New York Times stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

The most recent stock split for New York Times (NYT) was a 2:1 split on July 2, 1998. The combined total of all historical stock splits for New York Times result in 12 current shares for every original share available at the IPO in 1973.

New York Times reported $9.01B in Market Capitalization this June of 2025, considering the latest stock price and the number of outstanding shares.Data for New York Times | NYT - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last June in 2025.

New York Times reported 1.45 in Dividend Yield for its fiscal quarter ending in March of 2025. Data for New York Times | NYT - Dividend Yield including historical, tables and charts were last updated by Trading Economics this last June in 2025.

There are six diferent kinds of widgets we have;

Ticker - This Widget is used for your websites top or bottom for navigation bar. It is horizontal bar with symbols last prices, daily changes and daily percentage changes.

Tape Ticker - This is a stock market classic widget that simply displays symbols (prices, daily changes and daily changes of percentages ) with a sliding cursor that stops when your cursor stops in a position it will stop too. Simple, fancy and useful.

Single Ticker - It's a simple one-symbol sized ticker.

Converter - This widget works best on the right or left sidebar of your website with a fast, useful currency converter with the latest updates and unit prices.

Mini Converter - It’s also simple and beautiful converter best for mobile websites.

Historical Chart - You can view the historical data details for a single symbol with the Historical Chart Widget.

Life Times common stock net for the quarter ending March 31, 2025 was $0.002B, a 9.61% increase year-over-year. Life Times common stock net for 2024 was $0.002B, a 5.49% increase from 2023. Life Times common stock net for 2023 was $0.002B, a 1.24% increase from 2022. Life Times common stock net for 2022 was $0.002B, a 0.62% increase from 2021.

New York Times reported 28.02 in PE Price to Earnings for its fiscal quarter ending in March of 2025. Data for New York Times | NYT - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last June 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.

NYT New York Times Company (The) 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 per day. It has 72,406 rows. It features 6 columns including stock, opening price, highest price, and lowest price. It is 100% filled with non-null values.

Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.

There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.

Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.

A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.

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New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.

Content

Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.

Dataset

The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)

Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.

Starter Kernel(s)

• Simple LSTM Regression

Acknowledgements

Mining and updating of this dateset will depend upon Yahoo Finance .

Inspiration

Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting

Some Readings

• Applications of deep learning in stock market prediction: recent progress
• Stock predictions with state-of-the-art Transformer and Time Embeddings
• https://arxiv.org/pdf/1712.02136.pdf
• https://catanacapital.com/blog/unpredictable-black-swan-event-stock-market/
• https://par.nsf.gov/servlets/purl/10149715
• https://arxiv.org/pdf/1712.02136.pdf

*If you download and find the data useful your upvote is an explicit feedback for future works*

--- Original source retains full ownership of the source dataset ---

The latest closing stock price for Tesla as of June 17, 2025 is 316.39. An investor who bought $1,000 worth of Tesla stock at the IPO in 2010 would have $197,650 today, roughly 198 times their original investment - a 42.30% compound annual growth rate over 15 years. The all-time high Tesla stock closing price was 479.86 on December 17, 2024. The Tesla 52-week high stock price is 488.54, which is 54.4% above the current share price. The Tesla 52-week low stock price is 179.66, which is 43.2% below the current share price. The average Tesla stock price for the last 52 weeks is 291.40. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

This horizontal bar chart displays stocks over time by stocks over time using the aggregation count. The data is filtered where the stock is THK.MC. The data is about stocks per day.

Description

Historical hourly prices for stocks (7000+) traded on NASDAQ from 2021-01-01 to 2021-12-30.

Data

See provenance, times based on stock exchange location: - ticker (string): Symbol name. - name (string): Security name. - date (string): Trading date (Eastern Time Zone). - open (float): Open price on that day. - high (float): Maximum price on that day. - low (float): Minimum price on that day. - close (float): Close price on that day. - adjusted close (float): Close price adjusted for dividends and splits. - volume (int): Share volume traded on that day.

Usage

See getting started, data available as csv.

Legal

• Provided "as is" without guarantee of completeness.
• Photo by Alesia Kozik from Pexels.

The value of the DJIA index amounted to 43,191.24 at the end of March 2025, up from 21,917.16 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 29th of 2008, for instance, the Dow had a loss of 106.85 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 81.66 percent in one year, and 1933, year when the index registered a growth of 63.74 percent.

New York Times reported $2.74B in Assets for its fiscal quarter ending in March of 2025. Data for New York Times | NYT - Assets including historical, tables and charts were last updated by Trading Economics this last June in 2025.

The latest closing stock price for Microsoft as of June 18, 2025 is 480.24. An investor who bought $1,000 worth of Microsoft stock at the IPO in 1986 would have $8,056,718 today, roughly 8,057 times their original investment - a 25.94% compound annual growth rate over 39 years. The all-time high Microsoft stock closing price was 480.24 on June 18, 2025. The Microsoft 52-week high stock price is 481.00, which is 0.2% above the current share price. The Microsoft 52-week low stock price is 344.79, which is 28.2% below the current share price. The average Microsoft stock price for the last 52 weeks is 422.77. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

This poll, conducted December 8-10, 2006, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. Respondents were asked whether they approved of the way President George W. Bush was handling the presidency and issues such as foreign policy and the economy. Respondents voiced their concerns about the most important problem facing the country, the condition of the national economy, their own household's financial security, and whether the country was moving in the right direction. A series of questions addressed respondents' feelings about the newly elected United States Congress, and whether the United States should intervene in other countries' affairs. Views were sought on the war with Iraq, whether the Iraqi government was strong enough to withstand pressure from the insurgents, and whether the United States government should solicit the help of neighboring countries in the Middle East in its efforts to create stability in Iraq. Other questions addressed the recommendations made by the Iraq Study Group commissioned by Congress, and whether the United States had a responsibility to make sure Iraq had a stable government before withdrawing its troops. Respondents were also asked about their own opportunities to succeed compared to those of their parents' generation, whether they expected their children to have better opportunities than they did, how often they experienced stress in their daily life, and how often this stress was caused by financial difficulties. Additional topics addressed holiday spending, retirement savings and investments, the real estate and stock markets, and whether respondents rented or owned their home. Demographic information includes sex, age, race, education level, household income, marital status, religious preference, type of residential area (e.g., urban or rural), political party affiliation, political philosophy, voter registration status and participation history, the presence of household members between the ages of 18 and 24, whether respondents had children under 18, and whether they considered themselves to be born-again Christians.

This dataset provides realistic stock market data generated using Geometric Brownian Motion for price movements and Markov Chains for trend prediction. It is designed for time-series forecasting, financial modeling, and algorithmic trading simulations.

Key Features

• 1000 days of synthetic stock market data (from January 1, 2022, onwards).
• Multiple companies from diverse industries (Technology, Finance, Healthcare, Energy, Consumer Goods, Automotive, Aerospace, etc.).
• Stock price details: Open, High, Low, Close prices.
• Trading volume and market capitalization.
• Financial metrics: P/E Ratio, Dividend Yield, Volatility.
• Sentiment Score: A measure of market sentiment (-1 to 1 scale).
• Trend Labeling: Bullish, Bearish, or Stable, based on Markov Chain modeling.

Usage Scenarios

🔹 Time-Series Forecasting: Train models like LSTMs, Transformers, or ARIMA for stock price prediction.
🔹 Algorithmic Trading: Develop trading strategies based on trends and sentiment.
🔹 Feature Engineering: Explore correlations between financial metrics and stock movements.
🔹 Quantitative Finance Research: Analyze market trends using simulated yet realistic data.

PS: If you find this dataset helpful, please consider upvoting :)

Leverage Databento's real-time stock API to get tick data with full order book depth (MBO). Offering seamless intraday market replay in a single API call.

This horizontal bar chart displays stocks over time by stocks over time using the aggregation count. The data is filtered where the stock is NMEHF. The data is about stocks per day.