Key information about Japan Market Capitalization

• Japan Market Capitalization accounted for 6,358.159 USD bn in Feb 2025, compared with a percentage of 6,419.508 USD bn in the previous month
• Japan Market Capitalization is updated monthly, available from Jan 1957 to Feb 2025
• The data reached an all-time high of 6,919.601 USD bn in Sep 2021 and a record low of 4.473 USD bn in Aug 1957

CEIC converts monthly Market Capitalization into USD. Japan Exchange Group Inc. provides Market Capitalization in local currency. The Federal Reserve Board period end market exchange rate is used for currency conversions. Market Capitalization covers Tokyo Stock Exchange only, which is not the only equity market in the country but is highly representative. Before July 2013 Total Market Capitalization of TSE covers TSE 1st Section Shares, 2nd Section Shares and Mothers Shares. Since July 2013, Total Market Capitalization of TSE also covers Market Capitalization of Tokyo Pro Market Shares, JASDAQ Standard Shares and JASDAQ Growth Shares. On April 2022, the stock market was restructured into three new market segments (Prime Market, Standard Market, and Growth Market) replacing previous four market divisions: 1st Section, 2nd Section, Mothers, and JASDAQ (Standard and Growth).

As of May 2025, the combined monthly market capitalization of companies listed on the Tokyo Stock Exchange (TSE) in Japan amounted to around ***** trillion Japanese yen. There were more than *** thousand companies listed on the TSE as of the end of 2024.

Japan: Stock market capitalization, billion USD: The latest value from 2022 is 5380.48 billion U.S. dollars, a decline from 6544.3 billion U.S. dollars in 2021. In comparison, the world average is 1244.55 billion U.S. dollars, based on data from 74 countries. Historically, the average for Japan from 1975 to 2022 is 3052.32 billion U.S. dollars. The minimum value, 21.53 billion U.S. dollars, was reached in 1977 while the maximum of 6718.22 billion U.S. dollars was recorded in 2020.

As of December 2024, the combined market capitalization of companies listed on the Tokyo Stock Exchange (TSE) in Japan amounted to 996.3 trillion Japanese yen, reaching a decade high. Tokyo Stock Exchange Hosting over 3.9 thousand listed companies, the Tokyo Stock Exchange is the main stock exchange in Japan. It is operated by Japan Exchange Group, which was established in 2013 following the merger of Tokyo Stock Exchange Group and Osaka Securities Exchange. The company is one of the largest stock market operators in the world by market capitalization. Reorganization of market segments In April 2022, the TSE restructured its market segments. The stock exchange formerly consisted of four market divisions, which were reorganized into three; the Prime Market, where some of Japan’s largest corporations based on market capitalization are listed, the Standard Market, and the Growth Market for startups and emerging companies. The change was made to reduce ambiguity about the concept of each market division and create incentives for listed companies to increase their corporate value.

Key information about Japan Market Capitalization: % of GDP

• Japan Market Capitalization accounted for 163.5 % of its Nominal GDP in Dec 2024, compared with a percentage of 146.5 % in the previous year
• Japan Market Capitalization: % Nominal GDP is updated yearly, available from Dec 1994 to Dec 2024
• The data reached an all-time high of 163.5 % in Dec 2024 and a record low of 47.3 % in Dec 2002

CEIC calculates annual Market Capitalization as % of Nominal GDP from monthly Market Capitalization and annual Nominal GDP. Japan Exchange Group Inc. provides Market Capitalization in local currency. Economic and Social Research Institute provides Nominal GDP in local currency. Market Capitalization covers Tokyo Stock Exchange only, which is not the only equity market in the country but is highly representative. Before July 2013 Total Market Capitalization of TSE covers TSE 1st Section Shares, 2nd Section Shares and Mothers Shares. Since July 2013, Total Market Capitalization of TSE also covers Market Capitalization of Tokyo Pro Market Shares, JASDAQ Standard Shares and JASDAQ Growth Shares. On April 2022, the stock market was restructured into three new market segments (Prime Market, Standard Market, and Growth Market) replacing previous four market divisions: 1st Section, 2nd Section, Mothers, and JASDAQ (Standard and Growth).


Further information about Japan Market Capitalization: % of GDP

• In the latest reports, Prime Market recorded a monthly P/E ratio of 19.9 in Feb 2025
• Nikkei 225 Stock closed at 33,189.0 points in Jun 2023

************ was the leading domestic company in Japan based on market capitalization, which amounted to around **** trillion Japanese yen in May 2025. ****************************** ranked second with a market capitalization of about 24.4 trillion yen. With an aggregate market capitalization of over 996 trillion yen in 2024, the Tokyo Stock Exchange is one of the largest stock exchanges in the world.

Japan's main stock market index, the JP225, rose to 40151 points on June 27, 2025, gaining 1.43% from the previous session. Over the past month, the index has climbed 6.44% and is up 1.43% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on June of 2025.

Interactive daily chart of Japan's Nikkei 225 stock market index back to 1949. Each data point represents the closing value for that trading day and is denominated in japanese yen (JPY). The current price is updated on an hourly basis with today's latest value.

The New York Stock Exchange (NYSE) is the largest stock exchange in the world, with an equity market capitalization of almost ** trillion U.S. dollars as of June 2025. The following three exchanges were the NASDAQ, PINK Exchange, and the Frankfurt Exchange. What is a stock exchange? A stock exchange is a marketplace where stockbrokers, traders, buyers, and sellers can trade in equities products. The largest exchanges have thousands of listed companies. These companies sell shares of their business, giving the general public the opportunity to invest in them. The oldest stock exchange worldwide is the Frankfurt Stock Exchange, founded in the late sixteenth century. Other functions of a stock exchange Since these are publicly traded companies, every firm listed on a stock exchange has had an initial public offering (IPO). The largest IPOs can raise billions of dollars in equity for the firm involved. Related to stock exchanges are derivatives exchanges, where stock options, futures contracts, and other derivatives can be traded.

At the end of the fiscal year 2023, financial institutions, including insurance companies, investment trusts, and pension trusts, were the leading type of domestic investors in stocks in Japan, with stock holdings of around 291.1 trillion Japanese yen. Stock holdings of non-Japanese corporations and individuals amounted to 320.5 trillion yen. Tokyo Stock Exchange With a market capitalization of over 950 trillion Japanese yen and around 3.9 thousand constituents, the Tokyo Stock Exchange, operated by the Japan Exchange Group, is one of the largest stock exchanges in Asia and the world. In parallel to its reorganization in April 2022, a series of reforms were introduced to improve corporate governance of listed companies and make Japanese stocks more attractive to investors. Driven by global investors, the Nikkei 225 stock market index, Japan’s benchmark index, surpassed a 34 year-old record-high in February 2024. Private investors Stock holdings of individuals amounted to around 170.5 trillion yen in fiscal 2023. Japanese households hold a comparably large share of assets in cash and deposits. According to estimates, around 12 percent of the population were stock owners and equity and investment trusts accounted for around 19 percent of the financial assets of households. To boost private investment in stocks and bonds, an amended version of Japan’s tax-exempt investment scheme, Nippon Individual Savings Account (NISA), was launched in January 2024.

With LSEG's Tokyo Stock Exchange (TSE) Data, gain full access to benchmarks, indices, reference data, market depth data, and more.

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.

Nikkei 225 Index Target Price Prediction

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.

Nikkei 225: Surging Ahead or Approaching a Perilous Peak?

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 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.

Composition of uninterrupted trends observed in Nikkei 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.

Will the Nikkei 225 Index Recover?

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.

Nikkei 225: Rising Tide or Ticking Time Bomb?

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.

Nikkei 225 Index Soars on Positive Economic Data and Weaker Yen

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.

Nikkei 225 Forecast: Optimism Fuels Bullish Outlook for the Japanese Stock Market

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 Nikkei 225 Index reached an all-time high of ********* on *************. This was the highest value since the index peaked at ********* in 1989. The Nikkei 225 is a price-weighted stock market index that has been calculated by the Nihon Keizai Shimbun (Nikkei) newspaper since 1950. It comprises 225 constituents listed on the Prime Market of the Tokyo Stock Exchange (TSE).