Japan's main stock market index, the JP225, rose to 40790 points on July 23, 2025, gaining 2.55% from the previous session. Over the past month, the index has climbed 5.15% and is up 4.18% 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 July of 2025.

Japan's main stock market index, the JP225, rose to 41288 points on July 23, 2025, gaining 3.80% from the previous session. Over the past month, the index has climbed 6.44% and is up 5.45% 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 July of 2025.

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.

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 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.

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

Japan Nikkei 225 Futures: Trading Val: Sales: Fin Inst: Other Fin Inst data was reported at 4,155.910 JPY mn in 16 Jul 2018. This records a decrease from the previous number of 9,118.240 JPY mn for 09 Jul 2018. Japan Nikkei 225 Futures: Trading Val: Sales: Fin Inst: Other Fin Inst data is updated weekly, averaging 2,116.460 JPY mn from Jan 2014 (Median) to 16 Jul 2018, with 237 observations. The data reached an all-time high of 20,705.430 JPY mn in 07 Nov 2016 and a record low of 0.000 JPY mn in 26 Sep 2016. Japan Nikkei 225 Futures: Trading Val: Sales: Fin Inst: Other Fin Inst data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z033: Nikkei 225 Futures: Trading by Type of Investor.

Nikkei 225 Futures: Trading Val: Purchases: Fin Inst: Other Fin Inst data was reported at 3,820.460 JPY mn in 16 Jul 2018. This records a decrease from the previous number of 8,494.700 JPY mn for 09 Jul 2018. Nikkei 225 Futures: Trading Val: Purchases: Fin Inst: Other Fin Inst data is updated weekly, averaging 2,243.230 JPY mn from Jan 2014 (Median) to 16 Jul 2018, with 237 observations. The data reached an all-time high of 18,446.550 JPY mn in 07 Nov 2016 and a record low of 0.000 JPY mn in 28 Dec 2015. Nikkei 225 Futures: Trading Val: Purchases: Fin Inst: Other Fin Inst data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z033: Nikkei 225 Futures: Trading by Type of Investor.

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 Seen Rising on Tech Sector Strength.

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

Nikkei 225 Futures: Trading Vol: Balance: Inst: Business Cos data was reported at 2,510.000 Unit in 16 Jul 2018. This records a decrease from the previous number of 4,220.000 Unit for 09 Jul 2018. Nikkei 225 Futures: Trading Vol: Balance: Inst: Business Cos data is updated weekly, averaging 4,008.000 Unit from Jan 2014 (Median) to 16 Jul 2018, with 237 observations. The data reached an all-time high of 14,448.000 Unit in 05 Feb 2018 and a record low of 887.000 Unit in 30 Apr 2018. Nikkei 225 Futures: Trading Vol: Balance: Inst: Business Cos data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z033: Nikkei 225 Futures: Trading by Type of Investor.

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

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.

Trading Signals (Nikkei 225 Index Stock Forecast)

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

Nikkei 225 Futures: Trading Val: Balance: Fin Inst: City&Reg Banks data was reported at 162,601.526 JPY mn in 19 Nov 2018. This records a decrease from the previous number of 220,186.513 JPY mn for 12 Nov 2018. Nikkei 225 Futures: Trading Val: Balance: Fin Inst: City&Reg Banks data is updated weekly, averaging 126,637.910 JPY mn from Jan 2014 (Median) to 19 Nov 2018, with 255 observations. The data reached an all-time high of 435,836.873 JPY mn in 07 Nov 2016 and a record low of 2,210.850 JPY mn in 29 Dec 2014. Nikkei 225 Futures: Trading Val: Balance: Fin Inst: City&Reg Banks data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z033: Nikkei 225 Futures: Trading by Type of Investor.

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.

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

Key information about Japan P/E ratio

• Japan Prime Market recorded a monthly P/E ratio of 19.900 on Mar 2025, compared with 20.700 from the previous month.
• Japan Prime Market P/E ratio is updated monthly, with historical data available from Apr 2022 to Feb 2025.
• The P/E ratio reached an all-time high of 25.800 in Apr 2022 and a record low of 18.400 in Jun 2022.
• Japan Exchange Group Inc. provides monthly P/E Ratio. Japan Exchange Group Inc. does not provide month end data; thus monthly average is used instead. 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).

Japan Nikkei 225 Futures: Trading Val: Purchases: Total data was reported at 6,334,704.689 JPY mn in 16 Jul 2018. This records a decrease from the previous number of 9,265,220.664 JPY mn for 09 Jul 2018. Japan Nikkei 225 Futures: Trading Val: Purchases: Total data is updated weekly, averaging 7,495,745.380 JPY mn from Jan 2014 (Median) to 16 Jul 2018, with 237 observations. The data reached an all-time high of 34,729,714.168 JPY mn in 05 Mar 2018 and a record low of 1,923,070.849 JPY mn in 28 Dec 2015. Japan Nikkei 225 Futures: Trading Val: Purchases: Total data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z033: Nikkei 225 Futures: Trading by Type of Investor.

Forecast: Import of Bleached Kraft Paper and Paperboard Weighing 150-225 g/m2 to Japan 2024 - 2028 Discover more data with ReportLinker!

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 Reach New Heights?

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

Nikkei 225 Futures: Trading Val: Sales: Brokerage: Individuals data was reported at 612,167.146 JPY mn in 16 Jul 2018. This records a decrease from the previous number of 890,297.988 JPY mn for 09 Jul 2018. Nikkei 225 Futures: Trading Val: Sales: Brokerage: Individuals data is updated weekly, averaging 753,420.310 JPY mn from Jan 2014 (Median) to 16 Jul 2018, with 237 observations. The data reached an all-time high of 3,185,253.380 JPY mn in 24 Aug 2015 and a record low of 196,478.861 JPY mn in 01 May 2017. Nikkei 225 Futures: Trading Val: Sales: Brokerage: Individuals data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z033: Nikkei 225 Futures: Trading by Type of Investor.