Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Interest Rates (EMVMACROINTEREST) from Jan 1985 to Oct 2025 about volatility, uncertainty, equity, interest rate, interest, rate, and USA.

Graph and download economic data for Interest Rates and Price Indexes; Dow Jones U.S. Total Market Index, Level (BOGZ1FL073164013A) from 1970 to 2024 about mutual funds, equity, liabilities, interest rate, interest, rate, price index, indexes, price, and USA.

Graph and download economic data for Interest Rates and Price Indexes; Dow Jones U.S. Total Market Index, Level (BOGZ1FL073164013Q) from Q4 1970 to Q2 2025 about mutual funds, equity, liabilities, interest rate, interest, rate, price index, indexes, price, and USA.

Over 2021 the most commonly traded interest rate derivatives on the London Stock Exchange were three month futures for British pounds, of varying expiration dates. This was followed by futures on the euro interbank offered rate (Euribor), and then futures on the Sterling Overnight Interbank Average Rate (SONIA).

Interest rate futures are essentially a contact that fixes the interest rate on a loan or deposit for a period of time in the future, which (in the case of this statistic) is then tradable on a stock exchange. The type of future relates the underlying reference interest rate (LIBOR in the case of Sterling futures, or Eurobor, or SONIA).

This paper presents new evidence on how asset prices respond to new information about the money stock. It shows that the information content of money stock announcements and the response of asset prices to new information in the announcements vary with changes in the monetary policy regime, the Federal Reserve operating procedures, and the reserve accounting rules. While previous studies have examined how asset prices respond to the money stock announcements under the interest-rate targeting procedure and the nonborrowed reserve procedure, we have included new evidence from the borrowed reserve targeting procedure under both lagged and contemporaneous reserve accounting rules. Looking at how both forward exchange rates and other asset prices respond to the announcements, we distinguish between periods when the asset-price response reflected a change in the real interest rate and those when it reflected a change in the inflation premium. Finally, we show that the new contemporaneous reserve accounting rules have greatly reduced the information content of the money stock announcements.

Qurate Retail Inc. 8.0% Fixed Rate Cumulative Redeemable Preferred Stock is predicted to have moderate returns with low risk. The company has a strong financial position with consistent revenue and earnings growth. The preferred stock offers a fixed dividend rate, providing investors with a steady stream of income. However, the stock is subject to interest rate risk, as changes in interest rates could affect its market value.

As of 30 September 2025, the average monthly interest rate for bank loans taken out by small and medium enterprises (SMEs) in the United Kingdom (UK), based on the stock of outstanding loans, was at **** percent. The monthly interest rate for such loans have been declining since peaking in July 2024 at **** percent.

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

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View market daily updates and historical trends for Effective Federal Funds Rate. from United States. Source: Federal Reserve. Track economic data with YC…

This stock market dataset is designed for financial analysis and predictive modeling. It includes historical stock prices, technical indicators, macroeconomic factors, and sentiment scores to help in developing and testing machine learning models for stock trend prediction.

Dataset Features: Column Description Stock Random stock ticker (AAPL, GOOG, etc.) Date Random business date Open Open price High High price Low Low price Close Close price Volume Trading volume SMA_10 10-day Simple Moving Average RSI Relative Strength Index (10-90 range) MACD MACD indicator (-5 to 5) Bollinger_Upper Upper Bollinger Band Bollinger_Lower Lower Bollinger Band GDP_Growth Random GDP growth rate (2.5% to 3.5%) Inflation_Rate Inflation rate (1.5% to 3.0%) Interest_Rate Interest rate (0.5% to 5.0%) Sentiment_Score Random sentiment score (-1 to 1) Next_Close Next day's closing price (for regression) Target Binary classification (1: Price Increase, 0: Price Decrease)

Key Features: Stock Prices: Open, High, Low, Close, and Volume data. Technical Indicators: Simple Moving Average (SMA), Relative Strength Index (RSI), MACD, and Bollinger Bands. Macroeconomic Factors: Simulated GDP growth, inflation rate, and interest rates. Sentiment Scores: Randomized sentiment values between -1 and 1 to simulate market sentiment. Target Variables: Next-day close price (for regression) and price movement direction (for classification).

Turkey External Debt Stock: Treasury Guaranteed: Interest Rate: Combined data was reported at 110.000 USD mn in 2017. This records an increase from the previous number of 64.000 USD mn for 2016. Turkey External Debt Stock: Treasury Guaranteed: Interest Rate: Combined data is updated yearly, averaging 139.000 USD mn from Dec 2002 (Median) to 2017, with 16 observations. The data reached an all-time high of 271.000 USD mn in 2008 and a record low of 64.000 USD mn in 2016. Turkey External Debt Stock: Treasury Guaranteed: Interest Rate: Combined data remains active status in CEIC and is reported by Turkish Treasury. The data is categorized under Global Database’s Turkey – Table TR.JB014: Treasury Guaranteed External Debt Stock.

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.

This chapter reviews the behavior of financial asset prices in relation to consumption. The chapter lists some important stylized facts that characterize US data, and relates them to recent developments in equilibrium asset pricing theory. Data from other countries are examined to see which features of the US experience apply more generally. The chapter argues that to make sense of asset market behavior one needs a model in which the market price of risk is high, time-varying, and correlated with the state of the economy. Models that have this feature, including models with habit formation in utility, heterogeneous investors, and irrational expectations, are discussed. The main focus is on stock returns and short-term real interest rates, but bond returns are also considered.

United States - Equity Market Volatility Tracker: Macroeconomic News and Outlook: Interest Rates was 5.94573 Index in October of 2025, according to the United States Federal Reserve. Historically, United States - Equity Market Volatility Tracker: Macroeconomic News and Outlook: Interest Rates reached a record high of 23.32740 in October of 1987 and a record low of 1.74079 in May of 2017. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Equity Market Volatility Tracker: Macroeconomic News and Outlook: Interest Rates - last updated from the United States Federal Reserve on November of 2025.

The hypothesis is that an asset pricing model can explain real US short term bond interest rates. This is tested by using data to construct the three shocks of the model and inputting the shocks back into the model to produce the model generated US real bond interest rate from 1975-2020. This is then compared to the actual US data. The notable results are that the data matches the model generated data with a high correlation and relative volatility near one, indicating a close fit.

The quarterly data set presents all variables used to fit the model to the data, for 1975Q1 to 2020Q4. All data series after construction are transformed by taking natural logarithms and detrending them to be in deviations from their respective trends. In the filtered results, we used a Hodrick-Prescott filter with λ=1600.

In constructing real output, consumption, investment, government expenditures, and net exports real per capita series from raw data we follow Chari et al. (2007). [Chari, V. V., Kehoe, Patrick J., and McGrattan, Ellen R., 2007. "Business Cycle Accounting", Econometrica, vol. 75(3), pp. 781--836, May.] The final output series used is then obtained by deducted government expenditures and net exports from the total output to be consistent with our model.

Quarterly employment and physical capital are obtained by interpolating annual data using the method in Chari et al. (2007). The goods sector labor share is measured by the Total Full-Time and Part-Time Employees minus the Full-Time and Part-Time Employees in Finance and Insurance Services (FIS) and divided by the Civilian Noninstitutional Population. The proxy for the banking time share is the same as employees in FIS as divided by the Civilian Noninstitutional Population. Leisure is then the residual share. The quarterly physical capital stock is constructed as the sum of the annual Current cost net stock of consumer durables and fixed assets interpolated. It is transformed into real terms by normalizing with the implicit price deflator for durable goods.

The inflation measure is the CPI index, quarterly, with the percentage change from the year before (on an annual basis). Velocity measures are constructed by dividing real consumption with the respective real money stocks. The nominal series for exchange credit and deposits are transformed to real terms by normalizing with the CPI index.

The data can be used in conjunction with the Matlab Code files for the model, which are attached here. This allows one to replicate the model results.

Turkmenistan TM: External Debt: DOD: Stocks: Variable Rate data was reported at 143.055 USD mn in 2016. This records an increase from the previous number of 136.000 USD mn for 2015. Turkmenistan TM: External Debt: DOD: Stocks: Variable Rate data is updated yearly, averaging 110.903 USD mn from Dec 1970 (Median) to 2016, with 47 observations. The data reached an all-time high of 1.758 USD bn in 1999 and a record low of 0.000 USD mn in 1992. Turkmenistan TM: External Debt: DOD: Stocks: Variable Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Turkmenistan – Table TM.World Bank: External Debt: Debt Outstanding, Debt Ratio and Debt Service. Variable interest rate is long-term external debt with interest rates that float with movements in a key market rate; for example, the London interbank offered rate (LIBOR) or the U.S. prime rate. This item conveys information about the borrower's exposure to changes in international interest rates. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars.; ; World Bank, International Debt Statistics.; Sum;

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.

How accurate is machine learning in stock market? (TD 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

India Interest Rate: Share of Outstanding Floating Rate Rupee Loans: Public Sector Banks: Base Rate data was reported at 2.218 % in Sep 2024. This records a decrease from the previous number of 2.421 % for Jun 2024. India Interest Rate: Share of Outstanding Floating Rate Rupee Loans: Public Sector Banks: Base Rate data is updated quarterly, averaging 6.117 % from Sep 2019 (Median) to Sep 2024, with 21 observations. The data reached an all-time high of 14.381 % in Sep 2019 and a record low of 2.218 % in Sep 2024. India Interest Rate: Share of Outstanding Floating Rate Rupee Loans: Public Sector Banks: Base Rate data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Interest and Foreign Exchange Rates – Table IN.MB002: Bank Interest Rate: Share of Outstanding Floating Rate Rupee Loans.

This is dataset combining the stock prices for S&P 500 between 1927-12-30 and 2023-03-07 and FEDs interest rates. There is no info for interest rates before 1954. V1 version filters out missing rates before 1954.

View weekly updates and historical trends for 30 Year Mortgage Rate. from United States. Source: Freddie Mac. Track economic data with YCharts analytics.