United States Index: Value Line: Arithmetic data was reported at 6,053.860 21May1985=100 in Nov 2018. This records an increase from the previous number of 5,958.610 21May1985=100 for Oct 2018. United States Index: Value Line: Arithmetic data is updated monthly, averaging 1,326.970 21May1985=100 from Jan 1989 (Median) to Nov 2018, with 359 observations. The data reached an all-time high of 6,604.520 21May1985=100 in Aug 2018 and a record low of 216.890 21May1985=100 in Oct 1990. United States Index: Value Line: Arithmetic data remains active status in CEIC and is reported by Value Line. The data is categorized under Global Database’s United States – Table US.Z019: Valueline: Index.

United States Index: Value Line: Geometric data was reported at 528.470 NA in Oct 2018. This records a decrease from the previous number of 582.860 NA for Sep 2018. United States Index: Value Line: Geometric data is updated monthly, averaging 403.050 NA from Jan 2002 (Median) to Oct 2018, with 202 observations. The data reached an all-time high of 591.180 NA in Aug 2018 and a record low of 175.790 NA in Feb 2009. United States Index: Value Line: Geometric data remains active status in CEIC and is reported by Value Line. The data is categorized under Global Database’s United States – Table US.Z019: Valueline: Index.

指数：价值线：几何学的在10-01-2018达528.470NA，相较于09-01-2018的582.860NA有所下降。指数：价值线：几何学的数据按月更新，01-01-2002至10-01-2018期间平均值为403.050NA，共202份观测结果。该数据的历史最高值出现于08-01-2018，达591.180NA，而历史最低值则出现于02-01-2009，为175.790NA。CEIC提供的指数：价值线：几何学的数据处于定期更新的状态，数据来源于Value Line，数据归类于全球数据库的美国 – 表 US.Z019：价值线：指数。

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.

NASDAQ Composite Index NASDAQ Composite Index

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

Interactive chart of the S&P 500 stock market index since 1927. Historical data is inflation-adjusted using the headline CPI and each data point represents the month-end closing value. The current month is updated on an hourly basis with today's latest value.

United States - Producer Price Index by Industry: Line-Haul Railroads: Primary Services was 268.53200 Index Dec 1984=100 in May of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Line-Haul Railroads: Primary Services reached a record high of 268.53200 in May of 2025 and a record low of 100.00000 in December of 1984. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Industry: Line-Haul Railroads: Primary Services - last updated from the United States Federal Reserve on July of 2025.

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.

AEX Index: Will Europe's Top Performers Keep Soaring?

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.

Trading Signals (NASDAQ Composite 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

Graph and download economic data for Producer Price Index by Industry: Line-Haul Railroads: All Other Passenger Rail Service Classes (PCU482111482111507) from Jun 2005 to May 2025 about passenger, railroad, services, PPI, industry, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Industry: Line-Haul Railroads (PCU482111482111) from Jan 1969 to May 2025 about railroad, PPI, industry, inflation, price index, indexes, price, and USA.

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.

United States - Producer Price Index by Commodity: Machinery and Equipment: Carrier Line Equipment and External Modems was 71.23300 Index Dec 2000=100 in August of 2022, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Machinery and Equipment: Carrier Line Equipment and External Modems reached a record high of 100.10000 in January of 2001 and a record low of 71.20000 in October of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Machinery and Equipment: Carrier Line Equipment and External Modems - last updated from the United States Federal Reserve on July of 2025.

United States - Producer Price Index by Commodity: Telecommunication, Cable, and Internet User Services: Private Line Wired Telephone Service was 118.41600 Index Dec 2011=100 in April of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Telecommunication, Cable, and Internet User Services: Private Line Wired Telephone Service reached a record high of 119.77900 in February of 2025 and a record low of 92.57800 in July of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Telecommunication, Cable, and Internet User Services: Private Line Wired Telephone Service - last updated from the United States Federal Reserve on June of 2025.

Export values are the current value of exports (f.o.b.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2000). UNCTAD's export value indexes are reported for most economies. For selected economies for which UNCTAD does not publish data, the export value indexes are derived from export volume indexes (line 72) and corresponding unit value indexes of exports (line 74) in the IMF's International Financial Statistics.

Interactive chart of the S&P 500 stock market index over the last 10 years. Values shown are daily closing prices. The most recent value is updated on an hourly basis during regular trading hours.

指数：价值线：算术在11-01-2018达6,053.86021May1985=100，相较于10-01-2018的5,958.61021May1985=100有所增长。指数：价值线：算术数据按月更新，01-01-1989至11-01-2018期间平均值为1,326.97021May1985=100，共359份观测结果。该数据的历史最高值出现于08-01-2018，达6,604.52021May1985=100，而历史最低值则出现于10-01-1990，为216.89021May1985=100。CEIC提供的指数：价值线：算术数据处于定期更新的状态，数据来源于Value Line，数据归类于全球数据库的美国 – 表 US.Z019：价值线：指数。

Graph and download economic data for Producer Price Index by Industry: Line-Haul Railroads: Primary Services (PCU482111482111P) from Dec 1984 to May 2025 about railroad, primary, services, PPI, industry, inflation, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Industry: Line-Haul Railroads: Carload Rail Transportation of Coal (PCU48211148211141102) from Jan 2025 to May 2025 about coal, railroad, transportation, PPI, industry, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Industry: Line-Haul Railroads: Carload Rail Transportation of All Other Carload Rail Transportation (PCU48211148211141111) from Jan 2025 to May 2025 about railroad, transportation, PPI, industry, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Industry: Line-Haul Railroads: Carload Rail Transportation of Food and Kindred Products (PCU48211148211141104) from Jan 2025 to May 2025 about railroad, transportation, food, production, PPI, industry, price index, indexes, price, and USA.