In 2023, the S&P 500 Information Technology Index outperformed other sectors, with annual return of **** percent. On the other hand, the S&P 500 Utilities Index recorded the lowest returns, with a loss of *** percent.

As of November 14, 2021, all S&P 500 sector indices had recovered to levels above those of January 2020, prior to full economic effects of the global coronavirus (COVID-19) pandemic taking hold. However, different sectors recovered at different rates to sit at widely different levels above their pre-pandemic levels. This suggests that the effect of the coronavirus on financial markets in the United States is directly affected by how the virus has impacted various parts of the underlying economy. Which industry performed the best during the coronavirus pandemic? Companies operating in the information technology (IT) sector have been the clear winners from the pandemic, with the IT S&P 500 sector index sitting at almost ** percent above early 2020 levels as of November 2021. This is perhaps not surprising given this industry includes some of the companies who benefitted the most from the pandemic such as ************** and *******. The reason for these companies’ success is clear – as shops were shuttered and social gatherings heavily restricted due to the pandemic, online services such shopping and video streaming were in high demand. The success of the IT sector is also reflected in the performance of global share markets during the coronavirus pandemic, with tech-heavy NASDAQ being the best performing major market worldwide. Which industry performed the worst during the pandemic? Conversely, energy companies fared the worst during the pandemic, with the S&P 500 sector index value sitting below its early 2020 value as late as July 2021. Since then it has somewhat recovered, and was around ** percent above January 2020 levels as of October 2021. This reflects the fact that many oil companies were among the share prices suffering the largest declines over 2020. A primary driver for this was falling demand for fuel in line with the reduction in tourism and commuting caused by lockdowns all over the world. However, as increasing COVID-19 vaccination rates throughout 2021 led to lockdowns being lifted and global tourism reopening, demand has again risen - reflected by the recent increase in the S&P 500 energy index.

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.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-07-13 to 2025-07-11 about stock market, average, industry, and USA.

The Standard & Poor’s (S&P) 500 Index is an index of 500 leading publicly traded companies in the United States. In 2021, the index value closed at ******** points, which was the second highest value on record despite the economic effects of the global coronavirus (COVID-19) pandemic. In 2023, the index values closed at ********, the highest value ever recorded. What is the S&P 500? The S&P 500 was established in 1860 and expanded to its present form of 500 stocks in 1957. It tracks the price of stocks on the major stock exchanges in the United States, distilling their performance down to a single number that investors can use as a snapshot of the economy’s performance at a given moment. This snapshot can be explored further. For example, the index can be examined by industry sector, which gives a more detailed illustration of the economy. Other measures Being a stock market index, the S&P 500 only measures equities performance. In addition to other stock market indices, analysts will look to other indicators such as GDP growth, unemployment rates, and projected inflation. Similarly, since these indicators say something about the economic future, stock market investors will use these indicators to speculate on the stocks in the S&P 500.

US Stock Market Data (21st November 2023 – 2nd February 2024)

Overview

This dataset provides detailed historical data on the US stock market, covering the period from 21st November 2023 to 2nd February 2024. It includes daily performance metrics for major stocks and indices, enabling investors, analysts, and researchers to study short-term market trends, fluctuations, and patterns.

Dataset Contents

The dataset contains the following key attributes for each trading day:

Date: The trading date.

Ticker: Stock ticker symbol (e.g., AAPL for Apple, MSFT for Microsoft).

Open Price: The price at which the stock opened for trading.

Close Price: The price at which the stock closed for trading . High Price: The highest price reached during the trading session.

Low Price: The lowest price reached during the trading session.

Adjusted Close Price: The closing price adjusted for splits and dividend payouts.

Trading Volume: The total number of shares traded on that day.

Highlights

Time Period: Covers daily data for over two months of trading activity.

Market Scope: Includes data from a diverse set of stocks, industries, and sectors, reflecting the broader US market trends.

Indices and Major Stocks: Tracks key indices (e.g., S&P 500, NASDAQ) and major stocks across various sectors .

Potential Applications

Analyzing short-term market performance trends. Developing trading strategies or backtesting investment models. Exploring the impact of macroeconomic events on stock performance. Studying sector-wise performance in the US stock market.

Data Source

The data has been sourced from publicly available market records, ensuring reliability and accuracy. Each data point represents an official trading record from the respective exchange.

Usage Notes

The dataset is intended for educational, analytical, and research purposes only. Users should be mindful of potential market anomalies or external factors influencing data during this time frame.

Acknowledgments

Special thanks to the organizations and platforms that make financial market data accessible for analysis and research.

Euro Area's main stock market index, the EU50, fell to 5370 points on July 14, 2025, losing 0.25% from the previous session. Over the past month, the index has climbed 0.57% and is up 7.76% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on July of 2025.

High-Tech Companies on NASDAQ

Market Capitalization and Performance Metrics

By [source]

About this dataset

This dataset offers an insightful look into the performance of high-tech companies listed on the NASDAQ exchange in the United States. With information pertaining to over 8,000 companies in the electronics, computers, telecommunications, and biotechnology sectors, this is an incredibly useful source of insight for researchers, traders, investors and data scientists interested in acquiring information about these firms.

The dataset includes detailed variables such as stock symbols and names to provide quick identification of individual companies along with pricing changes and percentages from the previous day’s value as well as sector and industry breakdowns for comprehensive analysis. Other metrics like market capitalization values help to assess a firm’s relative size compared to competitors while share volume data can give a glimpse into how actively traded each company is. Additionally provided numbers include earnings per share breakdowns to gauge profits along with dividend pay date symbols for yield calculation purposes as well as beta values that further inform risk levels associated with investing in particular firms within this high-tech sector. Finally this dataset also collects any potential errors found amongst such extensive scrapes of company performance data giving users valuable reassurance no sensitive areas are missed when assessing various firms on an individual basis or all together as part of an overarching system

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How to use the dataset

This dataset is invaluable for researchers, traders, investors and data scientists who want to obtain the latest information about high-tech companies listed on the NASDAQ exchange in the United States. It contains data on more than 8,000 companies from a wide range of sectors such as electronics, computers, telecommunications, biotechnology and many more. In this guide we will learn how to use this dataset effectively.

Basics: The basics of working with this dataset include understanding various columns like symbol, name, price,pricing_changes, pricing_percentage_changes,sector,industry,market_cap,share_volume,earnings_per_share. Each column is further described below: - Symbol: This column gives you the stock symbol of the company. (String) - Name: This column gives you the name of the company. (String)
- Price: The current price of each stock given by symbol is mentioned here.(Float) - Pricing Changes: This represents change in stock price from previous day.(Float) - Pricing Percentage Changes :This provides percentage change in stock prices from previous day.(Float) - Sector : It give information about sector in which company belongs .(String). - Industry : Describe industry in which company lies.(string). - Market Capitalization : Give market capitalization .(String). - Share Volume : It refers to number share traded last 24 hrs.(Integer). - Earnings Per Share : It refer to earnings per share per Stock yearly divided by Dividend Yield ,Symbol Yield and Beta .It also involves Errors related with Data Set so errors specified here proviedes details regarding same if any errors occured while collecting data set or manipulation on it.. (float/string )

Advanced Use Cases: Now that we understand what each individual feature stands for it's time to delve deeper into optimizing returns using this data set as basis for our decision making processes such as selecting right portfolio formation techniques or selecting stocks wisely contrarian investment style etc. We can do a comparison using multiple factors like Current Price followed by Price Change percentage or Earnings feedback loop which would help us identify Potentially Undervalued investments both Short Term & Long Term ones at same time and We could dive into analysis showing Relationship between Price & Volumne across Sectors and

Research Ideas

• Analyzing stock trends - The dataset enables users to make informed decisions by tracking and analyzing changes in indicators such as price, sector, industry or market capitalization trends over time.
• Exploring correlations between different factors - By exploring the correlation between different factors such as pricing changes, earning per share or beta etc., it enables us to get a better understanding of how these elements influence each other and what implications it may have on our investments

Acknowledgements

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The NYSE U.S. Market Consumer Goods Sector Index tracks the performance of the U.S. domiciled equity components listed on the U.S. stock exchanges that offer goods and services in the consumer goods sector. Between December 2015 and June 2023, the index fluctuated but increased overall, standing at 3,209.19 index points as of June 2023.

The NYSE U.S. Market Industrials Sector Index tracks the performance of the U.S. domiciled equity components listed on the U.S. stock exchanges that offer goods and services in the industrials sector. The statistics shows the monthly development of the NYSE U.S. Market Industrials Sector Index from December 2015 to January 2023. During that time, the index fluctuated significantly, and stood at ******** as of January 2023.

In 2025, stock markets in the United States accounted for roughly ** percent of world stocks. The next largest country by stock market share was China, followed by the European Union as a whole. The New York Stock Exchange (NYSE) and the NASDAQ are the largest stock exchange operators worldwide. What is a stock exchange? The first modern publicly traded company was the Dutch East Industry Company, which sold shares to the general public to fund expeditions to Asia. Since then, groups of companies have formed exchanges in which brokers and dealers can come together and make transactions in one space. Stock market indices group companies trading on a given exchange, giving an idea of how they evolve in real time. Appeal of stock ownership Over half of adults in the United States are investing money in the stock market. Stocks are an attractive investment because the possible return is higher than offered by other financial instruments.

Investment Trust Market Outlook

The global investment trust market size was valued at approximately USD 2.5 trillion in 2023 and is projected to reach around USD 4.1 trillion by 2032, growing at a compound annual growth rate (CAGR) of 5.5% during the forecast period. The growth of this market is driven by several factors including increasing investor preference for diversified portfolios and the growing availability of various types of investment trusts to meet different investment goals. These factors are expected to propel the market significantly over the coming years.

Expanding middle-class populations and increasing disposable incomes in emerging economies are also contributing significantly to the growth of the investment trust market. With more individuals seeking avenues for better returns on their investments, investment trusts offer an attractive proposition due to their diversified nature and professional management. Additionally, the growing awareness about the benefits of investing in such diversified instruments, as opposed to individual stocks or bonds, is a crucial growth factor.

Technological advancements and digitalization have made it easier for investors to access investment trusts. Online platforms have simplified the process of investing, enabling real-time tracking and management of investment portfolios. This ease of access has broadened the market's appeal, attracting a younger, tech-savvy investor base. The integration of artificial intelligence and machine learning in these platforms further enhances their capabilities, making investment decisions more data-driven and informed.

The rising trend of sustainable and responsible investing is another significant driver for the investment trust market. Many investors are now seeking to align their portfolios with their personal values, focusing on environmental, social, and governance (ESG) criteria. Investment trusts that prioritize ESG factors are seeing increased demand, as investors look to not only generate financial returns but also contribute positively to society and the environment.

Regionally, North America and Europe dominate the investment trust market, primarily due to their well-established financial sectors and higher levels of investor sophistication. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The increasing economic development and growing middle-class population in countries like China and India are major contributors to this growth. As more individuals in these regions become financially literate, the demand for diverse investment options like investment trusts is expected to rise steadily.

Type Analysis

Equity investment trusts, fixed-income investment trusts, hybrid investment trusts, and other specialized types form the various segments of the investment trust market. Equity investment trusts, which primarily invest in stocks, remain the most popular due to their potential for high returns. These trusts appeal to investors looking for growth opportunities, particularly in sectors showing robust performance. The volatility of stock markets, however, poses a risk, making it essential for these trusts to maintain a well-diversified portfolio to mitigate potential losses.

Fixed-income investment trusts focus on bonds and other debt instruments, offering a more stable and predictable income stream, which is particularly attractive to conservative investors or those nearing retirement. These trusts typically have lower risk compared to equity trusts, but also potentially lower returns. With interest rates playing a critical role in their performance, the recent trends of fluctuating interest rates have made these trusts more appealing as they adapt to the changing economic landscape.

Hybrid investment trusts combine both equity and fixed-income investments, providing a balanced approach that appeals to a broader range of investors. These trusts aim to achieve a mix of income generation and capital appreciation, making them suitable for investors with moderate risk tolerance. The flexibility offered by hybrid trusts allows them to adjust their asset allocation based on market conditions, enhancing their appeal in uncertain economic climates.

Other types of investment trusts include those specializing in real estate, commodities, and niche sectors like technology or healthcare. These specialized trusts cater to investors looking to focus on specific sectors that they believe will outperform the broader market. While they offer t

Context

The algorithmic trading space is buzzing with new strategies. Companies have spent billions in infrastructures and R&D to be able to jump ahead of the competition and beat the market. Still, it is well acknowledged that the buy & hold strategy is able to outperform many of the algorithmic strategies, especially in the long-run. However, finding value in stocks is an art that very few mastered, can a computer do that?

Content

This Data repo contains two datasets:

1. Example_2019_price_var.csv. I built this dataset thanks to Financial Modeling Prep API and to pandas_datareader. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API, which is free and highly recommended). The column contains the percent price variation of each stock for the year 2019. In other words, it collects the percent price variation of each stock from the first trading day on Jan 2019 to the last trading day of Dec 2019. To compute this price variation I decided to consider the Adjusted Close Price.

2. Example_DATASET.csv. I built this dataset thanks to Financial Modeling Prep API. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API). Each column is a financial indicator that can be found in the 2018 10-K filings of each company. There are no Nans or empty cells. Furthermore, the last column is the CLASS of each stock, where:

   1. class = 1 if the price of the stock increases during 2019
   2. class = 0 if the price of the stock decreases during 2019

In other words, the last column is used to classify each stock in buy-worthy or not, and this relationship is what should allow a machine learning model to learn to recognize stocks that will increase their value from those that won't.

NOTE: the number of stocks does not match between the two datasets because the API did not have all the required financial indicators for some stocks. It is possible to remove from Example_2019_price_var.csv those rows that do not appear in Example_DATASET.csv.

Inspiration

I built this dataset during the 2019 winter holidays period, because I wanted to answer a simple question: is it possible to have a machine learning model learn the differences between stocks that perform well and those that don't, and then leverage this knowledge in order to predict which stock will be worth buying? Moreover, is it possible to achieve this simply by looking at financial indicators found in the 10-K filings?

Japan's main stock market index, the JP225, fell to 39519 points on July 14, 2025, losing 0.13% from the previous session. Over the past month, the index has climbed 3.15%, though it remains 4.25% 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 July of 2025.

Base Stock Market Outlook

The global base stock market size was valued at USD 28.5 billion in 2023 and is expected to reach USD 38.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 3.2% during the forecast period. The growth of this market is primarily driven by the increasing demand for lubricants, which in turn is influenced by the expanding automotive and industrial sectors. As industries strive for efficiency and sustainability, the demand for high-quality base stocks is expected to rise, fostering market growth over the forecast period.

One of the key growth factors for the base stock market is the booming automotive industry. The global automotive sector is continuously expanding due to increased vehicle production and sales, particularly in emerging economies. This surge in automotive production necessitates the use of high-performance lubricants, which are derived from premium base stocks. Moreover, the development of electric vehicles (EVs) and hybrid vehicles, which still require specialized lubricants, further propels the demand for high-quality base stocks. OEMs are increasingly focusing on advanced lubricants that can enhance vehicle performance and longevity, thereby driving the base stock market.

Another significant growth factor is the industrial sector's demand for efficient machinery and equipment maintenance. As industries strive to maximize operational efficiency and minimize downtime, the role of high-quality lubricants becomes crucial. Base stocks form the foundation of these lubricants, ensuring optimal performance under various operating conditions. The rising trend of industrial automation and the implementation of advanced machinery in manufacturing processes are expected to increase the demand for efficient lubricants, consequently boosting the base stock market.

Environmental regulations and the push for sustainability are also major drivers in the base stock market. Governments and regulatory bodies across the globe are imposing stringent regulations on emissions and waste management, leading to a higher demand for environmentally friendly lubricants. Bio-based oils, a segment within the base stock market, are gaining traction due to their biodegradability and lower environmental impact. As industries and consumers become more environmentally conscious, the preference for sustainable products, including bio-based lubricants, is expected to rise, further driving the market.

Blown Oil Base Oil is a unique type of base oil that undergoes a special process involving the introduction of air at high temperatures. This process alters the oil's properties, making it more viscous and enhancing its lubricating capabilities. Such characteristics make Blown Oil Base Oil particularly suitable for applications requiring enhanced viscosity and thermal stability. In the context of the base stock market, the demand for Blown Oil Base Oil is driven by industries that require lubricants with specific performance attributes, especially in sectors where machinery operates under extreme conditions. As the market evolves, the versatility of Blown Oil Base Oil presents opportunities for its increased adoption across various industrial applications.

Regionally, Asia Pacific is expected to dominate the base stock market during the forecast period. The region's strong industrial base, coupled with rapid urbanization and increasing automotive production, makes it a significant market for base stocks. North America and Europe also hold substantial shares, driven by technological advancements and stringent environmental regulations promoting the use of high-quality lubricants. Latin America, the Middle East, and Africa are emerging markets with growing industrial activities and vehicle ownership, presenting new opportunities for market growth.

Product Type Analysis

When analyzing the base stock market by product type, mineral oil, synthetic oil, and bio-based oil are the primary categories. Mineral oils have traditionally been the most widely used base stocks due to their cost-effectiveness and availability. These oils are derived from crude oil and are used in a variety of applications, including automotive and industrial lubricants. Despite their widespread use, mineral oils face challenges due to environmental concerns and the push for more sustainable alternatives. The preference f

The oil stock market chart provides a visual representation of the performance and trends in the oil stock market. It helps investors analyze the performance of the oil sector, identify patterns, and make informed investment decisions. Learn more about how oil stock market charts work and their customization options.

Graph and download economic data for Dow-Jones Industrial Stock Price Index for United States (M1109BUSM293NNBR) from Dec 1914 to Dec 1968 about stock market, industry, price index, indexes, price, and USA.

The Jarque-Bera statistic tests the null hypothesis of normality for the sample returns.

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.

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.

Tech Sector Outlook: Bullish Trend Expected for Dow Jones U.S. Technology 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