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.

In 2025, stock markets in the United States accounted for roughly ** percent of world stocks. The next largest country by stock market share was China, followed by the European Union as a whole. The New York Stock Exchange (NYSE) and the NASDAQ are the largest stock exchange operators worldwide. What is a stock exchange? The first modern publicly traded company was the Dutch East Industry Company, which sold shares to the general public to fund expeditions to Asia. Since then, groups of companies have formed exchanges in which brokers and dealers can come together and make transactions in one space. Stock market indices group companies trading on a given exchange, giving an idea of how they evolve in real time. Appeal of stock ownership Over half of adults in the United States are investing money in the stock market. Stocks are an attractive investment because the possible return is higher than offered by other financial instruments.

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:/...

Stock Data of Top USA Companies: Apple, Tesla, Amazon

👨‍💻 Author: Azhar Saleem

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Dataset Description

This dataset provides daily stock data for some of the top companies in the USA stock market, including major players like Apple, Microsoft, Amazon, Tesla, and others. The data is collected from Yahoo Finance, covering each company’s historical data from its starting date until today. This comprehensive dataset enables in-depth analysis of key financial indicators and stock trends for each company, making it valuable for multiple applications.

Column Descriptions

The dataset contains the following columns, consistent across all companies:

• Date: The date of the stock data entry.
• Open: The stock's opening price for the day.
• High: The highest price reached during the trading day.
• Low: The lowest price during the trading day.
• Close: The stock’s closing price for the day.
• Volume: The total number of shares traded on that day.
• Dividends: Any dividends paid out on that day.
• Stock Splits: Records stock split events, if any, on that day.

Potential Use Cases

1. Machine Learning & Deep Learning:

   • Stock Price Prediction: Use historical prices to train models for forecasting future stock prices.
   • Sentiment Analysis and Price Correlation: Combine with external sentiment data to predict price movements based on market sentiment.
   • Anomaly Detection: Detect unusual price patterns or volume spikes using classification algorithms.

2. Data Science:

   • Trend Analysis: Identify long-term trends for each company or compare trends between companies.
   • Volatility Analysis: Calculate volatility to assess risk and return patterns over time.
   • Correlation Analysis: Compare stock performance across companies to study market relationships.

3. Data Analysis:

   • Historical Performance: Review historical data to understand growth trends, market impact of stock splits, and dividends.
   • Seasonal Patterns: Analyze data for seasonal trends or recurring patterns across years.
   • Investment Strategy Backtesting: Test various investment strategies based on historical data to assess potential profitability.

4. Financial Research:

   • Economic Impact Studies: Investigate how major events affected stock prices across top companies.
   • Sector-Specific Analysis: Identify performance differences across sectors, such as tech, healthcare, and retail.

This dataset is a powerful tool for analysts, researchers, and financial enthusiasts, offering versatility across multiple domains from stock analysis to algorithmic trading models.

An important point in our data is that the data must be cleaned and the valume column is better because there is a lot of data noise in it.

These tables provide additional detail on the investment holdings of U.S. money market funds, based on a monthly dataset of security-level holdings for all U.S. money market funds. Table 1 reports the aggregate dollar amount of investments of U.S. money market funds since 2010, by the world region and country of the security issuer. Table 2 provides a finer level of detail by month, showing, for each country of issuer, the aggregate dollar amount of investments of U.S. money market funds by type of money fund (i.e., prime, government, and municipal bond funds), type of security (i.e., direct debt and deposits, repurchase agreement, asset-backed commercial paper, and other), and by maturity of the security. Table 3 depicts the asset allocation of U.S. money market fund portfolios over time. Tables 4, 5, and 6 show the asset allocation of prime, government, and tax-exempt money market funds, respectively, over time. The sum of the values in these three tables equals the total value of Table 3. Tables 7 and 8 report additional detail on the repurchase agreement holdings and the commercial paper holdings, respectively, for U.S. money market funds.

The value of the DJIA index amounted to ****** at the end of June 2025, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

Graph and download economic data for Quarterly Financial Report: U.S. Corporations: All Other Information: Capital Stock and Other Capital (Less Treasury Stock) (QFRD326519USNO) from Q4 2009 to Q2 2025 about stocks, information, finance, capital, Treasury, corporate, industry, and USA.

As of November 14, 2021, all S&P 500 sector indices had recovered to levels above those of January 2020, prior to full economic effects of the global coronavirus (COVID-19) pandemic taking hold. However, different sectors recovered at different rates to sit at widely different levels above their pre-pandemic levels. This suggests that the effect of the coronavirus on financial markets in the United States is directly affected by how the virus has impacted various parts of the underlying economy. Which industry performed the best during the coronavirus pandemic? Companies operating in the information technology (IT) sector have been the clear winners from the pandemic, with the IT S&P 500 sector index sitting at almost ** percent above early 2020 levels as of November 2021. This is perhaps not surprising given this industry includes some of the companies who benefitted the most from the pandemic such as ************** and *******. The reason for these companies’ success is clear – as shops were shuttered and social gatherings heavily restricted due to the pandemic, online services such shopping and video streaming were in high demand. The success of the IT sector is also reflected in the performance of global share markets during the coronavirus pandemic, with tech-heavy NASDAQ being the best performing major market worldwide. Which industry performed the worst during the pandemic? Conversely, energy companies fared the worst during the pandemic, with the S&P 500 sector index value sitting below its early 2020 value as late as July 2021. Since then it has somewhat recovered, and was around ** percent above January 2020 levels as of October 2021. This reflects the fact that many oil companies were among the share prices suffering the largest declines over 2020. A primary driver for this was falling demand for fuel in line with the reduction in tourism and commuting caused by lockdowns all over the world. However, as increasing COVID-19 vaccination rates throughout 2021 led to lockdowns being lifted and global tourism reopening, demand has again risen - reflected by the recent increase in the S&P 500 energy index.

This dataset provides financial information for a selection of companies listed on the S&P 500 index in the United States. It includes key metrics such as last recorded stock prices, highest and lowest stock prices, absolute and percentage changes, and trading volumes. The data is collected at a specific point in time and offers insights into the stock market performance of S&P 500 companies.

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.

Success.ai’s B2B Email Data for US Financial Services offers businesses comprehensive access to verified email addresses and contact details of key decision-makers across the financial services industry in the United States.

Sourced from over 170 million verified professional profiles and enriched with detailed firmographic data, this dataset is ideal for sales teams, marketers, and strategic planners looking to engage with banking executives, wealth managers, insurance specialists, and fintech leaders.

Backed by our Best Price Guarantee, Success.ai ensures that your outreach is guided by accurate, continuously updated, and AI-validated data.

Why Choose Success.ai’s Financial Services Email Data?

1. Verified B2B Email Data for Precision Outreach

   • Access verified work emails of decision-makers in banking, insurance, wealth management, investment firms, and fintech startups.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and ensuring high deliverability for your campaigns.

2. Focus on the US Financial Market

   • Includes profiles of professionals across major US financial hubs like New York, Chicago, San Francisco, and Miami, as well as regional banks, credit unions, and fintech disruptors.
   • Gain insights into industry trends, regulatory impacts, and market dynamics specific to the US financial ecosystem.

3. Continuously Updated Datasets

   • Real-time updates ensure that your data remains relevant, reflecting leadership changes, mergers, acquisitions, and new market entrants.
   • Stay aligned with evolving industry demands and customer needs.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible data usage and legal compliance for your campaigns.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with executives, financial advisors, compliance officers, and analysts across the US financial services sector.
• 50M Work Emails: AI-validated email data ensures precise communication and minimized email bounce rates.
• Firmographic Insights: Understand company sizes, revenue ranges, service offerings, and geographic presence to refine your targeting strategies.
• Decision-Maker Contact Details: Connect directly with key influencers and leaders shaping the US financial landscape.

Key Features of the Dataset:

1. Decision-Maker Email Profiles

   • Identify and engage with CEOs, CFOs, financial planners, compliance managers, and marketing directors responsible for driving financial strategies and regulatory compliance.
   • Target professionals overseeing technology adoption, customer engagement, and portfolio growth.

2. Advanced Filters for Tailored Campaigns

   • Filter contacts by industry segment (banking, insurance, investment management), company size, geographic location, or revenue bracket.
   • Tailor outreach efforts to align with specific financial services challenges, regulatory pressures, or customer preferences.

3. AI-Driven Enrichment

   • Profiles enriched with actionable data provide deeper insights, enabling personalized messaging and improving engagement outcomes with financial services stakeholders.

Strategic Use Cases:

1. Sales and Lead Generation

   • Offer SaaS solutions, compliance tools, or digital transformation services to financial services providers aiming to modernize operations and enhance customer experiences.
   • Build relationships with decision-makers in charge of vendor selection, procurement, and operational strategies.

2. Marketing and Outreach Campaigns

   • Target marketing teams and customer experience professionals to promote data-driven marketing tools, CRM platforms, or loyalty programs tailored to financial clients.
   • Leverage verified email data for multi-channel campaigns, driving higher engagement rates and conversions.

3. Fintech and Innovation Partnerships

   • Engage with fintech executives and banking leaders exploring digital payments, blockchain, AI-driven financial products, or open banking solutions.
   • Foster partnerships that accelerate innovation and enhance competitive positioning.

4. Regulatory Compliance and Risk Management

   • Connect with compliance officers and risk managers to present regulatory reporting tools, fraud detection systems, or cybersecurity solutions.
   • Address key pain points related to evolving compliance requirements and risk mitigation.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality B2B email data at competitive rates, ensuring maximum ROI for your outreach, marketing, and sales campaigns in the US financial sector.

2. Seamless Integration

   • Incorporate verified email data into CRM systems or marketing automation platforms via APIs or downloadable formats, streamlining data management and campaign execution.

3. Data Accuracy with AI Validation
   ...

Key information about United States Market Capitalization: % of GDP

• United States Market Capitalization accounted for 155.0 % of its Nominal GDP in Dec 2022, compared with a percentage of 205.0 % in the previous year
• US Market Capitalization: % Nominal GDP is updated yearly, available from Dec 1975 to Dec 2022
• The data reached an all-time high of 205.0 % in Dec 2021 and a record low of 36.7 % in Dec 1978

Key information about United States Market Capitalization

• United States Market Capitalization accounted for 40,297.980 USD bn in Dec 2022, compared with a percentage of 48,548.538 USD bn in the previous year
• US Market Capitalization is updated yearly, available from Dec 1975 to Dec 2022
• The data reached an all-time high of 48,548.538 USD bn in Dec 2021 and a record low of 703.800 USD bn in Dec 1975

Market Size statistics on the Financial Data Service Providers industry in the US

This statistic presents information on the financial assets of the credit unions in the United States from 2000 to 2018. In 2018, the financial assets of the credit unions in the United States was approximately **** trillion U.S. dollars.

This statistic shows the largest global stock exchanges globally as of March 2025, ranked by the value of electronic order book share trading. In that time, the NYSE Stock Market was the largest stock exchange worldwide, with the value of EOB shares traded amounting to *** trillion U.S. dollars. Stock exchanges — additional information Stock exchanges are an important part of the free market economic system and are the most important component of the stock market. A stock exchange provides the setting in which stockbrokers, sellers, buyers, and traders can be brought together to take part in the sale of shares, bonds, derivatives and other securities. The core function of a stock exchange is to enable the fair and orderly trading, as well as the provision of price information, of any securities being traded on that exchange. Originally the exchanges were physical places (in some world locations the goods are still traded over-the-counter) but with time, they took the shape of an electronic platform. In order that company shares may be bought, traded and sold on a stock exchange, the company is required to have undergone an initial public offering process (IPO) on that particular exchange. The initial public offering of Alibaba Group Holding, a Chinese company operating in the e-commerce sector, on the New York Stock Exchange in September 2014, was the largest listing in the United States since 1996. The IPO of Alibaba Group Holding raised approximately ***** billion U.S. dollars.

As of early 2025, companies in the information technology sector made up ** percent of the total market capitalization of all stock exchanges worldwide. Tech companies worldwide had a combined market capitalization of approximately ** trillion U.S. dollars. The second largest sector on stock markets worldwide was the financial services industry, with a market cap of ** trillion U.S. dollars, followed by the industrials sector with ** trillion U.S. dollars. On the other hand, real estate and utilities were the least represented sectors on stock markets worldwide.

Dataset Description:

Welcome to one of the most comprehensive financial datasets on Kaggle! This dataset encapsulates a quarter-century of daily U.S. stock market data, encompassing over 11,000 distinct tickers. It offers a broad spectrum of historical insights into the heart of the U.S. financial market, from blue-chip stocks to small-cap gems.

The data spans from the early dawn of the digital age to the present day, presenting a unique opportunity to analyze trends, patterns, and market behavior over significant economic cycles, events, and technological advancements.

Key Features of the Dataset:

• Extensive Coverage: The dataset includes data for over 11,000 tickers, making it an excellent resource for both breadth and depth analyses. Whether you're interested in a particular sector, market capitalization, or individual stocks, this dataset has you covered.

• Detailed Information: For each ticker, the dataset provides daily Open, Close, Volume, and Dividend data. This granularity allows for intricate technical analyses, machine learning modeling, backtesting trading strategies, or even building your own stock market simulator.

• Historical Dividend Data: The inclusion of dividend data makes this dataset particularly valuable for those interested in studying income-generating stocks or assessing the impact of dividend announcements on stock prices.

• Reliability and Consistency: The dataset has been meticulously compiled and validated, ensuring the accuracy and consistency of the data. It's been exported from an MS SQL Server database, thus maintaining the integrity and structure of the original data.

Potential Applications:

This dataset is a gold mine for researchers, data scientists, quantitative analysts, financial professionals, or anyone with an interest in financial markets. You can use it for a wide variety of purposes, such as:

• Developing and backtesting trading algorithms
• Conducting financial research and market analysis
• Teaching and learning about financial markets and data analysis
• Building predictive models for stock prices or market movements
• Investigating the impact of dividends on stock performance
• And much more!

In essence, this dataset offers a unique opportunity to dive into the fascinating world of the U.S. stock market. With its vast coverage, detailed information, and historical depth, it's a treasure trove of data waiting to be explored. Dive in and start discovering new insights today!

Context

This is a comprehensive dataset including numerous financial metrics that many professionals and investing gurus often use to value companies. This data is a look at the companies that comprise the S&P 500 (Standard & Poor's 500). The S&P 500 is a capitalization-weighted index of the top 500 publicly traded companies in the United States (top 500 meaning the companies with the largest market cap). The S&P 500 index is a useful index to study because it generally reflects the health of the overall U.S. stock market. The dataset was last updated in July 2020.

Content

There are 14 rows included in this dataset: ``` - 4 character variables: - Symbol: Ticker symbol used to uniquely identify each company on a particular stock market - Name: Legal name of the company - Sector: An area of the economy where businesses share a related product or service - SEC Filings: Helpful documents relating to a company

- 10 numeric variables:
  - Price: Price per share of the company
  - Price to Earnings (PE): The ratio of a company’s share price to its earnings per share
  - Dividend Yield: The ratio of the annual dividends per share divided by the price per share
  - Earnings Per Share (EPS): A company’s profit divided by the number of shares of its stock
  - 52 week high and low: The annual high and low of a company’s share price
  - Market Cap: The market value of a company’s shares (calculated as share price x number of shares)
  - EBITDA: A company’s earnings before interest, taxes, depreciation, and amortization; often used as a proxy for its profitability
  - Price to Sales (PS): A company’s market cap divided by its total sales or revenue over the past year
  - Price to Book (PB): A company’s price per share divided by its book value

### Acknowledgements

I found this data on the website datahub at https://datahub.io/core/s-and-p-500-companies-financials/r/1.html. All references and citations should be given to them.


### Inspiration

What useful information can you gleam from this dataset? Are these fundamentals enough to predict a high-quality company? How can you determine high from low quality? What would you liked to have seen in this dataset?