Japan's main stock market index, the JP225, rose to 38680 points on June 24, 2025, gaining 0.85% from the previous session. Over the past month, the index has climbed 3.06%, though it remains 1.26% lower than a year ago, 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.

Graph and download economic data for Nikkei Stock Average, Nikkei 225 from 1949-05-16 to 2025-06-23 about stocks, stock market, Japan, and indexes.

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 Nikkei Stock Average (Nikkei 225) closed at ********* points in May 2025. In February 2024, it surpassed an all-time high recorded 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).

In 2024, the Nikkei 225 index closed at ********* points. The index surpassed a 34-year-old record in February and reached a new all-time high in July 2024. 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.

Japan's main stock market index, the JP225, rose to 38723 points on June 24, 2025, gaining 0.96% from the previous session. Over the past month, the index has climbed 3.17%, though it remains 1.15% lower than a year ago, 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.

Key information about Japan Nikkei 225 Stock

• Japan Nikkei 225 Stock closed at 33,189.0 points in Jun 2023, compared with 30,887.9 points at the previous month end
• Japan Equity Market Index: Month End: Japan: Nikkei 225 Stock data is updated monthly, available from May 1949 to Jun 2023, with an average number of 9,363.1 points
• The data reached an all-time high of 38,915.9 points in Dec 1989 and a record low of 86.2 points in Jun 1950

CEIC calculates monthly Equity Market Index from daily Equity Market Index based on the last business day of the month. Nikkei provides Equity Market Index.


Further information about Japan Nikkei 225 Stock

• In the latest reports, Prime Market recorded a monthly P/E ratio of 20.0 in Jul 2023

The Nikkei 225 Index reached an all-time high of 42,426.77 on July 11, 2024. This was the highest value since the index peaked at 38,957.44 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).

Prices for Japan Stock Market Index (JPVIX) including live quotes, historical charts and news. Japan Stock Market Index (JPVIX) was last updated by Trading Economics this June 23 of 2025.

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).

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

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.

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

Predictions indicate a sustained upward trend for the Nikkei 225 index. Positive global economic conditions, strong corporate earnings, and government stimulus measures are expected to support growth. However, potential risks include geopolitical tensions, interest rate hikes, and supply chain disruptions, which could lead to volatility and a slowdown in growth.

Japan Index: Nikkei 300 Stock: Month End data was reported at 333.860 01Oct1982=100 in Nov 2018. This records an increase from the previous number of 330.290 01Oct1982=100 for Oct 2018. Japan Index: Nikkei 300 Stock: Month End data is updated monthly, averaging 257.950 01Oct1982=100 from Nov 1993 (Median) to Nov 2018, with 301 observations. The data reached an all-time high of 362.190 01Oct1982=100 in Sep 2018 and a record low of 145.670 01Oct1982=100 in May 2012. Japan Index: Nikkei 300 Stock: Month End data remains active status in CEIC and is reported by Nikkei. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

Japan Index: Nikkei Jasdaq Average: Month End data was reported at 3,563.170 NA in Oct 2018. This records a decrease from the previous number of 3,831.490 NA for Sep 2018. Japan Index: Nikkei Jasdaq Average: Month End data is updated monthly, averaging 1,562.840 NA from Feb 1994 (Median) to Oct 2018, with 297 observations. The data reached an all-time high of 4,239.850 NA in Jan 2018 and a record low of 627.000 NA in Oct 1998. Japan Index: Nikkei Jasdaq Average: Month End data remains active status in CEIC and is reported by Nikkei. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

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.

Stock market return (%, year-on-year) in Japan was reported at 22.23 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Japan - Stock market return (%, year-on-year) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

Japan OSE: Open Interest: Nikkei 225 Futures data was reported at 430,092.000 Unit in Oct 2018. This records an increase from the previous number of 420,954.000 Unit for Sep 2018. Japan OSE: Open Interest: Nikkei 225 Futures data is updated monthly, averaging 288,782.000 Unit from Sep 1988 (Median) to Oct 2018, with 362 observations. The data reached an all-time high of 567,770.000 Unit in May 2013 and a record low of 13,506.000 Unit in Sep 1988. Japan OSE: Open Interest: Nikkei 225 Futures data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z016: Osaka Exchange Inc: Futures and Options.

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.

Japan's Nikkei Soars to 33-Year High on U.S. Debt Deal, Weak 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