Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company SNL-Financial-LLC.

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.

Should You Buy SNL Right Now?

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

Ovaj skup podataka uključuje finansijske izvještaje, račune i blokade, te nekretnine. Podaci uključuju prihode, rashode, dobit, imovinu, obaveze i informacije o nekretninama u vlasništvu kompanije. Finansijski podaci, finansijski sažetak, sažetak kompanije, preduzetnik, zanatlija, udruženje, poslovni subjekti.

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.

SNL SUPPLY NETWORK LIMITED

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

This dataset contains locations of operating geothermal power plants within the United States as of the publication date. Geothermal power plant data was aggregated from SNL Financial LC, the Geothermal Energy Association (GEA), press releases and operator websites. NREL performed independent research to validate locations of geothermal plants based on aerial satellite imagery as of July 2014.

This dataset contains locations of geothermal power plants in development within the United States as of the publication date and includes attributes for planned capacity. Geothermal developing plant data was aggregated from SNL Financial LC, the Geothermal Energy Association (GEA), press releases, operator websites and geothermal lease data. NREL performed independent research to validate locations of geothermal projects under development as of July 2014.

Context

As cryptocurrency is gaining mainstream attention, this year the news of Elon Musk hosting the SNL made huge news in the crypto community. The coin who people relate to elon the most is dogecoin, he even tweets about dogecoin and posts memes about it.

Content

The Columns in the dataset are: Date: DD/MM/YYYY format Open: Opening price of the coin in that particular date Close: Closing price of the coin in that particular date High: Highest price of the coin in that particular date Low: Lowest price of the coin in that particular date

Acknowledgements

I downloaded the data from a website called Marketwatch, where we can see the price of stocks, cryptocurrency and other financial information.

Cash-Flow-Per-Share Time Series for Supply Network Ltd. Supply Network Limited provides aftermarket parts to the commercial vehicle market in Australia and New Zealand. The company sells truck and bus parts under the Multispares brand name, as well as offers a range of services comprising parts interpreting, procurement, supply management, and problem solving. Supply Network Limited was incorporated in 1986 and is headquartered in Pemulwuy, Australia.