Japan's main stock market index, the JP225, rose to 42268 points on September 2, 2025, gaining 0.19% from the previous session. Over the past month, the index has climbed 4.91% and is up 9.26% 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.

Graph and download economic data for Nikkei Stock Average, Nikkei 225 (NIKKEI225) from 1949-05-16 to 2025-09-02 about stocks, stock market, Japan, and indexes.

Japan's main stock market index, the JP225, fell to 42323 points on September 1, 2025, losing 0.93% from the previous session. Over the past month, the index has climbed 5.04% and is up 9.36% 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.

Dataset

This dataset was created by AritraChatterjee007

Released under CC0: Public Domain

Contents

Index Time Series for Xtrackers Nikkei 225 UCITS ETF 1C EUR. The frequency of the observation is daily. Moving average series are also typically included. NA

Nikkei 225 Index - Historical chart and current data through 2025.

Index Time Series for Rakuten-Nikkei 225 Leveraged. The frequency of the observation is daily. Moving average series are also typically included. NA

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

Japan Nikkei 225 Futures: Trading Val: Balance: Inst: Financial Inst data was reported at 268,850.955 JPY mn in 16 Jul 2018. This records a decrease from the previous number of 419,428.452 JPY mn for 09 Jul 2018. Japan Nikkei 225 Futures: Trading Val: Balance: Inst: Financial Inst data is updated weekly, averaging 348,976.978 JPY mn from Jan 2014 (Median) to 16 Jul 2018, with 237 observations. The data reached an all-time high of 3,928,552.800 JPY mn in 04 Jun 2018 and a record low of 71,503.450 JPY mn in 29 Dec 2014. Japan Nikkei 225 Futures: Trading Val: Balance: Inst: Financial 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.

Download Historical Nikkei 225 Index Indicies Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 4 companies listed on the Nagoya Stock Exchange (XNGO) in Japan. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Japan:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Japan:

Nikkei 225: The main index that tracks the performance of major companies listed on the Tokyo Stock Exchange. This index provides an overview of the overall market performance in Japan.

TOPIX: The index that tracks the performance of all domestic companies listed on the Tokyo Stock Exchange. This index reflects the performance of a broader range of companies in the Japanese market.

Company A: A prominent Japanese company with diversified operations across various sectors, such as automotive, electronics, or manufacturing. This company's stock is widely traded on the Tokyo Stock Exchange.

Company B: A leading financial institution in Japan, offering banking, insurance, or investment services. This company's stock is actively traded on the Tokyo Stock Exchange.

Company C: A major player in the Japanese consumer goods sector or other industries, involved in the production and distribution of consumer products. This company's stock is listed and actively traded on the Tokyo Stock Exchange.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Japan, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Japan ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Japan?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Japan exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, facilitating a convenient and secure payment process.

1. How do I receive...

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

Index Time Series for Nikkei225 Bull 2x ETF. The frequency of the observation is daily. Moving average series are also typically included. NA

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. 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. 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.

Download Historical Nikkei 225 - OSE(Day) Futures Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

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

Using all stocks listed in the Tokyo Stock Exchange and macroeconomic data for Japan, the dataset comprises the following series:

1. Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Monthly returns for 20 momentum portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Monthly returns for 25 price-to-cash flow-dividend yield portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
4. Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
5. Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology for all factors, except for RMW, which is determined using the return on assets as sorting variable, as in Hou, Xue and Zhang (2014). (Raw data source: Datastream database)
6. Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Japan. (Raw data source: OECD)
7. Consumer Confidence Index (CCI) for Japan. (Raw data source: OECD)
8. Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD)
9. Gross Domestic Product (GDP) for Japan. (Raw data source: OECD)
10. Consumer Price Index (CPI) growth rate for Japan. (Raw data source: OECD)

We have produced all return series using the following data from Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) price-to-cash flow ratio (PC series), and (vii) dividend yield (DY series). We have used the generic rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. We also exclude stocks with less than twelve observations in the period from July 1992 to June 2018. Accordingly, our sample comprises a total number of 5,312 stocks.

REFERENCES:

Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277. Hou K, Xue C, Zhang L. (2014). Digesting anomalies: An investment approach. Review of Financial Studies, 28, 650-705.

Download Historical Nikkei 225 - SGX Futures Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

Japan Nikkei 225 Futures: Trading Vol: Balance: Inst: Other Inst data was reported at 1,909.000 Unit in 26 Nov 2018. This records an increase from the previous number of 1,799.000 Unit for 19 Nov 2018. Japan Nikkei 225 Futures: Trading Vol: Balance: Inst: Other Inst data is updated weekly, averaging 1,634.500 Unit from Jan 2014 (Median) to 26 Nov 2018, with 256 observations. The data reached an all-time high of 7,069.000 Unit in 04 Jun 2018 and a record low of 369.000 Unit in 22 Aug 2016. Japan Nikkei 225 Futures: Trading Vol: Balance: Inst: Other 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.

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