According to a survey conducted in November 2020, the U.S. consumer staples stock expected to increase the most over the next five years is supermarket chain Costco. Costco garnered more than double the number of respondents than the next-most popular option, Clorox.

The Dow Jones U.S. Consumer Services index is expected to experience moderate growth in the near future. Key factors driving this growth include rising consumer spending, increased disposable income, and favorable economic conditions. However, risks associated with the index include rising inflation, geopolitical uncertainty, and supply chain disruptions.

The NYSE U.S. Market Consumer Services Sector Index tracks the performance of the U.S. domiciled equity components listed on the U.S. stock exchanges that offer goods and services in the consumer services sector. Between December 2015 and June 2023, the index fluctuated but increased overall. As of June 2023, the NYSE U.S. Market Consumer Services Index stood at ******** index points.

New York Stock Exchange: Index: S&P Consumer Staples Select Sector Index data was reported at 826.910 NA in Apr 2025. This records an increase from the previous number of 825.980 NA for Mar 2025. New York Stock Exchange: Index: S&P Consumer Staples Select Sector Index data is updated monthly, averaging 581.670 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 840.110 NA in Sep 2024 and a record low of 395.070 NA in Aug 2013. New York Stock Exchange: Index: S&P Consumer Staples Select Sector Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: S&P: Monthly.

The Dow Jones U.S. Consumer Services Capped Index is forecast to experience moderate growth over the coming period, driven by strong consumer spending in the post-pandemic recovery. However, risks remain, including the potential for further disruptions to the global supply chain, rising inflation, and the impact of geopolitical events on consumer sentiment.

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.

The Dow Jones U.S. Completion Total Stock Market 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

United States New York Stock Exchange: Index: Dow Jones US Consumer Services Index data was reported at 1,838.250 NA in Apr 2025. This records an increase from the previous number of 1,814.880 NA for Mar 2025. United States New York Stock Exchange: Index: Dow Jones US Consumer Services Index data is updated monthly, averaging 1,072.890 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 2,030.200 NA in Jan 2025 and a record low of 527.340 NA in Aug 2013. United States New York Stock Exchange: Index: Dow Jones US Consumer Services Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: Dow Jones: Monthly.

US retail investors had a relatively strong opinion on whether the stock market was more profitable than investments in cryptocurrencies. Nearly 32 percent of the respondents to a survey listed crypto as potentially having the most risk, against almost 38 percent preferring the stock market over virtual currencies in terms of profitability. One potential reason why this could be found at the US opinion on risk: slightly more respondents felt that the stock market was a more risky to invest in. This is quite different from answers given to these same questions but by consumers from the United Kingdom.

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 U.S. Consumer Services: Strength Despite Headwinds?

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 Equity Market Volatility Tracker: Macroeconomic News and Outlook: Consumer Spending And Sentiment (EMVMACROCONSUME) from Jan 1985 to May 2025 about volatility, uncertainty, equity, PCE, consumption expenditures, consumption, personal, and USA.

The Jarque-Bera statistic tests the null hypothesis of normality for the sample returns.

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.

Consumer Services Sector Anticipates Continued Growth, Dow Jones U.S. Consumer Services Index Shows.

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

United States New York Stock Exchange: Index: S&P Consumer Discretionary Select Sector data was reported at 1,994.230 NA in Apr 2025. This records a decrease from the previous number of 1,996.210 NA for Mar 2025. United States New York Stock Exchange: Index: S&P Consumer Discretionary Select Sector data is updated monthly, averaging 1,178.410 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 2,346.650 NA in Jan 2025 and a record low of 578.300 NA in Aug 2013. United States New York Stock Exchange: Index: S&P Consumer Discretionary Select Sector data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: S&P: Monthly.

The Consumer Sentiment Index in the United States stood at 64.7 in January 2025, an increase from the previous month. The index is normalized to a value of 100 in December 1964 and based on a monthly survey of consumers, conducted in the continental United States. It consists of about 50 core questions which cover consumers' assessments of their personal financial situation, their buying attitudes and overall economic conditions.

United States CSI: Savings: Stock Market Increase Probability: Next Yr: 75-99% data was reported at 32.000 % in May 2018. This records an increase from the previous number of 31.000 % for Apr 2018. United States CSI: Savings: Stock Market Increase Probability: Next Yr: 75-99% data is updated monthly, averaging 26.000 % from Jun 2002 (Median) to May 2018, with 191 observations. The data reached an all-time high of 38.000 % in Sep 2017 and a record low of 9.000 % in Mar 2009. United States CSI: Savings: Stock Market Increase Probability: Next Yr: 75-99% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H026: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the percent change that this one thousand dollar investment will increase in value in the year ahead, so that it is worth more than one thousand dollars one year from now?

This statistic shows the consumer perception of long-term investment in the United States in 2018, by age. In 2018, ** percent of respondents between 55 and 64 years said that real estate is a better long-term investment than investing in the stock market.

The forecast horizon is H = 30. The optimal lag order is determined by the AIC. Notes: TSI = total spillover index in (6); FROM = DSi⋅(H) in (7), total liquidity spillovers received by the i-th sector from all other sectors; TO = DS⋅i(H) in (8), total liquidity spillovers transmitted by the i-th sector to all other sectors; TO (own) = total liquidity spillovers generated by the i-th sector, including the contribution of its own; NET = NSi(H) in (9), net spillovers (the difference between transmitted liquidity shocks and received liquidity shocks).

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.

Is Consumer Spending Driving the U.S. Services 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

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 U.S. Real Estate: A True Reflection of the Market?

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.

Dow Jones U.S. Select Insurance Index: Poised for a Rebound?

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