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.

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

The MSCI Emerging Markets Latin America Index fluctuated significantly throughout the last few years. Between 2004 and 2023, the index peaked at 4,613.65 at the end of 2010, whereas it was at its lowest at the end of 2004. As of the end of 2023, the MSCI Emerging Markets Latin America Index stood at 2,662.81 points.

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

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 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.

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.

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 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.

Msci reported $4 in EPS Earnings Per Share for its fiscal quarter ending in March 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.

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.

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.

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

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

The stock market is the barometer of the economy that reflects the overall health and direction of the economic development and is affected by different factors including social, environmental and political. It is important to investigate the effect of the political instability on the stock market performance, especially on emerging economies. Therefore, we aim to study the relationship between political instability and stock market performance in Pakistan. To meet our objectives, we used past data from 1996 to 2021. Data are collected from the DataStream data base. MSCI indices are used as the proxy for the Stock market performance of the selected country. World governance six indicators are used in the study as the explanatory variable concentrating the political instability index as the main explanatory variable. Regression analysis is used but two-way robustness analysis was done for the accuracy of the findings through GMM methods and taking GDP as another endogenous variable. Our findings shows that the political stability has significant positive impact on the stock market performance while, political instability has negative impact on stock market performance. Moreover, other governance indicators has a significant positive impact on performance. However, political instability disrupts the operations and economical activities that leads to decrease the investor confidence and also decrease the foreign investment with the increment of the risk in the country. Moreover, our study has some implications for investors to develop the diversified portfolio to minimize the risk and policy makers can increase their foreign direct investment within the economy by controlling the political instability.

Using the MSCI emerging markets index, stock markets in emerging economies performed above those of developed economies in 2020, with an annual return of 18.31 percent. This compares to a 2020 annual return of 15.9 percent for the MSCI World Index, which tracks the stock markets of 23 developed economies.

United States New York Stock Exchange: Index: MSCI US High Dividend Yield Index data was reported at 2,832.712 NA in Apr 2025. This records a decrease from the previous number of 2,968.083 NA for Mar 2025. United States New York Stock Exchange: Index: MSCI US High Dividend Yield Index data is updated monthly, averaging 2,068.302 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 3,068.952 NA in Nov 2024 and a record low of 1,117.129 NA in Jan 2012. United States New York Stock Exchange: Index: MSCI US High Dividend Yield 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.

The stock market is the barometer of the economy that reflects the overall health and direction of the economic development and is affected by different factors including social, environmental and political. It is important to investigate the effect of the political instability on the stock market performance, especially on emerging economies. Therefore, we aim to study the relationship between political instability and stock market performance in Pakistan. To meet our objectives, we used past data from 1996 to 2021. Data are collected from the DataStream data base. MSCI indices are used as the proxy for the Stock market performance of the selected country. World governance six indicators are used in the study as the explanatory variable concentrating the political instability index as the main explanatory variable. Regression analysis is used but two-way robustness analysis was done for the accuracy of the findings through GMM methods and taking GDP as another endogenous variable. Our findings shows that the political stability has significant positive impact on the stock market performance while, political instability has negative impact on stock market performance. Moreover, other governance indicators has a significant positive impact on performance. However, political instability disrupts the operations and economical activities that leads to decrease the investor confidence and also decrease the foreign investment with the increment of the risk in the country. Moreover, our study has some implications for investors to develop the diversified portfolio to minimize the risk and policy makers can increase their foreign direct investment within the economy by controlling the political instability.

Download Historical MSCI Singapore Stock Index Futures Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

New York Stock Exchange: Index: MSCI US Momentum Net Total Return data was reported at 5,100.704 NA in Apr 2025. This records an increase from the previous number of 4,973.498 NA for Mar 2025. New York Stock Exchange: Index: MSCI US Momentum Net Total Return data is updated monthly, averaging 2,522.609 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 5,348.593 NA in Jan 2025 and a record low of 881.786 NA in Jan 2012. New York Stock Exchange: Index: MSCI US Momentum 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.