Euro Area's main stock market index, the EU50, fell to 5336 points on July 31, 2025, losing 1.06% from the previous session. Over the past month, the index has climbed 1.01% and is up 11.96% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on July of 2025.

Stock market return (%, year-on-year) in Denmark was reported at 31.18 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Denmark - Stock market return (%, year-on-year) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Stock market return (%, year-on-year) in Kenya was reported at 18.41 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Kenya - Stock market return (%, year-on-year) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Stock market index in Mexico, June, 2025 The most recent value is 130.44 points as of June 2025, a decline compared to the previous value of 131.33 points. Historically, the average for Mexico from January 1970 to June 2025 is 35.98 points. The minimum of 0 points was recorded in January 1970, while the maximum of 131.33 points was reached in May 2025. | TheGlobalEconomy.com

Taiwan Stock Exchange Weighted Index..............

Ireland ISEQ Equity: Irish Stock Exchange: Return Index: ESM data was reported at 2,572.880 30Dec2008=1000 in Oct 2018. This records a decrease from the previous number of 2,835.920 30Dec2008=1000 for Sep 2018. Ireland ISEQ Equity: Irish Stock Exchange: Return Index: ESM data is updated monthly, averaging 2,088.580 30Dec2008=1000 from Apr 2008 (Median) to Oct 2018, with 127 observations. The data reached an all-time high of 3,036.630 30Dec2008=1000 in Nov 2017 and a record low of 896.730 30Dec2008=1000 in Feb 2009. Ireland ISEQ Equity: Irish Stock Exchange: Return Index: ESM data remains active status in CEIC and is reported by Irish Stock Exchange. The data is categorized under Global Database’s Ireland – Table IE.Z001: Irish Stock Exchange: Index. The Irish Stock Exchange (ISE) renamed this securities market with effect from May 2010. The old name was Irish Enterprise Exchange (IEX) and it has been changed to Enterprise Securities Market (ESM).

Graph and download economic data for Financial Market: Share Prices for Italy (SPASTT01ITQ661N) from Q1 1957 to Q2 2025 about Italy and stock market.

Switzerland Equity Index: SIX Swiss Exchange: SPI Shares Return: Personal and Households Goods data was reported at 3,555.940 31Dec1999=1000 in Oct 2018. This records a decrease from the previous number of 3,909.570 31Dec1999=1000 for Sep 2018. Switzerland Equity Index: SIX Swiss Exchange: SPI Shares Return: Personal and Households Goods data is updated monthly, averaging 1,900.700 31Dec1999=1000 from Dec 1999 (Median) to Oct 2018, with 227 observations. The data reached an all-time high of 4,522.660 31Dec1999=1000 in Apr 2018 and a record low of 769.700 31Dec1999=1000 in Dec 2000. Switzerland Equity Index: SIX Swiss Exchange: SPI Shares Return: Personal and Households Goods data remains active status in CEIC and is reported by SIX Swiss Exchange. The data is categorized under Global Database’s Switzerland – Table CH.Z001: SIX Swiss Exchange: Stock Market Index.

Cape Verde: Stock market return, percent: The latest value from is percent, unavailable from percent in . In comparison, the world average is 0.00 percent, based on data from countries. Historically, the average for Cape Verde from to is percent. The minimum value, percent, was reached in while the maximum of percent was recorded in .

Stock market forecasting is one of the most challenging problems in today’s financial markets. According to the efficient market hypothesis, it is almost impossible to predict the stock market with 100% accuracy. However, Machine Learning (ML) methods can improve stock market predictions to some extent. In this paper, a novel strategy is proposed to improve the prediction efficiency of ML models for financial markets. Nine ML models are used to predict the direction of the stock market. First, these models are trained and validated using the traditional methodology on a historic data captured over a 1-day time frame. Then, the models are trained using the proposed methodology. Following the traditional methodology, Logistic Regression achieved the highest accuracy of 85.51% followed by XG Boost and Random Forest. With the proposed strategy, the Random Forest model achieved the highest accuracy of 91.27% followed by XG Boost, ADA Boost and ANN. In the later part of the paper, it is shown that only classification report is not sufficient to validate the performance of ML model for stock market prediction. A simulation model of the financial market is used in order to evaluate the risk, maximum draw down and returns associate with each ML model. The overall results demonstrated that the proposed strategy not only improves the stock market returns but also reduces the risks associated with each ML model.

Stock market return (%, year-on-year) in Oman was reported at 6.1763 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Oman - Stock market return (%, year-on-year) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

This dataset contains monthly and quarterly time-series data from 2012 to 2024 for Indonesian sovereign credit risk (∆CDS), global volatility (VIX), international equity proxy (MSCI World Index), Indonesia Stock Exchange Composite Index (IHSG), exchange rate (USD/IDR), and inflation. The dataset supports the empirical analysis in the article titled “The Interaction Between Sovereign Risk, Global Volatility, and Domestic Stock Returns: An Indonesian Case Study.

As of April 10, 2025, tech giants Apple, Microsoft, Nvidia, Alphabet (Google), and Amazon dominated the S&P 500 index and were among only eight companies with a market capitalization exceeding *** ******** U.S. dollars in the U.S.

Hong Kong Stock Exchange: Index: Total Return: Hang Seng China A Industry Top Index data was reported at 8,045.960 NA in Apr 2025. This records a decrease from the previous number of 8,290.750 NA for Mar 2025. Hong Kong Stock Exchange: Index: Total Return: Hang Seng China A Industry Top Index data is updated monthly, averaging 6,265.100 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 9,706.800 NA in Feb 2021 and a record low of 2,418.830 NA in Mar 2014. Hong Kong Stock Exchange: Index: Total Return: Hang Seng China A Industry Top Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.EDI.SE: Hong Kong Stock Exchange: Monthly.

CE Transact is the premier alternative data set for consumer spend on credit and debit cards, available as an aggregated feed. Hedge fund investors trust CE transaction data to track quarterly performance, company-reported KPIs, and earnings predictions for stock market strategic decision-making.

Graph and download economic data for Stock Market Turnover Ratio (Value Traded/Capitalization) for Italy (DDEM01ITA156NWDB) from 1975 to 2014 about Italy, ratio, and stock market.

Index: TSE: 1st Section: MA: Real Estate data was reported at 1,520.779 04Jan1968=100 in Jun 2018. This records a decrease from the previous number of 1,559.857 04Jan1968=100 for May 2018. Index: TSE: 1st Section: MA: Real Estate data is updated monthly, averaging 925.960 04Jan1968=100 from Dec 1987 (Median) to Jun 2018, with 367 observations. The data reached an all-time high of 2,363.700 04Jan1968=100 in Dec 1989 and a record low of 402.363 04Jan1968=100 in Apr 2003. Index: TSE: 1st Section: MA: Real Estate data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

The COVID-19 pandemic has emerged as a significant event of the current century, introducing substantial transformations in economic and social activities worldwide. The primary objective of this study is to investigate the relationship between daily COVID-19 cases and Pakistan stock market (PSX) return volatility. To assess the relationship between daily COVID-19 cases and the PSX return volatility, we collected secondary data from the World Health Organization (WHO) and the PSX website, specifically focusing on the PSX 100 index, spanning from March 15, 2020, to March 31, 2021. We used the GARCH family models for measuring the volatility and the COVID-19 impact on the stock market performance. Our E-GARCH findings show that there is long-term persistence in the return volatility of the stock market of Pakistan in the period of the COVID-19 timeline because ARCH alpha (ω1) and GARCH beta (ω2) are significant. Moreover, is asymmetrical effect is found in the stock market of Pakistan during the COVID-19 period due to Gamma (ѱ) being significant for PSX. Our DCC-GARCH results show that the COVID-19 active cases have a long-term spillover impact on the Pakistan stock market. Therefore, the need of strong planning and alternative platform should be needed in the distress period to promote the stock market and investor should advised to make diversified international portfolio by investing in high and low volatility stock market to save their income. This study advocated the implications for investors to invest in low volatility stock especially during the period of pandemics to protect their return on investment. Moreover, policy makers and the regulators can make effective policies to maintain financial stability during pandemics that is very important for the country’s economic development.

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.

Cotton Index: The Future of Textile Trade?

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 dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.