This dataset contains stock market data for prominent energy companies operating within the Oil and Gas Production/Exploration industry, namely BP, CNQ, CVX, COP, E, EQNR, HES, OXY, PBR, PXD, SHELL, TTE and WDS. It encompasses 5 years of trading data, including daily opening and closing prices, the highest and lowest prices recorded during the day, and the trading volume for each respective day. This dataset is an extremely valuable resource for investors, analysts, and researchers who want to evaluate and analyze the stock performance of major companies in the Petroleum and Gas Exploration/Production sector, such as BP, CNQ, CVX, COP, E, EQNR, HES, OXY, PBR, PXD, SHELL, TTE, and WDS. Due to its inclusion of daily stock price changes, this dataset holds great importance for those developing short-term and long-term investment strategies for these companies. Analysts can utilize this dataset to assess and examine the performance of leading companies in the Petroleum and Gas Exploration/Production sector, including BP, CNQ, CVX, COP, E, EQNR, HES, OXY, PBR, PXD, SHELL, TTE, and WDS. This dataset encompasses the differences between opening and closing prices, the highest and lowest values, trading volume, and other technical analysis tools, facilitating comparisons among these companies and providing valuable insights into sector trends. Investors and financial experts interested in evaluating the stock performance of BP, CNQ, CVX, COP, E, EQNR, HES, OXY, PBR, PXD, SHELL, TTE, and WDS in the Petroleum and Gas Exploration/Production sector will recognize the informative and valuable nature of this dataset. Through these data, investors can make more informed decisions, reduce risks, and explore potential opportunities. This comprehensive dataset offers reliable information about the performance of these significant companies and can serve as a valuable guide for future investment strategies.

This dataset contains stock market data for prominent energy companies operating within the Energy Services and Equipment industry, namely FTI, SLB, NE. And it encompasses 5 years of trading data for SLB and FTI, and a 3-year scope for NE. It includes daily opening and closing prices, as well as the highest and lowest prices recorded during the day, along with trading volume for each respective day. This dataset is a highly valuable resource for investors, analysts, and researchers to examine and analyze the stock performance of major companies in the Energy Services and Equipment sector, such as FTI, NE, and SLB. It includes daily stock price changes, making it extremely valuable for those developing short-term or long-term investment strategies for these companies. Analysts can use this dataset to evaluate the performance of FTI, NE, and SLB in the Energy Services and Equipment sector by studying the differences between opening and closing prices, the highest and lowest values, trading volume, and other technical analysis tools. This dataset allows for comparisons among these companies and provides a comprehensive overview of trends within the Energy Services and Equipment sector. In summary, this dataset serves as an informative and valuable source for investors and financial professionals interested in assessing the stock performance of companies like FTI, NE, and SLB in the Energy Services and Equipment sector. By utilizing this data, investors can make more informed decisions, minimize risks, and explore potential opportunities.

This dataset contains stock market data for prominent energy companies operating within the Refinery and Distribution, namely MPC, PSX and VLO. It encompasses 5 years of trading data, including daily opening and closing prices, the highest and lowest prices recorded during the day, and the trading volume for each respective day. This dataset, which provides the stock performance analysis of major companies in the Refinery and Distribution sector such as MPC, PSX, and VLO, holds significant value for investors, analysts, and researchers. Due to its inclusion of daily stock price changes, this dataset is a crucial tool for those evaluating both short-term and long-term investment strategies for these companies. Analysts interested in assessing the performance of leading companies in the Refinery and Distribution sector, including MPC, PSX, and VLO, can actively utilize this comprehensive dataset. Encompassing data on the differences between opening and closing prices, highest and lowest values, trading volume, and other technical analysis tools, this dataset provides essential information for understanding sector trends and making company comparisons. Investors and financial experts seeking to examine and evaluate the stock performance of MPC, PSX, and VLO in the Refinery and Distribution sector will greatly benefit from this valuable dataset. Through this data, investors can make more informed decisions, reduce risks, and discover potential opportunities. This comprehensive dataset serves as a reliable and detailed guide for future investment strategies, offering essential insights into the stock performance of these significant companies.

The gross replacement rate is a significant indicator that plays a central role in assessing the effectiveness of a pension system in providing retirement income to replace pre-retirement earnings, which serve as the primary source of income before retirement. This key metric is measured as a percentage and is calculated by dividing gross pension entitlement by gross pre-retirement earnings. By comparing the gross pension entitlement with the gross pre-retirement earnings, the gross replacement rate offers insights into how much of an individual's pre-retirement income is being replaced by their pension benefits during retirement. A high gross replacement rate indicates that retirees are receiving a substantial portion of their pre-retirement earnings as pension income, providing them with a more stable and secure financial situation after leaving the workforce. Conversely, a low gross replacement rate suggests that the pension system may not adequately meet retirees' income needs, potentially leading to financial challenges during retirement. Analyzing the gross replacement rate by gender is particularly important in understanding potential disparities in retirement income between men and women. Gender-specific differences in earnings, workforce participation, and pension contributions can lead to distinct replacement rates for male and female retirees. Ensuring equitable pension outcomes for all genders remains a critical objective for policymakers striving to create a fair and inclusive retirement system.