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 December 2 of 2025.

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

Japan's main stock market index, the JP225, rose to 49553 points on December 2, 2025, gaining 0.51% from the previous session. Over the past month, the index has declined 3.78%, though it remains 26.25% higher 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 December of 2025.

Graph and download economic data for Financial Market: Share Prices for Japan (SPASTT01JPQ661N) from Q1 1959 to Q3 2025 about stock market and Japan.

Nikkei 225 Weekly Margin Trading Data

(Japanese version follows / 日本語は下段)

📅 Updated weekly (based on Friday close, released on the 2nd business day of the following week).

Overview

• Weekly dataset of margin trading balances (general, exchange-regulated, total) and stock price data (OHLC, volume, turnover) for Nikkei 225 constituents.
• Values represent positions as of Friday market close (JST).
• Stock prices are aligned to the same weekly frequency (weekly candlestick).
• Scope: Nikkei 225 constituents only.
• Data collected from Japan Exchange Group (JPX).

Purpose

• For researchers, analysts, and data scientists exploring the relationship between stock prices and margin trading.
• Provides standardized weekly frequency suitable for backtesting and time-series analysis.

Frequency & Dating

• Frequency: Weekly
• date = week-ending Friday in YYYY-MM-DD (JST)
• Timezone: JST

Stock Prices (OHLC) Definition

• Open = Monday opening price of the week
• High / Low = Highest / lowest prices during the week
• Close = Friday closing price
• Prices are in JPY

Columns

• date — Week-ending Friday (JST), format YYYY-MM-DD
• code — JPX security code (string recommended)
• name_jp — Company name in Japanese
• name_en — Company name in English (official market usage)
• industry — JPX TOPIX 33 sector (English label)
• total_short_balance — Total margin short balance (shares)
• total_long_balance — Total margin long balance (shares)
• total_margin_ratio — Total margin ratio (unitless)
• general_short_balance — General margin short balance (shares)
• general_long_balance — General margin long balance (shares)
• general_margin_ratio — General margin ratio (unitless)
• institutional_short_balance — Exchange-regulated short balance (shares)
• institutional_long_balance — Exchange-regulated long balance (shares)
• institutional_margin_ratio — Exchange-regulated margin ratio (unitless)
• open_price — Candlestick Open (JPY)
• high_price — Candlestick High (JPY)
• low_price — Candlestick Low (JPY)
• close_price — Candlestick Close (JPY)
• trading_volume — Trading volume (shares)
• trading_value — Trading turnover (JPY)

Margin Trading in Japan: Two Systems

• Exchange-Regulated Margin (制度信用)
  • Rules set by Japanese stock exchanges (e.g., Tokyo Stock Exchange).
  • Standard settlement term: 6 months.
  • Trading conditions (collateral ratio, margin calls) are standardized.
• General Margin (一般信用)
  • Rules set by individual brokers.
  • Terms vary: may be short-term (few days) or no fixed term.
  • Trading costs (interest, fees) and conditions differ by broker.
• This dataset separates both systems, enabling detailed analysis of investor behavior.

Notes

• Margin ratios are dimensionless (often described as “x倍” in Japanese).
• In the unlikely case where the denominator (short balance) would be 0, the ratio is computed using 1 instead of 0 to avoid division errors. However, for Nikkei 225 constituents, such cases are practically nonexistent.
• This dataset covers Nikkei 225 constituents only.
• All figures are unadjusted for corporate actions (e.g., stock splits, reverse splits).
  This applies to all data fields, including stock prices, margin balances, trading volume, and turnover.

日本語説明

日経225 信用倍率・信用残高データ（週次）

📅 毎週更新！（毎週金曜引け時点のデータを翌週第2営業日に反映）

概要

• 本データセットは、**日経225 採用銘柄**について、**週次**の信用取引残高（**一般信用／制度信用／合計**）および株価データ（**OHLC**、出来高、売買代金）を収録しています。
• 信用残データは 金曜引け後（JST）時点のものです。
• 株価データは週足に整形し、信用残と同じ**週次**頻度に揃えています。
• 対象は 日経225採用銘柄のみ。
• 日本取引所グループ（JPX）で公表されているデータを元に作成しています。

目的

• 株価と信用残の関係性を研究・分析する研究者、アナリスト、データサイエンティスト向け。
• バックテストや時系列解析に適した標準化された週次頻度データを提供します。

頻度と日付

• すべて**週次**です。
• date は 週末の金曜日（JST） を YYYY-MM-DD 形式で表記。
• タイムゾーンは JST。

株価（OHLC）の定義

• 始値（Open）：週初（月曜日）の寄り付き
• 高値／安値（High / Low）：週内の最高値／最安値
• 終値（Close）：週末（金曜日）の終値
• 価格の単位は 円（JPY）。

カラム

• date — 週末の金曜日（JST、YYYY-MM-DD）
• code — 証券コード（文字列推奨）
• name_jp — 日本語社名
• name_en — 英語社名（市場での正式表記）
• industry — JPX 33業種（英語表記）
• total_short_balance — 合計 売り残（株）
• total_long_balance — 合計 買い残（株）
• total_margin_ratio — 合計 信用倍率（無次元）
• general_short_balance — 一般信用 売り残（株）
• general_long_balance — 一般信用 買い残（株）
• general_margin_ratio — 一般信用の**信用倍率**（無次元）
• institutional_short_balance — 制度信用 売り残（株）
• institutional_long_balance — 制度信用 買い残（株）
• institutional_margin_ratio — 制度信用の**信用倍率**（無次元）
• open_price — 週足の**始値**（円）
• high_price — 週足の**高値**（円）
• low_price — 週足の**安値**（円）
• close_price — 週足の**終値**（円）
• `tradi...

Graph and download economic data for Nikkei Stock Average, Nikkei 225 (NIKKEI225) from 1949-05-16 to 2025-12-02 about stocks, stock market, Japan, and indexes.

Dataset

This dataset was created by ka.na.o

Contents

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Post-Christmas, Asian markets rise as retail and tourism stocks thrive, with gains in Japan's Nikkei 225 and increased oil prices boosting the momentum.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Content

Daily price data for indexes tracking stock exchanges from all over the world (United States, China, Canada, Germany, Japan, and more). The data was all collected from Yahoo Finance, which had several decades of data available for most exchanges.

Prices are quoted in terms of the national currency of where each exchange is located.

Acknowledgements

Data collected from Yahoo Finance Photo by Jason Leung on Unsplash

In 2024, the Tokyo Stock Price Index (TOPIX) hit a daily closing low of ******** points on August 5, when Japan's stock market experienced a historic crash. TOPIX is a free-float adjusted market capitalization-weighted index that has been published by the Tokyo Stock Exchange (TSE) since 1969. The market capitalization as of the base date (January 4, 1968) is set at 100 points.

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

Graph and download economic data for Share Prices: All Shares/Broad: Total for Japan (SPASTT01JPQ657N) from Q2 1959 to Q3 2025 about stock market and Japan.

Using all stocks listed on the Japanese equity market and macroeconomic data for Japan, the dataset comprises the following series:

1. Japan_25_Portfolios_MV_PTBV: Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Japan_25_Portfolios_MV_PE: Monthly returns for 25 size-PE portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Japan_50_Portfolios_SECTOR: Monthly returns for 50 industry portfolios. (Raw data source: Datastream database)
4. Japan_3 Factors: Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
5. Japan_5 Factors: Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology. (Raw data source: Datastream database)
6. Japan_NUCLEAR_Y: Instrument in years with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
7. Japan_NUCLEAR_M: Instrument in months with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
8. Japan_RF_M: Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD)
9. Company data: Names and general data of the companies that constitute the sample. (Raw data source: Datastream database)
10. Number of stocks in portfolios: Number of stocks included each year in Japan_25_Portfolios_MV_PTBV, Japan_25_Portfolios_MV_PE and Japan_50_Portfolios_SECTOR. (Raw data source: Datastream database)

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-earnings ratio (PE series), and (vii) industry (SECTOR 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. Accordingly, our sample comprises a total number of 5,212 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.

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

Japan PIA: Equity & Inv Fund Shares (EIF): Net: U.S. Dollar data was reported at 343.498 JPY bn in May 2018. This records a decrease from the previous number of 729.211 JPY bn for Apr 2018. Japan PIA: Equity & Inv Fund Shares (EIF): Net: U.S. Dollar data is updated monthly, averaging 215.306 JPY bn from Jan 2014 (Median) to May 2018, with 53 observations. The data reached an all-time high of 987.799 JPY bn in Feb 2016 and a record low of -593.927 JPY bn in Sep 2017. Japan PIA: Equity & Inv Fund Shares (EIF): Net: U.S. Dollar data remains active status in CEIC and is reported by Bank of Japan. The data is categorized under Global Database’s Japan – Table JP.O018: Portfolio Investment Assets (PIA) by Quotation Currency: BPM6.

Graph and download economic data for Share Prices: All Shares/Broad: Total for Japan (SPASTT01JPM657N) from Feb 1959 to Oct 2025 about stock market and Japan.

Snapshot img

Securities Exchanges Market Size 2025-2029

The securities exchanges market size is forecast to increase by USD 56.67 billion at a CAGR of 12.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing demand for investment opportunities. This trend is fueled by a global economic recovery and a rising interest in various asset classes, particularly in emerging markets. Another key driver is the increasing focus on sustainable and environmental, social, and governance (ESG) investing. This shift reflects a growing awareness of the importance of long-term value creation and the role of exchanges in facilitating socially responsible investments. This trend is driven by the expanding securities business units, including stocks, bonds, mutual funds, and other securities, which cater to the needs of investment firms and individual investors. However, the market is not without challenges. Increasing market volatility poses a significant risk for exchanges and their clients.
Furthermore, the rapid digitization of trading and the emergence of alternative trading platforms are disrupting traditional exchange business models. To navigate these challenges, exchanges must adapt by investing in technology, expanding their product offerings, and building strong regulatory frameworks. Data analytics and big data are also crucial tools for e-brokerage firms to gain insights and make informed decisions. By doing so, they can capitalize on the market's growth potential and maintain their competitive edge. Geopolitical tensions, economic instability, and regulatory changes can all contribute to market fluctuations and uncertainty.

What will be the Size of the Securities Exchanges Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic market, financial instrument classification plays a crucial role in facilitating efficient trade matching through advanced execution quality metrics and order book liquidity. Quantitative trading models leverage options clearing corporation data to optimize portfolio holdings, while trade matching engines utilize high-speed data storage solutions and portfolio optimization algorithms to minimize latency and enhance market depth indicators. Data center infrastructure and network bandwidth capacity are essential components for supporting complex algorithmic trading strategies, including latency reduction and price volatility forecasting. Market impact measurement and risk assessment methodologies are integral to managing market impact and mitigating fraud, ensuring regulatory compliance through transaction reporting standards and regulatory compliance software.

Exchange traded funds (ETFs) have gained popularity, necessitating robust quote dissemination systems and trade surveillance analytics. Server virtualization and cybersecurity threat mitigation strategies further strengthen the market's resilience, enabling seamless integration of data-driven quantitative models and sophisticated fraud detection algorithms. Additionally, users of online trading platforms can easily monitor the performance of their assets thanks to real-time stock data.

How is this Securities Exchanges Industry segmented?

The securities exchanges industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Service

  Market platforms
  Capital access platforms
  Others


Trade Finance Instruments

  Equities
  Derivatives
  Bonds
  Exchange-traded funds
  Others


Type

  Large-cap exchanges
  Mid-cap exchanges
  Small-cap exchanges


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Switzerland
    UK


  APAC

    China
    Hong Kong
    India
    Japan


  Rest of World (ROW)

By Service Insights

The Market platforms segment is estimated to witness significant growth during the forecast period. The market is characterized by advanced technologies and systems that enable efficient price discovery, manage settlement risk, and ensure regulatory compliance. Market platforms, which include trading platforms, order-matching systems, and market data dissemination, hold the largest share of the market. These platforms facilitate the buying and selling of securities, providing market liquidity and transparency. Real-time market surveillance and high-frequency trading infrastructure are crucial components, ensuring fair and orderly markets and enabling efficient trade execution. Financial modeling techniques and algorithmic trading platforms optimize trading strategies, while electronic communication networks and central counterparty clearing minimize r

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