3 datasets found
  1. Former Wells Fargo Executive to Pay $3 Million for Role in Fraudulent Sales...

    • kappasignal.com
    Updated May 30, 2023
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    KappaSignal (2023). Former Wells Fargo Executive to Pay $3 Million for Role in Fraudulent Sales Practices (Forecast) [Dataset]. https://www.kappasignal.com/2023/05/former-wells-fargo-executive-to-pay-3.html
    Explore at:
    Dataset updated
    May 30, 2023
    Dataset provided by
    ACPrINC
    Authors
    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.

    Former Wells Fargo Executive to Pay $3 Million for Role in Fraudulent Sales Practices

    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. N

    West Fargo, ND Annual Population and Growth Analysis Dataset: A...

    • neilsberg.com
    csv, json
    Updated Jul 30, 2024
    + more versions
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    Neilsberg Research (2024). West Fargo, ND Annual Population and Growth Analysis Dataset: A Comprehensive Overview of Population Changes and Yearly Growth Rates in West Fargo from 2000 to 2023 // 2024 Edition [Dataset]. https://www.neilsberg.com/insights/west-fargo-nd-population-by-year/
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Jul 30, 2024
    Dataset authored and provided by
    Neilsberg Research
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    West Fargo, North Dakota
    Variables measured
    Annual Population Growth Rate, Population Between 2000 and 2023, Annual Population Growth Rate Percent
    Measurement technique
    The data presented in this dataset is derived from the 20 years data of U.S. Census Bureau Population Estimates Program (PEP) 2000 - 2023. To measure the variables, namely (a) population and (b) population change in ( absolute and as a percentage ), we initially analyzed and tabulated the data for each of the years between 2000 and 2023. For further information regarding these estimates, please feel free to reach out to us via email at research@neilsberg.com.
    Dataset funded by
    Neilsberg Research
    Description
    About this dataset

    Context

    The dataset tabulates the West Fargo population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of West Fargo across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

    Key observations

    In 2023, the population of West Fargo was 40,400, a 1.05% increase year-by-year from 2022. Previously, in 2022, West Fargo population was 39,980, an increase of 1.32% compared to a population of 39,461 in 2021. Over the last 20 plus years, between 2000 and 2023, population of West Fargo increased by 24,866. In this period, the peak population was 40,400 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

    Content

    When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

    Data Coverage:

    • From 2000 to 2023

    Variables / Data Columns

    • Year: This column displays the data year (Measured annually and for years 2000 to 2023)
    • Population: The population for the specific year for the West Fargo is shown in this column.
    • Year on Year Change: This column displays the change in West Fargo population for each year compared to the previous year.
    • Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

    Good to know

    Margin of Error

    Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

    Custom data

    If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

    Inspiration

    Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

    Recommended for further research

    This dataset is a part of the main dataset for West Fargo Population by Year. You can refer the same here

  3. d

    Oregon New Market Tax Credit (NMTC)

    • catalog.data.gov
    • data.oregon.gov
    • +1more
    Updated Sep 27, 2024
    + more versions
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    data.oregon.gov (2024). Oregon New Market Tax Credit (NMTC) [Dataset]. https://catalog.data.gov/dataset/oregon-new-market-tax-credit-nmtc-2016-2020
    Explore at:
    Dataset updated
    Sep 27, 2024
    Dataset provided by
    data.oregon.gov
    Area covered
    Oregon
    Description

    This program has ended. Please see notes below and datasets for past clients. Reporting covers July 1 to June 30, annually. For more information on Oregon's program visit https://www.oregon.gov/biz/programs/NMTC/Pages/default.aspx Please see notes regarding specific columns in the table below. Eligibility Determination made by Community Development Entity - *Wells Fargo Community Development took over the credits for Albina Community Bank when they ceased operations. Total Costs of Professionals Fees - *CDE does not charge fees. Maximum Amount of Tax Credit Made Available to Quality Equity Investor in Current Tax Year - * Advantage Capital has one QEI

  4. Not seeing a result you expected?
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Share
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Click to copy link
Link copied
Close
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KappaSignal (2023). Former Wells Fargo Executive to Pay $3 Million for Role in Fraudulent Sales Practices (Forecast) [Dataset]. https://www.kappasignal.com/2023/05/former-wells-fargo-executive-to-pay-3.html
Organization logo

Former Wells Fargo Executive to Pay $3 Million for Role in Fraudulent Sales Practices (Forecast)

Explore at:
Dataset updated
May 30, 2023
Dataset provided by
ACPrINC
Authors
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.

Former Wells Fargo Executive to Pay $3 Million for Role in Fraudulent Sales Practices

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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