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 (BOGZ1FL073164013Q) from Q4 1970 to Q2 2025 about mutual funds, equity, liabilities, interest rate, interest, rate, price index, indexes, price, and USA.

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

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.

The U.S. federal funds rate peaked in 2023 at its highest level since the 2007-08 financial crisis, reaching 5.33 percent by December 2023. A significant shift in monetary policy occurred in the second half of 2024, with the Federal Reserve implementing regular rate cuts. By December 2024, the rate had declined to 4.48 percent. What is a central bank rate? The federal funds rate determines the cost of overnight borrowing between banks, allowing them to maintain necessary cash reserves and ensure financial system liquidity. When this rate rises, banks become more inclined to hold rather than lend money, reducing the money supply. While this decreased lending slows economic activity, it helps control inflation by limiting the circulation of money in the economy. Historic perspective The federal funds rate historically follows cyclical patterns, falling during recessions and gradually rising during economic recoveries. Some central banks, notably the European Central Bank, went beyond traditional monetary policy by implementing both aggressive asset purchases and negative interest rates.

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 main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

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.

In 2025, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the financial crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

The Federal Reserve's balance sheet has undergone significant changes since 2007, reflecting its response to major economic crises. From a modest *** trillion U.S. dollars at the end of 2007, it ballooned to approximately **** trillion U.S. dollars by October 29, 2025. This dramatic expansion, particularly during the 2008 financial crisis and the COVID-19 pandemic—both of which resulted in negative annual GDP growth in the U.S.—showcases the Fed's crucial role in stabilizing the economy through expansionary monetary policies. Impact on inflation and interest rates The Fed's expansionary measures, while aimed at stimulating economic growth, have had notable effects on inflation and interest rates. Following the quantitative easing in 2020, inflation in the United States reached ***** percent in 2022, the highest since 1991. However, by August 2025, inflation had declined to *** percent. Concurrently, the Federal Reserve implemented a series of interest rate hikes, with the rate peaking at **** percent in August 2023, before the first rate cut since September 2021 occurred in September 2024. Financial implications for the Federal Reserve The expansion of the Fed's balance sheet and subsequent interest rate hikes have had significant financial implications. In 2024, the Fed reported a negative net income of ***** billion U.S. dollars, a stark contrast to the ***** billion U.S. dollars profit in 2022. This unprecedented shift was primarily due to rapidly rising interest rates, which caused the Fed's interest expenses to soar to over *** billion U.S. dollars in 2023. Despite this, the Fed's net interest income on securities acquired through open market operations reached a record high of ****** billion U.S. dollars in the same year.

This is not going to be an article or Op-Ed about Michael Jordan. Since 2009 we've been in the longest bull-market in history, that's 11 years and counting. However a few metrics like the stock market P/E, the call to put ratio and of course the Shiller P/E suggest a great crash is coming in-between the levels of 1929 and the dot.com bubble. Mean reversion historically is inevitable and the Fed's printing money experiment could end in disaster for the stock market in late 2021 or 2022. You can read Jeremy Grantham's Last Dance article here. You are likely well aware of Michael Burry's predicament as well. It's easier for you just to skim through two related videos on this topic of a stock market crash. Michael Burry's Warning see this YouTube. Jeremy Grantham's Warning See this YouTube. Typically when there is a major event in the world, there is a crash and then a bear market and a recovery that takes many many months. In March, 2020 that's not what we saw since the Fed did some astonishing things that means a liquidity sloth and the risk of a major inflation event. The pandemic represented the quickest decline of at least 30% in the history of the benchmark S&P 500, but the recovery was not correlated to anything but Fed intervention. Since the pandemic clearly isn't disappearing and many sectors such as travel, business travel, tourism and supply chain disruptions appear significantly disrupted - the so-called economic recovery isn't so great. And there's this little problem at the heart of global capitalism today, the stock market just keeps going up. Crashes and corrections typically occur frequently in a normal market. But the Fed liquidity and irresponsible printing of money is creating a scenario where normal behavior isn't occurring on the markets. According to data provided by market analytics firm Yardeni Research, the benchmark index has undergone 38 declines of at least 10% since the beginning of 1950. Since March, 2020 we've barely seen a down month. September, 2020 was flat-ish. The S&P 500 has more than doubled since those lows. Look at the angle of the curve: The S&P 500 was 735 at the low in 2009, so in this bull market alone it has gone up 6x in valuation. That's not a normal cycle and it could mean we are due for an epic correction. I have to agree with the analysts who claim that the long, long bull market since 2009 has finally matured into a fully-fledged epic bubble. There is a complacency, buy-the dip frenzy and general meme environment to what BigTech can do in such an environment. The weight of Apple, Amazon, Alphabet, Microsoft, Facebook, Nvidia and Tesla together in the S&P and Nasdaq is approach a ridiculous weighting. When these stocks are seen both as growth, value and companies with unbeatable moats the entire dynamics of the stock market begin to break down. Check out FANG during the pandemic. BigTech is Seen as Bullet-Proof me valuations and a hysterical speculative behavior leads to even higher highs, even as 2020 offered many younger people an on-ramp into investing for the first time. Some analysts at JP Morgan are even saying that until retail investors stop charging into stocks, markets probably don’t have too much to worry about. Hedge funds with payment for order flows can predict exactly how these retail investors are behaving and monetize them. PFOF might even have to be banned by the SEC. The risk-on market theoretically just keeps going up until the Fed raises interest rates, which could be in 2023! For some context, we're more than 1.4 years removed from the bear-market bottom of the coronavirus crash and haven't had even a 5% correction in nine months. This is the most over-priced the market has likely ever been. At the night of the dot-com bubble the S&P 500 was only 1,400. Today it is 4,500, not so many years after. Clearly something is not quite right if you look at history and the P/E ratios. A market pumped with liquidity produces higher earnings with historically low interest rates, it's an environment where dangerous things can occur. In late 1997, as the S&P 500 passed its previous 1929 peak of 21x earnings, that seemed like a lot, but nothing compared to today. For some context, the S&P 500 Shiller P/E closed last week at 38.58, which is nearly a two-decade high. It's also well over double the average Shiller P/E of 16.84, dating back 151 years. So the stock market is likely around 2x over-valued. Try to think rationally about what this means for valuations today and your favorite stock prices, what should they be in historical terms? The S&P 500 is up 31% in the past year. It will likely hit 5,000 before a correction given the amount of added liquidity to the system and the QE the Fed is using that's like a huge abuse of MMT, or Modern Monetary Theory. This has also lent to bubbles in the housing market, crypto and even commodities like Gold with long-term global GDP meeting many headwinds in the years ahead due to a...

An index that can be used to gauge broad financial conditions and assess how these conditions are related to future economic growth. The index is broadly consistent with how the FRB/US model generally relates key financial variables to economic activity. The index aggregates changes in seven financial variables: the federal funds rate, the 10-year Treasury yield, the 30-year fixed mortgage rate, the triple-B corporate bond yield, the Dow Jones total stock market index, the Zillow house price index, and the nominal broad dollar index using weights implied by the FRB/US model and other models in use at the Federal Reserve Board. These models relate households' spending and businesses' investment decisions to changes in short- and long-term interest rates, house and equity prices, and the exchange value of the dollar, among other factors. These financial variables are weighted using impulse response coefficients (dynamic multipliers) that quantify the cumulative effects of unanticipated permanent changes in each financial variable on real gross domestic product (GDP) growth over the subsequent year. The resulting index is named Financial Conditions Impulse on Growth (FCI-G). One appealing feature of the FCI-G is that its movements can be used to measure whether financial conditions have tightened or loosened, to summarize how changes in financial conditions are associated with real GDP growth over the following year, or both.

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.

Graph and download economic data for Dividend Yield of Common Stocks on the New York Stock Exchange, Composite Index for United States from Jan 1926 to Feb 1969 about dividends, composite, stock market, NY, yield, interest rate, interest, rate, indexes, and USA.

View market daily updates and historical trends for US Discount Rate. from United States. Source: Federal Reserve. Track economic data with YCharts analyt…

NASDAQ Stock Data with Economic Indicators

Overview

This dataset comprises historical stock price data for NASDAQ-listed companies, combined with a selection of key economic indicators. It is designed to provide a comprehensive view of market behavior, facilitating financial analysis and predictive modeling. Users can explore relationships between stock performance and various economic factors.

Features

The dataset includes the following features:

• Date: The date of the recorded stock prices (formatted as YYYY-MM-DD).

• Open: The price at which the stock opened for trading on a given day.

• High: The highest price reached by the stock during the trading day.

• Low: The lowest price recorded during the trading day.

• Close: The price at which the stock closed at the end of the trading day.

• Volume: The total number of shares traded during the day.

• Interest Rate: The prevailing interest rate, which influences economic activity and stock performance.

• Exchange Rate: The exchange rate for the USD against other currencies, reflecting international market influences.

• VIX: The Volatility Index, a measure of market risk and investor sentiment, often referred to as the "fear index."

• Gold: The price of gold per ounce, which serves as a traditional safe-haven asset and is often inversely correlated with stock prices.

• Oil: The price of crude oil, an essential commodity that influences various sectors, especially transportation and manufacturing.

• TED Spread: The difference between the interest rates on interbank loans and short-term U.S. government debt, which indicates credit risk in the banking system.

• EFFR (Effective Federal Funds Rate): The interest rate at which depository institutions lend reserve balances to other depository institutions overnight, influencing overall economic activity.

Use Cases

This dataset is suitable for a variety of applications, including: - Financial Analysis: Evaluate historical trends in stock prices relative to economic indicators. - Predictive Modeling: Develop machine learning models to forecast stock price movements based on historical data and economic variables. - Time Series Analysis: Conduct analyses over different time frames (daily, weekly, monthly, yearly) to identify patterns and anomalies.

Data Source

The data is sourced from reputable financial APIs and databases: - Yahoo Finance: Historical stock prices. - Federal Reserve Economic Data (FRED): Economic indicators such as interest rates and VIX. - Alpha Vantage / Quandl: Commodity prices for gold and oil.

Conclusion

This dataset provides a rich foundation for analysts, researchers, and data scientists interested in the intersection of stock market performance and macroeconomic conditions. Its structured features and comprehensive nature make it a valuable resource for both academic and practical financial inquiries.

Graph and download economic data for Interest Rates and Price Indexes; Dow Jones U.S. Total Market Index, Percent Change in Index (BOGZ1PC073164013Q) from Q1 1971 to Q2 2025 about mutual funds, equity, liabilities, interest rate, interest, rate, price index, indexes, price, and USA.

United States US: Market Capitalization: Listed Domestic Companies data was reported at 32,120.703 USD bn in 2017. This records an increase from the previous number of 27,352.201 USD bn for 2016. United States US: Market Capitalization: Listed Domestic Companies data is updated yearly, averaging 11,322.354 USD bn from Dec 1980 (Median) to 2017, with 38 observations. The data reached an all-time high of 32,120.703 USD bn in 2017 and a record low of 1,263.561 USD bn in 1981. United States US: Market Capitalization: Listed Domestic Companies data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Financial Sector. Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

The benchmark interest rate in India was last recorded at 5.50 percent. This dataset provides - India Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

Real time prices in the diamond market are reflected by the so-called Diamond Financial Index (DFX) which is available on a daily base since April 2018.

As diamond prices are influenced by many factors like trade barriers, political instability, operational disruptions like mine closures or economic downturns resp. upturns, it is not an easy task to predict the development of future diamond prices.

To predict prices, indicators are needed. Empirical findings support the argument that diamond prices respond to economic downturns resp. upturns and are therefore also correlated with inflation rates and interest rates resp. fed rates. Also gold prices could be an indicator for the development of diamond prices.

Because the US are playing quite a big role in the diamond business, the following US rates can be considered:

o inflation rate (10-year breakeven inflation rate) o interest rate (10-year treasury inflation-indexed security, constant maturity, risk-free) o fed rate (effective federal funds rate)

Moreover, gold prices could be considered as an indicator.

Content

The following five datasets have been downloaded from the following websites and merged to one dataset:

o diamond price (DFX): https://www.investing.com/indices/get-diamonds-general o inflation rate: https://fred.stlouisfed.org/series/T10YIE o interest rate: https://fred.stlouisfed.org/series/DFII10 o fed rate: https://fred.stlouisfed.org/series/DFF o gold price: https://www.boerse-online.de/rohstoffe/historisch/goldpreis/usd/

To merge the datasets, date has been used as index. A few missing values in the datasets have been filled in by copying the value from the day before (see file "diamond_data_merged_with_other_variables.csv").

Please note: I added one additional version of the dataset where ID is used as index (not date). Missing values are not filled in in this version (see file "df_diamond_data_merged_with_other_variables.csv"). I would recommend using the dataset "diamond_data_merged_with_other_variables.csv" with date as index.

Inspiration

The following questions could be answered:

o How did diamond prices, inflation rate, interest rate, fed rate and gold price develop since 2018? o How is the correlation between diamond prices and inflation rate, interest rate, fed rate and gold prices? o How will diamond prices develop in the future?

When it comes to price prediction machine learning has been successful in predicting stock market prices through a host of different time series models. There is also a limited but quite restrictive application in predicting cryptocurrency prices. Often neural networks like LSTM (Long Short Term Memory) are used. LSTM oder other models, e.g. ARIMA, could be also used here.