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

Block reported $166.63M in Stock for its fiscal quarter ending in September of 2025. Data for Block | SQ - Stock including historical, tables and charts were last updated by Trading Economics this last November in 2025.

Block reported $40.44B in Market Capitalization this December of 2025, considering the latest stock price and the number of outstanding shares.Data for Block | SQ - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last December in 2025.

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

Block reported -5.7M in Dividend Yield for its fiscal quarter ending in June of 2020. Data for Block | SQ - Dividend Yield including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Block reported $0.71 in EPS Earnings Per Share for its fiscal quarter ending in December of 2024. Data for Block | SQ - EPS Earnings Per Share including historical, tables and charts were last updated by Trading Economics this last December in 2025.

The “Tesla Stock Price Data (Last One Year)” dataset is a comprehensive collection of historical stock market information, focusing on Tesla Inc. (TSLA) for the past year. This dataset serves as a valuable resource for financial analysts, investors, researchers, and data enthusiasts who are interested in studying the trends, patterns, and performance of Tesla’s stock in the financial markets.It consists of 9 columns referring to date, high and low prices, open and closing value, volume, cumulative open and of course changing of price.At a first glance in order to better understand the data we should plot the time series of each attribute.The cumulative Open Interest(OI) is the total open contracts that are being held in a particular Future or Call or Put contracts on the Exchange. We can see that the biggest drop of the stock happened in January of 2023 and after 5 to 6 months it regained its stock value round the summer of the same year with opening and closing price around 300.As a next step we are going to plot some more plots in order ro better understand the relation between our target column(change price) with every other attribute. In order to interpret the results:

Linear Regression:

Mean Absolute Error (MAE): 6.28 This model, on average, predicts the “Price Change” within approximately 6.28 units of the true value. Mean Squared Error (MSE): 52.97 MSE measures the average of squared differences, and this value suggests some variability in prediction errors. Root Mean Squared Error (RMSE): 7.28 RMSE is the square root of MSE and is in the same units as the target variable. An RMSE of 7.28 indicates the typical prediction error. R-squared (R2): 0.0868 R-squared represents the proportion of the variance in the target variable explained by the model. An R2 of 0.0868 suggests that the model explains only a small portion of the variance, indicating limited predictive power. Decision Tree Regression:

Mean Absolute Error (MAE): 9.21 This model, on average, predicts the “Price Change” within approximately 9.21 units of the true value, which is higher than the Linear Regression model. Mean Squared Error (MSE): 150.69 The MSE is relatively high, indicating larger prediction errors and more variability. Root Mean Squared Error (RMSE): 12.28 RMSE of 12.28 is notably higher, suggesting that this model has larger prediction errors. R-squared (R2): -1.598 The negative R-squared value indicates that the model performs worse than a horizontal line as a predictor, indicating a poor fit. Random Forest Regression:

Mean Absolute Error (MAE): 6.99 This model, on average, predicts the “Price Change” within approximately 6.99 units of the true value, similar to Linear Regression. Mean Squared Error (MSE): 62.79 MSE is lower than the Decision Tree model but higher than Linear Regression, suggesting intermediate prediction accuracy Root Mean Squared Error (RMSE): 7.92 RMSE is also intermediate, indicating moderate prediction errors. R-squared (R2): -0.0824 The negative R-squared suggests that the Random Forest model does not perform well and has limited predictive power.

As retail investing continues to reshape global markets, understanding who participates, how they invest, and what influences their decisions has never been more crucial. Stock ownership in the U.S. is reaching levels not seen since before the 2008 crisis, with younger generations, mobile platforms, and new asset classes like ETFs...

Block reported $17.29 in PE Price to Earnings for its fiscal quarter ending in March of 2025. Data for Block | SQ - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last December in 2025.

AI-powered price forecasts for SQ stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

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.

PERSHING SQUARE HOLDINGS LTD Forecast & Analysis

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

Block reported $5.81B in Debt for its fiscal quarter ending in June of 2025. Data for Block | SQ - Debt including historical, tables and charts were last updated by Trading Economics this last December in 2025.

The average square foot value of apartments in the United States increased dramatically in recent years, peaking at about *** U.S. dollars per square foot in 2021. This was more than double the average value just a decade ago.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

In 2024, Block's market capitalization increased by nearly ***percent compared to the previous year. That year, its market capitalization amounted to around **** billion U.S. dollars. Additionally, the company’s price-to-earnings (P/E) increased from -****** to ******between 2023 and 2024, indicating that Block's stock price grew faster than its earnings over the period.

Block reported $2.9B in Net Income for its fiscal quarter ending in December of 2024. Data for Block | SQ - Net Income including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Index Time Series for Leverage Shares 3x Square ETP Securities GBX. The frequency of the observation is daily. Moving average series are also typically included.

A list of the top 50 Pershing Square Capital Management holdings showing which stocks are owned by Bill Ackman's hedge fund.

Block reported $3.72B in Cost of Sales for its fiscal quarter ending in December of 2024. Data for Block | SQ - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last December in 2025.

In 2024, Poland noted the highest industrial and logistics property market stock among Central and Eastern European countries, reaching **** million square meters. Following was Czechia, with a total stock of **** million square meters.