As of June 17, 2024, the most shorted stock was for, the American holographic technology services provider, MicroCloud Hologram Inc., with 66.64 percent of their total float having been shorted. This is a change from mid-January 2021, when video game retailed GameStop had an incredible 121.07 percent of their available shares in a short position. In effect this means that investors had 'borrowed' more shares (with a future promise to return them) than the total number of shares available for public trading. Owing to this behavior of professional investors, retail investors enacted a campaign to drive up the stock price of Gamestop, leading to losses of billions when investors had to repurchase the stock they had borrowed. At this time, a similar – but less effective – social media campaign was also carried out for the stock price of cinema operator AMC, and the price of silver. What is short selling? Short selling is essentially where an investor bets on a share price falling by: borrowing a number of shares selling these shares while the price is still high; purchasing the same number again once the price falls; then returning the borrowed shares at a profit. Of course, a profit will only be made if the share price does fall; should the share price rise the investor will then need to purchase the shares back at a higher price, and thus incur a loss. Short selling can lead to some very large profits in a short amount of time, with Tesla stock generating over one billion dollars in short sell profits during the first week of March 2020 alone, owing to the financial crash caused by the coronavirus (COVID-19) pandemic. However, owing to the short-term, opportunistic nature of short selling, these returns look less impressive when considered as net profits from short sell positions over the full year. The risks of short selling Short selling carries greater risks than traditional investments, and for this reason financial advisors often recommend against this strategy for ‘retail’ (i.e. non-professional) investors. The reason for this is that losses from short selling are potentially uncapped, whereas losses from traditional investments are limited to the initial cost. For example, if someone purchases 100 dollars of shares, the maximum they can lose is the 100 dollars the spent on those shares. However, say someone borrows 100 dollars of shares instead, betting on the price falling. If these shares are then sold for 100 dollars but the price subsequently rises, the losses could greatly exceed the initial investment should the price rise to, say, 500 dollars. The risks of short selling can be seen by looking again at Tesla, with the company causing the greatest losses over 2020 from short selling at over 40 billion U.S. dollars.

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.

External debt stocks, short-term (DOD, current US$) in China was reported at 1287403080192 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. China - External debt stocks, short-term (DOD, current US$) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

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.

External debt stocks, short-term (DOD, current US$) in Guyana was reported at 60278439 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Guyana - External debt stocks, short-term (DOD, current US$) - actual values, historical data, forecasts and projections were sourced from the World Bank on May of 2025.

Asset Price Dataset Description

This dataset is a comprehensive collection of historical financial data on a specific asset, covering a wide range of information related to daily prices, trading volume and technical indicators. It is designed to provide a detailed, multi-faceted view of asset performance over time, enabling in-depth analysis and the application of various financial strategies.

Information on the columns of the dataset

1. Date: The specific date of the entry.
2. Opening: The opening price of the asset at the beginning of the day.
3. High: The highest price reached by the asset during the day.
4. Low: The lowest price reached by the asset during the day.
5. Closing: The price of the asset at the end of the day.
6. Adjusted Closing: The closing price adjusted for dividends and stock splits.
7. Volume: The number of shares traded during the day.
8. Amplitude: The difference between the highest and lowest price of the day (High - Low).
9. MA7: Moving average of the closing price of the last 7 days.
10. MA14: Moving average of the closing price of the last 14 days.
11. MA30: Moving average of the closing price over the last 30 days.
12. Daily Return: The percentage change in the closing price in relation to the previous day.
13. ATR (Average True Range): Moving average of the True Range (TR) for a given period, used to measure volatility.
14. RSI (Relative Strength Index): Relative Strength Index, a momentum indicator that measures the speed and change of price movements.
15. Annual growth percentage: Percentage of annual growth.
16. Percentage of daily growth: Percentage of daily growth.
17. Absolute Daily Growth: Daily absolute growth, the absolute difference in the closing price compared to the previous day.
18. Day: The day of the week.
19. Month: The month of the year.
20. TR (True Range): The biggest difference between:
21. The maximum price of the day minus the minimum price of the day.
22. The maximum price of the day minus the closing price of the previous day.
23. The minimum price of the day minus the closing price of the previous day.

Applicability

1. Trend Analysis:
   • Through historical data, it is possible to identify short and long-term price trends, helping analysts and investors make informed decisions about buying and selling assets.
2. Development of Negotiation Strategies:
   • The data can be used to develop and test automated trading strategies, including the use of moving averages, relative strength indexes (RSI), and other technical indicators.
3. Volatility Study:
   • With metrics such as Average True Range (ATR), the dataset allows measuring asset volatility over time, essential for risk management strategies and understanding asset stability.
4. Performance Assessment:
   • The detailed history of opening, closing, high and low prices, as well as trading volume, allows an accurate assessment of the asset's performance in different periods.
5. Modeling and Forecasting:
   • The data can be used to build predictive models using machine learning and statistical analysis techniques, providing predictions about future price movements.
6. Education and Research:
   • For students and researchers, the dataset offers a rich source of real data to study financial markets, test hypotheses and perform simulations.

Importance

1. Informed Decision Making:
   • Access to detailed historical data allows investors and analysts to make evidence-based decisions, reducing uncertainty and risk associated with financial markets.
2. Backtesting:
   • It is possible to apply trading strategies to historical data to verify their effectiveness before implementing them in the real market, a crucial process for developing robust trading systems.
3. Comparative Performance Analysis:
   • With consistent data, you can compare the asset's performance over different periods or with other assets, providing a clear perspective on its relative performance.
4. Pattern Identification:
   • The dataset allows the identification of patterns and anomalies in price movements, which can be explored to develop trading strategies or to better understand the factors that influence the market.
5. Risk Management:
   • Analyzing volatility and price behavior over time helps in building risk management strategies, essential for preserving capital and optimizing returns.

This dataset is a valuable tool for anyone involved in financial markets, from individual investors to market analysts and academic researchers, providing the necessary foundation for detailed analysis and informed financial decisions.

1. Overview

This dataset provides monthly stock price data for the MAG7 over the past 20 years (2004–2024). The data includes key financial metrics such as opening price, closing price, highest and lowest prices, trading volume, and percentage change. The dataset is valuable for financial analysis, stock trend forecasting, and portfolio optimization.

2. What is MAG7 ?

MAG7 refers to the seven largest and most influential technology companies in the U.S. stock market : - Microsoft (MSFT) - Apple (AAPL) - Google (Alphabet, GOOGL) - Amazon (AMZN) - Nvidia (NVDA) - Meta (META) - Tesla (TSLA)

These companies are known for their market dominance, technological innovation, and significant impact on global stock indices such as the S&P 500 and Nasdaq-100.

3. Dataset Details

The dataset consists of historical monthly stock prices of MAG7, retrieved from Investing.com. It provides an overview of how these stocks have performed over two decades, reflecting market trends, economic cycles, and technological shifts.

4. Columns Descriptions

• Date The recorded month and year (DD-MM-YYYY)
• Price The closing price of the stock at the end of the month
• Open The price at which the stock opened at the beginning of the month
• High The highest stock price recorded in the month
• Low The lowest stock price recorded in the month
• Vol. The total trading volume for the month
• Change % The percentage change in stock price compared to the previous month # 5. Data Source & Format The dataset was obtained from Investing.com and downloaded in CSV format. The data has been processed to ensure consistency and accuracy, with date formats standardized for time-series analysis. # 6. Potential Use Cases This dataset can be used for :
• 📈 Stock price trend analysis over 20 years
• 📊 Building financial models for long-term investing
• 🔎 Machine learning applications in stock market prediction
• 📉 Evaluating market volatility and economic impact on MAG7 stocks

7. Limitations & Considerations

• ⚠️ The dataset is limited to monthly data, meaning short-term price fluctuations are not captured.
• ⚠️ Trading volume (Vol.) may vary in availability due to differences in reporting.
• ⚠️ External factors such as stock splits, dividends, and market crashes are not explicitly noted but may impact historical trends.

Masonite International Ordinary Shares (Canada) stock may experience moderate growth in the short term due to increased demand for housing materials. However, the stock faces risks associated with supply chain disruptions, rising costs, and competition from substitutes.

External debt stocks, short-term (DOD, current US$) in Chad was reported at 34619134 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Chad - External debt stocks, short-term (DOD, current US$) - actual values, historical data, forecasts and projections were sourced from the World Bank on May 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.

Daktronics (DAKT) Stock: Soaring or Short-Circuiting?

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

Daktronics's growth potential is promising. Analysts predict strong revenue from its digital display products, driven by rising demand from the sports, entertainment, and transportation industries. However, competition from lower-cost providers and the potential impact of economic downturns on discretionary spending pose risks to the company's performance.

In 2024 thus far, on average, there were 20,259 companies with short-time work arrangements in Germany. Due to the COVID-19 pandemic, the number of companies introducing short-time work increased drastically during 2020 and 2021.

Garrett Motion's stock exhibits high volatility, with significant fluctuations in price over short periods. Recent financial performance indicates a risk of further price swings. The company faces intense competition in the automotive industry, particularly from established players with more extensive resources. Market conditions, technological advancements, and supply chain disruptions could also impact its future performance.

Australia Liabilities: Stock: Pension Funds: Short Term Loans & Placements data was reported at 2,273.000 AUD mn in Dec 2024. This records a decrease from the previous number of 2,359.000 AUD mn for Sep 2024. Australia Liabilities: Stock: Pension Funds: Short Term Loans & Placements data is updated quarterly, averaging 560.000 AUD mn from Jun 1988 (Median) to Dec 2024, with 147 observations. The data reached an all-time high of 2,655.000 AUD mn in Jun 2022 and a record low of 0.000 AUD mn in Mar 1992. Australia Liabilities: Stock: Pension Funds: Short Term Loans & Placements data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.AB022: SNA08: SESCA08: Funds by Sector: Financial Corporations: Pension Funds: Stock.

External debt stocks, short-term (DOD, current US$) in Ecuador was reported at 1739695000 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Ecuador - External debt stocks, short-term (DOD, current US$) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Material stocks of buildings, infrastructure, machinery and other short-lived products form the biophysical basis of production and consumption. They are a crucial lever for resource efficiency and a sustainable circular economy, and for climate change mitigation. Here, we provide a global, country-level database of national-level material stocks differentiated by four end-uses and four summary material groups, for 177 countries from 1900 to 2016.

This MAT_STOCKS database is derived from the economy-wide, dynamic, inflow-driven stock-flow model of Material Inputs, Stocks and Outputs (MISO2) (Wiedenhofer et al. 2024). MISO2 covers 14 supply chain processes from raw material extraction to processing, trade, recycling and waste management, as well as 13 end-use types of stocks. Further information on the model and its system definition, as well as the model input data and assumptions and data processing procedures can be found in the accompanying peer-reviewed publication. The model code and exemplary input data can be found in the GitHub repository.

The MAT_STOCKS database version 1.0 provided here is summarized from the more detailed modeling presented in (Wiedenhofer et al. 2024). The dataset here gives:

• Material stocks by 4 main end-uses: buildings, infrastructure, machinery and other short-lived products (summarized from 13 detailed end-uses modeled) (S_10)
• Material stocks and flows by 4 main material groupings: biomass, non-metallic minerals, metals, as well as fossil-fuels derived materials (summarized from 23 raw materials and 20 stock-building materials modeled)
• Flows: Gross Additions to Stocks (F_9_10) and End-of-Life/Waste potentials (F_10_11)
• 177 countries
• 1900 to 2016

All units in kilotons. Paramter names are in accordance with the system definition given in the publication.

Additionally, this repository includes all data presented in the figures of the related journal article.

Further information

This dataset complements the following scientific article:

Wiedenhofer, Dominik and Streeck, Jan and Wieland, Hanspeter and Grammer, Benedikt and Baumgart, Andre and Plank, Barbara and Helbig, Christoph and Pauliuk, Stefan and Haberl, Helmut and Krausmann, Fridolin, From Extraction to End-uses and Waste Management: Modelling Economy-wide Material Cycles and Stock Dynamics Around the World (2024). Journal of Industrial Ecology, https://doi.org/10.1111/jiec.13575

The model code and its documentation are available on Github and Zenodo (see links below). For further information please see the publications. You can also contact Dominik Wiedenhofer dominik.wiedenhofer(a)boku.ac.at and visit our website to learn more about our project: MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Funding

This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950), and the European Union's Horizon Europe programme (CircEUlar, grant agreement No 101056810). Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or granting authorities.

Belgium Financial Assets: Stock: HN: Debt Securities: Short Term data was reported at 4,289.000 EUR mn in Dec 2024. This records an increase from the previous number of 3,770.000 EUR mn for Sep 2024. Belgium Financial Assets: Stock: HN: Debt Securities: Short Term data is updated quarterly, averaging 715.000 EUR mn from Dec 1998 (Median) to Dec 2024, with 105 observations. The data reached an all-time high of 23,307.000 EUR mn in Jun 2024 and a record low of 146.000 EUR mn in Mar 2013. Belgium Financial Assets: Stock: HN: Debt Securities: Short Term data remains active status in CEIC and is reported by National Bank of Belgium. The data is categorized under Global Database’s Belgium – Table BE.AB018: Funds by Sector: ESA 2010: Households and NPISHs: Stock.

Graph and download economic data for Insurance Companies; Short-Term Debt Securities; Asset, Transactions (BOGZ1FU524022405Q) from Q4 1946 to Q4 2024 about short-term, transactions, insurance, debt, securities, assets, and USA.

Netherlands Financial Assets: Stock: TE: DS: Short-Term Securities data was reported at 73,062.000 EUR mn in Dec 2024. This records a decrease from the previous number of 75,620.000 EUR mn for Sep 2024. Netherlands Financial Assets: Stock: TE: DS: Short-Term Securities data is updated quarterly, averaging 46,804.500 EUR mn from Mar 1999 (Median) to Dec 2024, with 104 observations. The data reached an all-time high of 83,543.000 EUR mn in Dec 2020 and a record low of 29,834.000 EUR mn in Mar 2012. Netherlands Financial Assets: Stock: TE: DS: Short-Term Securities data remains active status in CEIC and is reported by Statistics Netherlands. The data is categorized under Global Database’s Netherlands – Table NL.AB002: ESA 2010: Funds by Sector: Total Economy: Stock.

Czech Republic Financial Assets: Stock: HH: Loans: Short Term data was reported at 6.000 CZK mn in Dec 2024. This records a decrease from the previous number of 79.000 CZK mn for Sep 2024. Czech Republic Financial Assets: Stock: HH: Loans: Short Term data is updated quarterly, averaging 1.000 CZK mn from Mar 2008 (Median) to Dec 2024, with 68 observations. The data reached an all-time high of 92.000 CZK mn in Dec 2012 and a record low of 0.000 CZK mn in Dec 2021. Czech Republic Financial Assets: Stock: HH: Loans: Short Term data remains active status in CEIC and is reported by Czech National Bank. The data is categorized under Global Database’s Czech Republic – Table CZ.AB010: Funds by Sector: ESA 2010: Non Consolidated: Households: Stock.