Dow reported $123.1 in PE Price to Earnings for its fiscal quarter ending in June of 2025. Data for Dow | DOW - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last October in 2025.

View quarterly updates and historical trends for S&P 500 Operating P/E Ratio. from United States. Source: Standard and Poor's. Track economic data with YC…

View quarterly updates and historical trends for S&P 500 Operating P/E Ratio Forward Estimate. from United States. Source: Standard and Poor's. Track econ…

NASDAQ reported $32.28 in PE Price to Earnings for its fiscal quarter ending in June of 2025. Data for NASDAQ | NDAQ - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last October in 2025.

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.

NASDAQ Composite Index NASDAQ Composite Index

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

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.

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.

Dow Jones New Zealand Index Target Price Prediction

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

View monthly updates and historical trends for S&P 500 Shiller CAPE Ratio. from United States. Source: Robert Shiller. Track economic data with YCharts an…

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.

The Dow Jones U.S. Completion Total Stock Market Index

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

Price-Earnings-Ratio Time Series for Core Scientific, Inc. Common Stock. Core Scientific, Inc. provides digital asset mining services in the United States. It operates through three segments: Digital Asset Self-Mining; Digital Asset Hosted Mining; and HPC Hosting. The company offers digital infrastructure, software solutions, and services; and operates licensing data center space facilities. It also deploys and operates own large fleet of miners within owned digital infrastructure as part of a pool of users that process transactions conducted on one or more blockchain networks; and provides hosting services for digital asset mining customers, which include deployment, monitoring, trouble shooting, optimization, and maintenance of its customers' digital asset mining equipment. In addition, the company provides electrical power, repair, and other infrastructure services to operate, maintain, and earn digital assets; and sells mining equipment to customers. Core Scientific, Inc. was founded in 2017 and is headquartered in Dover, Delaware.

Key information about India P/E ratio

• India SENSEX recorded a daily P/E ratio of 21.540 on 26 Mar 2025, compared with 21.740 from the previous day.
• India SENSEX P/E ratio is updated daily, with historical data available from Dec 1988 to Mar 2025.
• The P/E ratio reached an all-time high of 36.210 in Feb 2021 and a record low of 15.670 in Mar 2020.
• BSE Limited provides daily P/E Ratio.

Price-Earnings-Ratio Time Series for Real Brokerage Inc. The Real Brokerage Inc., together with its subsidiaries, operates as a real estate technology company in the United States and Canada. It offers brokerage, title, mortgage broker, and wallet services. The company was founded in 2014 and is based in Miami, Florida.

Price-Earnings-Ratio Time Series for ADTRAN Inc. ADTRAN Holdings, Inc. provides networking and communications platforms, software, systems, and services in the United States, Germany, the United Kingdom, and internationally. It operates through two segments, Network Solutions, and Services & Support. It offers residential gateways; ethernet passive optical network ONUs; gigabit passive optical network/XGS-PON ONTs; traditional SSE, routers, and switches; edge cloud; carrier ethernet network interface devices; Optical Line Terminals; Packet Aggregation, Copper Access, and Oscilloquartz; optical transport and engine solutions; infrastructure monitoring solution; and training, professional, software, and managed services. The company provides various software, such as Mosaic One SaaS, n-Command, Procloud, MCP, AOE, and ACI-E. It serves large, medium, and small service providers; alternative service providers, such as utilities, municipalities and fiber overbuilders; cable/MSOs; and SMBs and distributed enterprises. The company was incorporated in 1985 and is headquartered in Huntsville, Alabama.

Price-Earnings-Ratio Time Series for Alarm.com Holdings Inc. Alarm.com Holdings, Inc. provides various Internet of Things (IoT) and solutions for residential, multi-family, small business, and enterprise commercial markets in North America and internationally. The company operates through Alarm.com and Other segments. It offers solutions to control and monitor security systems, as well as to IoT devices, including door locks, garage doors, thermostats, and video cameras; and video analytics, AI deterrence, vacation watch, video doorbells, intelligent integration, live streaming, secure cloud storage, and video alerts. The company also provides scenes, video analytics triggers, thermostat schedules, responsive savings, precision comfort, energy usage monitoring, places feature, whole home water safety, and solar monitoring solutions, as well as heating, ventilation, and air conditioning monitoring services. In addition, it offers demand response programs, commercial grade video, commercial video analytics, access control, cell connectors, enterprise dashboard and multi-site management, connected fleet, energy savings, protection for valuables and inventory, temperature monitoring, and daily safeguard solutions. Further, the company provides a permission-based online portal that provides account management, sales, marketing, training, and support tools; service dashboard, a unified interface that displays key operational and customer experience indicators; installation and support services; MobileTech Application and Remote Toolkit; video health reports; smart gateway; AI-powered enhancements to professional monitoring and false alarm reduction; Web services and business intelligence; sales, marketing, and training services; and home builder programs. Additionally, it offers electric utility grid and water management, indoor gunshot detection, and health and wellness and data-rich emergency response solutions. The company was founded in 2000 and is headquartered in Tysons, Virginia.

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.

Surging Services: Will Dow Jones CPI Signal Continued Consumer Strength?

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

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Context

45 financial metrics and ratios of every company included in the Nasdaq-100 stock market index (as of 09/2021) for the last five fiscal years. Some metrics or ratios might not be calculated, depending on the company's profitability [...].

Inspiration

The dataset offers a vast variety of possibilities for data exploration, data preparation and visualization, classification or clustering of the different companies, and the prediction of future developments of certain metrics and ratios.

Covered Metrics and Ratios

Besides the stock symbol, the company name and the respective GICS sector and GICS subsector classification, the datasets comprises information about (1) Asset Turnover, (2) Buyback Yield, (3) CAPEX to Revenue, (4) Cash Ratio, (5) Cash to Debt, (6) COGS to Revenue, (7) Beneish M-Score, (8) Altman Z-Score, (9) Current Ratio, (10) Days Inventory, (11) Debt to Equity, (12) Debt to Assets, (13) Debt to EBITDA, (14) Debt to Revenue, (15) E10 (by Prof. Robert Shiller), (16) Effective Interest Rate, (17) Equity to Assets, (18) Enterprise Value to EBIT, (19) Enterprise Value to EBITDA, (20) Enterprise Value to Revenue, (21) Financial Distress, (22) Financial Strength, (23) Joel Greenblatt Earnings Yield (by Joel Greenblatt), (24) Free Float Percentage, (25) Piotroski F-Score, (26) Goodwill to Assets, (27) Gross Profit to Assets, (28) Interest Coverage, (29) Inventory Turnover, (30) Inventory to Revenue, (31) Liabilities to Assets, (32) Long-term Debt to Assets, (33) Price-to-Book-Ratio, (34) Price-to-Earnings-Ratio, (35) Price-to-Earnings-Ratio (Non-Recurring Items), (36) Price-Earnings-Growth-Ratio, (37) Price-to-Free-Cashflow, (38) Price-to-Operating-Cashflow, (39) Predictability, (40) Profitability, (41) Rate of Return, (42) Scaled Net Operating Assets, (43) Year-over-Year EBITDA Growth, (44) Year-over-Year EPS Growth, (45) Year-over-Year Revenue Growth

Note, that the dates defining a fiscal year may vary from company to company.

Acknowledgements

The contents are provided by wikipedia.de and gurufocus.com from where the data was scraped.

Price-Earnings-Ratio Time Series for Hub Group Inc. Hub Group, Inc., a supply chain solutions provider, offers transportation and logistics management services in North America. It operates in two segments, Intermodal and Transportation Solutions (ITS), and Logistics. The ITS segment offers intermodal and dedicated trucking services, including freight transportation, truckload, less-than-truckload, flatbed, temperature-controlled, and dedicated and regional trucking services. The Logistics segment provides transportation management, freight brokerage, shipment optimization, load consolidation, mode selection, carrier management, load planning and execution, warehousing, fulfillment, cross-docking, and consolidation and final mile delivery services. It also provides trucking transportation services, including dry van, expedited, less-than-truckload, and refrigerated and flatbed services. As of December 31, 2024, the company operated a fleet of approximately 2,300 tractors, 3,200 employee drivers, 500 independent owner-operators, and 4,700 trailers; and owned approximately 50,000 dry and 53-foot containers, as well as 900 refrigerated 53-foot containers. It serves a range of industries, including retail, consumer products, automotive, and durable goods. The company was founded in 1971 and is headquartered in Oak Brook, Illinois.

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.

Dow Jones U.S. Telecommunications: Industry in Transition?

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

Price-Earnings-Ratio Time Series for United Bankshares Inc. United Bankshares, Inc., through its subsidiaries, provides commercial and retail banking products and services in the United States. The company accepts checking, savings, and time and money market accounts; individual retirement accounts; and demand deposits, statement and special savings, and NOW accounts. Its loan products include commercial loans and leases to small to mid-size industrial and commercial companies, as well as automobile dealers, service, retail and wholesale merchants; construction and real estate loans, such as commercial and residential mortgages, and loans secured by owner-occupied real estate; personal, automobiles, boats, recreational vehicles, credit card receivables, commercial, and floor plan loans; and home equity loans. In addition, the company provides credit cards; trust, safe deposit boxes, wire transfers, and other banking products and services; investment and security services; buying and selling federal fund services; automated teller machine services; and internet and automated telephone banking services. Further, it offers community banking services, such as asset management, real property title insurance, financial planning, mortgage banking, and brokerage services, as well as custody of assets, investment management, escrow services, and related fiduciary activities. United Bankshares, Inc. was incorporated in 1982 and is headquartered in Charleston, West Virginia.

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.

Will the Dow Jones Industrial Average Index Rise Today?

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