Files:- stock_exchanges_data.csv:This file provides data on key financial indicators for 82 global stock exchanges, including Market Capitalization, Capitalization-to-GDP Ratio, Value Traded, Value Traded to GDP Ratio, Share Turnover Velocity, Capitalization per Listed Company, and Number of Trades. The data reflects the year 2023 and serves as the foundation for clustering and classification analysis within the study, focusing on identifying development patterns and key factors influencing stock exchange stability and competitiveness.- research_code.ipynb:This Jupyter Notebook contains the complete Python code used for the analysis conducted in the study. It includes data preparation, clustering, classification, Shapley values calculation, and all other analytical steps described in the paper. The notebook is fully reproducible based on the provided dataset.Raw data (csv files). Source: The World Federation of Exchanges (WFE) and International Monetary Fund (IMF)

Germany DE: Market Capitalization: Listed Domestic Companies: % of GDP data was reported at 45.385 % in 2022. This records a decrease from the previous number of 57.564 % for 2021. Germany DE: Market Capitalization: Listed Domestic Companies: % of GDP data is updated yearly, averaging 31.800 % from Dec 1975 (Median) to 2022, with 48 observations. The data reached an all-time high of 64.691 % in 1999 and a record low of 7.519 % in 1980. Germany DE: Market Capitalization: Listed Domestic Companies: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Financial Sector. Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values.;World Federation of Exchanges database.;Weighted average;Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

Greece GR: Market Capitalization: Listed Domestic Companies: % of GDP data was reported at 25.266 % in 2017. This records an increase from the previous number of 19.286 % for 2016. Greece GR: Market Capitalization: Listed Domestic Companies: % of GDP data is updated yearly, averaging 34.131 % from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 83.191 % in 2007 and a record low of 11.737 % in 2011. Greece GR: Market Capitalization: Listed Domestic Companies: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Greece – Table GR.World Bank.WDI: Financial Sector. Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values.; ; World Federation of Exchanges database.; Weighted average; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

Egypt EG: Stocks Traded: Total Value: % of GDP data was reported at 6.130 % in 2017. This records an increase from the previous number of 3.028 % for 2016. Egypt EG: Stocks Traded: Total Value: % of GDP data is updated yearly, averaging 7.695 % from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 58.855 % in 2008 and a record low of 3.028 % in 2016. Egypt EG: Stocks Traded: Total Value: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Egypt – Table EG.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values.; ; World Federation of Exchanges database.; Weighted average; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

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.

Nifty 50: A Hold for Now, But Long-Term Gains Are Possible

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This paper analyses the relationship between stock market capitalization and real GDP in ten Central and Eastern European countries (CEECs) that joined the European Union in 2004 and 2007, with the objective of determining if the financial markets have played a role as a driver of the economic development in these countries or vice versa. The methodology is based on the application of three different measures of causality between the relevant variables, in order to determine the existence and the direction of causality. Using a cointegrated Vector Autoregressive model (VAR), the authors study the relationship between the relevant variables through the following tests: Granger causality test, Toda-Yamamoto approach and Frequency Domain approach. The results obtained suggest evidence of the existence of this relationship, in both directions, in a significant number of this group of countries, and especially in those there is a long-term relationship.

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.

Cloudflare (NET) Navigates the Web 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

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.

S&P 500: A Bull or a Bear?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The data set was collected to investigate the impact of IFRS adoption on the performance of the 3 biggest stock markets in Sub-Saharan Africa (South Africa, Nigeria and Kenya). Some control variables like Market capitalization to GDP, real GDP growth, number of listed companies were also included in the model.

This study aims to observe the effects of financial metrics, free float shareholders, GDP growth and firm age on dividend payout policy in both the short- and long-term scenarios. The study introduces the dynamic panel regression models, i.e. Autoregressive Integrated Moving Average (ARIMA) for time series data and Weighted Least Squares (WLS) to account for autocorrelation and heteroscedasticity limitation. The dataset includes all companies listed on the Stock Exchange of Thailand during the years 2013–2023. The study finds that in the long-term, GDP growth negatively relates to dividend payout policy in all industrial sectors. Financial metrics, free float shareholders, GDP growth and firm age affect a mixed picture of industrial sectors on dividend payout policy. In the short-term, previous dividend payments significantly influence dividend payment policy. Furthermore, a higher debt-to-equity ratio, firm age, and free cashflows influence dividend payout policy in various industrial sectors. Also shown by the analysis is that factors influence short-run adjustment to half-life analysis.

The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United Kingdom UK: Market Capitalization: Listed Domestic Companies: % of GDP data was reported at 64.629 % in 2008. This records a decrease from the previous number of 125.114 % for 2007. United Kingdom UK: Market Capitalization: Listed Domestic Companies: % of GDP data is updated yearly, averaging 87.239 % from Dec 1975 (Median) to 2008, with 34 observations. The data reached an all-time high of 177.400 % in 1999 and a record low of 6.368 % in 1980. United Kingdom UK: Market Capitalization: Listed Domestic Companies: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Financial Sector. Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values.; ; World Federation of Exchanges database.; Weighted average; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

The financial indicators are based on data compiled according to the 2008 SNA "System of National Accounts, 2008". Many indicators are expressed as a percentage of Gross Domestic Product (GDP) or as a percentage of Gross Disposable Income (GDI) when referring to the Households and NPISHs sector. The definition of GDP and GDI are the following:

Gross Domestic Product:
Gross Domestic Product (GDP) is derived from the concept of value added. Gross value added is the difference of output and intermediate consumption. GDP is the sum of gross value added of all resident producer units plus that part (possibly the total) of taxes on products, less subsidies on products, that is not included in the valuation of output [System of National Accounts, 2008, par. 2.138]. GDP is also equal to the sum of final uses of goods and services (all uses except intermediate consumption) measured at purchasers’ prices, less the value of imports of goods and services [System of National Accounts, 2008, par. 2.139]. GDP is also equal to the sum of primary incomes distributed by producer units [System of National Accounts, 2008, par. 2.140].

Gross Disposable Income:
Gross Disposable Income (GDI) is equal to net disposable income which is the balancing item of the secondary distribution income account plus the consumption of fixed capital. The use of the Gross Disposable Income (GDI), rather than net disposable income, is preferable for analytical purposes because there are uncertainty and comparability problems with the calculation of consumption of fixed capital. GDI measures the income available to the total economy for final consumption and gross saving [System of National Accounts, 2008, par. 2.145].

Definition of Debt:
Debt is a commonly used concept, defined as a specific subset of liabilities identified according to the types of financial instruments included or excluded. Generally, debt is defined as all liabilities that require payment or payments of interest or principal by the debtor to the creditor at a date or dates in the future. Consequently, all debt instruments are liabilities, but some liabilities such as shares, equity and financial derivatives are not debt [System of National Accounts, 2008, par. 22.104]. According to the SNA, most debt instruments are valued at market prices. However, some countries do not apply this valuation, in particular for securities other than shares, except financial derivatives (AF33). In this dataset, for financial indicators referring to debt, the concept of debt is the one adopted by the SNA 2008 as well as by the International Monetary Fund in “Public Sector Debt Statistics – Guide for compilers and users” (Pre-publication draft, May 2011). Debt is thus obtained as the sum of the following liability categories, whenever available / applicable in the financial balance sheet of the institutional sector:special drawing rights (AF12), currency and deposits (AF2), debt securities (AF3), loans (AF4), insurance, pension, and standardised guarantees (AF6), and other accounts payable (AF8). This definition differs from the definition of debt applied under the Maastricht Treaty for European countries. First, gross debt according to the Maastricht definition excludes not only financial derivatives and employee stock options (AF7) and equity and investment fund shares (AF5) but also insurance pensions and standardised guarantees (AF6) and other accounts payable (AF8). Second, debt according to Maastricht definition is valued at nominal prices and not at market prices.

To view other related indicator datasets, please refer to:
Institutional Investors Indicators [add link]
Household Dashboard [add link]

Costa Rica CR: Market Capitalization: Listed Domestic Companies: % of GDP data was reported at 3.223 % in 2022. This records an increase from the previous number of 3.142 % for 2021. Costa Rica CR: Market Capitalization: Listed Domestic Companies: % of GDP data is updated yearly, averaging 4.905 % from Dec 1993 (Median) to 2022, with 25 observations. The data reached an all-time high of 16.193 % in 1999 and a record low of 3.014 % in 2003. Costa Rica CR: Market Capitalization: Listed Domestic Companies: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Financial Sector. Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values.;World Federation of Exchanges database.;Weighted average;Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

Poland PL: Market Capitalization: Listed Domestic Companies: % of GDP data was reported at 38.396 % in 2017. This records an increase from the previous number of 29.421 % for 2016. Poland PL: Market Capitalization: Listed Domestic Companies: % of GDP data is updated yearly, averaging 27.648 % from Dec 1995 (Median) to 2017, with 23 observations. The data reached an all-time high of 49.321 % in 2007 and a record low of 3.211 % in 1995. Poland PL: Market Capitalization: Listed Domestic Companies: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Poland – Table PL.World Bank.WDI: Financial Sector. Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values.; ; World Federation of Exchanges database.; Weighted average; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

Short-run and speed of adjustment to long-run equilibrium.

Descriptive Statistics and Boxcox transformation (λ).

The Belt and Road financial system resilience dataset reflects the level of resilience of the financial system of each country, and the higher the value of the data, the stronger the resilience of the financial system of the countries along the Belt and Road. The financial system resilience includes money market development resilience, banking system resilience, and stock market resilience. The data products are prepared with reference to the World Bank's statistical database, using broad money growth (annual percentage), real interest rates, net domestic credit (current local currency) as a percentage of GDP, and net domestic credit (current local currency) for countries along the Belt and Road from 2000 to 2019. The financial system resilience product is prepared through a comprehensive diagnosis based on sensitivity and adaptability analysis, taking into account year-on-year changes in each indicator, using year-on-year data on six indicators: bank liquidity reserves as a percentage of bank assets, bank capital as a percentage of assets, and total stock transactions as a percentage of GDP. The financial system resilience dataset for countries along the "Belt and Road" is an important reference for the analysis and comparison of the current financial system resilience of each country.

France FR: Stocks Traded: Total Value: % of GDP data was reported at 40.973 % in 2014. This records an increase from the previous number of 39.327 % for 2013. France FR: Stocks Traded: Total Value: % of GDP data is updated yearly, averaging 13.969 % from Dec 1975 (Median) to 2014, with 40 observations. The data reached an all-time high of 108.977 % in 2007 and a record low of 0.850 % in 1977. France FR: Stocks Traded: Total Value: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values.; ; World Federation of Exchanges database.; Weighted average; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

United States US: Stocks Traded: Total Value: % of GDP data was reported at 205.181 % in 2017. This records a decrease from the previous number of 225.893 % for 2016. United States US: Stocks Traded: Total Value: % of GDP data is updated yearly, averaging 155.485 % from Dec 1984 (Median) to 2017, with 34 observations. The data reached an all-time high of 320.992 % in 2008 and a record low of 27.431 % in 1984. United States US: Stocks Traded: Total Value: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values.; ; World Federation of Exchanges database.; Weighted average; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.