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.

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

Moderna's stock sees a 3.6% decline as JPMorgan lists it as a top short idea, citing financial and regulatory concerns amid market volatility.

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.

While I usually focus on micro cap stocks I’m bullish on this company Duck Creek Technologies. Ticker $DCT. Given the 5-year chart a correction is occurring in the stock price. My buy call on it is around $26.50. It’s currently $30, despite the 7.5% increase in short float volumted reported by short interest API. I like their product-market fit and revenue growth. Duck Creek Technologies, Inc. provides software-as-a-service core systems to the property and casualty insurance industry in the United States and internationally. Their Q4 Earnings were not as bad as some made them out to be. Expected to earn $0.02 per share, pro forma, on sales of $69.1 million, Duck Creek turned in a $0.02 per share profit on sales of $70.8 million -- not a huge earnings beat, but a beat nonetheless. Recurring revenue at the software provider increased 41%, and subscription revenue grew 35%. Total sales for the quarter were up 21%. This is still a fastly accelerating company. The company provides Duck Creek Policy, a solution that enables insurers to develop and launch new insurance products and manage various aspects of policy administration ranging from product definition to quoting, binding, and servicing. I like the industry they are in very much and in a correction I’m quite confidence this is a decent long-term investment (not necessarily a swing). Their market cap is about $4 Billion. They are practically unknown on Stocktwits. This is always a good thing, as you can get the stock at undervalued levels more easily with social amplification tampering with the intrinsic value too much. There’s more short-term pain however for this company which means the stock will go down for a while. Duck Creek's guidance for the first quarter of fiscal 2022, and for the year as a whole, was pretty weak. For the year's first quarter, Duck Creek predicted revenue ranging from $68 million to $70 million, which at the midpoint would just barely surpass Wall Street's predicted $68.7 million. This is why my buy call is much lower then even current levels which have been in correction territory. In a note on October 5th, 2022, RBC Capital warned that this implies a "meaningful" slowdown in the company's growth going forward, and J.P. Morgan downgraded the stock on the weak forecast, which it said no longer supports the company's "cloud and growth thesis." Still overall analysts remain bullish on this business model and company. So I advise you to add it to your watchlist and buy an entry position if it continues to correct into the $20s. Wishing you a great week guys and a productive weekend.

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.

Consistent growth in assets under management (AUM) has immensely benefited the Hedge Funds industry over the past five years. Industry servicers invest capital they receive from a variety of investor types across a broad range of asset classes and investment strategies. Operators collect a fee for the amount of money they manage for these clients and a percentage of gains they are able to generate on invested assets. This business model helped industry revenue climb at a CAGR of 7.7% to $127.4 billion over the past five years, including an expected incline of 5.7% in 2024. Despite economic volatility in 2020 due to the pandemic lowering interest rates, an incline in the value of stocks in 2020 positively affected many hedge funds. The S&P 500 climbed 16.3% in 2020, which helped increase AUM. Although industry professionals question the relevance of benchmarking hedge fund returns against equity performance, given that hedge funds rely on a range of instruments other than stocks, the industry's poor performance relative to the S&P 500 has begun to raise concern from some investors. These trends have affected the industry's structure, with the traditional 2.0 and 20.0 structure of a flat fee on total AUM and a right-to-earned profit deteriorating into a 1.4 and 16.0 arrangement. As a result, industry profit, measured as earnings before interest and taxes, has been hindered over the past five years. Industry revenue is expected to grow at a CAGR of 3.1% to $148.5 billion over the next five years. AUM is forecast to continue increasing at a consistent rate, partly due to the diversification benefits that hedge funds provide. Nonetheless, increased regulation stemming from the global financial crisis and an escalating focus on the industry's tax structure has the potential to harm industry profit. Further economic uncertainty stemming from heightened inflation and persistently high interest rates is anticipated to dampen any large-scale growth for the industry as more hedge funds take a hawkish approach in their investment portfolio moving forward. Regardless, the number of new hedge funds is forecast to trend with AUM and revenue over the next five years.

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.

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.

This chapter reviews the behavior of financial asset prices in relation to consumption. The chapter lists some important stylized facts that characterize US data, and relates them to recent developments in equilibrium asset pricing theory. Data from other countries are examined to see which features of the US experience apply more generally. The chapter argues that to make sense of asset market behavior one needs a model in which the market price of risk is high, time-varying, and correlated with the state of the economy. Models that have this feature, including models with habit formation in utility, heterogeneous investors, and irrational expectations, are discussed. The main focus is on stock returns and short-term real interest rates, but bond returns are also considered.

In the second quarter of 2024, the value of the international debt capital market transactions amounted to approximately 1.4 trillion U.S. dollars. The debt market is the part of the capital market on which fixed-interest securities are traded. These securities include, for example, government, municipal, corporate or mortgage bonds. Bonds – additional information The bond market, also known as the credit or fixed income market, is a market that trades in debt. The two most well known parts of the bond market are the primary and secondary capital markets. The primary market is the market that deals with the issuance of new securities and is an important part of the financial markets system. The bonds issued on the primary market are subsequently traded on the secondary markets. A bond is an instrument of indebtedness. The issuer of the bond is obliged to pay the bond holder the principal amount and the pre-agreed interest when the bond reaches maturity. The interest rates are generally payable at fixed intervals. Bonds provide the borrower with external funds in order to finance long-term investments, or, where government bonds are concerned, to finance government expenditure. Bonds are most often bought and traded by institutions such as central banks, pension funds or hedge funds. They are generally seen as being less volatile that stocks, especially the short and medium termed bonds. Bonds suffer from less day-to-day volatility than stocks but are still subject to risk. They are subject to credit and liquidity risks, among others.

Top 10 countries of last or next residence for short-term migrants. Inflow, Outflow and Residency. Estimates from the International Passenger Survey, annual table.

Snapshot img

Hedge Fund Market in US Size 2025-2029

The US hedge fund market size is forecast to increase by USD 738 billion at a CAGR of 8.1% between 2024 and 2029.

US Hedge Fund Market is experiencing significant growth due to increasing investor interest in alternative investment options. This trend is driven by the desire for higher returns and risk diversification, leading to a surge in assets under management. Furthermore, technological advancements are transforming the hedge fund industry, enabling companies to offer innovative solutions and improve operational efficiency. However, the market is not without challenges. Regulatory constraints continue to pose significant obstacles, with stringent regulations governing fund operations, investor protection, and transparency.
Compliance with these regulations requires substantial resources and expertise, presenting a significant challenge for hedge fund managers. Companies seeking to capitalize on market opportunities and navigate these challenges effectively must stay informed of regulatory developments and invest in robust compliance frameworks. Additionally, leveraging technology to streamline operations and enhance transparency can help hedge funds remain competitive and meet investor demands.

What will be the Size of the Hedge Fund Market in US during the forecast period?

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US hedge funds market activities and evolving patterns continue to unfold, shaping the industry's landscape. Hedge funds employ various strategies, such as quantitative methods, algorithmic trading, and relative value strategies, to manage risk and generate alpha. Investor relations play a crucial role in attracting and retaining capital from high-net-worth individuals, family offices, pension funds, and institutional investors. Fund of funds and multi-strategy funds offer diversification, while big data analytics and alternative data inform investment decisions. Machine learning and artificial intelligence enhance risk management and performance measurement. Regulatory compliance and transparency are essential components of hedge fund operations, ensuring liquidity and mitigating drawdowns.
Market dynamics are influenced by various factors, including hedge fund leverage, volatility, and capacity. Hedge fund managers must navigate these complexities to deliver competitive returns, employing due diligence and effective fee structures. Hedge fund distribution channels, such as conferences and sales efforts, facilitate access to new investors. The hedge fund market is a continually evolving ecosystem, where technology, regulatory requirements, and investor expectations shape the industry's future. Hedge fund liquidation and exit strategies, performance fees, and risk appetite are critical considerations for hedge fund managers and investors alike. Ultimately, the hedge fund industry's success hinges on its ability to adapt and innovate in a rapidly changing financial landscape.

How is this Hedge Fund in US Industry segmented?

The hedge fund in US industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Offshore
  Domestic
  Fund of funds


Method

  Long and short equity
  Event driven
  Global macro
  Others


End-user

  Institutional
  Individual


Fund Structure

  Small (
  Medium (USD500M-USD2B)
  Large (>USD2B)


Investor Type

  Institutional
  High-Net-Worth Individuals


Geography

  North America

    US

By Type Insights

The offshore segment is estimated to witness significant growth during the forecast period.

The offshore segment of the hedge fund market in the US houses funds that are managed or marketed by American firms but are domiciled and operated in offshore jurisdictions. These funds, located in financial centers known for their favorable regulatory environments, tax treatment, and legal infrastructure, offer investors tax efficiency through lower or zero taxation on investment income, capital gains, and distributions. The reduced regulatory burden in offshore jurisdictions enables greater flexibility in fund operations, investment strategies, and disclosure obligations, making offshore hedge funds an appealing choice for tax-conscious investors. Portfolio construction, risk management, and hedge fund allocation strategies are crucial elements for these funds, with relative value and long-short equity strategies commonly employed.

Performance fees and management fees are the primary revenue sources for hedge fund managers, while family offices and institutional investors provide significant hedge fund capital. Regulatory compliance and due diligence are essential for investors, ensuring transparency and performance measurement. Hedge fund research, risk appetite, and investor r

The Australian Securities Exchange (ASX) was established in July 2006 after the Australian Stock Exchange merged with the Sydney Futures Exchange, making it one of the top 20 global exchange groups by market capitalization. ASX facilitates trading in leading stocks, ETFs, derivatives, fixed income, commodities, and energy, commanding over 80% of the market share in the Australian Cash Market, with the S&P/ASX 200 as its main index. We offer comprehensive real-time market information services for all instruments in the ASX Level 1 and Level 2 (full market depth) products, and also provide Level 1 data as a delayed service. You can access this data through various means tailored to your specific needs and workflows, whether for trading via electronic low latency datafeeds, using our desktop services equipped with advanced analytical tools, or through our end-of-day valuation and risk management products.

ABSTRACT The study sought to apply the model developed by Gokhale et al. (2015) to identify the existence of overreaction and behavioral biases in the Brazilian stock market and analyze its performance as an investment strategy on the São Paulo Stock, Commodities, and Futures Exchange (BM&FBOVESPA) in the short term and long term, as well as test its robustness with time window simulations. The impacts of behavioral finance on capital markets can affect economic decisions, perpetuate or increase asset pricing anomalies, and in more extreme and persistent situations contribute to the formation of bubbles that can compromise the entire financial system of a country. The study pioneers an innovative methodology in the Brazilian stock market for identifying behavioral biases and obtaining abnormal returns and higher returns than the Ibovespa. The research uses the model developed by Gokhale, Tremblay, and Tremblay (2015) in three samples with quotations data for Brazilian publicly-traded companies that compose the Ibovespa and IBrA in the period from 2005 to 2016. With the R statistical software, the Fundamental Valuation Index (FVI) was calculated for each sample share and each year. From the FVI index, the undervalued shares were identified, indicating that the sales price does not reflect their economic fundamentals, and portfolio simulations were carried out for investment over three months or the next year. The results indicate the possible existence of overreaction and behavioral biases in the Brazilian stock market, which lead to the possibility of higher abnormal returns than those of the Ibovespa. Similar to the US market, at the end of the 2006-2016 period simulated portfolios yielded more than 274%, while the Ibovespa yielded approximately 80%. The robustness tests attest to the effectiveness of the model. The various investment portfolios, simulated over different time horizons, yielded more than the Ibovespa on average. The study also confirmed the assumptions of Gokhale, Tremblay, and Tremblay (2015) regarding the model's inadequacy for short-term strategies.

SPDR Nuveen Bloomberg Short Te p/s ratio from 1970 to 1969. P/s ratio can be defined as the price to sales or PS ratio is calculated by taking the latest closing price and dividing it by the most recent sales per share number. The PS ratio is an additional way to assess whether a stock is over or under valued and is used primarily in cases where earnings are negative and the PE ratio cannot be utilized.

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.

Data Source The data of HSI - Enhanced separation of long-term memory from short-term memory on top of LSTM: Neural network-based stock index forecasting