The outbreak of the coronavirus (COVD-19) pandemic has changed the way many people communicate, personally and professionally. An increase in working from home (WFH) and social distancing has made face-to-face contact with relatives, friends, and colleagues harder. Tracing the share price of Zoom – a prominent video communications service – shows how central web conferencing has become to keeping people in contact throughout the pandemic. While the price has increased steadily throughout 2020, a positive announcement regarding the efficiency of a COVID-19 vaccine made on November 9, 2020, resulted in Zoom’s share price falling from 500.11 U.S. dollars to 403.58 U.S. dollars on November 10, 2020. Since then the share price has stumbled downwards, landing on 61 U.S. dollars on July 29, 2024. Despite the fall from grace on the stock market, Zoom's business is more robust than ever, both in terms of revenue and income. The company has really cashed in on the opportunity provided by the pandemic and has grown its business tremendously. The work-from-home experiment A recent survey showed that in companies with digital output, 75 percent of respondents work either entirely in a work-from-home (WFH) setting, or in a hybrid arrangement. Web conferencing software is experiencing an increase in spending as a result, with 67 percent of respondents planning to increase their spending in this area. Services such as Zoom are certain to see a reduction in user numbers when the pandemic is brought under control, but usage is unlikely to return to pre-pandemic levels. In a recent survey of 1,428 CIOs and IT leaders across 83 countries, 94 percent of respondents said they expect at least some of their workforce to WFH post-COVID-19. Hardware sales defy forecasts As well as increases in software and services that enable WFH, physical hardware has also seen an increase in sales, likely due to workers setting up offices at home. Following an initial dip caused by supply chain disruptions, increased demand, especially in the education and business sectors, saw PC shipments return to growth. This defies forecasts made during the initial phases of the pandemic, when analysts expected a drop of anywhere from 1.6 to 11.5 percent in the shipments of personal computing devices.

The main stock market index in the United States (US500) decreased 176 points or 2.99% since the beginning of 2025, 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 March of 2025.

Use our Stock prices dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

This statistic shows the stock price development of selected petroleum companies from January 2, 2020 to April 15, 2024. After the Russian invasion of Ukraine in February 2022, oil prices increased sharply in the first quarter of 2022 since many countries depend on Russian oil. Petroleum companies highly benefited from inclined oil prices, and saw significant increases in their share prices.

Stocks of video game retailer GameStop exploded in January 2021, effectively doubling in value on a daily basis. At the close of trading on January 27, GameStop Corporation's stock price reaching 86.88 U.S. dollars per share - or +134 percent compared to the day before. On December 30, 2020, the price was valued at 4.82 U.S. dollars per share. The cause of this dramatic increase is a concerted effort via social media to raise the value of the company's stock, intended to negatively affect professional investors planning to ‘short sell’ GameStop shares. As professional investors started moving away from GameStop the stock price began to fall, stabilizing at around 11-13 U.S. dollars in mid-February. However, stock prices unexpectedly doubled again on February 24, and continued to rise, reaching 66.25 U.S. dollars at the close of trade on March 10. The reasons for this second increase are not fully clear. At the close of trade on January 29, 2025, GameStop shares were trading at nearly 27.5 U.S. dollars. Who are GameStop? GameStop are a retailer of video games and associated merchandise headquartered in a suburbs of Dallas, Texas, but with stores throughout North America, Europe, Australia and New Zealand. As of February 2020 the group maintained just over 5,500 stores, variously under the GameStop, EB Games, ThinkGeek, and Micromania-Zing brands. The company's main revenue source in 2020 was hardware and accessories - a change from 2019, when software sales were the main source of revenue. While the company saw success in the decade up to 2016 (owing to the constant growth of the video game industry), GameStop experienced declining sales since because consumers increasingly purchased video games digitally. It is this continual decline, combined with the effect of the global coronavirus pandemic on traditional retail outlets, that led many institutional investors to see GameStop as a good opportunity for short selling. What is short selling? Short selling is where an investor effectively bets on a the price of a financial asset falling. To do this, an investor borrows shares (or some other asset) via an agreement that the same number of shares be returned at a future date. They can then sell the borrowed shares, and purchase the same number back once the price has fallen to make a profit. Obviously, this strategy only works when the share price does fall – otherwise the borrowed stocks need to be repurchased at a higher price, causing a loss. In the case of GameStop, a deliberate campaign was arranged via social media (particularly Reddit) for individuals to purchase GameStop shares, thus driving the price higher. As a result, some estimates place the loss to institutional investors in January 2021 alone at around 20 billion U.S. dollars. However, once many of these investors had 'closed out' their position by returning the shares they borrowed, demand for GameStop stock fell, leading to the price reduction seen early in early February. A similar dynamic was seen at the same time with the share price of U.S. cinema operator AMC.

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.

The main stock market index in Hong Kong (HK50) increased 3587 points or 17.88% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on March of 2025.

Overview

This dataset contains historical daily prices for all tickers currently trading on NASDAQ. The up to date list is available from nasdaqtrader.com. The historic data is retrieved from Yahoo finance via yfinance python package.

It contains prices for up to 01 of April 2020. If you need more up to date data, just fork and re-run data collection script also available from Kaggle.

Data Structure

The date for every symbol is saved in CSV format with common fields:

• Date - specifies trading date
• Open - opening price
• High - maximum price during the day
• Low - minimum price during the day
• Close - close price adjusted for splits
• Adj Close - adjusted close price adjusted for both dividends and splits.
• Volume - the number of shares that changed hands during a given day

All that ticker data is then stored in either ETFs or stocks folder, depending on a type. Moreover, each filename is the corresponding ticker symbol. At last, symbols_valid_meta.csv contains some additional metadata for each ticker such as full name.

The price of Amazon shares traded on the Nasdaq stock exchange fluctuated significantly but increased for the most part during the period between 2010 and 2025, peaking at 237.68 U.S. dollar per share in January 2025. Expansion during the pandemic Due to the rise of online shopping worldwide during the Covid-19 pandemic, Amazon's share prices saw an increase as the company experienced dramatic growth. As a result, the company's net sales revenue increased by almost 400 billion U.S. dollars between 2019 to 2024, growing ever since. However, the surge in Amazon's operations significantly increased the company's fulfillment expenses and shipping costs after 2020. The shift towards offline shopping and cost increases after the pandemic resulted in significant layoffs in 2022. Amazon Web Services Amazon is not only the world's most valuable retailer but also the leader in the cloud computing industry through Amazon Web Services (AWS). AWS is a platform that offers storage, servers, and networking to individuals, businesses, and organizations. Amazon's success is driven by its excellence in diverse sectors, but AWS stands as the primary source of profit. The cloud service has consistently grown in profitability, generating nearly 40 billion U.S. dollars in profit in 2024.

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.

Can we predict stock market using machine learning? (WY Stock Forecast)

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

Long term historical dataset of the NASDAQ Composite stock market index since 1971. 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.

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.

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 main stock market index in Japan (JP225) decreased 2147 points or 5.38% since the beginning of 2025, 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 March of 2025.

The main stock market index in Russia (MOEX) increased 264 points or 9.16% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Russia. Russia Stock Market Index MOEX CFD - values, historical data, forecasts and news - updated on March of 2025.

Vector Group stock analysts predict a moderate increase in value, but there is a moderate risk associated with this prediction. This is primarily due to the company's reliance on traditional tobacco products, which face declining demand and increasing regulatory challenges. However, the company's recent investments in alternative products, such as e-cigarettes and cannabis, could mitigate this risk and support future growth.

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.

Probabilistic AI: A New Approach to Artificial Intelligence

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 Federal National Mortgage Association, commonly known as Fannie Mae, was created by the U.S. congress in 1938, in order to maintain liquidity and stability in the domestic mortgage market. The company is a government-sponsored enterprise (GSE), meaning that while it was a publicly traded company for most of its history, it was still supported by the federal government. While there is no legally binding guarantee of shares in GSEs or their securities, it is generally acknowledged that the U.S. government is highly unlikely to let these enterprises fail. Due to these implicit guarantees, GSEs are able to access financing at a reduced cost of interest. Fannie Mae's main activity is the purchasing of mortgage loans from their originators (banks, mortgage brokers etc.) and packaging them into mortgage-backed securities (MBS) in order to ease the access of U.S. homebuyers to housing credit. The early 2000s U.S. mortgage finance boom During the early 2000s, Fannie Mae was swept up in the U.S. housing boom which eventually led to the financial crisis of 2007-2008. The association's stated goal of increasing access of lower income families to housing finance coalesced with the interests of private mortgage lenders and Wall Street investment banks, who had become heavily reliant on the housing market to drive profits. Private lenders had begun to offer riskier mortgage loans in the early 2000s due to low interest rates in the wake of the "Dot Com" crash and their need to maintain profits through increasing the volume of loans on their books. The securitized products created by these private lenders did not maintain the standards which had traditionally been upheld by GSEs. Due to their market share being eaten into by private firms, however, the GSEs involved in the mortgage markets began to also lower their standards, resulting in a 'race to the bottom'. The fall of Fannie Mae The lowering of lending standards was a key factor in creating the housing bubble, as mortgages were now being offered to borrowers with little or no ability to repay the loans. Combined with fraudulent practices from credit ratings agencies, who rated the junk securities created from these mortgage loans as being of the highest standard, this led directly to the financial panic that erupted on Wall Street beginning in 2007. As the U.S. economy slowed down in 2006, mortgage delinquency rates began to spike. Fannie Mae's losses in the mortgage security market in 2006 and 2007, along with the losses of the related GSE 'Freddie Mac', had caused its share value to plummet, stoking fears that it may collapse. On September 7th 2008, Fannie Mae was taken into government conservatorship along with Freddie Mac, with their stocks being delisted from stock exchanges in 2010. This act was seen as an unprecedented direct intervention into the economy by the U.S. government, and a symbol of how far the U.S. housing market had fallen.

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.

The main stock market index in Sweden (Stockholm) increased 140 points or 5.65% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Sweden. Sweden Stock Market Index - values, historical data, forecasts and news - updated on March of 2025.