The benchmark interest rate in the United States was last recorded at 4 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

U.S. Economic and Financial Dataset

Dataset Description

This dataset combines historical U.S. economic and financial indicators, spanning the last 50 years, to facilitate time series analysis and uncover patterns in macroeconomic trends. It is designed for exploring relationships between interest rates, inflation, economic growth, stock market performance, and industrial production.

Key Features

• Frequency: Monthly
• Time Period: Last 50 years from Nov-24
• Sources:
  • Federal Reserve Economic Data (FRED)
  • Yahoo Finance

Dataset Feature Description

1. Interest Rate (Interest_Rate):

   • The effective federal funds rate, representing the interest rate at which depository institutions trade federal funds overnight.

2. Inflation (Inflation):

   • The Consumer Price Index for All Urban Consumers, an indicator of inflation trends.

3. GDP (GDP):

   • Real GDP measures the inflation-adjusted value of goods and services produced in the U.S.

4. Unemployment Rate (Unemployment):

   • The percentage of the labor force that is unemployed and actively seeking work.

5. Stock Market Performance (S&P500):

   • Monthly average of the adjusted close price, representing stock market trends.

6. Industrial Production (Ind_Prod):

   • A measure of real output in the industrial sector, including manufacturing, mining, and utilities.

Dataset Statistics

1. Total Entries: 599
2. Columns: 6
3. Memory usage: 37.54 kB
4. Data types: float64

Feature Overview

• Columns:
  • Interest_Rate: Monthly Federal Funds Rate (%)
  • Inflation: CPI (All Urban Consumers, Index)
  • GDP: Real GDP (Billions of Chained 2012 Dollars)
  • Unemployment: Unemployment Rate (%)
  • Ind_Prod: Industrial Production Index (2017=100)
  • S&P500: Monthly Average of S&P 500 Adjusted Close Prices

Executive Summary

This project explores the interconnected dynamics of key macroeconomic indicators and financial market trends over the past 50 years, leveraging data from the Federal Reserve Economic Data (FRED) and Yahoo Finance. The dataset integrates critical variables such as the Federal Funds Rate, Inflation (CPI), Real GDP, Unemployment Rate, Industrial Production, and the S&P 500 Index, providing a holistic view of the U.S. economy and financial markets.

The analysis focuses on uncovering relationships between these variables through time-series visualization, correlation analysis, and trend decomposition. Key findings are included in the Insights section. This project serves as a robust resource for understanding long-term economic trends, policy impacts, and market behavior. It is particularly valuable for students, researchers, policymakers, and financial analysts seeking to connect macroeconomic theory with real-world data.

Potential Use Cases

• Economic Analysis: Examine relationships between interest rates, inflation, GDP, and unemployment.
• Stock Market Prediction: Study how macroeconomic indicators influence stock market trends.
• Time Series Modeling: Perform ARIMA, VAR, or other models to forecast economic trends.
• Cyclic Pattern Analysis: Identify how economic shocks and recoveries impact key indicators.

Snap of Power Analysis

https://github.com/user-attachments/assets/1b40e0ca-7d2e-4fbc-8cfd-df3f09e4fdb8">

To ensure sufficient power, the dataset covers last 50 years of monthly data i.e., around 600 entries.

Key Insights derived through EDA, time-series visualization, correlation analysis, and trend decomposition

• Interest Rate and Inflation Dynamics: The interest Rate and inflation exhibit an inverse relationship, especially during periods of aggressive monetary tightening by the Federal Reserve.
• Economic Growth and Market Performance: GDP growth and the S&P 500 Index show a positive correlation, reflecting how market performance often aligns with overall economic health.
• Labor Market and Industrial Output: Unemployment and industrial production demonstrate a strong inverse relationship. Higher industrial output is typically associated with lower unemployment
• Market Behavior During Economic Shocks: The S&P 500 experienced sharp declines during significant crises, such as the 2008 financial crash and the COVID-19 pandemic in 2020. These events also triggered increased unemployment and contractions in GDP, highlighting the interplay between markets and the broader economy.
• Correlation Highlights: S&P 500 and GDP have a strong positive correlation. Interest rates negatively correlate with GDP and inflation, reflecting monetary policy impacts. Unemployment is negatively correlated with industrial production but positively correlated with interest rates.

Link to GitHub Repo

https:/...

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Canada was last recorded at 2.25 percent. This dataset provides - Canada Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Sweden was last recorded at 1.75 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Russia was last recorded at 16.50 percent. This dataset provides the latest reported value for - Russia Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in the United Kingdom was last recorded at 4 percent. This dataset provides - United Kingdom Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Minority-Interest-Expense Time Series for Bank of America Corp. Bank of America Corporation, through its subsidiaries, provides various financial products and services for individual consumers, small and middle-market businesses, institutional investors, large corporations, and governments worldwide. The company operates through four segments: Consumer Banking, Global Wealth & Investment Management (GWIM), Global Banking, and Global Markets. The Consumer Banking segment offers traditional and money market savings accounts, certificates of deposit and IRAs, checking accounts, and investment accounts and products; credit and debit cards; residential mortgages and home equity loans; and direct and indirect loans, such as automotive, recreational vehicle, and consumer personal loans. The GWIM segment provides investment management, brokerage, banking, and trust and retirement products and services; wealth management solutions; and customized solutions, including specialty asset management services. The Global Banking segment offers lending products and services, including commercial loans, leases, commitment facilities, trade finance, and commercial real estate and asset-based lending; treasury solutions, such as treasury management, foreign exchange, short-term investing options, and merchant services; working capital management solutions; debt and equity underwriting and distribution, and merger-related and other advisory services; and fixed-income and equity research services. The Global Markets segment provides market-making, financing, securities clearing, settlement, and custody services; securities and derivative products; and risk management products using interest rate, equity, credit, currency and commodity derivatives, foreign exchange, fixed-income, and mortgage-related products. Bank of America Corporation was founded in 1784 and is based in Charlotte, North Carolina.

This table contains 39 series, with data for starting from 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Financial market statistics (39 items: Government of Canada Treasury Bills, 1-month (composite rates); Government of Canada Treasury Bills, 2-month (composite rates); Government of Canada Treasury Bills, 3-month (composite rates);Government of Canada Treasury Bills, 6-month (composite rates); ...).

This dataset contains synthetic real estate transaction data for neighborhoods in the Vancouver metropolitan area, covering the period from 2004 to 2024. The dataset simulates market trends, house price fluctuations, and contains features commonly associated with real estate listings, such as property type, number of bedrooms, bathrooms, square footage, and more. No real-world data is used; this dataset is entirely computer-generated for educational and demonstration purposes.

The dataset includes additional features such as:

A price surge from late 2020 to early 2022 to reflect real-world trends during that period of low-interest rates.

Randomly introduced outliers and noise to simulate abnormal transactions, such as significantly higher or lower sale prices.

This dataset can be used for educational purposes, machine learning projects, and time series forecasting demonstrations. It is a great tool for practicing data cleaning, exploratory data analysis (EDA), feature engineering, and modeling in the context of real estate.

Columns:

Neighborhood: The neighborhood where the property is located, broken down by cities in Vancouver (e.g., Kitsilano, Mount Pleasant, Guildford).

Year: Year of the transaction (2004-2024).

Season: Season of the year (Spring, Summer, Fall, Winter).

Property Type: Type of the property (House, Condo, Townhouse, Duplex, Triplex).

Bedrooms: Number of bedrooms in the property.

Bathrooms: Number of bathrooms in the property.

Year Built: The year the property was built.

Renovation Year: Year of the most recent renovation, if applicable.

Garage Type: Type of garage (None, Single, Double, Triple).

Square Footage (House): The size of the house in square feet.

Square Footage (Land): The size of the land in square feet.

Basement: Whether the basement is finished or not (Finished, Not Finished).

Legal Units: Number of legal units in the property (e.g., 0-2).

Market Price: The market price of the property for the given year and season, including random noise and seasonal fluctuations.

Usage Notes:

This dataset is synthetic and does not represent actual transactions. It is intended for educational purposes and should not be used for real-world financial or investment decisions.

It can be used for projects focused on time series forecasting, regression analysis, and data visualization.

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

About Dataset

Context

Lending Club is a peer-to-peer lending platform that allows borrowers to obtain loans directly from individual and institutional investors. The company, which was founded in 2007, acts as a middleman, connecting borrowers with lenders, and charging fees for its services. Loans issued through Lending Club can be used for a variety of purposes, including debt consolidation, home improvement, and small business expenses. The platform is available to borrowers and investors in the United States.

About the dataset

The Lending Club loan dataset is a collection of information on loans issued through the Lending Club platform. The dataset includes information on the borrower, such as credit score, income, and employment history, as well as details about the loan, including the loan amount, interest rate, and loan status. The dataset also includes information on the loan's purpose and the borrower's credit history.

Content

This is a cleaned version of the following Lending Club Dataset: here, which has Lending Club loans ranging from 2007 to 2020 Q3.

The script used to build this cleaned version can be found here.

The data dictionary can be found here.

Here are the changes applied to the dataset: * Created a data-catalog in CSV * Changed the dataset to Feather (a fast, lightweight, and easy-to-use binary file format for storing data frames) * The Feather keeps all datatype metadata stored (like category datatype), and it is much smaller than a CSV file * Changed all numerical columns to proper datatype in order to reduce memory usage and to improve the dataset usability * All string/object columns were transformed to category pandas data type * All data columns were changed to pandas datetime * All numerical string columns are fixed to numerical (for example, int_rate) * Added a smaller resampled version of dataset in CSV to be previewed on Kaggle

Context

On UN SDGs (focus:EU27) I posted on 2019-12-30 a dataset containing indicators from Eurostat, but then on 2020-04-24 (lockdown), decided to expand the dataset by searching within the Worldbank huge collection of indicators those that could be useful in my interim publications focusing on the same themes.

Hence, selected 33 indicators (actually 34, but one is across- the GINI index estimate), and created a dataset containing those indicators, adding a sample notebook focused on EU27 to show the content.

Eventually, both the WorldBank and Eurostat data (routinely updated by their sources) will be integrated within the digital side (that I will share online to allow others to develop their own analyses) of the book that I have been keeping on the back burner since 2015 (see below).

UPDATE 2020-11-26: added to the series of datasets supporting my ongoing publications on sustainable digital transformation also another dataset: EU 27 Energy sources - consumption 1990-2018 - energy production, import, export by source - consumption by sector

UPDATE 2022-11-17: extended the series to 2021, with data extracted 2022-09-16

Content

Shared the "raw" data as downloaded from the World Bank webite, with the following limited preparation steps:

1. extracted the "dimensions", i.e. the Country and Indicator codes and descriptions
2. unified the 34 CSVs (34 indicators, see below) in a single CSV, smaller after leaving just the codes for the "dimensions" and the actual values
3. selected as period of reference 2000-2021, to focus on the XXI century (albeit 2000 data might actually refer to 1999 or earlier information).

Selection rationale

The criteria for selection of those 33 indicators was:

1. should be foreseen a routine update by the source (in this case, yearly), if needed
2. are considered non-partisan (i.e. do not represent a specific agenda, and therefor could be more acceptable as components to indicators to both political activists and business advocates)
3. each indicator should be provided by a "domain expert source" and then collated by an acknowledged "super-partes" (again, to improve credibility and perception as non-biased)
4. last but not least: each indicator should be useful, alone or with others, to help set an agenda for action (e.g. used the indicators on access to Internet, workforce inclusion, urbanization, etc)

Release date and timeframe coverage

The updated collated dataset was released on 2022-11-17, and contains data from 1999 until 2021

"Raw" WorldBank indicators list

The "keycode" (e.g. TX.VAL.TECH.MF.ZS) can be used on WorldBank search index to both see the metadata, the timeframe available, preview, and select other export options

In order to enable to check on the metadata if the information might be of interested to you, this is the list of the indicators: | index | key | description | | --- | --- | --- | | 0 | CM.MKT.LCAP.GD.ZS | Market capitalization of listed domestic companies (% of GDP) | | 1 | EG.IMP.CONS.ZS | Energy imports, net (% of energy use) | | 2 | EG.USE.ELEC.KH.PC | Electric power consumption (kWh per capita) | | 3 | EN.URB.LCTY.UR.ZS | Population in the largest city (% of urban population) | | 4 | FP.CPI.TOTL.ZG | Inflation, consumer prices (annual %) | | 5 | FR.INR.LNDP | Interest rate spread (lending rate minus deposit rate, %) | | 6 | FS.AST.PRVT.GD.ZS | Domestic credit to private sector (% of GDP) | | 7 | GB.XPD.RSDV.GD.ZS | Research and development expenditure (% of GDP) | | 8 | GC.NLD.TOTL.GD.ZS | Net lending (+) / net borrowing (-) (% of GDP) | | 9 | GC.TAX.TOTL.GD.ZS | Tax revenue (% of GDP) | | 10 | GC.XPN.TOTL.GD.ZS | Expense (% of GDP) | | 11 | IC.BUS.DISC.XQ | Business extent of disclosure index (0=less disclosure to 10=more disclosure) | | 12 | IC.CRD.INFO.XQ | Depth of credit information index (0=low to 8=high) | | 13 | IC.GOV.DURS.ZS | Time spent dealing with the requirements of government regulations (% of senior management time) | | 14 | IC.LGL.CRED.XQ | Strength of legal rights index (0=weak to 12=strong) | | 15 | IC.REG.DURS.FE | Time required to start a business, female (days) | | 16 | IC.REG.DURS.MA | Time required to start a business, male (days) | | 17 | IC.TAX.TOTL.CP.ZS | Total tax and contribution rate (% of profit) | | 18 | IT.CEL.SETS.P2 | Mobile cellular subscriptions (per 100 people) | | 19 | IT.MLT.MAIN.P2 | Fixed telephone subscriptions (per 100 people) | | 20 | IT.NET.BBND.P2 | Fixed broadband subscriptions (per 100 people) | | 21 | LP.LPI.OVRL.XQ | Logistics performance index: Overall (1=low to 5=high) | | 22 | MS.MIL.XPND.GD.ZS | Military expenditure (% of GDP) | | 23 | NY.GDP.MKTP.KD.ZG | GDP growth (ann...

A table that shows in detail by CUSIP, the interest rate, the STRIP CUSIP, maturity date, and amounts outstanding for securities held in unstripped form, stripped form and amount that have been reconstituted. STRIP stands for Separate Trading of Registered Interest and Principal of Securities. This is a security that has been stripped down into separate securities representing the principal and each interest payment. Each payment has its own identification number and can be traded individually. These securities are also known as zero-coupon bonds.

The benchmark interest rate in Turkey was last recorded at 39.50 percent. This dataset provides the latest reported value for - Turkey Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

A table that shows in detail by CUSIP, interest rate, the issue date, maturity date, interest payment dates and amounts outstanding for unmatured Bills, Notes, Bonds, Treasury Inflation-Protected Securities and Floating Rate Notes as of the last business day of the month.

The benchmark interest rate in Switzerland was last recorded at 0 percent. This dataset provides - Switzerland Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Pakistan was last recorded at 11 percent. This dataset provides - Pakistan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Norway was last recorded at 4 percent. This dataset provides the latest reported value for - Norway Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.