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.

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).

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.

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.

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.

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).

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

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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:/...

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.

S&P 500: A Bull or a Bear?

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

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.

Year: The year of the observation.

Month: The month of the observation.

Interest Rate: The prevailing interest rate for the given month.

Unemployment Rate: The unemployment rate in percentage terms for that time period.

Index Price: A synthetic stock market index price representing overall market trends.

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

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...

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.

Inflation: Friend or Foe to the Stock Market?

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

As of June 17, 2024, the most shorted stock was for, the American holographic technology services provider, MicroCloud Hologram Inc., with 66.64 percent of their total float having been shorted. This is a change from mid-January 2021, when video game retailed GameStop had an incredible 121.07 percent of their available shares in a short position. In effect this means that investors had 'borrowed' more shares (with a future promise to return them) than the total number of shares available for public trading. Owing to this behavior of professional investors, retail investors enacted a campaign to drive up the stock price of Gamestop, leading to losses of billions when investors had to repurchase the stock they had borrowed. At this time, a similar – but less effective – social media campaign was also carried out for the stock price of cinema operator AMC, and the price of silver. What is short selling? Short selling is essentially where an investor bets on a share price falling by: borrowing a number of shares selling these shares while the price is still high; purchasing the same number again once the price falls; then returning the borrowed shares at a profit. Of course, a profit will only be made if the share price does fall; should the share price rise the investor will then need to purchase the shares back at a higher price, and thus incur a loss. Short selling can lead to some very large profits in a short amount of time, with Tesla stock generating over one billion dollars in short sell profits during the first week of March 2020 alone, owing to the financial crash caused by the coronavirus (COVID-19) pandemic. However, owing to the short-term, opportunistic nature of short selling, these returns look less impressive when considered as net profits from short sell positions over the full year. The risks of short selling Short selling carries greater risks than traditional investments, and for this reason financial advisors often recommend against this strategy for ‘retail’ (i.e. non-professional) investors. The reason for this is that losses from short selling are potentially uncapped, whereas losses from traditional investments are limited to the initial cost. For example, if someone purchases 100 dollars of shares, the maximum they can lose is the 100 dollars the spent on those shares. However, say someone borrows 100 dollars of shares instead, betting on the price falling. If these shares are then sold for 100 dollars but the price subsequently rises, the losses could greatly exceed the initial investment should the price rise to, say, 500 dollars. The risks of short selling can be seen by looking again at Tesla, with the company causing the greatest losses over 2020 from short selling at over 40 billion U.S. dollars.

The Average Interest Rates on U.S. Treasury Securities dataset provides average interest rates on U.S. Treasury securities on a monthly basis. Its primary purpose is to show the average interest rate on a variety of marketable and non-marketable Treasury securities. Marketable securities consist of Treasury Bills, Notes, Bonds, Treasury Inflation-Protected Securities (TIPS), Floating Rate Notes (FRNs), and Federal Financing Bank (FFB) securities. Non-marketable securities consist of Domestic Series, Foreign Series, State and Local Government Series (SLGS), U.S. Savings Securities, and Government Account Series (GAS) securities. Marketable securities are negotiable and transferable and may be sold on the secondary market. Non-marketable securities are not negotiable or transferrable and are not sold on the secondary market. This is a useful dataset for investors and bond holders to compare how interest rates on Treasury securities have changed over time.

Graph and download economic data for Dividend Yield of Preferred Stock on the New York Stock Exchange for United States (M13048USM156NNBR) from Jan 1910 to Apr 1964 about dividends, stock market, NY, yield, interest rate, interest, rate, and USA.

Descriptive statistics of SHIBOR and SHSML.

Historical dataset of the 12 month LIBOR rate back to 1986. The London Interbank Offered Rate is the average interest rate at which leading banks borrow funds from other banks in the London market. LIBOR is the most widely used global "benchmark" or reference rate for short term interest rates.

Sharp economic volatility, the continued effects of high interest rates and mixed sentiment among investors created an uneven landscape for stock and commodity exchanges. While trading volumes soared in 2020 due to the pandemic and favorable financial conditions, such as zero percent interest rates from the Federal Reserve, the continued effects of high inflation in 2022 and 2023 resulted in a hawkish pivot on interest rates, which curtailed ROIs across major equity markets. Geopolitical volatility amid the Ukraine-Russia and Israel-Hamas wars further exacerbated trade volatility, as many investors pivoted away from traditional equity markets into derivative markets, such as options and futures to better hedge on their investment. Nonetheless, the continued digitalization of trading markets bolstered exchanges, as they were able to facilitate improved client service and stronger market insights for interested investors. Revenue grew an annualized 0.1% to an estimated $20.9 billion over the past five years, including an estimated 1.9% boost in 2025. A core development for exchanges has been the growth of derivative trades, which has facilitated a significant market niche for investors. Heightened options trading and growing attraction to agricultural commodities strengthened service diversification among exchanges. Major companies, such as CME Group Inc., introduced new tradeable food commodities for investors in 2024, further diversifying how clients engage in trades. These trends, coupled with strengthened corporate profit growth, bolstered exchanges’ profit. Despite current uncertainty with interest rates and the pervasive fear over a future recession, the industry is expected to do well during the outlook period. Strong economic conditions will reduce investor uncertainty and increase corporate profit, uplifting investment into the stock market and boosting revenue. Greater levels of research and development will expand the scope of stocks offered because new companies will spring up via IPOs, benefiting exchange demand. Nonetheless, continued threat from substitutes such as electronic communication networks (ECNs) will curtail larger growth, as better technology will enable investors to start trading independently, but effective use of electronic platforms by incumbent exchange giants such as NASDAQ Inc. can help stem this decline by offering faster processing via electronic trade floors and prioritizing client support. Overall, revenue is expected to grow an annualized 3.5% to an estimated $24.8 billion through the end of 2031.

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.