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.

Surging Services: Will Dow Jones CPI Signal Continued Consumer Strength?

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 Stock Prices: 12 Months Expectation: Increase data was reported at 36.100 % in Apr 2025. This records a decrease from the previous number of 39.900 % for Mar 2025. United States Stock Prices: 12 Months Expectation: Increase data is updated monthly, averaging 36.200 % from Jun 1987 (Median) to Apr 2025, with 455 observations. The data reached an all-time high of 57.200 % in Nov 2024 and a record low of 18.100 % in Mar 2008. United States Stock Prices: 12 Months Expectation: Increase data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H052: Consumer Confidence Index: Stock Price Expectation. [COVID-19-IMPACT]

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.

United States SCE: Stock Price: Probability That US Stock Prices will be Higher 1 Year from Now data was reported at 35.662 % in Apr 2025. This records an increase from the previous number of 33.832 % for Mar 2025. United States SCE: Stock Price: Probability That US Stock Prices will be Higher 1 Year from Now data is updated monthly, averaging 39.618 % from Jun 2013 (Median) to Apr 2025, with 143 observations. The data reached an all-time high of 51.840 % in Apr 2020 and a record low of 33.767 % in Jun 2022. United States SCE: Stock Price: Probability That US Stock Prices will be Higher 1 Year from Now data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.H085: Survey of Consumer Expectations: Financial.

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.

LON:STG Stock: The Stock Market Bubble Is About to Burst

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: Eco: Recent Buss Conditions: Favorable Stock Market data was reported at 5.000 % in May 2018. This records an increase from the previous number of 4.000 % for Apr 2018. United States CSI: Eco: Recent Buss Conditions: Favorable Stock Market data is updated monthly, averaging 2.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 11.000 % in Mar 2017 and a record low of 0.000 % in Feb 2009. United States CSI: Eco: Recent Buss Conditions: Favorable Stock Market data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H028: Consumer Sentiment Index: Economic Conditions. During the last few months, have your heard of any favorable or unfavorable changes in business conditions? The question was: What did you hear?

Envestnet®| Yodlee®'s Consumer Spending 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?

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: Analytics B2C 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.

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.

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.

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 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: 100% data was reported at 11.000 % in Oct 2018. This records an increase from the previous number of 10.000 % for Sep 2018. United States CSI: Savings: Stock Market Increase Probability: Next Yr: 100% data is updated monthly, averaging 6.000 % from Jun 2002 (Median) to Oct 2018, with 196 observations. The data reached an all-time high of 13.000 % in Jan 2018 and a record low of 1.000 % in Nov 2011. United States CSI: Savings: Stock Market Increase Probability: Next Yr: 100% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s United States – Table US.H029: 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?

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

The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. The data regularly include the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. The surveys conducted in 2004 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Opinions were collected regarding respondents' appraisals of present market conditions for purchasing houses, automobiles, computers, and other durables. Also explored in this survey, were respondents' types of savings and financial investments, loan use, family income, and retirement planning. Other topics in this series typically include ownership, lease, and use of automobiles, respondents' use of personal computers at home and in the office, and respondents' familiarity with and use of the Internet. Demographic information includes ethnic origin, sex, age, marital status, and education.

The latest closing stock price for Consumer Portfolio Services as of May 27, 2025 is 9.01. An investor who bought $1,000 worth of Consumer Portfolio Services stock at the IPO in 1992 would have $2,432 today, roughly 2 times their original investment - a 3.81% compound annual growth rate over 33 years. The all-time high Consumer Portfolio Services stock closing price was 17.88 on September 09, 1997. The Consumer Portfolio Services 52-week high stock price is 12.73, which is 41.3% above the current share price. The Consumer Portfolio Services 52-week low stock price is 7.85, which is 12.9% below the current share price. The average Consumer Portfolio Services stock price for the last 52 weeks is 9.70. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

United States CSI: Eco: Recent Buss Conditions: Unfavorable Stock Market data was reported at 4.000 % in May 2018. This records a decrease from the previous number of 7.000 % for Apr 2018. United States CSI: Eco: Recent Buss Conditions: Unfavorable Stock Market data is updated monthly, averaging 1.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 38.000 % in Nov 1987 and a record low of 0.000 % in Dec 2013. United States CSI: Eco: Recent Buss Conditions: Unfavorable Stock Market data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H028: Consumer Sentiment Index: Economic Conditions. During the last few months, have your heard of any favorable or unfavorable changes in business conditions? The question was: What did you hear?

CSI: Savings: Stock Market Increase Probability: Next Yr: 1-24% data was reported at 12.000 % in May 2018. This stayed constant from the previous number of 12.000 % for Apr 2018. CSI: Savings: Stock Market Increase Probability: Next Yr: 1-24% data is updated monthly, averaging 18.000 % from Jun 2002 (Median) to May 2018, with 191 observations. The data reached an all-time high of 32.000 % in Feb 2009 and a record low of 8.000 % in Jan 2018. CSI: Savings: Stock Market Increase Probability: Next Yr: 1-24% 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?

Taiwan TWSE: Equity Market Index: Trading & Consumers' Goods data was reported at 265.650 31Dec1994=100 in Oct 2018. This records a decrease from the previous number of 276.610 31Dec1994=100 for Sep 2018. Taiwan TWSE: Equity Market Index: Trading & Consumers' Goods data is updated monthly, averaging 115.695 31Dec1994=100 from Jan 1995 (Median) to Oct 2018, with 286 observations. The data reached an all-time high of 276.680 31Dec1994=100 in Jun 2018 and a record low of 49.070 31Dec1994=100 in Apr 2003. Taiwan TWSE: Equity Market Index: Trading & Consumers' Goods 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.

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.

Short/Long Term Stocks: SPB 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

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.

Madison Square Garden Entertainment (MSGE) : A Rollercoaster Ride Ahead?

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

Tata Consumer Products Ltd reported 1.07T in Market Capitalization this June of 2025, considering the latest stock price and the number of outstanding shares.Data for Tata Consumer Products Ltd | TACN - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last June in 2025.