Graph and download economic data for Dow Jones Transportation Average (DJTA) from 2015-07-15 to 2025-07-14 about stock market, transportation, average, and USA.

The year-end closing values of the Dow Jones Transportation Index increased more than twofold since 2012. The value of Dow Jones Transportation Index amounted to 15,895.75 points in 2024. 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.

The observations for the Dow Jones Transportation Average represent the daily index value at market close. The market typically closes at 4 PM ET, except for holidays when it sometimes closes early.

Originally made up of only railroad stocks, the Dow Jones Transportation Average now provides a view of the 20 transportation companies in US stock market. For more information, please visit Dow Jones Transportation Average (http://us.spindices.com/indices/equity/dow-jones-transportation-average).

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Taiwan TWSE: Equity Market Index: Shipping and Transportation data was reported at 64.480 31Dec1994=100 in Nov 2018. This records an increase from the previous number of 61.170 31Dec1994=100 for Oct 2018. Taiwan TWSE: Equity Market Index: Shipping and Transportation data is updated monthly, averaging 77.430 31Dec1994=100 from Jan 1995 (Median) to Nov 2018, with 287 observations. The data reached an all-time high of 133.910 31Dec1994=100 in Apr 2008 and a record low of 29.280 31Dec1994=100 in Oct 2001. Taiwan TWSE: Equity Market Index: Shipping and Transportation data remains active status in CEIC and is reported by Taiwan Stock Exchange Corporation. The data is categorized under Global Database’s Taiwan – Table TW.Z001: Taiwan Stock Exchange (TWSE): Indices.

China Index: Shenzhen Stock Exchange: Transportation data was reported at 1,141.150 03Apr1991=100 in 14 May 2025. This records an increase from the previous number of 1,121.110 03Apr1991=100 for 13 May 2025. China Index: Shenzhen Stock Exchange: Transportation data is updated daily, averaging 1,107.063 03Apr1991=100 from Jul 2001 (Median) to 14 May 2025, with 5786 observations. The data reached an all-time high of 2,901.943 03Apr1991=100 in 15 Jun 2015 and a record low of 634.256 03Apr1991=100 in 29 Nov 2012. China Index: Shenzhen Stock Exchange: Transportation data remains active status in CEIC and is reported by Shenzhen Stock Exchange. The data is categorized under High Frequency Database’s Financial and Futures Market – Table CN.ZA: Shenzhen Stock Exchange: Indices: Daily.

Graph and download economic data for Producer Price Index by Industry: Railroad Rolling Stock Manufacturing: Street, Subway, Trolley, and Rapid Transit Cars (New and Rebuilt) and Rebuilt Passenger/Freight Cars (PCU33651033651053) from Dec 2001 to May 2016 about railroad, stocks, transportation, vehicles, new, manufacturing, PPI, industry, inflation, price index, indexes, price, and USA.

United States - Producer Price Index by Commodity: Transportation Services: Mailing Services (Market Dominant Services) was 162.69300 Index Dec 2012=100 in April of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Transportation Services: Mailing Services (Market Dominant Services) reached a record high of 162.73300 in October of 2024 and a record low of 100.00000 in January of 2013. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Transportation Services: Mailing Services (Market Dominant Services) - last updated from the United States Federal Reserve on June of 2025.

Japan Index: TSE: 1st Section: MA: Warehousing & Harbour Transport Service data was reported at 1,635.353 04Jan1968=100 in Nov 2018. This records a decrease from the previous number of 1,670.778 04Jan1968=100 for Oct 2018. Japan Index: TSE: 1st Section: MA: Warehousing & Harbour Transport Service data is updated monthly, averaging 1,498.276 04Jan1968=100 from Dec 1987 (Median) to Nov 2018, with 372 observations. The data reached an all-time high of 3,872.940 04Jan1968=100 in Dec 1989 and a record low of 761.419 04Jan1968=100 in Dec 2002. Japan Index: TSE: 1st Section: MA: Warehousing & Harbour Transport Service data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z002: All Stock Exchange: Market Indices.

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.

PTSI: Could This Transportation Stock Deliver Big Returns?

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

Qatar Stock Exchange: Index: QE All Share Transportation Index data was reported at 5,590.720 NA in Apr 2025. This records a decrease from the previous number of 5,635.670 NA for Mar 2025. Qatar Stock Exchange: Index: QE All Share Transportation Index data is updated monthly, averaging 2,493.090 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 5,635.670 NA in Mar 2025 and a record low of 1,316.950 NA in Jul 2012. Qatar Stock Exchange: Index: QE All Share Transportation Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Qatar – Table QA.EDI.SE: Qatar Stock Exchange: Monthly.

Baltic Dry rose to 1,783 Index Points on July 14, 2025, up 7.22% from the previous day. Over the past month, Baltic Dry's price has fallen 9.72%, and is down 10.54% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on July of 2025.

Graph and download economic data for Producer Price Index by Commodity: Transportation Services: Mailing Services (Market Dominant Services) (WPU30160107) from Dec 2012 to May 2025 about postal, transportation, services, commodities, PPI, inflation, price index, indexes, price, and USA.

The Freight Transport by Sea industry in China is mainly engaged in shipping exported items to their destination countries and territories from China and shipping imported items from other countries. Therefore, the industry's performance is extremely sensitive to the global economic climate as well as situation of China economy, as these determine the international trade between China and other countries.China has been the largest country in goods trade since 2017. In 2023, total import and export value of goods in China reached $5.9 trillion, down by 5.0% from 2022, ranking the first in the world for seven consecutive years despite the decrease. Largest international trade scale has stimulated the demand for ocean shipping services. Industry revenue is expected to increase at an annualized 5.8% over the five years through 2024. This includes anticipated increase of 8.1% in the current year, to $59.7 billion, mainly due to the low freight rates.The freight transportation volume by sea in China in 2020 and 2022 has increased by 0.6% and 2.2% year on year, mainly due to the repeated COVID-19 pandemic in China. However, the efficiency of global logistics turnover has decreased due to the prevention and control measures of COVID-19 pandemic. Freight rates have stayed high since the second half year of 2020 resulted by the serious congestions in some ports and short supply of container positions. Industry revenue realized year-on-year growth of 19.8% in 2020, 88.3% in 2021 and 13.4% in 2022. Freight rates have dropped dramatically in 2023, leading to a decrease in industry revenue, by 52.1% from 2022.Industry revenue is forecast to rise at an annualized 8.1% over the five years through 2029, to $88.1 billion. The recovery of China's economy and the largest international trade scale between China and other countries will continue to support the industry growth. The implementation of RCEP will also stimulate international trade between China and member countries, and further promote the demand for ocean shipping services.

United States - Equity Market Volatility Tracker: Transportation, Infrastructure, and Public Utilities was 1.35681 Index in May of 2025, according to the United States Federal Reserve. Historically, United States - Equity Market Volatility Tracker: Transportation, Infrastructure, and Public Utilities reached a record high of 2.13067 in February of 2025 and a record low of 0.00000 in March of 1985. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Equity Market Volatility Tracker: Transportation, Infrastructure, and Public Utilities - last updated from the United States Federal Reserve on July of 2025.

United States - Producer Price Index by Industry: Railroad Rolling Stock Manufacturing: Street, Subway, Trolley, and Rapid Transit Cars, and Railway Maintenance Equipment and Parts was 201.75500 Index Jun 1984=100 in May of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Railroad Rolling Stock Manufacturing: Street, Subway, Trolley, and Rapid Transit Cars, and Railway Maintenance Equipment and Parts reached a record high of 202.02300 in March of 2025 and a record low of 99.80000 in October of 1984. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Industry: Railroad Rolling Stock Manufacturing: Street, Subway, Trolley, and Rapid Transit Cars, and Railway Maintenance Equipment and Parts - last updated from the United States Federal Reserve on July of 2025.

Morocco Index: Casablanca Stock Exchange: Transport data was reported at 3,157.970 03Mar1993=1000 in Jun 2018. This records a decrease from the previous number of 3,279.200 03Mar1993=1000 for May 2018. Morocco Index: Casablanca Stock Exchange: Transport data is updated monthly, averaging 1,266.960 03Mar1993=1000 from Jun 2003 (Median) to Jun 2018, with 181 observations. The data reached an all-time high of 3,354.870 03Mar1993=1000 in Aug 2017 and a record low of 842.330 03Mar1993=1000 in Jul 2006. Morocco Index: Casablanca Stock Exchange: Transport data remains active status in CEIC and is reported by Casablanca Stock Exchange. The data is categorized under Global Database’s Morocco – Table MA.Z001: Casablanca Stock Exchange: Index.

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.

JBHT J.B. Hunt Transport Services Inc. Common Stock

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.

Ryvyl Revolutionizing Transportation: Will (RVYL) Stock Lead the Charge?

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

The Inland Water Freight Transport Market, valued at $14.5 billion in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) exceeding 3.73% from 2025 to 2033. This expansion is fueled by several key drivers. Increasing global trade necessitates efficient and cost-effective transportation solutions, and inland waterways offer a compelling alternative to congested roads and railways, particularly for bulk commodities. Furthermore, growing environmental concerns are pushing businesses towards more sustainable transportation methods, with inland waterways presenting a lower carbon footprint compared to road or air freight. The market is segmented by transportation type (liquid bulk and dry bulk) and vessel type (cargo ships, container ships, tankers, and others). The dominance of specific vessel types will likely shift based on evolving cargo demands and technological advancements in ship design and efficiency. Geographic variations in market share reflect the presence of extensive navigable waterways and industrial hubs in specific regions. North America, with its extensive river systems and significant industrial activity, is expected to hold a substantial market share, followed by Asia-Pacific, driven by its burgeoning economies and increasing trade volumes. However, regulatory hurdles, infrastructure limitations in certain regions, and fluctuations in fuel prices present ongoing challenges to market growth. Companies like American Commercial Barge Line, Ingram Barge, and CMA CGM are key players, competing on service offerings, efficiency, and geographical reach. The forecast period anticipates continuous market expansion, driven by sustained global trade, infrastructure development, and a push towards greener logistics. The future of the inland water freight transport market hinges on strategic investments in infrastructure modernization, including dredging projects and waterway improvements. Technological innovation, such as the adoption of autonomous vessels and enhanced navigation systems, will also play a critical role in improving efficiency and safety. Furthermore, government policies promoting sustainable transportation and easing regulatory burdens will be crucial in unlocking the market's full potential. The competitive landscape will see existing players consolidating their positions and new entrants capitalizing on emerging opportunities. Successfully navigating these dynamics will require a strategic blend of operational excellence, technological adaptation, and a keen understanding of evolving regulatory environments. Recent developments include: February 2024: Shri Sarbananda Sonowal launched significant projects for the development of waterways, unveiled projects worth INR 308 crore (USD 37.17 million) for Northeast India, and inaugurated Passenger-cum-Cargo Terminal at Bogibeel near Dibrugarh, Inland Water Transport Terminal at Sonamura in Tripura and upgraded terminals at Karimganj and Badarpur in Assam.November 2023: Amazon became the first e-commerce company to work with the Inland Waterways Authority of India to facilitate cargo shipments on Indian waters. A pilot route will be launched between Patna and Kolkata. The aim is to establish a network for cargo shipment on inland waterways. A pilot will soon be initiated on the water route between Patna and Kolkata with the support of IWAI and its carriers.September 2023: Maersk launched the world's first container ship on bio-methanol, a renewable and sustainable fuel source. This initiative represents a significant step toward reducing the shipping industry's carbon emissions and environmental impact.. Key drivers for this market are: 4., The Need for Reasonable Cargo Transportation4.; Increasing Industrial Production Index. Potential restraints include: 4., The Need for Reasonable Cargo Transportation4.; Increasing Industrial Production Index. Notable trends are: The Demand for Transporting Goods in Large Quantities is Increasing.