2 datasets found
  1. k

    Splunk (SPLK) Stock: A Deep Dive into Future Growth? (Forecast)

    • kappasignal.com
    Updated Mar 31, 2024
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    KappaSignal (2024). Splunk (SPLK) Stock: A Deep Dive into Future Growth? (Forecast) [Dataset]. https://www.kappasignal.com/2024/03/splunk-splk-stock-deep-dive-into-future.html
    Explore at:
    Dataset updated
    Mar 31, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    Splunk (SPLK) Stock: A Deep Dive into Future Growth?

    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

  2. Global Clickstream Analytics Market 2017-2021

    • technavio.com
    Updated Sep 21, 2017
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    Technavio (2017). Global Clickstream Analytics Market 2017-2021 [Dataset]. https://www.technavio.com/report/global-clickstream-analytics-market
    Explore at:
    Dataset updated
    Sep 21, 2017
    Dataset provided by
    TechNavio
    Authors
    Technavio
    License

    https://www.technavio.com/content/privacy-noticehttps://www.technavio.com/content/privacy-notice

    Time period covered
    2021 - 2025
    Area covered
    Global
    Description

    Snapshot img { margin: 10px !important; } Overview of the global clickstream analytics market

    Research analysis on the global clickstream analytics market identifies that benefits such as the availability of detailed customer segmentation will be one of the major factors that will have a positive impact on the growth of the market. A detailed segmentation of the customers will help businesses customize their offerings according to the services and advertisements that a customer prefers. This will provide a better quality of user interaction, will increase conversion rates, and lead to high customer loyalty. Clickstream analytics will provide the necessary data, which, when used on mapping or predictive models, allows for detailed customer segmentation. Technavio’s market research analysts predict that this market will grow at a CAGR of almost 14% by 2021.

    In terms of geography, Americas accounted for the major shares of the clickstream analytics market during 2016. The growth in the penetration of the Internet is major factor driving the clickstream analytics market growth in this region. The access to large amounts of data gives businesses a higher chance of monetizing their advertisements with clickstream data. The demand of clickstream analytics will continue to increase in the region with the rise in accuracy of the data and rise in user registrations.

    Competitive landscape and key vendors

    This market appears to be highly fragmented, owing to the presence of numerous vendors. According to the clickstream analytics market outlook, the increasing adoption of clickstream analytics will increase the number of vendors who enter the market, which in turn, will intensify the level of competition among the players. Though the competition is intense among the players in the developed markets such as North America and Europe, the rising adoption of clickstream analytics in the emerging markets will strengthen the competitive environment among the players in these regions as well.

    The leading vendors in the market are -

    Google
    IBM
    Microsoft
    Oracle
    

    The other prominent vendors in the market are Adobe Systems, AT INTERNET, SAP, Splunk, Talend, Verto Analytics, Vlocity, and webtrends.

    Segmentation by data type and analysis of the clickstream analytics market

    Master
    Transaction
    

    Master data is mainly essential in e-commerce platforms since it provides vendors with information on the kind of customers who refer the website or customer segments that are interested in buying certain products. During 2016, the master data segment accounted for major shares of the clickstream analytics market. It has been expected that the clickstream analytics market size & share will grow in the forthcoming years.

    Segmentation by source and analysis of the clickstream analytics market

    Host server
    Third- party agreements
    Network topology
    Tracking host computer
    

    Based on the clickstream analytics market forecast, the host server segment accounted for major share of this market during 2016. When a user requests a web page, the server records information such as the user’s IP address, history of URLs visited, and the type of browser in the server log. Organizations are entering into partnerships with companies that provide products to analyze these logs. They are also partnering with companies that provide consulting services to process clickstream data and aggregate them with e-mail data or online sales data to have a comprehensive view of their clients.

    Key questions answered in the report include

    What will the market size and the growth rate be in 2021?
    What are the key factors driving the global clickstream analytics market?
    What are the key market trends impacting the growth of the global clickstream analytics market?
    What are the challenges to market growth?
    Who are the key vendors in the global clickstream analytics market?
    What are the market opportunities and threats faced by the vendors in the global clickstream analytics market?
    Trending factors influencing the market shares of the Americas, APAC, and EMEA.
    What are the key outcomes of the five forces analysis of the global clickstream analytics market? 
    

    Technavio also offers customization on reports based on specific client requirement.

  3. Not seeing a result you expected?
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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
KappaSignal (2024). Splunk (SPLK) Stock: A Deep Dive into Future Growth? (Forecast) [Dataset]. https://www.kappasignal.com/2024/03/splunk-splk-stock-deep-dive-into-future.html

Splunk (SPLK) Stock: A Deep Dive into Future Growth? (Forecast)

Explore at:
Dataset updated
Mar 31, 2024
Dataset authored and provided by
KappaSignal
License

https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

Description

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.

Splunk (SPLK) Stock: A Deep Dive into Future Growth?

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

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