Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-12-02 to 2025-12-01 about stock market, average, industry, and USA.

Dow Jones - Historical chart and current data through 2025.

The value of the DJIA index amounted to ****** at the end of June 2025, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

There have been ups and downs in the history of the Dow Jones Industrial Average index (DJIA). Some years brought as much as ** percent of decrease in its value. Great Recession, however, took the largest toll on the Dow. In 1931, the index lost ***** percent of its value.

Index history

Dow Jones Industrial Average index (DJIA) is one of the most important stock market indices worldwide. It was created in 1896 by Charles Dow and Edward Jones. DJIA is the second oldest U.S. stock index after the Dow Jones Transportation Average, which was established in 1984.

Index components

DJIA reflects the performance of thirty large U.S. publicly traded companies. When the index was created, it was primarily composed of industrial companies, hence the index name. With time, the economic situation in the U.S. has changed and apart from industrial companies, which played a huge role in the market in the **** and the beginning of the **** century, also companies from other leading industries were incorporated into the index. At present, the DJIA index is composed of most renowned U.S. corporations, such as Coca Cola, Microsoft or Walt Disney.

The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

The statistic shows the best years of the Dow Jones Industrial Average index from 1897 to 2024. The best year in the history of the index was 1915, when the index value increased by ***** percent to close at ***** points compared to ***** in the previous year.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Graph and download economic data for Dow-Jones Industrial Stock Price Index for United States (M1109BUSM293NNBR) from Dec 1914 to Dec 1968 about stock market, industry, price index, indexes, price, and USA.

The Dow Jones Composite Index finished the year 2024 at 13,391.71 points, an increase compared to the previous year. Even with the economic effects of the global coronavirus (COVID-19) pandemic, 2021 had the highest point of the index in the past two decades. What is Dow Jones Composite Index? The Dow Jones Composite Index is one of the indices from the Dow Jones index family. It is composed of 65 leading U.S. companies: 30 stocks forming the Dow Jones Industrial Average index, 20 stocks from the Dow Jones Transportation index and 15 stocks from the Dow Jones Utility Average index. Importance of stock indices A stock market index shows an average performance of companies from a given section of the market. It is usually a weighted average, meaning that such factors as price of companies or their market capitalization are taken into consideration when calculating the index value. Stock indices are very useful for the financial market participants, as they instantly show the sentiments prevailing on a given market. They are also commonly used as a benchmark against portfolio performance, showing if a given portfolio has outperformed, or underperformed the market.

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.

Dow Jones Industrial Average 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

Throughout the 1920s, prices on the U.S. stock exchange rose exponentially, however, by the end of the decade, uncontrolled growth and a stock market propped up by speculation and borrowed money proved unsustainable, resulting in the Wall Street Crash of October 1929. This set a chain of events in motion that led to economic collapse - banks demanded repayment of debts, the property market crashed, and people stopped spending as unemployment rose. Within a year the country was in the midst of an economic depression, and the economy continued on a downward trend until late-1932.

It was during this time where Franklin D. Roosevelt (FDR) was elected president, and he assumed office in March 1933 - through a series of economic reforms and New Deal policies, the economy began to recover. Stock prices fluctuated at more sustainable levels over the next decades, and developments were in line with overall economic development, rather than the uncontrolled growth seen in the 1920s. Overall, it took over 25 years for the Dow Jones value to reach its pre-Crash peak.

Index Time Series for SPDR Dow Jones Industrial Average ETF Trust. The frequency of the observation is daily. Moving average series are also typically included. The Trust"s Portfolio consists of substantially all of the component common stocks that comprise the DJIA, which are weighted in accordance with the terms of the Trust Agreement.

The Dow Jones Industrial Average is (DJIA) is possibly the most well-known and commonly used stock index in the United States. It is a price-weighted index that assesses the stock prices of 30 prominent companies, whose combined prices are then divided by a regularly-updated divisor (0.15199 in February 2021), which gives the index value. The companies included are rotated in and out on a regular basis; as of mid-2022, the longest mainstay on the list is Procter & Gamble, which was added in 1932; whereas Amgen, Salesforce, and Honeywell were all added in 2020. As one of the oldest indices for stock market analysis, the impact of major events, recessions, and economic shocks or booms can be tracked and contextualized over longer periods of time.

Due to inflation, unadjusted figures appear to be more sporadic in recent years, however the greatest fluctuations came in the earliest years of the index. In the given period, the greatest decline came in the wake of the Wall Street Crash in 1929; by 1932 average values had fallen to just one fifth of their 1929 average, from roughly 314 to 65.

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.

The statistic shows the best days of the Dow Jones Industrial Average index from 1897 to 2024. The best day in the history of the index was October 6, 1931, when the index value increased by almost ** percent - although it should be noted that this occured one day after the Dow Jones experienced its fourth-worst day of all time, dropping over **** percent. The largest gain in points occurred on October 13, 2008.

** Overview** This dataset contains stock price data for 5 different stocks along with major market indices (Dow Jones, NASDAQ, and S&P 500). The data has been enhanced with various technical indicators and features commonly used in financial analysis and algorithmic trading.

Dataset Statistics

• Number of rows: 2569
• Number of columns: 221
• Date range: 2015-01-05 to 2025-03-21
• Number of stocks: 5
• Number of market indices: 3

Feature Naming Conventions

• Features ending with numbers (e.g., return_1, close_2) refer to specific stocks (1-5)
• Features with X_Y format (e.g., ma10_3, beta_2_nasdaq_20) have the following pattern:
  • First number/name refers to the parameter or stock
  • Second number/name refers to the stock or index
  • Third number (if present) refers to the time window
• Correlation features (e.g., corr_1_2) show correlation between two stocks (stock 1 and stock 2)

Feature Categories

Basic Price Data

• Date: Trading date in YYYY-MM-DD format
• return_X: Daily return (percentage price change) for stock X (where X is 1-5)
• open_X: Opening price for stock X
• high_X: Highest price during the trading day for stock X
• low_X: Lowest price during the trading day for stock X
• close_X: Closing price for stock X
• adjusted_X: Adjusted closing price for stock X (accounts for dividends and splits)
• volume_X: Trading volume (number of shares traded) for stock X

Market Index Data

• returns_dj: Daily return for Dow Jones Industrial Average
• close_dj: Closing price for Dow Jones Industrial Average
• returns_nasdaq: Daily return for NASDAQ Composite Index
• close_nasdaq: Closing price for NASDAQ Composite Index
• returns_SP500: Daily return for S&P 500 Index
• close_SP500: Closing price for S&P 500 Index

Moving Averages and Trend Indicators

• maX_Y: X-day simple moving average of closing price for stock Y
• emaX_Y: X-day exponential moving average of closing price for stock Y
• envelope_upper_X: Upper price envelope (5% above MA10) for stock X
• envelope_lower_X: Lower price envelope (5% below MA10) for stock X

Momentum and Volatility Indicators

• rocX_Y: X-day Rate of Change (percentage) for stock Y
• volatility_X: 20-day rolling standard deviation of returns for stock X
• rsi_X: 14-day Relative Strength Index for stock X (momentum indicator, 0-100)
• macd_X: Moving Average Convergence Divergence for stock X (ema12 - ema26)
• macd_signal_X: 9-day EMA of MACD for stock X
• macd_hist_X: MACD histogram for stock X (macd - macd_signal)

Volume Indicators

• volume_ma10_X: 10-day moving average of trading volume for stock X
• volume_ratio_X: Ratio of current volume to 10-day volume MA for stock X

Price Ratio Indicators

• high_low_ratio_X: Ratio of high price to low price for stock X (daily range)
• close_open_ratio_X: Ratio of close price to open price for stock X (intraday movement)

Correlation and Beta Indicators

• beta_X_Y_Z: Z-day rolling beta of stock X to index Y (measure of volatility relative to market)
• corr_X_Y: 20-day rolling correlation between returns of stock X and stock Y (ranges from -1 to 1)

Example Usage

import pandas as pd
import matplotlib.pyplot as plt

# Load the dataset
df = pd.read_csv('enhanced_stock_dataset.csv')
df['Date'] = pd.to_datetime(df['Date'])

# Plot closing prices for all stocks
plt.figure(figsize=(12, 6))
for i in range(1, 6):
  plt.plot(df['Date'], df[f'close_{i}'], label=f'Stock {i}')
plt.title('Stock Closing Prices')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.grid(True)
plt.show()

Notes

• This dataset contains engineered features that can be directly used for machine learning models
• All NaN values have been filled using forward and backward filling methods
• The correlation features (corr_X_Y) show the relationship between different stocks
• Beta values show the relationship between each stock and the market indices

Graph and download economic data for Dow Jones Transportation Average (DJTA) from 2015-12-03 to 2025-12-02 about stock market, transportation, average, and USA.

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? (Dow Jones Industrial Average 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

China's main stock market index, the SHANGHAI, fell to 3898 points on December 2, 2025, losing 0.42% from the previous session. Over the past month, the index has declined 1.98%, though it remains 15.36% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.