The statistic shows the development of the MSCI World USD Index from 1986 to 2024. The 2024 year-end value of the MSCI World USD index amounted to ******** points. MSCI World USD index – additional information The MSCI World Index, developed by Morgan Stanley Capital International (MSCI), is one of the most important stock indices. It includes stocks from developed countries all over the world and is regarded as benchmark of global stock market. According to MSCI, this index covers about ** percent of the free float-adjusted market capitalization in each country. As seen on the statistics above, in 2024, MSCI World USD index reported its highest value since 1986 amounting, a threefold increase from the figure recorded in 2013, when the year-end value of the MSCI World index was equal to ********. Along with the S&P Global Broad Market, the MSCI World is one of the most important global stock market performance indexes. Aside of including markets around the globe, these two indexes are global in a sense that they disregard where the companies are domiciled or traded, whereas other important indexes such as the Dow Jones Industrial Average, the Japanese index Nikkei 225, Wilshire 5000, the NASDAQ 100 index, have different approaches.

The net returns offered by the MSCI World and MSCI All Country World Index (ACWI) outperformed the rate of return provided by the MSCI Emerging Markets index. On a one-year rate of return, the MSCI World and ACWI offered similar net return rates of around 12 and a similar three-year return of nine percent, while the MSCI Emerging Markets provided returns of 4.21 and 2.34 percent.

MSCI World index is predicted to experience a moderate increase. The predicted range for the index is between a slight increase and a significant increase. The risk associated with this prediction is moderate, as there are some factors that could potentially impact the index's performance.

Prices for Global Equity Index including live quotes, historical charts and news. Global Equity Index was last updated by Trading Economics this July 13 of 2025.

Browse LSEG's MSCI Global Equity Indexes and gain extensive equity market coverage for over 75 countries in the developed, emerging and frontier markets.

Sri Lanka's S&P All-World Equity Index Volatility is -6.88% which is the 52nd highest in the world ranking. Transition graphs on S&P All-World Equity Index Volatility in Sri Lanka and comparison bar charts (USA vs. China vs. Japan vs. Sri Lanka), (Australia vs. Romania vs. Sri Lanka) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

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.

MSCI World Index Forecast: Mixed Outlook

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 MSCI World index dropped sharply in the four weeks between February 16 and March 15, 2020, shedding 31.3 percent of its value. This, of course, was due to the economic impact of the global coronavirus (COVID-19) pandemic. It was not until November 2020 that the index recovered to the levels seen in early 2020. On October 13, 2024, it reached over 3,730 index points, the highest value during the observed period. 1,583 companies from 23 developed economies are included in the MSCI World Index. While a world index in the sense of covering developed markets in North America, Western Europe, and the Asia-Pacific region, it has been criticized for how it excludes companies located in large developing economies such as China, Russia, Brazil, India, and South Africa.

Peru's S&P All-World Equity Index Volatility is -12.47% which is the 65th highest in the world ranking. Transition graphs on S&P All-World Equity Index Volatility in Peru and comparison bar charts (USA vs. China vs. Japan vs. Peru), (Saudi Arabia vs. Malaysia vs. Peru) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

India's S&P All-World Equity Index Volatility is 15.77% which is the 17th highest in the world ranking. Transition graphs on S&P All-World Equity Index Volatility in India and comparison bar charts (USA vs. China vs. Japan vs. India), (China vs. United States of America vs. India) are used for easy understanding. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

The average for 2024 based on 138 countries was 5.56 points. The highest value was in Finland: 7.74 points and the lowest value was in Afghanistan: 1.72 points. The indicator is available from 2013 to 2024. Below is a chart for all countries where data are available.

Consumer price index (2010 = 100) in World was reported at 136 in 2011, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Consumer price index (2010 = 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

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.

MSCI World Index: Global Peaks or Precipice?

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

Graph and download economic data for Global Price Index of All Commodities (PALLFNFINDEXM) from Jan 2003 to Apr 2025 about World, commodities, price index, indexes, and price.

Twelve Data is a technology-driven company that provides financial market data, financial tools, and dedicated solutions. Large audiences - from individuals to financial institutions - use our products to stay ahead of the competition and success.

At Twelve Data we feel responsible for where the markets are going and how people are able to explore them. Coming from different technological backgrounds, we see how the world is lacking the unique and simple place where financial data can be accessed by anyone, at any time. This is what distinguishes us from others, we do not only supply the financial data but instead, we want you to benefit from it, by using the convenient format, tools, and special solutions.

We believe that the human factor is still a very important aspect of our work and therefore our ethics guides us on how to treat people, with convenient and understandable resources. This includes world-class documentation, human support, and dedicated solutions.

Graph and download economic data for Global Price Index of All Commodities (PALLFNFINDEXQ) from Q1 2003 to Q1 2025 about World, commodities, price index, indexes, and price.

LSEG is one of the world’s largest sources of index pricing, composition content, and history. Browse LSEG Indices.

This dataset provides values for CORRUPTION INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The average for 2024 based on 174 countries was 59 index points. The highest value was in Singapore: 84 index points and the lowest value was in North Korea: 3 index points. The indicator is available from 1995 to 2024. Below is a chart for all countries where data are available.

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.

MSCI World Index: Global Pathfinder or Market Mirage?

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