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 just *** week in March 2020, investors with a short position on Tesla stock were able to generate profits of over *** billion U.S. dollars. From around mid-February 2020, the global coronavirus (COVID-19) pandemic sent global stock markets into a tailspin as entire countries closed down their economy in order to slow the spread of the virus. While the effect on financial markets was catastrophic for many most investors, once class of investor was able to profit handsomely off the disaster - short sellers. Short selling is a process whereby investors effectively borrow a certain number of shares for a period of time, with the aim of selling them when the price is high, then repurchasing at a lower price in order to return them.

Over the course of 2020, U.S. short sellers generated a net profits of around 1.28 billion U.S. dollars from short selling Exxon Mobil stock. While a very large number, this pales in comparison to the net annual losses of from short selling of over 40 billion U.S. dollars for Tesla stock. Short selling is a process whereby investors effectively borrow a certain number of shares for a period of time, with the aim of selling them when the price is high, then repurchasing at a lower price in order to return them.

Over the course of 2020, U.S. short sellers lost over 40 billion U.S. dollars to shorts of Tesla - a value significantly higher than other companies. While short selling can generate some very large profits in a small amount of time, the practice can also lead to some very large losses should stock prices rise, confounding investors' expectations. Short selling is a process whereby investors effectively borrow a certain number of shares for a period of time, with the aim of selling them when the price is high, then repurchasing at a lower price in order to return them.

Dataset Card for "short-interest-stocks"

More Information needed

The dataset contains the file required for training and testing and split accordingly.

There are two groups of features that you can use for prediction:

1. Fundamentals and ratios: Values collected form statements and balance sheets for each ticker
2. Technical indicators and strategy flags: Technical indicators calculated on close value of each day and buy and sell signals generated using some commonly used trading strategies.

Files found in Fundamentals folder is a processed format of the files found in raw folder. Ratios and other values are stretched to match the length of the closing price column such that the value in the pe_ratio column for example is the PE ratio from the most recent quarter and this applies for every column.

Technical indicators are calculated with the default parameters used in Pandas_TA package.

Data is collected form finance.yahoo.com and macrotrends.net Timeframe for the given data is different from one ticker to another because of unavailability of some stocks for a given time frame on either of the websites.

All code required to collect the data and perform preprocessing and feature engineering to get the data in the given format can be found in the following notebooks:

1. https://www.kaggle.com/code/mohammedobeidat/us-stocks-data-collection
2. https://www.kaggle.com/code/mohammedobeidat/us-stocks-technicals-feature-engineering-and-eda
3. https://www.kaggle.com/code/mohammedobeidat/us-stocks-fundamentals-preprocessing-and-eda

Files

• {<>_ticker_train}.csv - the training set
• {<>_ticker_train}.csv - the test set

Columns

Columns names are supposed to be self-explanatory assuming you are familiar with the stock market. Some acronyms you may encounter:

1. tmm is short for Trailing Twelve Months
2. pe is short for Price to Earnings
3. pb is short for Price to Book Value
4. ps is short for Price to Sales
5. fcf is short for Free Cash Flow
6. eps is short for Earnings per Share

In 2024, ** 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 ** 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.

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.

Overall, this project was meant test the relationship between social media posts and their short-term effect on stock prices. We decided to use Reddit posts from financial specific subreddit communities like r/wallstreetbets, r/investing, and r/stocks to see the changes in the market associated with a variety of posts made by users. This idea came to light because of the GameStop short squeeze that showed the power of social media in the market. Typically, stock prices should purely represent the total present value of all the future value of the company, but the question we are asking is whether social media can impact that intrinsic value. Our research question was known from the start and it was do Reddit posts for or against a certain stock provide insight into how the market will move in a short window. To solve this problem, we selected five large tech companies including Apple, Tesla, Amazon, Microsoft, and Google. These companies would likely give us more data in the subreddits and would have less volatility day to day allowing us to simulate an experiment easier. They trade at very high values so a change from a Reddit post would have to be significant giving us proof that there is an effect.

Next, we had to choose our data sources for to have data to test with. First, we tried to locate the Reddit data using a Reddit API, but due to circumstances regarding Reddit requiring approval to use their data we switched to a Kaggle dataset that contained metadata from Reddit. For our second data set we had planned to use Yahoo Finance through yfinance, but due to the large amount of data we were pulling from this public API our IP address was temporarily blocked. This caused us to switch our second data to pull from Alpha Vantage. While this was a large switch in the public it was a minor roadblock and fixing the Finance pulling section allowed for everything else to continue to work in succession. Once we had both of our datasets programmatically pulled into our local vs code, we implemented a pipeline to clean, merge, and analyze all the data. At the end, we implement a Snakemake workflow to ensure the project was easily reproducible. To continue, we utilized Textblob to label our Reddit posts with a sentiment value of positive, negative, or neutral and provide us with a correlation value to analyze with. We then matched the time frame of each post with the stock data and computed any possible changes, found a correlation coefficient, and graphed our findings.

To conclude the data analysis, we found that there is relatively small or no correlation between the total companies, but Microsoft and Google do show stronger correlations when analyzed on their own. However, this may be due to other circumstances like why the post was made or if the market had other trends on those dates already. A larger analysis with more data from other social media platforms would be needed to conclude for our hypothesis that there is a strong correlation.

The smooth functioning of the stock lending market is essential for enabling short selling and maintaining effective arbitrage. Yet, little is known about how this low-transparency market responds to acute disruptions such as short squeezes. Short selling plays a central role in price efficiency, without which prices would disproportionately reflect the beliefs of the optimist. Cheap access to shares from lending institutions facilitates this process. The January 2021 short squeeze – centered around GameStop but involving a wider array of highly shorted equities – created extreme market conditions. While regulatory and academic attention has largely been focused on the squeeze’s effect on price volatility and more visible metrics of market quality, the squeeze’s effects on the stock lending market have not been thoroughly explored. Through an OLS regression framework, this paper analyzes how borrowing costs behaved across the highly shorted segment of the market, as compared to the non-shorted, broader market segment. The results show that during the squeeze, borrow fees increased, but only in the highly shorted group. In the post-squeeze period, borrow fees fell significantly, but again only within the highly shorted group. The stability of control group metrics supports the idea that the observed effects were concentrated solely within highly shorted equities. These results contribute to the literature on short sale constraints, bringing further implications for inefficiencies beyond what the existing literature had shown. Furthermore, this paper provides evidence that the lending market distortions brought on by a short squeeze may persist beyond the event window, interfering with effective arbitrage.

Tweet Sentiment's Impact on Stock Returns

862,231 Labeled Instances

By [source]

About this dataset

This dataset contains 862,231 labeled tweets and associated stock returns, providing a comprehensive look into the impact of social media on company-level stock market performance. For each tweet, researchers have extracted data such as the date of the tweet and its associated stock symbol, along with metrics such as last price and various returns (1-day return, 2-day return, 3-day return, 7-day return). Also recorded are volatility scores for both 10 day intervals and 30 day intervals. Finally, sentiment scores from both Long Short - Term Memory (LSTM) and TextBlob models have been included to quantify the overall tone in which these messages were delivered. With this dataset you will be able to explore how tweets can affect a company's share prices both short term and long term by leveraging all of these data points for analysis!

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For more datasets, click here.

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How to use the dataset

In order to use this dataset, users can utilize descriptive statistics such as histograms or regression techniques to establish relationships between tweet content & sentiment with corresponding stock return data points such as 1-day & 7-day returns measurements.

The primary fields used for analysis include Tweet Text (TWEET), Stock symbol (STOCK), Date (DATE), Closing Price at the time of Tweet (LAST_PRICE) a range of Volatility measures 10 day Volatility(VOLATILITY_10D)and 30 day Volatility(VOLATILITY_30D ) for each Stock which capture changes in market fluctuation during different periods around when Twitter reactions occur. Additionally Sentiment Polarity analysis undertaken via two Machine learning algorithms LSTM Polarity(LSTM_POLARITY)and Textblob polarity provide insight into whether people are expressing positive or negative sentiments about each company at given times which again could influence thereby potentially influence Stock Prices over shorter term periods like 1-Day Returns(1_DAY_RETURN),2-Day Returns(2_DAY_RETURN)or longer term horizon like 7 Day Returns*7DAY RETURNS*.Finally MENTION field indicates if names/acronyms associated with Companies were specifically mentioned in each Tweet or not which gives extra insight into whether company specific contexts were present within individual Tweets aka “Company Relevancy”

Research Ideas

• Analyzing the degree to which tweets can influence stock prices. By analyzing relationships between variables such as tweet sentiment and stock returns, correlations can be identified that could be used to inform investment decisions.
• Exploring natural language processing (NLP) models for predicting future market trends based on textual data such as tweets. Through testing and evaluating different text-based models using this dataset, better predictive models may emerge that can give investors advance warning of upcoming market shifts due to news or other events.
• Investigating the impact of different types of tweets (positive/negative, factual/opinionated) on stock prices over specific time frames. By studying correlations between the sentiment or nature of a tweet and its effect on stocks, insights may be gained into what sort of news or events have a greater impact on markets in general

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: reduced_dataset-release.csv | Column name | Description | |:----------------------|:-------------------------------------------------------------------------------------------------------| | TWEET | Text of the tweet. (String) | | STOCK | Company's stock mentioned in the tweet. (String) | | DATE | Date the tweet was posted. (Date) | | LAST_PRICE | Company's last price at the time of tweeting. (Float) ...

This paper uncovers a new finding of sustainable cross-sectional variations in stock returns explained by mood fluctuations across the days of the week. Long/short leg of illiquid anomaly returns are extensively related to the days of the week, and the magnitude of excess returns is also striking [Long leg refers to portfolio deciles that earn higher excess returns. Historical evidence suggests that more illiquid stock earn higher excess returns (Amihud, 2002; Corwin and Schultz, 2012)]. The speculative leg of illiquid anomalies is the long leg (Birru, 2018) [The speculative leg falls into the long leg of anomaly because more illiquid stocks are sensitive to investor sentiment (Birru, 2018)]. Therefore, the long (speculative) leg experiences more sustainable high returns on Friday than the short (non-speculative) leg. At the same time, relatively higher long (speculative) leg returns were witnessed on Friday than Monday with a greater magnitude difference. These cross-sectional variations in illiquid stocks on specific days are consistent with the explanation of the limit to arbitrage. The observed variations in cross-sectional returns are sustained and consistent with plenty of evidence from psychology research regarding the low mood on Monday and high mood on Friday.

Browse ProShares UltraPro Short QQQ (SQQQ) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

Nasdaq TotalView-ITCH is the proprietary data feed that provides full order book depth for Nasdaq market participants.

Origin: Directly captured at Equinix NY4 (Secaucus, NJ) with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP.

Supported data encodings: DBN, CSV, JSON Learn more

Supported market data schemas: MBO, MBP-1, MBP-10, BBO-1s, BBO-1m, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics, Status, Imbalance Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

NYSE Integrated is a proprietary data feed that disseminates full order book updates from the New York Stock Exchange (XNYS). It delivers every quote and order at each price level, along with any event that updates the order book after an order is placed, such as trade executions, modifications, or cancellations.

NYSE is the leading venue for listing blue-chip companies and large-cap stocks. Powered by NYSE's Pillar platform, its hybrid market model of floor-based auction and electronic trading allows it to capture a significant portion of trading activity during the US equity market open and close. As of January 2025, the NYSE represented approximately 6.31% of the average daily volume (ADV) across all exchange-listed US securities, including those listed on Nasdaq, other NYSE venues, and Cboe exchanges.

NYSE is also the only exchange to offer Designated Market Maker (DMM) privileges, allowing the floor to send D-Quote Orders, short for Discretionary Orders, throughout the day. Most D-Quote Orders execute in the closing auction, where they're known as Closing D Orders and allow traders to access the NYSE closing auction after 3:50 PM. This creates significant price discovery during the NYSE Closing Auction, where interest represented via the floor contributes more than 40% of total volume.

NYSE is also unique for being the only exchange with a Parity/Priority Allocation model for matching. This resembles a mixed FIFO and pro-rata matching algorithm, where the participant who sets the best price is matched first, and then the remaining shares are allocated to other orders entered by floor brokers at that price (parity allocation). Floor brokers may utilize e-Quotes to to receive such parity allocation of incoming executions.

With L3 granularity, NYSE Integrated captures information beyond the L1, top-of-book data available through SIP feeds, enabling accurate modeling of the book imbalances, queue dynamics, and the auction process. This data includes explicit trade aggressor side, odd lots, and imbalances. Auction imbalances offer valuable insights into NYSE’s opening and closing auctions by providing details like imbalance quantity, paired quantity, imbalance reference price, and book clearing price.

Historical data is available for usage-based rates or with any Databento US Equities subscription. Visit our pricing page for more details or to upgrade your plan.

Asset class: Equities

Origin: Directly captured at Equinix NY4 (Secaucus, NJ) with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP.

Supported data encodings: DBN, CSV, JSON (Learn more)

Supported market data schemas: MBO, MBP-1, MBP-10, TBBO, Trades, BBO-1s, BBO-1m, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Imbalance, Statistics, Status (Learn more)

Resolution: Immediate publication, nanosecond-resolution timestamps

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.

Stocks of crude oil in the United States increased by 7.07million barrels in the week ending July 4 of 2025. This dataset provides the latest reported value for - United States Crude Oil Stocks Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

FinFeedAPI provides equity market data covering over 11,000 symbols, featuring historical T+1 data with an unlimited loopback period. We deliver everything from detailed trade records and multiple levels of order book depth (Level 1-3) to crucial regulatory and system messages.

Our data is engineered for performance, featuring nano-second precision timestamps. This ensures a competitive edge for high-frequency trading by enabling fair, accurate, and auditable transaction sequencing, critical for regulatory compliance. Access comprehensive equity market intelligence directly through our robust API offerings.

Why FinFeedAPI?

Market Coverage & Data Depth: - Historical Data: T+1 data on 11K+ symbols with unlimited historical lookback. - Trade Feeds: Detailed trade records including timestamps, sizes, prices, and conditions (e.g., odd lot, intermarket sweep, extended hours). - Level 1 Quotes: Best bid/ask prices, sizes, and timestamps. - Level 2 Price Book: Market depth with multiple bid/ask prices and aggregate order sizes. - Level 3 Order Book: The complete order book detailing individual orders.

Essential Messages: - Admin Messages: Trading status, official open/close prices, auction states, short sale restrictions, retail liquidity indicators, security directory. - System Events: Exchange-level notifications for key trading session phases.

Precision & Reliability: - Nano-second Timestamps: Ensuring fair, accurate, and auditable transaction sequencing for HFT and compliance. - Institutional Trust: Relied upon by financial institutions for dependable equity market information.

Financial institutions and trading firms rely on FinFeedAPI for mission-critical equity market intelligence. We are committed to delivering clean, precise, and comprehensive data when it matters most. If you require dependable and granular stock market data, FinFeedAPI provides the actionable insights you need.

Brazil's main stock market index, the IBOVESPA, fell to 136187 points on July 11, 2025, losing 0.41% from the previous session. Over the past month, the index has declined 1.17%, though it remains 5.66% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Brazil. Brazil Stock Market (BOVESPA) - values, historical data, forecasts and news - updated on July of 2025.

Germany's main stock market index, the DE40, fell to 24255 points on July 11, 2025, losing 0.82% from the previous session. Over the past month, the index has climbed 2.04% and is up 29.37% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Germany. Germany Stock Market Index (DE40) - values, historical data, forecasts and news - updated on July of 2025.