Darktrace stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

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.

Dark Shadows Over DARK Stock?

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

AI-powered price forecasts for DARK.L stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

Darktrace stock faces mixed predictions and risks. Some analysts anticipate strong financial performance and market expansion, leading to potential upside. However, concerns remain regarding competition from established cybersecurity players, the impact of economic headwinds on IT spending, and the company's relatively high valuation. Investors should carefully consider these factors before making investment decisions.

Darktrace reported 4.04B in Market Capitalization this October of 2024, considering the latest stock price and the number of outstanding shares.Data for Darktrace | DARK - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last August in 2025.

On March 9, 2020, also referred to as Black Monday 2020, a global stock market crash took place, stemming from the collapse of the OPEC deal and the economic impact of the coronavirus (COVID-19). As a result, Russian oil companies suffered the most significant falls in shares. Lukoil and Rosneft saw their shares plunge by **** and **** percent, respectively.

Black Hills Stock - Diese Werte, historische Daten, Prognosen, Statistiken, Diagramme und ökonomische Kalender - Jul 2025.Data for Black Hills | Stock including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Comprehensive collection of financial reports and documents for Dark Point Games Spolka Akcyjna (DPG)

The most recent stock split for Stanley Black & Decker (SWK) was a 2:1 split on June 4, 1996. The combined total of all historical stock splits for Stanley Black & Decker result in 3 current shares for every original share available at the IPO in 1984.

A list of the top 50 Delphi Management holdings showing which stocks are owned by Scott Black's hedge fund.

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.

LON:DARK DARKTRACE PLC

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

Turkey Cement: Stock: Black Sea data was reported at 109,890.000 Ton in Aug 2018. This records a decrease from the previous number of 111,746.000 Ton for Jul 2018. Turkey Cement: Stock: Black Sea data is updated monthly, averaging 81,689.000 Ton from Jan 1999 (Median) to Aug 2018, with 236 observations. The data reached an all-time high of 234,699.000 Ton in Sep 2017 and a record low of 27,827.000 Ton in May 2006. Turkey Cement: Stock: Black Sea data remains active status in CEIC and is reported by Turkish Cement Manufacturers Association. The data is categorized under Global Database’s Turkey – Table TR.EA026: Cement and Clinker Production, Stocks and Sales.

Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.

There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.

Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.

A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.

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New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.

Content

Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.

Dataset

The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)

Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.

Starter Kernel(s)

• Simple LSTM Regression

Acknowledgements

Mining and updating of this dateset will depend upon Yahoo Finance .

Inspiration

Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting

Some Readings

• Applications of deep learning in stock market prediction: recent progress
• Stock predictions with state-of-the-art Transformer and Time Embeddings
• https://arxiv.org/pdf/1712.02136.pdf
• https://catanacapital.com/blog/unpredictable-black-swan-event-stock-market/
• https://par.nsf.gov/servlets/purl/10149715
• https://arxiv.org/pdf/1712.02136.pdf

*If you download and find the data useful your upvote is an explicit feedback for future works*

--- Original source retains full ownership of the source dataset ---

Report of Carbon Black Feed Stock is currently supplying a comprehensive analysis of many things which are liable for economy growth and factors which could play an important part in the increase of the marketplace in the prediction period. The record of Carbon Black Feed Stock Industry is providing the thorough study on the grounds of market revenue discuss production and price happened. The report also provides the overview of the segmentation on the basis of area, contemplating the particulars of earnings and sales pertaining to marketplace.

Black Hills stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Option pricing is crucial in enabling investors to hedge against risks. The Black–Scholes option pricing model is widely used for this purpose. This paper investigates whether the Black–Scholes model is a good indicator of option pricing in the United States stock market. We examine the relevance of the Black–Scholes model to certain stocks using paired sample t-test and Corrado and Miller’s approximation for the implied volatility. Empirical tests are applied to determine the significance of the relationship between the actual market values and the Black–Scholes model values. Paired sample t-tests are applied to 582 call options and 579 put options. The empirical test results show that there is no significant difference between the actual market premium value and the Black–Scholes model premium value for seven out of nine stocks considered for call options, and four out of nine stocks considered for put options. Thus, we conclude that the Black–Scholes option pricing model can be used to price call options but is not suitable for pricing put options in the United States stock market.

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.

Chegg (CHGG) Stock: Dark Clouds Looming Ahead?

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

Carbon Black Feed Stock Market Outlook

The global carbon black feed stock market is projected to witness significant growth over the coming years. The market size, valued at approximately USD 4.5 billion in 2023, is anticipated to reach USD 7.2 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 5.3% during the forecast period. This growth can be attributed to a variety of factors including increasing demand from industries such as automotive and construction, which are substantial consumers of carbon black in various applications. The expansion of these industries, particularly in emerging economies, plays a pivotal role in driving the market's expansion.

One of the primary growth factors for the carbon black feed stock market is the burgeoning automotive industry. As the automotive sector evolves, the demand for high-performance tires, which utilize carbon black as a critical reinforcement material, continues to rise. Moreover, the shift towards electric vehicles (EVs) is also contributing to this demand, as these vehicles often utilize specialized tires designed to reduce resistance and improve efficiency. In addition to tires, carbon black is increasingly used in automotive components and coatings due to its excellent conductive properties and ability to improve durability, which are essential in modern automotive manufacturing processes.

Another significant growth factor is the rapid industrialization and urbanization in regions like Asia Pacific and Latin America. These regions are experiencing an upsurge in construction activities, further propelling the demand for carbon black in construction materials, coatings, and plastics. The construction industry uses carbon black for its UV protection and insulation properties, which improve the longevity and durability of building materials. As governments and private sectors invest heavily in infrastructure development, the need for such materials is expected to increase, subsequently boosting the carbon black feed stock market.

Additionally, technological advancements and increased R&D activities are also driving the market forward. Innovations in production processes have led to the development of specialty grades of carbon black, which offer enhanced performance characteristics for specific applications. These advancements allow for more efficient and environmentally friendly production methods, aligning with global sustainability goals and regulations. The development of bio-based carbon black feed stocks represents another promising area, as industries aim to reduce their carbon footprint and reliance on non-renewable resources, thus creating new opportunities for market expansion.

The regional outlook for the carbon black feed stock market indicates a strong presence in Asia Pacific, which holds a significant market share due to the region's rapidly growing industrial base. North America and Europe also represent substantial markets, driven by the continuous demand from the automotive and construction industries. Meanwhile, regions like the Middle East & Africa and Latin America are witnessing gradual growth due to increasing investments in infrastructure and manufacturing sectors. Each of these regions presents unique opportunities and challenges, influencing the overall dynamics of the global carbon black feed stock market.

Astm Grade Carbon Black is gaining attention as a significant variant within the carbon black feed stock market. This grade is specifically formulated to meet stringent industry standards, offering enhanced properties that cater to specialized applications. Industries such as automotive and electronics are increasingly relying on Astm Grade Carbon Black for its superior performance in terms of durability and conductivity. The development of this grade aligns with the market's shift towards high-performance materials that can withstand demanding operational environments. As the demand for precision and quality in manufacturing processes grows, Astm Grade Carbon Black is poised to play a crucial role in meeting these evolving industry requirements.

Grade Analysis

In the carbon black feed stock market, the grade segment is categorized into standard grade and specialty grade. Standard grade carbon black is widely used across several applications due to its cost-effectiveness and versatility. It serves as a reinforcing agent in rubber products, especially in the tire manufacturing industry, which is one of the largest consume

A list of the top 50 Dark Forest Capital Management Lp holdings showing which stocks are owned by Dark Forest Capital Management Lp's hedge fund.

Stanley Black & Decker stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.