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 MSCI World index dropped sharply in the four weeks between February 16 and March 15, 2020, shedding **** 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 July 20, 2025, it reached over ***** 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.

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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.

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 ******** rate of return, the MSCI World and ACWI offered similar net return rates of around ** and a similar ********** return of **** percent, while the MSCI Emerging Markets provided returns of **** and **** percent.

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

United States New York Stock Exchange: Index: MSCI US Real Estate Index data was reported at 232.090 NA in May 2024. This stayed constant from the previous number of 232.090 NA for Apr 2024. United States New York Stock Exchange: Index: MSCI US Real Estate Index data is updated monthly, averaging 217.340 NA from Dec 2012 (Median) to May 2024, with 138 observations. The data reached an all-time high of 333.710 NA in Dec 2021 and a record low of 159.970 NA in Aug 2013. United States New York Stock Exchange: Index: MSCI US Real Estate Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: MSCI: Monthly.

Msci stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

United States New York Stock Exchange: Index: MSCI US Gross Total Return data was reported at 25,491.880 NA in Apr 2025. This records a decrease from the previous number of 25,623.120 NA for Mar 2025. United States New York Stock Exchange: Index: MSCI US Gross Total Return data is updated monthly, averaging 11,158.379 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 27,650.960 NA in Jan 2025 and a record low of 4,706.759 NA in Jan 2012. United States New York Stock Exchange: Index: MSCI US Gross Total Return data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: MSCI: Monthly.

Since January 2015, the MSCI Europe Index has fluctuated, dropping significantly following the beginning of the COVID-19 pandemic. In March 2020, the index dropped to ******** points. After that, the MSCI Europe index increased in the following months, peaking at over ***** points at the end of December 2021. Since then, the index has fluctuated significantly and reached a value of ******** as of February 2025. The MSCI Europe Index, developed by Morgan Stanley Capital International (MSCI), is one of the most important stock indices in the region. It includes stocks from developed countries and is regarded as a benchmark of the European stock market.

United States New York Stock Exchange: Index: MSCI US Growth Index Net Total Return data was reported at 24,589.422 NA in Apr 2025. This records an increase from the previous number of 23,991.494 NA for Mar 2025. United States New York Stock Exchange: Index: MSCI US Growth Index Net Total Return data is updated monthly, averaging 8,873.335 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 27,433.036 NA in Jan 2025 and a record low of 3,519.068 NA in Jan 2012. United States New York Stock Exchange: Index: MSCI US Growth Index Net Total Return data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: MSCI: Monthly.

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: Reflecting Global Economic Trends or Inflated Valuations?

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

Green bond indices make it easier for investors to track the performance of green bonds and compare it with other investments. Bloomberg Barclays MSCI Global Green Bond Index was launched in 2014 with the aim provide a benchmark for the green bonds market. Between 2015 and 2020, the Bloomberg Barclays MSCI Global Green Bond Index saw an overall increase, reaching a value of 121.91 as of the end of 2020. By the end of 2022, however, the index value fell to 86.94, before increasing again to 96.09 by the end of 2023.

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: Where Will it Take Us?

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.

The MSCI World Index: A Global Benchmark?

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

Zum Ende des Jahres 2024 schloss der MSCI World Index bei einem Stand von etwa ******** Punkten. Dies ist ein Anstieg gegenüber dem Schlussstand am Ende des Vorjahres um rund ** Prozent.Abgebildet werden jeweils die Schlussstände des MSCI World Index eines Jahres.Der MSCI World Index (auch "The World Index" genannt) ist einer der wichtigsten Aktienindizes der Welt. Er wird von dem US-amerikanischen Finanzdienstleister Morgan Stanley Capital International in drei Varianten berechnet, als Kursindex (Price), als Performanceindex ohne Berücksichtigung von Quellensteuern (Gross) und als Performanceindex mit Berücksichtigung von Quellensteuern (Net). Veröffentlicht wurde hier der Kursindex. Der Index beinhaltet Aktien aus 23 Ländern (Stand: Ende Dezember 2024) und wird seit dem 31. Dezember 1969 (Startwert: 100 Punkte) berechnet. Aktien aus Entwicklungsländern (Emerging Markets) und Aktien kleiner Unternehmen (Small Caps) werden nicht berücksichtigt.

United States New York Stock Exchange: Index: MSCI US Small Cap Index data was reported at 945.471 NA in Apr 2025. This records a decrease from the previous number of 969.458 NA for Mar 2025. United States New York Stock Exchange: Index: MSCI US Small Cap Index data is updated monthly, averaging 647.487 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 1,145.056 NA in Nov 2024 and a record low of 314.601 NA in May 2012. United States New York Stock Exchange: Index: MSCI US Small Cap Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: MSCI: Monthly.

Im Jahr 2024 verzeichnete der MSCI World Index (Kursindex in US-Dollar) eine Wertsteigerung von etwa ** Prozent. Einen Rekordwert im negativen Sinne verzeichnete der Index im Jahr 2008. Auf dem Höhepunkt der Finanzkrise brach der MSCI World Index innerhalb von 12 Monaten um rund ** Prozent ein.Der MSCI World ist der bekannteste Index für den Weltaktienmarkt. Er wird fortlaufend berechnet und enthält rund 1.500 Unternehmen aus 23 entwickelten Ländern. Unternehmen aus Schwellenländern sind nicht vertreten. Mit einem Anteil von rund 70 Prozent haben US-Konzerne das mit Abstand größte Gewicht im Index.

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

Msci reported $4.17 in EPS Earnings Per Share for its fiscal quarter ending in June of 2025. Data for Msci | MSCI - EPS Earnings Per Share including historical, tables and charts were last updated by Trading Economics this last July in 2025.