We have developed an open access database of CFO turnover and dismissal in S&P 1500 firms from 2000 to 2022. Specifically, we conduct an extensive search for archival documentation of CFO turnover events and code for various types of departure. Our dataset identifies eleven mutually exclusive categories of CFO turnover—including death or illness, dismissals, internal reassignments, resignations, and retirements—and includes evidence for our coding.

We also provide our coding scheme for 2022 and 2023 version respectively, as well as user manual for understanding key variables in our dataset.

CFO Email List Looking to connect with top CFOs all over the United States, the United Kingdom, Australia, and beyond? Our CFO Email List gives access to thousands of confirmed and decision-ready financial executives. Fully compliant with international data laws (GDPR, CANSPAM), this database contains important data points to tailor your approach and was created for B2B marketers, consultants, and software vendors. Our list enables you to quickl

Brazil Startup Indicators: Founders: Performance: CFO: Chief Financial Officer data was reported at 1.800 % in 2023. This records a decrease from the previous number of 2.800 % for 2022. Brazil Startup Indicators: Founders: Performance: CFO: Chief Financial Officer data is updated yearly, averaging 1.800 % from Dec 2021 (Median) to 2023, with 3 observations. The data reached an all-time high of 2.800 % in 2022 and a record low of 1.400 % in 2021. Brazil Startup Indicators: Founders: Performance: CFO: Chief Financial Officer data remains active status in CEIC and is reported by Brazilian Association of Startups. The data is categorized under Brazil Premium Database’s Investment – Table BR.OH004: Startups: Profile of Startup Founders.

Aggregate payroll data from CFO system

MB "Jūsų cfo" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

People Authority (PA) compiles a comprehensive database of individual profiles. The data is sourced from various feeds, including contributions from external datasets and third-party providers. Current data providers encompass public officials and directors, analysts and brokers, contacts from buy and sell-side, and insiders. The aggregated data forms a single, authoritative entry for each individual. Each entry is given a unique, permanent identifier, created through the mastering of essential attributes, both public and private, to accurately identify and describe the person. The Permid enables users to make extensive and detailed connections about individuals. Our approach advocates a standardized, federated collection system. No single entity is in charge of overseeing the individuals in PA. Various content groups utilize PA as the definitive source to reference an individual, and, if necessary, they can create or update records directly in the database using contribution tools.

FY 2003 Franchise Fund Annual Report CFO Letter

Supporting data of data entitled "On the mere exigency "

Historical ownership data of CFO by Keystone Financial Group

Cfo team MB financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

Explore the progression of average salaries for graduates in Cfo Training from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Cfo Training relative to other fields. This data is essential for students assessing the return on investment of their education in Cfo Training, providing a clear picture of financial prospects post-graduation.

The CFO Advisory market has emerged as a crucial component in today's rapidly evolving financial landscape, offering strategic insights and solutions to help organizations navigate complex economic challenges. At its core, CFO Advisory services are designed to enhance financial leadership, improve performance manage

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.

CFO CFOAM 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

MB "CFO Here & Now" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The purpose of this data release is to provide data in support of the Bureau of Land Management's (BLM) Reasonably Foreseeable Development (RFD) Scenario by estimating water-use associated with oil and gas extraction methods within the BLM Carlsbad Field Office (CFO) planning area, located in Eddy and Lea Counties as well as part of Chaves County, New Mexico. Three comma separated value files and two python scripts are included in this data release. It was determined that all reported oil and gas wells within Chaves County from the FracFocus and New Mexico Oil Conservation Division (NM OCD) databases were outside of the CFO administration area and were excluded from well_records.csv and modeled_estimates.csv. Data from Chaves County are included in the produced_water.csv file to be consistent with the BLM’s water support document. Data were synthesized into comma separated values which include, produced_water.csv (volume) from NM OCD, well_records.csv (including _location and completion) from NM OCD and FracFocus, and modeled_estimates.csv (using FracFocus as well as Ball and others (2020) as input data). The results from modeled_estimates.csv were obtained using a previously published regression model (McShane and McDowell, 2021) to estimate water use associated with unconventional oil and gas activities in the Permian Basin (Valder and others, 2021) for the period of interest (2010-2021). Additionally, python scripts to process, clean, and categorize FracFocus data are provided in this data release.

The Court Funds Office (CFO) provides a banking and administration service for the Civil Courts throughout England and Wales and their database holds the account number, case name, year carried over, date account opened and the credit details.

This is the data used for item analysis, calculating reliability and validity

C-level Executives are professionals who hold the highest job position in an organization. They are responsible for making crucial administrative and financial decisions. They actively participate in strategic business planning, task delegation, stock decisions, and personnel management. Organizations usually have one or more C-level executives, starting with the CEO, but may also have a COO, CFO, CIO, CTO, and CMO.

At Hir Infotech, we can provide this dataset in a clean i.e error-free and accurate manner. This data can be classified on the basis of job title, industry, or state.

This dataset can be customized in any format you like like .json, .csv, or .xml

A sample can be given to you upon request. Please do reach out to us for further help or inquiries