Japan's main stock market index, the JP225, fell to 48089 points on October 10, 2025, losing 1.01% from the previous session. Over the past month, the index has climbed 8.38% and is up 21.42% 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 October 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 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.

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

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.

Can stock prices be predicted? (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 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 index: Navigating Key Levels Amid Global Uncertainty

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: 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.

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.

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

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.

Japan Nikkei 225 Futures: Trading Val: Balance: Brokerage data was reported at 12,451,801.160 JPY mn in 19 Nov 2018. This records a decrease from the previous number of 17,203,155.111 JPY mn for 12 Nov 2018. Japan Nikkei 225 Futures: Trading Val: Balance: Brokerage data is updated weekly, averaging 13,732,582.643 JPY mn from Jan 2014 (Median) to 19 Nov 2018, with 255 observations. The data reached an all-time high of 52,696,182.185 JPY mn in 24 Aug 2015 and a record low of 3,404,826.964 JPY mn in 28 Dec 2015. Japan Nikkei 225 Futures: Trading Val: Balance: Brokerage 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 Unbleached Kraft Paper and Paperboard Weighing 225 g/m2 or More to Japan 2024 - 2028 Discover more data with ReportLinker!

The evaluation indexes of AGA-LSTM model and other DL models in Nikkei225 date set.

The Japan bancassurance market size reached USD 141 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 225 Billion by 2033, exhibiting a growth rate (CAGR) of 5.4% during 2025-2033

IMARC Group provides an analysis of the key trends in each segment of the Japan bancassurance market report, along with forecasts at the country and regional levels from 2025-2033. Our report has categorized the market based on product type and model type.

Explore the forecast for the Japanese market for hearing aids, with a projected volume of 1.5M units and a value of $236M by 2035.

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).

The evaluation indexes of AGA-LSTM model and other DL models in DJIA date set.

Nikkei 225 Futures: Trading Vol: Sales: Brokerage data was reported at 255,702.000 Unit in 16 Jul 2018. This records a decrease from the previous number of 374,275.000 Unit for 09 Jul 2018. Nikkei 225 Futures: Trading Vol: Sales: Brokerage data is updated weekly, averaging 361,713.000 Unit from Jan 2014 (Median) to 16 Jul 2018, with 237 observations. The data reached an all-time high of 1,423,706.000 Unit in 24 Aug 2015 and a record low of 88,885.000 Unit in 28 Dec 2015. Nikkei 225 Futures: Trading Vol: Sales: Brokerage 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.

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Statistical description of 50 optimal parameter combinations.