The main stock market index of United States, the US500, rose to 6008 points on June 9, 2025, gaining 0.13% from the previous session. Over the past month, the index has climbed 2.80% and is up 12.07% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on June of 2025.

Historical dataset of the Dow Jones Industrial Average (DJIA) stock market index for the last 100 years. Historical data is inflation-adjusted using the headline CPI and each data point represents the month-end closing value. The current month is updated on an hourly basis with today's latest value.

The main stock market index of United States, the US500, rose to 6000 points on June 6, 2025, gaining 1.03% from the previous session. Over the past month, the index has climbed 6.55% and is up 12.22% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on June of 2025.

Graph and download economic data for Index of Common Stock Prices, New York Stock Exchange for United States (M11007USM322NNBR) from Jan 1902 to May 1923 about New York, stock market, indexes, and USA.

Ten years of daily data for the Dow Jones Industrial Average (DJIA) market index. Each point of the dataset is represented by the daily closing price for the DJIA. Historical data can be downloaded via the red button on the upper right corner of the chart.

The Dow Jones Industrial Average (DJIA) index dropped around 8,000 points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at 44,910.65 points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over 29,000 points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than 3,500 points in the week from February 21 to February 28, which was a fall of 12.4 percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

Stock Analysis Software Market Size And Forecast

Stock Analysis Software Market size was valued at USD 145.6 Million in 2023 and is projected to reach USD 450.68 Million by 2031, growing at a CAGR of 15.17% during the forecast period 2024-2031.

Global Stock Analysis Software Market Drivers

The market drivers for the Stock Analysis Software Market can be influenced by various factors. These may include:

Growing Interest from Investors: As more people and organizations engage in the stock market, there is an increasing need for tools that help monitor and evaluate investments. Automation and Efficiency: Software adoption is fueled by traders' and investors' need for automated solutions that will expedite their analysis and decision-making. Data Accessibility: An abundance of financial data, such as current stock prices and corporate details, presents prospects for thorough analytical instruments. Advanced Technologies: Adding AI and machine learning to stock analysis software improves its capacity for prediction and provides more individualized insights, which draws in more users. Growth in Retail Trading: Individual investors' need for user-friendly stock analysis tools has been fueled by the growing acceptance of retail trading platforms. Regulatory Compliance: Software solutions that support compliance are in great demand as financial markets become more regulated. Cost-Effectiveness: By eliminating the need for human analysts, automated analysis systems can offer both individual and institutional investors a more affordable option. Cross-platform Integration: Users seeking coherent investing ecosystems will find stock research software more appealing if it interfaces with other financial tools and platforms. Global Market Expansion: Software that can assess equities across multiple locations and adhere to international regulations is needed as stock markets become increasingly global. User-Friendly Interfaces: The movement toward more user-friendly interfaces increases the accessibility of stock analysis software, which encourages non-professional investors to use it.

United Kingdom's main stock market index, the GB100, rose to 8838 points on June 6, 2025, gaining 0.30% from the previous session. Over the past month, the index has climbed 3.25% and is up 7.19% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on June of 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.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

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.

Historical dataset for the S&P 500 stock market index since 1927. Historical data is inflation-adjusted using the headline CPI and each data point represents the month-end closing value. The current month is updated on an hourly basis with today's latest value.

Japan's main stock market index, the JP225, rose to 38094 points on June 9, 2025, gaining 0.93% from the previous session. Over the past month, the index has climbed 1.19%, though it remains 2.42% lower than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on June of 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.

The Dow Jones U.S. Completion Total Stock Market Index

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 Dow Jones Industrial Average (DJIA) is a stock market index used to analyze trends in the stock market. While many economists prefer to use other, market-weighted indices (the DJIA is price-weighted) as they are perceived to be more representative of the overall market, the Dow Jones remains one of the most commonly-used indices today, and its longevity allows for historical events and long-term trends to be analyzed over extended periods of time. Average changes in yearly closing prices, for example, shows how markets developed year on year. Figures were more sporadic in early years, but the impact of major events can be observed throughout. For example, the occasions where a decrease of more than 25 percent was observed each coincided with a major recession; these include the Post-WWI Recession in 1920, the Great Depression in 1929, the Recession of 1937-38, the 1973-75 Recession, and the Great Recession in 2008.

Stock Market Performance and Corporate Board Member Profiles

Analyzing the Influences of Leadership on Stock Market Performance

By Jon Loyens [source]

About this dataset

This powerful dataset brings together publically-available information from leading stock markets with extensive details about corporate board members. For each company, discover not only their board composition and background, but also current market dynamics, trends and rule changes affecting them. Whether you're a teacher looking to add more detail to a class presentation or an investor seeking a competitive edge in the market - this dataset provides comprehensive insights into the world of stocks and those that play an influential role on its direction. Unprecedented access awaits as you explore hypothetical investments and strategies or actual risks associated with established entities today

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How to use the dataset

Using this dataset, you can gain a better understanding of the relationship between corporate board members and stock market performance. You can analyze the data to determine the average performance of board members at different companies and compare it to the overall performance of other stocks. In addition, you can look into correlations between individual stocks, various industries, and different groups of companies with similar board membership profiles. This dataset provides an overview of all major stocks across multiple industries with detailed insights on each stock's current and past market performance as well as corporate boards

Research Ideas

• Analyzing the performance of individual board members in relation to their company’s stock market performance.
• Determining if certain board members are better at making decisions that benefit the company’s stock market position across all companies they have a stake in.
• Identifying correlations between trends in different companies' stocks and external factors such as the influence of particular board members or other events associated with that company's sectors or markets

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: boardmembers.csv | Column name | Description | |:--------------------|:-----------------------------------| | BoardMemberName | Name of the board member. (String) | | CompanyName | Name of the company. (String) | | Source | Source of the data. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Jon Loyens.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-06-08 to 2025-06-06 about stock market, average, industry, and USA.

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.

LON:STG Stock: The Stock Market Bubble Is About to Burst

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

In 2024, 62 percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years, and is still below the levels before the Great Recession, when it peaked in 2007 at 65 percent. What is the stock market? The stock market can be defined as a group of stock exchanges, where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the Financial Crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

Historical dataset of the S&P 500 stock market index over the last 10 years. Values shown are daily closing prices. The most recent value is updated on an hourly basis during regular trading hours.