Japan's main stock market index, the JP225, rose to 45791 points on September 18, 2025, gaining 2.23% from the previous session. Over the past month, the index has climbed 5.15% and is up 23.24% 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 September of 2025.

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

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 September 19 of 2025.

Dataset

This dataset was created by ka.na.o

Contents

It contains the following files:

Japan Index: NSE: Stock Price Index: 2nd Section Composite data was reported at 3,638.890 04Jan1968=100 in Oct 2018. This records an increase from the previous number of 3,634.600 04Jan1968=100 for Sep 2018. Japan Index: NSE: Stock Price Index: 2nd Section Composite data is updated monthly, averaging 1,350.530 04Jan1968=100 from Feb 1999 (Median) to Oct 2018, with 237 observations. The data reached an all-time high of 3,655.090 04Jan1968=100 in Jul 2018 and a record low of 871.670 04Jan1968=100 in Nov 2002. Japan Index: NSE: Stock Price Index: 2nd Section Composite data remains active status in CEIC and is reported by Nagoya Stock Exchange. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

Graph and download economic data for Volatility of Stock Price Index for Japan (DDSM01JPA066NWDB) from 1984 to 2021 about stocks, volatility, Japan, price index, indexes, and price.

Japan TSE: C: PE Ratio: 2nd Sec: Precision Instruments data was reported at 14.500 Unit in Feb 2019. This records a decrease from the previous number of 15.000 Unit for Jan 2019. Japan TSE: C: PE Ratio: 2nd Sec: Precision Instruments data is updated monthly, averaging 15.200 Unit from Jan 2013 (Median) to Feb 2019, with 74 observations. The data reached an all-time high of 23.400 Unit in Jul 2018 and a record low of 0.900 Unit in Apr 2014. Japan TSE: C: PE Ratio: 2nd Sec: Precision Instruments data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z010: Tokyo Stock Exchange: Price Earnings Ratio.

Stock price volatility in Japan was reported at 19.26 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Japan - Stock price volatility - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Japan TSE: C: Average: PE Ratio: PM: Wholesale Trade data was reported at 12.200 Times in Apr 2025. This stayed constant from the previous number of 12.200 Times for Mar 2025. Japan TSE: C: Average: PE Ratio: PM: Wholesale Trade data is updated monthly, averaging 11.400 Times from Apr 2022 (Median) to Apr 2025, with 37 observations. The data reached an all-time high of 14.600 Times in May 2022 and a record low of 9.300 Times in Jun 2022. Japan TSE: C: Average: PE Ratio: PM: Wholesale Trade data remains active status in CEIC and is reported by Japan Exchange Group Inc.. The data is categorized under Global Database’s Japan – Table JP.Z: Tokyo Stock Exchange: Price Earnings Ratio. [COVID-19-IMPACT]

Japan: Stock price volatility, percent: The latest value from 2021 is 19.26 percent, a decline from 19.31 percent in 2020. In comparison, the world average is 20.14 percent, based on data from 87 countries. Historically, the average for Japan from 1984 to 2021 is 19.68 percent. The minimum value, 9.88 percent, was reached in 1984 while the maximum of 38.22 percent was recorded in 2009.

In 2024, the average price-to-book (P/B) ratio of stocks on the Prime Market of the Tokyo Stock Exchange (TSE) in Japan was ***. The average P/B ratio of stocks on the Standard Market was ***.

Graph and download economic data for Financial Market: Share Prices for Japan (SPASTT01JPM661N) from Jan 1959 to Aug 2025 about stock market and Japan.

Graph and download economic data for Price Level of the Capital Stock for Japan (PLKCPPJPA670NRUG) from 1954 to 2019 about stocks, capital, Japan, and price.

Japan TSE: C: Average: PE Ratio: PM: Metal Products data was reported at 16.300 Times in Apr 2025. This records a decrease from the previous number of 16.600 Times for Mar 2025. Japan TSE: C: Average: PE Ratio: PM: Metal Products data is updated monthly, averaging 15.400 Times from Apr 2022 (Median) to Apr 2025, with 37 observations. The data reached an all-time high of 20.500 Times in Mar 2024 and a record low of 11.400 Times in Mar 2023. Japan TSE: C: Average: PE Ratio: PM: Metal Products data remains active status in CEIC and is reported by Japan Exchange Group Inc.. The data is categorized under Global Database’s Japan – Table JP.Z: Tokyo Stock Exchange: Price Earnings Ratio. [COVID-19-IMPACT]

The Nikkei Stock Average (Nikkei 225) closed at ********* points in August 2025. In the same month, the index reached a new all-time high. 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).

TSE: C: Average: PE Ratio: SM: Fishery, Agriculture & Forestry data was reported at 20.000 Times in Apr 2025. This records a decrease from the previous number of 20.100 Times for Mar 2025. TSE: C: Average: PE Ratio: SM: Fishery, Agriculture & Forestry data is updated monthly, averaging 14.800 Times from Apr 2022 (Median) to Apr 2025, with 37 observations. The data reached an all-time high of 20.100 Times in Mar 2025 and a record low of 10.900 Times in Jun 2022. TSE: C: Average: PE Ratio: SM: Fishery, Agriculture & Forestry data remains active status in CEIC and is reported by Japan Exchange Group Inc.. The data is categorized under Global Database’s Japan – Table JP.Z: Tokyo Stock Exchange: Price Earnings Ratio.

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

Graph and download economic data for Nikkei Stock Average, Nikkei 225 (NIKKEI225) from 1949-05-16 to 2025-09-19 about stocks, stock market, Japan, and indexes.

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 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