Eli Lilly reported $15.56B in Sales Revenues for its fiscal quarter ending in June of 2025. Data for Eli Lilly | LLY - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last September in 2025.

This graph shows the revenue of Eli Lilly and Company from 2007 to 2024. Eli Lilly is one of the top global pharmaceutical companies. In 2024, the company generated over ** billion U.S. dollars of revenue.

Eli Lilly reported $5.66B in Net Income for its fiscal quarter ending in June of 2025. Data for Eli Lilly | LLY - Net Income including historical, tables and charts were last updated by Trading Economics this last September in 2025.

In 2024, Trulicity was Eli Lilly's third-biggest revenue generator, with about *** billion U.S. dollars. The highest revenue generating drug was Mounjaro with **** billion U.S. dollars. Eli Lilly is a multinational pharmaceutical company that is headquartered in Indianapolis, Indiana. Eli Lilly Company Eli Lilly was founded in **** in the United States. Currently, the company has around 47 thousand employees globally. The company specializes in global health initiatives, diabetes care and a multitude of other products concerning human health. Eli Lilly currently markets products in around *** countries globally. As of 2023, the company was among the top pharmaceutical companies based on both prescription sales and research and development spending. Diabetes products by Eli Lilly Eli Lilly is the manufacturer of several of the global top diabetic pharmaceutical products and treatments. It is the second ranked diabetes-focused pharmaceutical company based on revenue. Mounjaro is currently Eli Lilly’s top drug used in the treatment of type II diabetes. Thus, it took over the position of Eli Lilly's former top anti-diabetic, Trulicity.

In fiscal year 2024, Eli Lilly and Company's revenue by geographical region are as follows: CHINA: $1.66 B, Europe: $6.92 B, JAPAN: $1.81 B, Non-US: $4.27 B, UNITED STATES: $30.38 B.

Eli Lilly reported $7.02B in EBIT for its fiscal quarter ending in June of 2025. Data for Eli Lilly | LLY - Ebit including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Eli Lilly reported $13.11B in Gross Profit on Sales for its fiscal quarter ending in June of 2025. Data for Eli Lilly | LLY - Gross Profit On Sales including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Eli Lilly reported $6.78B in Pre-Tax Profit for its fiscal quarter ending in June of 2025. Data for Eli Lilly | LLY - Pre Tax Profit including historical, tables and charts were last updated by Trading Economics this last September 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.

Eli Lilly (LLY) : A Prescription for Growth

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

Eli Lilly reported $2.45B in Cost of Sales for its fiscal quarter ending in June of 2025. Data for Eli Lilly | LLY - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Historical ownership data of LLY by State of Alaska Department of Revenue

Historical holdings data showing quarterly positions, market values, shares held, and portfolio percentages for LLY held by State of Alaska Department of Revenue from Q3 2013 to Q2 2025

Eli Lilly reported $6.31 in EPS Earnings Per Share for its fiscal quarter ending in June of 2025. Data for Eli Lilly | LLY - EPS Earnings Per Share including historical, tables and charts were last updated by Trading Economics this last September 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.

Lilly's (LLY) Future Outlook Bright Amidst Strong Drug Pipeline, Analyst Forecasts.

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

Eli Lilly reported $40M in Interest Income for its fiscal quarter ending in June of 2025. Data for Eli Lilly | LLY - Interest Income including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Eli Lilly reported 37 in PE Price to Earnings for its fiscal quarter ending in June of 2025. Data for Eli Lilly | LLY - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last September in 2025.

MB "LILY Beauty House" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

UAB "CALLA lily" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Lily Lake. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Lily Lake. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Lily Lake, the median household income stands at $172,768 for householders within the 45 to 64 years age group, followed by $133,750 for the 25 to 44 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $125,750.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Lily Lake median household income by age. You can refer the same here

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.

Lilly's (LLY) Promising Pipeline Fuels Bullish Outlook, Analysts Predict.

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