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

The analysis considers the role of non-performing loans (NPLs) for bank lending rates on newly granted loans. It is based on euro area data. The focus is on an effect caused by the stock of NPLs that extends beyond losses that banks have already incorporated into their reported capital positions. The paper assesses the channels through which such an effect occurs most importantly whether it runs through banks' idiosyncratic funding costs.

File 0 contains a description of the data used for the analysis. It does not contain actual data as most data used for the analysis is confidential. The file contains the names of the Stata-dta-Files in which the datasets are stored. These Stata-dta-Files are the starting point for the data processing which is activated by the code in the subsequent Stata-do-Files.

Files 1-3 contain the code for processing SNL and Bankscope / Orbis data. This data includes the banking group level data for the analysis (most importantly NPL / regulatory capital data). File 1 contains the code for the processing of SNL data. File 2 contains the code for the of the Bankscope / Orbis data. File 3 contains the code for merging SNL and Bankscope / Orbis data.

Files 4-6 contain the code for processing the CSDB data which includes data on the cost of bond funding on the banking group level, iBSI / iMIR data which includes data on lending rates and lending volumes on the single bank level and the macroeconomic data. File 4 contains the code for the processing of the CSDB data. Note that this data is initially on the single security level and is processed such, that information on costs of bond funding on the banking group level is retrieved. File 5 contains the code for the processing of the iBSI / iMIR data. File 6 contains the code for the processing of the macroeconomic variables.

File 7 contains the code for merging all datasets. File 8 contains the code for producing the descriptive statistics in Section 3 of the paper. File 9 contains the code for the estimation of Equations 1 and 3 of the paper. File 10 contains the code for the estimation of Equations 1 and 3 with random samples (Appendix D of the paper). File 11 contains the code for estimations with loan growth as dependent variable (Section 5.2 of the paper).

Files 12 and 13 contain code for the data processing and estimation of Equation 2 on the banking group level.

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

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

The northeastern North Carolina coastal system, from False Cape, Virginia, to Cape Lookout, North Carolina, has been studied by a cooperative research program that mapped the Quaternary geologic framework of the estuaries, barrier islands, and inner continental shelf. This information provides a basis to understand the linkage between geologic framework, physical processes, and coastal evolution at time scales from storm events to millennia. The study area attracts significant tourism to its parks and beaches, contains a number of coastal communities, and supports a local fishing industry, all of which are impacted by coastal change. Knowledge derived from this research program can be used to mitigate hazards and facilitate effective management of this dynamic coastal system. This regional mapping project produced spatial datasets of high-resolution geophysical (bathymetry, backscatter intensity, and seismic reflection) and sedimentary (core and grab-sample) data. The high-resolution geophysical data were collected during numerous surveys within the back-barrier estuarine system, along the barrier island complex, in the nearshore, and along the inner continental shelf. Sediment cores were taken on the mainland and along the barrier islands, and both cores and grab samples were taken on the inner shelf. Data collection was a collaborative effort between the U.S. Geological Survey (USGS) and several other institutions including East Carolina University (ECU), the North Carolina Geological Survey, and the Virginia Institute of Marine Science (VIMS). The high-resolution geophysical data of the inner continental shelf were collected during six separate surveys conducted between 1999 and 2004 (four USGS surveys north of Cape Hatteras: 1999-045-FA, 2001-005-FA, 2002-012-FA, 2002-013-FA, and two USGS surveys south of Cape Hatteras: 2003-003-FA and 2004-003-FA) and cover more than 2600 square kilometers of the inner shelf. Single-beam bathymetry data were collected north of Cape Hatteras in 1999 using a Furuno fathometer. Swath bathymetry data were collected on all other inner shelf surveys using a SEA, Ltd. SwathPLUS 234-kHz bathymetric sonar. Chirp seismic data as well as sidescan-sonar data were collected with a Teledyne Benthos (Datasonics) SIS-1000 north of Cape Hatteras along with boomer seismic reflection data (cruises 1999-045-FA, 2001-005-FA, 2002-012-FA and 2002-013-FA). An Edgetech 512i was used to collect chirp seismic data south of Cape Hatteras (cruises 2003-003-FA and 2004-003-FA) along with a Klein 3000 sidescan-sonar system. Sediment samples were collected with a Van Veen grab sampler during four of the USGS surveys (1999-045-FA, 2001-005-FA, 2002-013-FA, and 2004-003-FA). Additional sediment core data along the inner shelf are provided from previously published studies. A cooperative study, between the North Carolina Geological Survey and the Minerals Management Service (MMS cores), collected vibracores along the inner continental shelf offshore of Nags Head, Kill Devils Hills and Kitty Hawk, North Carolina in 1996. The U.S. Army Corps of Engineers collected vibracores along the inner shelf offshore of Dare County in August 1995 (NDC cores) and July-August 1995 (SNL cores). These cores are curated by the North Carolina Geological Survey and were used as part of the ground validation process in this study. Nearshore geophysical and core data were collected by the Virginia Institute of Marine Science. The nearshore is defined here as the region between the 10-m isobath and the shoreline. High-resolution bathymetry, backscatter intensity, and chirp seismic data were collected between June 2002 and May 2004. Vibracore samples were collected in May and July 2005. Shallow subsurface geophysical data were acquired along the Outer Banks barrier islands using a ground-penetrating radar (GPR) system. Data were collected by East Carolina University from 2002 to 2005. Rotasonic cores (OBX cores) from five drilling operations were collected from 2002 to 2006 by the North Carolina Geological Survey as part of the cooperative study with the USGS. These cores are distributed throughout the Outer Banks as well as the mainland. The USGS collected seismic data for the Quaternary section within the Albemarle-Pamlico estuarine system between 2001 and 2004 during six surveys (2001-013-FA, 2002-015-FA, 2003-005-FA , 2003-042-FA, 2004-005-FA, and 2004-006-FA). These surveys used Geopulse Boomer and Knudsen Engineering Limited (KEL) 320BR Chirp systems, except cruise 2003-042-FA, which used an Edgetech 424 Chirp and a boomer system. The study area includes Albemarle Sound and selected tributary estuaries such as the South, Pungo, Alligator, and Pasquotank Rivers; Pamlico Sound and trunk estuaries including the Neuse and Pamlico Rivers; and back-barrier sounds including Currituck, Croatan, Roanoke, Core, and Bogue.

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.