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.

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.

Download Nikkei 225 Yen Futures (CME) (NH1 Index) historical futures data — 1m, 5m, 10m, 30m, 1h, Daily — from 2008-Jan-23 to 2025-Jul-16

The yield on Japan 30 Year Bond Yield rose to 3.40% on December 2, 2025, marking a 0.01 percentage points increase from the previous session. Over the past month, the yield has edged up by 0.36 points and is 1.11 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Japan 30 Year Bond Yield - values, historical data, forecasts and news - updated on December of 2025.

These data accompany the publication "Trends, Reversion, and Critical Phenomena in Financial Markets".

They contain daily data from Jan 1992 to Dec 2019 on 24 financial markets, namely

• 6 equity indices: S&P 500, TSE 60, DAX 30, FTSE 100, Nikkei 225, Hang Seng
• 6 Interest rates for government bonds: US 10-year, Canada 10-year, Germany 10-year, UK 10-year, Japan 10-year, Australia 3-year
• 6 FX rates: CAD/USD, EUR/USD, GBP/USD, JPY/USD, AUD/USD, NZD/USD
• 6 Commodities: Crude Oil, Natural Gas, Gold, Copper, Soybeans, Live Cattle

The data are provided in 13 columns:

• Column 1: date
• Column 2: market
• Column 3: daily log return of futures on that market, normalized to have mean 0 and standard deviation 1 over the 28-year time period
• Columns 4-13: trend strengths in that market over 10 different time horizons of (2,4,8,16,32,64,128,256,512,1024) business days.

The trend strengths are defined in the accompanying paper. They are cut off at plus/minus 2.5. The daily log returns were computed from daily futures prices, rolled 5 days prior to first notice, which were taken from Bloomberg. The following mean returns and volatilites were used to normalize the daily log returns in column 3:

Market Mean St. Dev.

S&P 500 2.217% 1.100% TSE 60 2.416% 1.067% DAX 30 1.199% 1.366% FTSE 100 1.053% 1.103% Nikkei 225 -0.483% 1.486% Hang Seng 0.768% 1.674% US 10-year 3.734% 0.366% Can. 10-year 3.637% 0.376% Ger. 10-year 4.141% 0.337% UK 10-year 2.983% 0.419% Jap. 10-year 4.453% 0.249% Aus. 3-year 3.029% 0.074% CAD/USD 0.048% 0.479% EUR/USD -0.222% 0.619% GBP/USD 0.316% 0.597% JPY/USD -0.761% 0.667% AUD/USD 0.851% 0.725% NZD/USD 1.563% 0.724% Crude Oil 0.093% 2.243% Natural Gas -2.649% 2.985% Gold 0.580% 0.987% Copper 0.936% 1.586% Soybeans 0.631% 1.360% Live Cattle 0.483% 0.894%

Context

Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.

There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.

Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.

A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.

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New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.

Content

Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.

Dataset

The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)

Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.

Starter Kernel(s)

• Simple LSTM Regression

Acknowledgements

Mining and updating of this dateset will depend upon Yahoo Finance .

Inspiration

Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting

Some Readings

• Applications of deep learning in stock market prediction: recent progress
• Stock predictions with state-of-the-art Transformer and Time Embeddings
• https://arxiv.org/pdf/1712.02136.pdf
• https://catanacapital.com/blog/unpredictable-black-swan-event-stock-market/
• https://par.nsf.gov/servlets/purl/10149715
• https://arxiv.org/pdf/1712.02136.pdf

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