AT&T stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

The latest closing stock price for AT&T as of June 17, 2025 is 27.66. An investor who bought $1,000 worth of AT&T stock at the IPO in 1983 would have $113,915 today, roughly 114 times their original investment - a 11.96% compound annual growth rate over 42 years. The all-time high AT&T stock closing price was 28.42 on June 10, 2025. The AT&T 52-week high stock price is 29.03, which is 5% above the current share price. The AT&T 52-week low stock price is 17.90, which is 35.3% below the current share price. The average AT&T stock price for the last 52 weeks is 23.44. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

AT&T reported $192.41B in Market Capitalization this July of 2025, considering the latest stock price and the number of outstanding shares.Data for AT&T | T - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last July in 2025.

AT&T reported $33.78B in Current Assets for its fiscal quarter ending in March of 2025. Data for AT&T | T - Current Assets including historical, tables and charts were last updated by Trading Economics this last July in 2025.

AT&T market cap as of July 10, 2025 is $194.89B. AT&T market cap history and chart from 2010 to 2025. Market capitalization (or market value) is the most commonly used method of measuring the size of a publicly traded company and is calculated by multiplying the current stock price by the number of shares outstanding.

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.

T^C AT&T Inc. Depositary Shares each representing a 1/1000th interest in a share of 4.750% Perpetual Preferred Stock Series C

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

AT&T reported $126.16B in Debt for its fiscal quarter ending in March of 2025. Data for AT&T | T - Debt including historical, tables and charts were last updated by Trading Economics this last July 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.

AT&T Global Notes: A Solid Investment for the Future? (TBB)

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

AT&T reported $48B in Current Liabilities for its fiscal quarter ending in March of 2025. Data for AT&T | T - Current Liabilities including historical, tables and charts were last updated by Trading Economics this last July 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.

AT&T (T): Navigating the Telecommunications Tide - What's Ahead?

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

Browse AT&T Inc. (T) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Consolidated last sale, exchange BBO and national BBO across all US equity options exchanges. Includes single name stock options (e.g. TSLA), options on ETFs (e.g. SPY, QQQ), index options (e.g. VIX), and some indices (e.g. SPIKE and VSPKE). This dataset is based on the newer, binary OPRA feed after the migration to SIAC's OPRA Pillar SIP in 2021. OPRA is notable for the size of its data and we recommend users to anticipate several TBs of data per day for the full dataset in its highest granularity (MBP-1).

Origin: Options Price Reporting Authority

Supported data encodings: DBN, JSON, CSV Learn more

Supported market data schemas: MBP-1, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, TBBO, Trades, Statistics, Definition Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

The statistic depicts the churn rate of AT&T in regards to wireless subscribers from 2007 to 2017. In 2017, the churn rate was at **** percent.

AT&T - additional information

AT&T is an American multinational telecommunications company based in Dallas, Texas. The company was recently ranked as being the most valuable telecom brand in the world and the company naturally enjoys the lead in the market in the United States as well. The company’s operating revenue has been strong and consistent over the past years and has consistently sat at more than 100 billion US dollars annually for some time. AT&T’s net profit however has fluctuated somewhat in recent times but has also been as high as almost ** billion US in the past.

Employee numbers at AT&T have been declining annually since 2007, due to a number of various cuts across the business. Despite the cuts, AT&T was still the telecommunications company with the greatest number of employees as of 2012. In the United States AT&T’s greatest competitor is Verizon Communications, who also enjoys a significant share of the wireless market across the country. Verizon was also among the most valuable telecommunications brands worldwide with a value of more than ** billion as of 2013.

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.

AT&T (T) Rising: A Stellar Investment or a Communication Conundrum?

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 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.

TBC AT&T Inc. 5.625% Global Notes due 2067

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 December 2024, the telecommunications company AT&T had around 141 thousand employees, a sharp decrease from the previous year. AT&T on cost-cutting trend AT&T's workforce has declined year-on-year since 2019, with a 40 percent reduction through to January 2024. In 2020, AT&T's CEO, John Stankey, committed the operator to finding six billion U.S. dollars in savings by 2023, with a reduction in labor costs listed as a key target. In Spring 2023, the operator announced further measures in the form of office closures across the country, with a new policy requiring certain management staff to attend an office at least three times a week. While these measures did not include layoffs, AT&T stated that the new requirements may spur employees to seek opportunities elsewhere. Moreover, the company's 2022 decision to spin off its interest in the media company WarnerMedia significantly reduced its workforce by over 20 percent. AT&T’s revenue AT&T is a leader in the wireless telecommunications sector, servicing the most wireless subscriptions among U.S. operators. Despite this, the company faces strong competition from rival network operators Verizon and T-Mobile US, particularly in the 5G space. AT&T ranked second in U.S. 5G coverage in 2024, and third in typical 5G download speeds. T-Mobile US held the top spot in both metrics, having invested heavily in its 5G network in an attempt to challenge AT&T and Verizon.

The wireless telecommunication carrier industry has witnessed significant shifts recently, driven by evolving consumer demands and technological advancements. The popularity of smartphones and rising data consumption habits have mainly driven growth. Households have chosen to disconnect their landlines to cut costs and receive network access away from home. Industry revenue was bolstered during the current period by a surge in mobile internet demand. The revival of unlimited data and call plans prompted industry-wide adjustments to pricing and data offerings. While competition has intensified, leading to price wars and slender margins, carriers have embraced bundled offerings of value-added services, like streaming subscriptions, to distinguish themselves. Despite these efforts, revenue growth remains sluggish amid high operational costs and a saturated market. Overall, Wireless Telecommunications Carriers' revenue has modestly grown at an annualized rate of 0.1% to total $340.3 billion in 2025, when revenue will climb an estimated 6.0%, as the early shift to fifth-generation (5G) enables businesses to renegotiate the current product-price paradigm with consumers. The industry is defined by a transition from primarily providing voice services to focusing on providing data services. Technological change, namely the shift from fourth-generation (4G) wireless data services to 5G, continues to shape the industry. Companies expand scope through mergers and acquisitions, acquiring spectrum and niche customer bases. The battle for wireless spectrum intensified as 5G technology became a focal point, requiring carriers to secure valuable frequency bands through hefty investments. For instance, Verizon's $45 billion expenditure in the C-band spectrum auction highlights the critical importance of spectrum acquisition. While Federal Communications Commission (FCC) regulations have curtailed large-scale consolidations, strategic alliances and mergers have been common to share infrastructure and expand market reach. Also, unlimited data plans have shaken up cost structures and shifted consumers to new providers. Following the expansion of unlimited data and calls, profit is poised to inch downward as the cost of acquiring new customers begins to mount. Profitability is additionally hindered by supply chain disruptions, which still loom large, as equipment delays and price hikes impact rollout timeliness. Industry revenue is forecast to incline at an annualized 5.4% through 2030, totaling an estimated $443.5 billion, driven by the expansion of mobile devices using data services and increasing average revenue per user. As the rollout of 5G networks increases the speed of wireless data services, more consumers will view on-the-go internet access as an essential function of mobile phones. Moving forward, the industry landscape will be characterized by the heightened competition among carriers for wireless spectrum, an already scarce resource and efforts to connect more Americans in remote parts of the country to fast and reliable internet. Subscriber saturation presents a formidable challenge, compelling carriers to focus on existing customers and innovative service packages. Companies like AT&T and Verizon are pioneering flexible infrastructure projects, which could redefine the industry’s operational efficiency. Despite facing spectrum supply limitations, the industry is poised to benefit from seamless connectivity solutions for various sectors, potentially redefining wireless carriers’ roles in an increasingly interconnected world.

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.

What's the Future Hold for AT&T (T) Fractional Preferred 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

The Digital Subscriber Line (DSL) internet market, while facing competition from faster technologies like fiber and cable, maintains a significant presence, particularly in underserved areas with limited infrastructure alternatives. The market, estimated at $15 billion in 2025, demonstrates a Compound Annual Growth Rate (CAGR) of 2% from 2025 to 2033. This relatively modest growth reflects the gradual decline of DSL as consumers and businesses migrate to higher-bandwidth options. However, DSL continues to serve as a cost-effective, readily available solution for many, sustaining a niche market. Factors such as affordability, existing infrastructure in some regions, and the slower pace of broadband expansion in rural areas contribute to its continued relevance. Key players like AT&T, Verizon, and CenturyLink, leveraging their existing infrastructure, continue to offer DSL services, adapting their strategies by bundling it with other services or targeting specific customer segments with price-sensitive needs. Despite the steady decline, certain market trends support a sustainable market share for DSL internet. These include the ongoing need for reliable internet access in remote regions where fiber rollout is challenging and expensive. Further, targeted marketing toward price-conscious consumers, coupled with improvements in DSL technology providing marginally better speeds, could help to prolong its lifespan. However, the persistent restraint of technological obsolescence and the compelling value proposition of faster broadband alternatives remain significant challenges that might influence the reduced CAGR compared to faster-growing segments of the internet service market. Effective strategies for DSL providers must focus on niche market penetration and adapting to the evolving needs of a consumer base increasingly demanding higher bandwidth capabilities.

The Smart Home Security Services market is experiencing robust growth, projected to reach a significant market size by 2033, driven by a 24.10% Compound Annual Growth Rate (CAGR) from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of smart home technologies, including interconnected security devices and remote monitoring capabilities, offers homeowners enhanced convenience and peace of mind. Rising concerns about home security breaches and property crime further stimulate demand for sophisticated security solutions. The market is segmented by product (video surveillance – encompassing security cameras, monitors, and storage devices; access control systems, including facial, fingerprint, and iris recognition), and installation type (professional versus self-installation). Professional installation services remain prevalent, particularly for complex systems, while self-installation options cater to the DIY market segment, driving competition and innovation. Key players like ADT Security Services, AT&T, Comcast, and Vivint are leading this growth through strategic partnerships, technological advancements, and expansion into new markets. Geographic expansion is also a significant driver. North America currently holds a substantial market share, but the Asia-Pacific region is showing rapid growth potential due to increasing urbanization and rising disposable incomes. The market faces challenges such as cybersecurity concerns and the complexities associated with integrating multiple smart home devices seamlessly. However, ongoing technological developments and a focus on user-friendly interfaces are mitigating these challenges. The future trajectory of the Smart Home Security Services market is promising, with considerable opportunities for growth in both developed and emerging economies. The integration of artificial intelligence (AI) and machine learning (ML) into security systems will further enhance capabilities and market appeal. This includes features such as advanced threat detection, predictive analytics, and automated responses. The convergence of smart home security with other smart home functionalities, like energy management and home automation, is creating a holistic ecosystem that adds value for consumers. The ongoing focus on data privacy and security is shaping industry standards and driving the adoption of robust encryption and authentication protocols. The market’s continued growth depends on addressing consumer concerns, particularly related to cost and complexity, while consistently delivering innovative solutions that prioritize user experience and security. Competitive pressures will necessitate continuous improvement in product features, service offerings, and cost-effectiveness to maintain market share. Recent developments include: June 2020: Swann, engaged in providing do-it-yourself security solutions, announced the release of the Swann Wire-Free Security Camera, a new, completely wireless camera that can be set up in seconds and used for monitoring indoors or outdoors in homes. The camera is stocked with modern smart security features, including free face recognition and cloud or local storage., September 2020: Hangzhou Hikvision Digital Technology Co. Ltd launched Hik-ProConnect, a convergent, cloud-based security service solution where users can converge Hikvision devices to cover video, intrusion, access control, intercom, and more to address their security needs. Users can also authorize their professional security advisors to complete necessary system management, such as remote system health checks and maintenance.. Key drivers for this market are: Growing Safety Concerns, Decreasing Costs of Sensors. Potential restraints include: Growing Safety Concerns, Decreasing Costs of Sensors. Notable trends are: Video Surveillance systems are expected to register significant growth in forecasted period.

Discover the potential rise in smartphone prices due to tariff concerns, as highlighted by T-Mobile's CEO, and how it might affect consumer purchasing behavior.