25 datasets found
  1. w

    Monthly food price inflation estimates by country - Afghanistan, Armenia,...

    • microdata.worldbank.org
    Updated Jul 9, 2025
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    Bo Pieter Johannes Andrée (2025). Monthly food price inflation estimates by country - Afghanistan, Armenia, Bangladesh...and 33 more [Dataset]. https://microdata.worldbank.org/index.php/catalog/4509
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    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Bo Pieter Johannes Andrée
    Time period covered
    2008 - 2025
    Area covered
    Bangladesh
    Description

    Abstract

    Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

    Geographic coverage notes

    The data cover the following areas: Afghanistan, Armenia, Bangladesh, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Dem. Rep., Congo, Rep., Gambia, The, Guinea, Guinea-Bissau, Haiti, Indonesia, Iraq, Kenya, Lao PDR, Lebanon, Liberia, Libya, Malawi, Mali, Mauritania, Mozambique, Myanmar, Niger, Nigeria, Philippines, Senegal, Somalia, South Sudan, Sri Lanka, Sudan, Syrian Arab Republic, Yemen, Rep.

  2. Datasets used for the paper: "Dynamic Financial Analysis (DFA) of General...

    • zenodo.org
    zip
    Updated Apr 4, 2025
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    Benjamin Avanzi; Benjamin Avanzi; Yanfeng Li; Greg Taylor; Bernard Wong; Bernard Wong; Yanfeng Li; Greg Taylor (2025). Datasets used for the paper: "Dynamic Financial Analysis (DFA) of General Insurers under Climate Change" [Dataset]. http://doi.org/10.5281/zenodo.15098758
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    zipAvailable download formats
    Dataset updated
    Apr 4, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Benjamin Avanzi; Benjamin Avanzi; Yanfeng Li; Greg Taylor; Bernard Wong; Bernard Wong; Yanfeng Li; Greg Taylor
    License

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

    Time period covered
    Apr 4, 2025
    Description

    Introduction

    This Data folder accompanies the code supporting the paper "Dynamic Financial Analysis (DFA) of General Insurers under Climate Change". The corresponding code can be downloaded from the following GitHub repository: link. In this paper, we introduce a climate-dependent DFA framework that integrates climate risk into DFA, providing a comprehensive assessment of its long-term impact on the general insurance industry.

    After downloading the Data folder, unzip it and place it in the same directory as the code downloaded from the GitHub repository. Once this setup is complete, users can run the RMarkdown file without any additional configuration.

    The main data folders used are outlined below:

    • Economic: Contains historical data on macroeconomic variables (e.g., GDP, interest rates, and inflation rates) and projections of GDP, population, and oil and gas production under each SSP scenario.

    • Equity return: Contains the pseudo All-Ordinaries Shares total returns series and the financial statements of a representative oil and gas producer. Due to licensing restrictions, we are unable to provide the actual Total Returns series of the All-Ordinaries Shares Index and the financial statements of Woodside Energy Limited from FactSet. Users are encouraged to obtain this data directly from FactSet.

    • Hazards loss: Includes the ICA and EM-DAT datasets on catastrophe insurance losses in Australia.

    • Precipitations, FWI, SST and MSLP, Near-surface temperature, and Air temperature: Contain historical observations of precipitation, fire weather index, sea-surface temperature, mean sea-level pressure, near-surface temperature, and air temperature at the grid cell level across Australia (or nearby ocean areas).

    • CMIP6_ensemble_precipitation, CMIP6_ensemble_SST, CMIP6_ensemble_MSLP, CMIP6_ensemble_near_surface_temperature, and CMIP6_ensemble_air_temperature: Contain CMIP6 ensemble projections of the corresponding climate variables.

    Details of the dataset download sources can be found on the GitHub page linked above.

  3. t

    12.5.4 Transport insurance. United Kingdom. | Consumer Prices Index...

    • timeseriesexplorer.com
    Updated May 22, 2024
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    Time Series Explorer (2024). 12.5.4 Transport insurance. United Kingdom. | Consumer Prices Index including owner occupiers' housing costs (CPIH) [Dataset]. https://www.timeseriesexplorer.com/e807cf2c5864a86310f26a4dd681c191/88120d0283384cdc8b5b9ac663223610/
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    Dataset updated
    May 22, 2024
    Dataset provided by
    Time Series Explorer
    Office for National Statistics
    Area covered
    United Kingdom
    Description

    Unit of measurement: 2015=100. United Kingdom, 12.5.4 Transport insurance. CPIH is the most comprehensive measure of inflation. It extends CPI to include a measure of the costs associated with owning, maintaining and living in one's own home, known as owner occupiers' housing costs (OOH), along with council tax. This dataset provides CPIH time series (2005 to latest published month), allowing users to customise their own selection, view or download.

  4. Consumer Price Index 2021 - West Bank and Gaza

    • pcbs.gov.ps
    Updated May 18, 2023
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    Palestinian Central Bureau of Statistics (2023). Consumer Price Index 2021 - West Bank and Gaza [Dataset]. https://www.pcbs.gov.ps/PCBS-Metadata-en-v5.2/index.php/catalog/711
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    Dataset updated
    May 18, 2023
    Dataset authored and provided by
    Palestinian Central Bureau of Statisticshttp://pcbs.gov.ps/
    Time period covered
    2021
    Area covered
    Palestine, West Bank
    Description

    Abstract

    The Consumer price surveys primarily provide the following: Data on CPI in Palestine covering the West Bank, Gaza Strip and Jerusalem J1 for major and sub groups of expenditure. Statistics needed for decision-makers, planners and those who are interested in the national economy. Contribution to the preparation of quarterly and annual national accounts data.

    Consumer Prices and indices are used for a wide range of purposes, the most important of which are as follows: Adjustment of wages, government subsidies and social security benefits to compensate in part or in full for the changes in living costs. To provide an index to measure the price inflation of the entire household sector, which is used to eliminate the inflation impact of the components of the final consumption expenditure of households in national accounts and to dispose of the impact of price changes from income and national groups. Price index numbers are widely used to measure inflation rates and economic recession. Price indices are used by the public as a guide for the family with regard to its budget and its constituent items. Price indices are used to monitor changes in the prices of the goods traded in the market and the consequent position of price trends, market conditions and living costs. However, the price index does not reflect other factors affecting the cost of living, e.g. the quality and quantity of purchased goods. Therefore, it is only one of many indicators used to assess living costs. It is used as a direct method to identify the purchasing power of money, where the purchasing power of money is inversely proportional to the price index.

    Geographic coverage

    Palestine West Bank Gaza Strip Jerusalem

    Analysis unit

    The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

    Universe

    The target population for the CPI survey is the shops and retail markets such as grocery stores, supermarkets, clothing shops, restaurants, public service institutions, private schools and doctors.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A non-probability purposive sample of sources from which the prices of different goods and services are collected was updated based on the establishment census 2017, in a manner that achieves full coverage of all goods and services that fall within the Palestinian consumer system. These sources were selected based on the availability of the goods within them. It is worth mentioning that the sample of sources was selected from the main cities inside Palestine: Jenin, Tulkarm, Nablus, Qalqiliya, Ramallah, Al-Bireh, Jericho, Jerusalem, Bethlehem, Hebron, Gaza, Jabalia, Dier Al-Balah, Nusseirat, Khan Yunis and Rafah. The selection of these sources was considered to be representative of the variation that can occur in the prices collected from the various sources. The number of goods and services included in the CPI is approximately 730 commodities, whose prices were collected from 3,200 sources. (COICOP) classification is used for consumer data as recommended by the United Nations System of National Accounts (SNA-2008).

    Sampling deviation

    Not apply

    Mode of data collection

    Computer Assisted Personal Interview [capi]

    Research instrument

    A tablet-supported electronic form was designed for price surveys to be used by the field teams in collecting data from different governorates, with the exception of Jerusalem J1. The electronic form is supported with GIS, and GPS mapping technique that allow the field workers to locate the outlets exactly on the map and the administrative staff to manage the field remotely. The electronic questionnaire is divided into a number of screens, namely: First screen: shows the metadata for the data source, governorate name, governorate code, source code, source name, full source address, and phone number. Second screen: shows the source interview result, which is either completed, temporarily paused or permanently closed. It also shows the change activity as incomplete or rejected with the explanation for the reason of rejection. Third screen: shows the item code, item name, item unit, item price, product availability, and reason for unavailability. Fourth screen: checks the price data of the related source and verifies their validity through the auditing rules, which was designed specifically for the price programs. Fifth screen: saves and sends data through (VPN-Connection) and (WI-FI technology).

    In case of the Jerusalem J1 Governorate, a paper form has been designed to collect the price data so that the form in the top part contains the metadata of the data source and in the lower section contains the price data for the source collected. After that, the data are entered into the price program database.

    Cleaning operations

    The price survey forms were already encoded by the project management depending on the specific international statistical classification of each survey. After the researcher collected the price data and sent them electronically, the data was reviewed and audited by the project management. Achievement reports were reviewed on a daily and weekly basis. Also, the detailed price reports at data source levels were checked and reviewed on a daily basis by the project management. If there were any notes, the researcher was consulted in order to verify the data and call the owner in order to correct or confirm the information.

    At the end of the data collection process in all governorates, the data will be edited using the following process: Logical revision of prices by comparing the prices of goods and services with others from different sources and other governorates. Whenever a mistake is detected, it should be returned to the field for correction. Mathematical revision of the average prices for items in governorates and the general average in all governorates. Field revision of prices through selecting a sample of the prices collected from the items.

    Response rate

    Not apply

    Sampling error estimates

    The findings of the survey may be affected by sampling errors due to the use of samples in conducting the survey rather than total enumeration of the units of the target population, which increases the chances of variances between the actual values we expect to obtain from the data if we had conducted the survey using total enumeration. The computation of differences between the most important key goods showed that the variation of these goods differs due to the specialty of each survey. For example, for the CPI, the variation between its goods was very low, except in some cases such as banana, tomato, and cucumber goods that had a high coefficient of variation during 2019 due to the high oscillation in their prices. The variance of the key goods in the computed and disseminated CPI survey that was carried out on the Palestine level was for reasons related to sample design and variance calculation of different indicators since there was a difficulty in the dissemination of results by governorates due to lack of weights. Non-sampling errors are probable at all stages of data collection or data entry. Non-sampling errors include: Non-response errors: the selected sources demonstrated a significant cooperation with interviewers; so, there wasn't any case of non-response reported during 2019. Response errors (respondent), interviewing errors (interviewer), and data entry errors: to avoid these types of errors and reduce their effect to a minimum, project managers adopted a number of procedures, including the following: More than one visit was made to every source to explain the objectives of the survey and emphasize the confidentiality of the data. The visits to data sources contributed to empowering relations, cooperation, and the verification of data accuracy. Interviewer errors: a number of procedures were taken to ensure data accuracy throughout the process of field data compilation: Interviewers were selected based on educational qualification, competence, and assessment. Interviewers were trained theoretically and practically on the questionnaire. Meetings were held to remind interviewers of instructions. In addition, explanatory notes were supplied with the surveys. A number of procedures were taken to verify data quality and consistency and ensure data accuracy for the data collected by a questioner throughout processing and data entry (knowing that data collected through paper questionnaires did not exceed 5%): Data entry staff was selected from among specialists in computer programming and were fully trained on the entry programs. Data verification was carried out for 10% of the entered questionnaires to ensure that data entry staff had entered data correctly and in accordance with the provisions of the questionnaire. The result of the verification was consistent with the original data to a degree of 100%. The files of the entered data were received, examined, and reviewed by project managers before findings were extracted. Project managers carried out many checks on data logic and coherence, such as comparing the data of the current month with that of the previous month, and comparing the data of sources and between governorates. Data collected by tablet devices were checked for consistency and accuracy by applying rules at item level to be checked.

    Data appraisal

    Other technical procedures to improve data quality: Seasonal adjustment processes

  5. EnhancedHousingPricesData

    • kaggle.com
    Updated Dec 17, 2023
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    Yaroslav53 (2023). EnhancedHousingPricesData [Dataset]. https://www.kaggle.com/datasets/yaroslav53/enhancedhousingmarketdata
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 17, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Yaroslav53
    Description

    EnhancedHousingMarketData.csv is an auxiliary dataset for the "Housing Prices" competition, containing key economic and demographic indicators vital for real estate market analysis. It includes data on non-farm employment, housing price index, per capita income, total quarterly wages, quantitative indexes of real GDP, total GDP, real GDP, stable population, employed individuals, and the average weekly wage in the private sector, along with the unemployment rate. This dataset aids in better understanding the factors influencing housing prices and allows for a more in-depth analysis of the real estate market.

    "**TotalNonfarmEmployees**" - reflects the total number of employees working outside the agricultural sector. This figure includes workers in industries such as manufacturing, construction, trade, transportation, education, healthcare, and other non-agricultural sectors, making it a key indicator of economic activity and employment in the region.

    "**HousingPriceIndex**" - represents a housing price index, reflecting changes in real estate prices in a specific region for a given month. This index can be used to analyze trends in the real estate market and assess the overall economic conditions.

    "**AnnualPerCapitaIncome**" - represents the annual per capita income, measured yearly. This indicator reflects the average income per resident in a specific region over a year, serving as an important measure of the population's economic well-being.

    "**QuarterlyTotalWages**" - represents the total quarterly wages, measured in dollars and adjusted for seasonal variations. This metric reflects the sum of wages paid by employers insured for unemployment insurance over a calendar quarter. It includes components such as vacation pay, bonuses, and tips.

    "**TotalRealGDPChainIndex**" - represents the total annual quantitative index of real GDP, encompassing data from all private sectors and the government. It is based on the Fisher chain-weighted method, tracking changes in production volume or expenditures while eliminating the effects of price changes. This index is useful for comparing the volumes of production or expenditures across different time periods.

    "**TotalGDP**" - describes the total Gross Domestic Product (GDP), measured in millions of dollars and calculated annually without seasonal adjustments. This metric encompasses all private sectors and the government, reflecting the market value of all final goods and services produced within an agglomeration. The agglomeration GDP represents the gross output minus intermediate costs, serving as a key indicator of economic activity and production volume.

    "**TotalRealGDP**" - represents the total real Gross Domestic Product, measured in millions of chained 2012 dollars and calculated annually without seasonal adjustments. This metric includes data from all private sectors and the government. The real GDP for agglomerations is a measure of the gross product of each agglomeration, adjusted for inflation, and based on national prices for goods and services produced in the agglomeration.

    "**StablePopulation**" - reflects the stable population, measured in thousands of people and calculated annually without seasonal adjustments. This metric represents population estimates as of July 1st each year, providing reliable data for analyzing demographic trends and planning purposes.

    "**EmployedIndividuals**" - represents the number of employed individuals, measured in persons without seasonal adjustment and updated monthly. The data are derived from the Current Population Survey (CPS). Employed individuals include those who did any paid work, owned a business or farm, worked 15 hours or more as unpaid workers in a family business, or were temporarily absent from their job for various reasons. This metric is important for analyzing employment levels and the economic activity of the population.

    "**AverageWeeklyWagePrivate**" - denotes the average weekly wage of private enterprise employees, measured in dollars per week and calculated quarterly without seasonal adjustment. It includes payments made by employers insured against unemployment over the quarter, encompassing vacation pay, bonuses, stock options, tips, and other components. This metric is important for assessing the level of wages in the private sector.

    "**UnemploymentRate**" - represents the unemployment rate, measured in percentages and calculated monthly without seasonal adjustments. This metric indicates the proportion of the unemployed within the total labor force, providing key information about the labor market's condition and the population's economic activity.

  6. What will collision insurance cover in the event of an accident? (Forecast)

    • kappasignal.com
    Updated May 19, 2023
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    KappaSignal (2023). What will collision insurance cover in the event of an accident? (Forecast) [Dataset]. https://www.kappasignal.com/2023/05/what-will-collision-insurance-cover-in.html
    Explore at:
    Dataset updated
    May 19, 2023
    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.

    What will collision insurance cover in the event of an accident?

    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

  7. Will the Insurance Index Weather the Storm? (Forecast)

    • kappasignal.com
    Updated Sep 8, 2024
    + more versions
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    KappaSignal (2024). Will the Insurance Index Weather the Storm? (Forecast) [Dataset]. https://www.kappasignal.com/2024/09/will-insurance-index-weather-storm.html
    Explore at:
    Dataset updated
    Sep 8, 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.

    Will the Insurance Index Weather the Storm?

    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

  8. H

    Blue Chip Economic Indicators

    • dataverse.harvard.edu
    Updated Mar 5, 2025
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    Joseph Aguinaldo (2025). Blue Chip Economic Indicators [Dataset]. http://doi.org/10.7910/DVN/M2WNLO
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 5, 2025
    Dataset provided by
    Harvard Dataverse
    Authors
    Joseph Aguinaldo
    License

    https://dataverse.harvard.edu/api/datasets/:persistentId/versions/6.0/customlicense?persistentId=doi:10.7910/DVN/M2WNLOhttps://dataverse.harvard.edu/api/datasets/:persistentId/versions/6.0/customlicense?persistentId=doi:10.7910/DVN/M2WNLO

    Time period covered
    Jan 1, 1976 - Dec 31, 2024
    Description

    Blue Chip Economic Indicators - Monthly surveys of top analysts at some of America’s largest manufacturers, banks, insurance companies, and brokerage firms about their insights on U.S. economic growth, inflation, interest rates, and a host of other critical indicators of future business activity – including GDP (Gross Domestic Product), Consumer Price Index (CPI), industrial production, income, corporate profits, treasury bill rates, unemployment rates, housing starts and vehicle sales. Blue Chip Economic Indicators provides forecasts for this year and next from each panel member, plus an average or consensus, of their forecasts for each variable—there also are five to nine quarters of quarterly forecasts. DATA AVAILABLE FOR YEARS: 1976-2024

  9. k

    What is comprehensive insurance? (Forecast)

    • kappasignal.com
    Updated Sep 22, 2023
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    KappaSignal (2023). What is comprehensive insurance? (Forecast) [Dataset]. https://www.kappasignal.com/2023/09/what-is-comprehensive-insurance.html
    Explore at:
    Dataset updated
    Sep 22, 2023
    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.

    What is comprehensive insurance?

    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

  10. Dow Jones U.S. Select Insurance Index: Poised for a Rebound? (Forecast)

    • kappasignal.com
    Updated Apr 25, 2024
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    KappaSignal (2024). Dow Jones U.S. Select Insurance Index: Poised for a Rebound? (Forecast) [Dataset]. https://www.kappasignal.com/2024/04/dow-jones-us-select-insurance-index.html
    Explore at:
    Dataset updated
    Apr 25, 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.

    Dow Jones U.S. Select Insurance Index: Poised for a Rebound?

    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

  11. Enstar Group (ESGR): Navigating the Insurance Landscape - What's Next?...

    • kappasignal.com
    Updated Feb 26, 2024
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    KappaSignal (2024). Enstar Group (ESGR): Navigating the Insurance Landscape - What's Next? (Forecast) [Dataset]. https://www.kappasignal.com/2024/02/enstar-group-esgr-navigating-insurance.html
    Explore at:
    Dataset updated
    Feb 26, 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.

    Enstar Group (ESGR): Navigating the Insurance Landscape - What's Next?

    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

  12. SIGIP Selective Insurance Group Inc. Depositary Shares each representing a...

    • kappasignal.com
    Updated Feb 21, 2023
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    KappaSignal (2023). SIGIP Selective Insurance Group Inc. Depositary Shares each representing a 1/1000th interest in a share of 4.60% Non-Cumulative Preferred Stock Series B (Forecast) [Dataset]. https://www.kappasignal.com/2023/02/sigip-selective-insurance-group-inc.html
    Explore at:
    Dataset updated
    Feb 21, 2023
    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.

    SIGIP Selective Insurance Group Inc. Depositary Shares each representing a 1/1000th interest in a share of 4.60% Non-Cumulative Preferred Stock Series B

    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

  13. Selective Insurance: Fractional Ownership - A Tiny Slice of Stability...

    • kappasignal.com
    Updated Jan 26, 2024
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    KappaSignal (2024). Selective Insurance: Fractional Ownership - A Tiny Slice of Stability (SIGIP) (Forecast) [Dataset]. https://www.kappasignal.com/2024/01/selective-insurance-fractional.html
    Explore at:
    Dataset updated
    Jan 26, 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.

    Selective Insurance: Fractional Ownership - A Tiny Slice of Stability (SIGIP)

    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

  14. k

    Hippo (HIPO) Insurance: Riding the Wave of Change? (Forecast)

    • kappasignal.com
    Updated Aug 29, 2024
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    KappaSignal (2024). Hippo (HIPO) Insurance: Riding the Wave of Change? (Forecast) [Dataset]. https://www.kappasignal.com/2024/08/hippo-hipo-insurance-riding-wave-of.html
    Explore at:
    Dataset updated
    Aug 29, 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.

    Hippo (HIPO) Insurance: Riding the Wave of Change?

    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

  15. QBE QBE INSURANCE GROUP LIMITED (Forecast)

    • kappasignal.com
    Updated Dec 11, 2022
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    KappaSignal (2022). QBE QBE INSURANCE GROUP LIMITED (Forecast) [Dataset]. https://www.kappasignal.com/2022/12/qbe-qbe-insurance-group-limited.html
    Explore at:
    Dataset updated
    Dec 11, 2022
    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.

    QBE QBE INSURANCE GROUP 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

  16. Sabre Insurance Group (SBRE): Soaring or Stumbling? (Forecast)

    • kappasignal.com
    Updated Apr 6, 2024
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    KappaSignal (2024). Sabre Insurance Group (SBRE): Soaring or Stumbling? (Forecast) [Dataset]. https://www.kappasignal.com/2024/04/sabre-insurance-group-sbre-soaring-or.html
    Explore at:
    Dataset updated
    Apr 6, 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.

    Sabre Insurance Group (SBRE): Soaring or Stumbling?

    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

  17. WTM White Mountains Insurance Group Ltd. Common Stock (Forecast)

    • kappasignal.com
    Updated Dec 24, 2022
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    KappaSignal (2022). WTM White Mountains Insurance Group Ltd. Common Stock (Forecast) [Dataset]. https://www.kappasignal.com/2022/12/wtm-white-mountains-insurance-group-ltd.html
    Explore at:
    Dataset updated
    Dec 24, 2022
    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.

    WTM White Mountains Insurance Group Ltd. Common Stock

    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

  18. Lemonade (LMND) - A Drop of Insurance, A Sea of Growth (Forecast)

    • kappasignal.com
    Updated Sep 26, 2024
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    KappaSignal (2024). Lemonade (LMND) - A Drop of Insurance, A Sea of Growth (Forecast) [Dataset]. https://www.kappasignal.com/2024/09/lemonade-lmnd-drop-of-insurance-sea-of.html
    Explore at:
    Dataset updated
    Sep 26, 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.

    Lemonade (LMND) - A Drop of Insurance, A Sea of 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

  19. Aon's (AON) Global Reach: Insurance Giant Charts a Course for Growth...

    • kappasignal.com
    Updated Oct 20, 2024
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    KappaSignal (2024). Aon's (AON) Global Reach: Insurance Giant Charts a Course for Growth (Forecast) [Dataset]. https://www.kappasignal.com/2024/10/aons-aon-global-reach-insurance-giant.html
    Explore at:
    Dataset updated
    Oct 20, 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.

    Aon's (AON) Global Reach: Insurance Giant Charts a Course for 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

  20. k

    International General Insurance (IGIC) Stock Forecast: Buckle Up for a Bumpy...

    • kappasignal.com
    Updated Jun 11, 2024
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    KappaSignal (2024). International General Insurance (IGIC) Stock Forecast: Buckle Up for a Bumpy Ride, But the Destination is Profitable (Forecast) [Dataset]. https://www.kappasignal.com/2024/06/international-general-insurance-igic.html
    Explore at:
    Dataset updated
    Jun 11, 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.

    International General Insurance (IGIC) Stock Forecast: Buckle Up for a Bumpy Ride, But the Destination is Profitable

    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

Share
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Click to copy link
Link copied
Close
Cite
Bo Pieter Johannes Andrée (2025). Monthly food price inflation estimates by country - Afghanistan, Armenia, Bangladesh...and 33 more [Dataset]. https://microdata.worldbank.org/index.php/catalog/4509

Monthly food price inflation estimates by country - Afghanistan, Armenia, Bangladesh...and 33 more

Explore at:
2 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jul 9, 2025
Dataset authored and provided by
Bo Pieter Johannes Andrée
Time period covered
2008 - 2025
Area covered
Bangladesh
Description

Abstract

Food price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes food price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following areas: Afghanistan, Armenia, Bangladesh, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Dem. Rep., Congo, Rep., Gambia, The, Guinea, Guinea-Bissau, Haiti, Indonesia, Iraq, Kenya, Lao PDR, Lebanon, Liberia, Libya, Malawi, Mali, Mauritania, Mozambique, Myanmar, Niger, Nigeria, Philippines, Senegal, Somalia, South Sudan, Sri Lanka, Sudan, Syrian Arab Republic, Yemen, Rep.

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