This statistic shows the stock price development of selected companies in the airline industry from January 6, 2020 to February 3, 2025. Due to implications of national lockdowns and travel restrictions, stock values of airlines dropped significantly in March 2020. Since then, stock prices started to increase again, and slightly recovering to pre-pandemic levels.

American Airlines stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

In 2024, Delta Air Lines and United Airlines were the leading airlines in the U.S., with a domestic market share of 21 percent. That year, American Airlines had the second-largest market share of 20 percent. U.S. airlines' domestic market share The passenger air transportation market is a thriving industry, taking individuals to locations around the globe. American Airlines was the third largest airline in the North America based on operating revenue, reaching nearly 40.5 billion U.S. dollars in 2023. Passenger airlines can face much scrutiny for their passenger satisfaction and comfort. A 2025 North American Airline Satisfaction Study by J.D. Power & Associates listed Southwest Airlines as the best long-haul, closely followed by low-cost carrier JetBlue Airways. United Airlines, Delta Air Lines, American Airlines and Southwest Airlines are the top-ranked airlines based on 2024 domestic market share. Delta operates out of Atlanta, and Hartsfield-Jackson Atlanta International Airport, Delta’s hub, sees the most passenger traffic in the United States. Chicago-headquartered United Airlines is a subsidiary of United Continental Holdings. United has flights to 210 domestic destinations and 120 destinations internationally.

United States New York Stock Exchange: Index: NYSE Arca Airline Index data was reported at 49.027 NA in Apr 2025. This records a decrease from the previous number of 54.703 NA for Mar 2025. United States New York Stock Exchange: Index: NYSE Arca Airline Index data is updated monthly, averaging 81.491 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 119.060 NA in Jan 2018 and a record low of 36.270 NA in Aug 2012. United States New York Stock Exchange: Index: NYSE Arca Airline Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: Monthly.

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.

AAL American Airlines Group Inc. Common Stock

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

United Airlines Holdings stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Delta Air Lines was the most valuable airline in the world as of April 2025, with a market value of **** billion U.S. dollars. Ryanair Holdings ranked second, with **** billion dollars worth of market value. Market valuation The market value of a company typically refers to the market capitalization of a publicly traded firm, and is calculated by multiplying the number of shares by the current share price. A company’s market value also serves as an indicator of its business prospects. Other factors such as profitability, debt load, and metrics like earnings before tax, depreciation, and amortization (EBITDA) are also considered when assessing a company's overall value.
Delta and Southwest: southern roots, global reach Southwest Airlines is the world’s largest low-cost carrier and the fourth-leading domestic carrier in the United States, operating from its headquarters at Dallas Love Field. Another powerhouse rooted in the American South is Delta Air Lines, one of the largest airlines in the world in terms of passengers carried. With its headquarters at the world’s busiest airport, Hartsfield-Jackson Atlanta International Airport, the airline is a member of the SkyTeam airline alliance.

United Airlines Holdings reported $32.73B in Market Capitalization this August of 2025, considering the latest stock price and the number of outstanding shares.Data for United Airlines Holdings | UAL - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last August 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.

SAVE Spirit Airlines Inc. Common Stock

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 dataset is about stocks. It has 4 rows and is filtered where the company is Southwest Airlines. It features 8 columns including stock name, company, exchange, and exchange symbol.

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.

Soaring High with LUV: Will Southwest Airlines Stock Take Off?

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

European airline stocks recently saw their worst drop in over six months, driven by declining bookings and rising fuel costs, despite earlier gains from strong summer bookings.

The closure of Ukrainian airspace on *****************, in response to the Russian invasion has greatly impacted the aviation sector. Ryanair, which has a ** percent market share in Ukraine, has cancelled all flights for the foreseeable future. Ukraine’s aviation sector Ukraine has a small aviation market, accounting for only *** percent of the European passenger traffic and just under one percent of the global traffic in 2021. The country’s flag carrier is Ukraine International Airlines, which is based in Kyiv. The airline posted a profit loss in 2020 due to reduced operations during the coronavirus pandemic but managed to almost double its passenger traffic in 2021 to *** million. As of *************, some ** aircraft were stored at airports around the country, including the largest cargo airplane Antonov AN-225 Mriya, which was destroyed during the Battle of Antonov Airport on February 24, 2022.

Impact on European air traffic

To sanction Russia’s invasion, ** countries worldwide have closed their airspace to Russian airlines. In response, the Russian Federation banned airlines from ** countries, along with all ** members of the European Union. In Europe, airlines already cancelled flights to Russia, with Germany reporting the highest cancellation rate. The airspace closure has forced airlines to find alternative routes, which will make travel routes longer, more fuel intensive, and will increase their operating costs.

Southwest Airlines stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

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.

(UIL) United Airlines Is Set To Soar

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 dataset is about stocks. It has 4 rows and is filtered where the company is China Southern Airlines. It features 8 columns including stock name, company, exchange, and exchange symbol.

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.

International Consolidated Airlines Group (IAG): Soaring High or Facing Turbulence?

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 dataset is about stocks. It has 4 rows and is filtered where the company is Singapore Airlines Official Website. It features 8 columns including stock name, company, exchange, and exchange symbol.

The global commercial airlines market, valued at $778.61 billion in 2025, is projected to experience robust growth, driven by several key factors. Increased air travel demand, fueled by rising disposable incomes in emerging economies and a growing middle class, is a significant contributor. Technological advancements, including the adoption of more fuel-efficient aircraft and improved air traffic management systems, are further enhancing operational efficiency and reducing costs. The expansion of low-cost carriers and increased airline alliances are also stimulating competition and driving down fares, making air travel more accessible to a wider population. However, the market faces challenges such as fluctuating fuel prices, geopolitical instability impacting air travel routes, and the ongoing need for substantial investments in infrastructure to accommodate growing passenger numbers. The COVID-19 pandemic’s lingering impact also presents a significant headwind, though recovery is underway. Segmentation analysis reveals that the passenger segment dominates revenue streams, while international flights contribute significantly to overall market value. North America and Europe currently hold the largest regional market shares, but the Asia-Pacific region is expected to exhibit the most substantial growth over the forecast period due to rapid economic expansion and increasing urbanization in countries like China and India. The competitive landscape is highly fragmented, with numerous global and regional players vying for market share. Established legacy carriers are facing increased competition from low-cost airlines and are adapting their strategies to remain competitive. Mergers and acquisitions are likely to continue shaping the industry structure as airlines seek to expand their networks and improve operational efficiency. The future of the commercial airlines market hinges on successful navigation of these challenges, leveraging technological innovation, strategic partnerships, and sustainable business practices. Continued growth is anticipated, albeit at a moderated pace compared to pre-pandemic levels, underpinned by the long-term trend of increasing air travel demand globally. Specific regional growth rates will vary, reflecting unique economic and political circumstances.

US Airlines Twitter (Over time)

Study the trend customer satisfaction over time

About this dataset

The columns in the dataset include index, unit id, golden, unit state, trusted judgments, last judgment at, airline sentiment, airline sentiment confidence, negative reason, negative reason confidence, airline_sentiment_gold and retweet count. There is also text included for each tweet as well as tweet location and user timezone.

Using this dataset, you can get a feel for how customers of various airlines feel about their service. You can use the data to analyze trends over time or compare different airlines. Some research ideas include using airline sentiment to predict the stock market or using the negativereason data to help airlines improve their customer service

How to use the dataset

Looking at this dataset, you can get a feel for how customers of various airlines feel about their service. The data includes the airline, the tweet text, the date of the tweet, and various other information. You can use this to analyze trends over time or compare different airlines

Research Ideas

• Using airline sentiment to predict the stock market - is there a correlation between how the public perceives an airline and how that airline's stock performs?
• Using negativereason data to help airlines improve their customer service - which negative reasons are mentioned most often? Are there certain airlines that are consistently mentioned for specific reasons?
• Use the tweet data to map out airline hot spots - where do people tend to tweet about certain airlines the most? Is there a geographic pattern to sentiment about specific airlines?

Acknowledgements

If you use this dataset in your research, please credit Social Media Data

License

License: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for non-commercial purposes only. - Adapt - remix, transform, and build upon the material for non-commercial purposes only. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - You may not: - Use the material for commercial purposes.

Columns

File: Airline-Sentiment-2-w-AA.csv | Column name | Description | |:---------------------------|:-----------------------------------------------------------------------------| | _golden | This column is the gold standard column. (Boolean) | | _unit_state | This column is the state of the unit. (String) | | _trusted_judgments | This column is the number of trusted judgments. (Numeric) | | _last_judgment_at | This column is the timestamp of the last judgment. (String) | | airline_sentiment | This column is the sentiment of the tweet. (String) | | negativereason | This column is the negative reason for the sentiment. (String) | | airline_sentiment_gold | This column is the gold standard sentiment of the tweet. (String) | | name | This column is the name of the airline. (String) | | negativereason_gold | This column is the gold standard negative reason for the sentiment. (String) | | retweet_count | This column is the number of retweets. (Numeric) | | text | This column is the text of the tweet. (String) | | tweet_coord | This column is the coordinates of the tweet. (String) | | tweet_created | This column is the timestamp of the tweet. (String) | | tweet_location | This column is the location of the tweet. (String) | | user_timezone | This column is the timezone of the user. (String) |