of monetary neutrality, which argues that changes in money supply affect nominal variables without impacting real economic factors.

The dataset is structured around a combination of theoretical insights and simulations that analyze the effectiveness of monetary neutrality in the Colombian context, given both domestic and international economic challenges such as the war in Ukraine and agricultural sector disruptions. Through simulations, the dataset demonstrates the effects of monetary expansion on variables like inflation, production, and employment, providing a framework for understanding current economic trends and proposing solutions to socio-economic challenges in Colombia.

Data source: Bangladesh Bank [24].

While higher interest rates increase the cost of credit financing for businesses, this study finds that the direct impact of this traditional credit transmission mechanism on corporate bankruptcy risk is limited. Instead, our research reveals that changes in corporate behavior induced by rising debt financing costs are the root cause of bankruptcy risk. In the short term, an increase in interest rates drives businesses to substitute supply chain financing for credit financing in pursuit of profit maximization. This mismatch of short-term debt and long-term investments undermines the sustainability of the supply chain, ultimately reducing financial security—sacrificing safety for profitability. In the long term, higher interest rates exacerbate the overcapacity problem in industries, increasing the unsustainability of the production and sales balance. Using data from China’s construction industry, this study empirically tests these findings and, based on the main conclusions, provides policy suggestions regarding the long- and short-term effects of monetary policy on the sustainable development of China’s construction industry: (1) focus on short-term interest rate risks and be vigilant against commercial credit bubbles; (2) long-term monetary policy should prioritize industrial structure optimization.

Financial Access and Usage

Global Comparative Ratios Across 189 Jurisdictions

By International Monetary Fund [source]

About this dataset

This dataset provides an unprecedented opportunity to explore global financial access and usage trends from 2004-2016 from 189 of the world's reporting jurisdictions—which cover over 99 percent of the total adult population. With 152 time series and 47 indicator ratios, this Financial Access Survey gives insight into ways that access to and usage of financial services differ by households vs small/medium enterprises, life vs non-life insurance, deposits & microfinance institutions as well as credit unions & financial cooperatives. Utilizing this data, we can gain a better understanding of how policies or shifts in the global economy may influence or relate to access or utilization of services in certain regions while having comparable cross-economy comparisons. The IMF Monetary and Financial Statistics Manual Compilation Guide is utilized for all methodologies used in accumulating these datasets, while all data is available “as-is” with no guarantee provided either express or implied. Are you looking for ways to implement insightful macroeconomic analyses? Download FAS 2004–2016 now!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The Financial Access Survey provides global supply-side data on access to and usage of financial services by households and firms for 189 reporting jurisdictions, covering 99 percent of the world’s adult population. With a robust selection of time series in this dataset, users can make meaningful insights into trends over time or across countries concerning various indicators related to access and usage of financial services. To help users navigate this large dataset, the following guide explains how to use the data most effectively.

Understanding The Dataset Columns

The columns in the dataset provide information about each indicator such as country name, indicator name, code for that indicator, its attribute (i.e., rate/ratio), when data is available for that particular indicator. Once you have identified an interesting measure/indicator whether it be credit union density or life insurance penetration rate measure in a given country during a certain year period then you can look up those numbers from the rows provided in this dataset .

Understanding The Different Years Available & Comparing Numbers Over Time

It is useful for users to compare different indicators over time by looking at specific years within this dataset which will allow us to see if rates are increasing or decreasing worldwide patterns across these trends among different countries based on these various measures listed provided in this survey such as mortgage lending rate or ratio GDP per capita etc that have been collected . We can therefore make use of our knowledge off economic changes that have occurred over time within certain parts of world , no matter if they are longer term economic effects due increases certain capabilities within a geographical area or shorter term changes due taxation laws by governments etc driving some people either towards using or away from using certain kinds financial products .

#### Comparing Between Countries

This datasets allows us direct comparisons between different countries with regards how many people are currently making use particular types off finances services , we certainly be able analyse current international relationships between services providers as well customers where ever concerned about particular attributes mentioned above whether being deposit interest rates small business credits terms tenders so forth . Knowing more about related dynamics helps build better user experiences with providers who understand needs risks impacts generating larger customer bases globally which often beneficial both parties involved exchange relationship so not forget always keep cross border motif whenever eye process from afar !

Research Ideas

• Comparing the access to and usage of financial services in different countries to better inform research policy decisions.
• Analyzing trends in financial access and usage by jurisdiction over time, to identify areas needing improvement in order to promote financial inclusion and stability.
• Cross-referencing FAS data with macroeconomic indicators such as GDP information to measure the potential impact of changes in level of access on economic growth or other metrics specific to a country or region of interest

Acknowledgements

If you use this dataset in yo...

China’s crude oil import has increased sharply since 2002. Its expenditure on oil import now accounts for around 10% of its total commodity import. Thus, there is potential imported inflation or deflation due to oil price fluctuations and China’s central bank may respond to it. We quantitatively analyze the impact of oil prices on China’s benchmark interest rate and monetary supply by a 6-variable structural vector auto-regression model. We draw that: 1) In response to an increase of oil price, China’s central bank generally upgrades interest rate. If oil price rises by 10 US dollars, the 6-month lending base rate will increase by around 0.13 percentage point in 3 months. 2) The effects of price shocks deepen after the oil pricing reform, and specifically, it can explain 19.8% of the variations in monetary policies in one year after October 2008, compared with the 0.83% before October 2001.

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.

DTE Energy (DTE) Stock Forecast: Powering Profits in 2024?

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 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.

Tesla Stock: An Analysis of its Growth, Volatility, and Future Prospects

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 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.

IMNN Stock Forecast

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