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.

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.

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.

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?

The NYSE U.S. Market Consumer Goods 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 goods sector. Between December 2015 and June 2023, the index fluctuated but increased overall, standing at 3,209.19 index points as of June 2023.

Envestnet®| Yodlee®'s Bank Transaction Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

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 CSI: Savings: Stock Market Increase Probability: Next Yr: Mean data was reported at 59.400 % in May 2018. This records a decrease from the previous number of 60.800 % for Apr 2018. United States CSI: Savings: Stock Market Increase Probability: Next Yr: Mean data is updated monthly, averaging 54.500 % from Jun 2002 (Median) to May 2018, with 191 observations. The data reached an all-time high of 66.700 % in Jan 2018 and a record low of 34.000 % in Mar 2009. United States CSI: Savings: Stock Market Increase Probability: Next Yr: Mean 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.

United States CSI: Savings: Stock Market Increase Probability: Next Yr: 25-49% data was reported at 10.000 % in May 2018. This records an increase from the previous number of 8.000 % for Apr 2018. United States CSI: Savings: Stock Market Increase Probability: Next Yr: 25-49% data is updated monthly, averaging 10.000 % from Jun 2002 (Median) to May 2018, with 191 observations. The data reached an all-time high of 19.000 % in Feb 2010 and a record low of 4.000 % in Apr 2015. United States CSI: Savings: Stock Market Increase Probability: Next Yr: 25-49% 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 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.

JPMorgan American Investment Trust (JAM): Questioning the Future

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

Card Stock Market Outlook

The global card stock market size was valued at approximately USD 2.8 billion in 2023 and is projected to grow to USD 4.2 billion by 2032, at a compound annual growth rate (CAGR) of 4.6% during the forecast period. This robust growth is driven by increasing demand in the packaging and printing industries, along with a burgeoning interest in crafting and DIY activities globally.

One of the primary growth factors fueling the card stock market is the rising demand for sustainable and eco-friendly packaging solutions. As consumers and businesses alike become more environmentally conscious, the demand for recyclable and biodegradable card stock has surged. This trend is particularly evident in the packaging sector, where companies are increasingly opting for card stock over plastic to meet consumer preferences and regulatory requirements aimed at reducing plastic waste.

The growth of the e-commerce industry is another significant driver for the card stock market. With the rapid expansion of online retailing, the need for secure and appealing packaging solutions has increased. Card stock is often used in packaging for its durability and printability, which helps in creating visually attractive and sturdy packaging. Moreover, the rise in personalized and custom packaging trends among e-commerce platforms has further amplified the demand for high-quality card stock.

Additionally, the increasing popularity of crafting and DIY activities has spurred the demand for various types of card stock. With more people engaging in hobbies such as scrapbooking, card-making, and other creative projects, the market for card stock has expanded significantly. This trend is further bolstered by the proliferation of social media platforms, where users share their crafting ideas and projects, thereby inspiring others and driving demand for crafting materials, including card stock.

From a regional perspective, North America and Europe hold significant shares in the card stock market, driven by high levels of consumer awareness and stringent environmental regulations. Asia Pacific, however, is expected to witness the fastest growth during the forecast period due to increasing industrialization, rising disposable income, and the growing e-commerce sector. Latin America and the Middle East & Africa are also anticipated to exhibit moderate growth, supported by expanding packaging and printing industries in these regions.

Product Type Analysis

The card stock market can be segmented by product type into coated card stock, uncoated card stock, textured card stock, recycled card stock, and others. Coated card stock holds a significant share due to its smooth surface and excellent printability, which makes it ideal for high-quality printing applications. It is widely used in business cards, brochures, and luxury packaging, where visual appeal is paramount. The coating enhances the card's durability and resistance to moisture, making it suitable for various commercial uses.

Uncoated card stock, on the other hand, is preferred for applications that require a more natural and tactile feel. It is often used in stationery, greeting cards, and certain types of packaging where a rustic or minimalist aesthetic is desired. The lack of coating allows for better ink absorption, which can be advantageous for certain printing techniques and crafting projects.

Textured card stock offers a unique advantage with its distinct surface patterns, adding a tactile dimension to printed materials. This type of card stock is popular in high-end invitations, business cards, and special event stationery. The textured surface can range from subtle linen-like patterns to more pronounced embossing, catering to diverse design needs.

Recycled card stock is gaining traction due to the growing emphasis on sustainability. Made from post-consumer waste, this type of card stock appeals to eco-conscious consumers and businesses. It is used in a variety of applications, including packaging, printing, and crafting, and offers a viable alternative to traditional paper products with a lower environmental footprint.

Other types of card stock include specialty variants tailored for specific applications, such as metallic finishes, which are used for luxury packaging and special occasions. These niche products, while not as widely used as the more common types, play an important role in meeting the diverse needs of the market and offering unique solutions for specific projects.

United States CSI: Savings: Stock Market Increase Probability: Next Yr: 0% data was reported at 2.000 % in May 2018. This stayed constant from the previous number of 2.000 % for Apr 2018. United States CSI: Savings: Stock Market Increase Probability: Next Yr: 0% data is updated monthly, averaging 3.000 % from Jun 2002 (Median) to May 2018, with 191 observations. The data reached an all-time high of 10.000 % in Mar 2009 and a record low of 0.000 % in Feb 2007. United States CSI: Savings: Stock Market Increase Probability: Next Yr: 0% 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?

The North America Consumer Battery Market is segmented by Technology Type (Lithium-ion Batteries, Zinc-Carbon Batteries, Alkaline Batteries, and Others) and Geography (The US, Canada, and the Rest of North America). The report offers the market size and forecasts in revenue (USD billion) for all the above segments.

In 2024, private labels accounted for **** percent of the annual value of sales of consumer goods in the United States. In 2023, store brands performed a little lower with a **** percent dollar share of total consumer goods sales.

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.

DJ US Healthcare: Poised for Recovery?

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 is the package of supporting files. In this package, the readers can find the primary dataset in the XLSX/XLS files. The description of the data is also provided. (ZIP)

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.