Economic Optimism Index in the United States decreased to 48.60 points in July from 49.20 points in June of 2025. This dataset provides the latest reported value for - United States IBD/TIPP Economic Optimism Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

VN30 Index: Analysts Bullish Amidst Market Optimism.

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

Based on professional technical analysis and AI models, deliver precise price‑prediction data for Optimism on 2025-08-08. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.

United States SBOI: sa: Prices: Price Plans data was reported at 28.000 % in Oct 2018. This records an increase from the previous number of 24.000 % for Sep 2018. United States SBOI: sa: Prices: Price Plans data is updated monthly, averaging 19.000 % from Jan 1975 (Median) to Oct 2018, with 438 observations. The data reached an all-time high of 46.000 % in Oct 1979 and a record low of 0.000 % in Mar 2009. United States SBOI: sa: Prices: Price Plans data remains active status in CEIC and is reported by National Federation of Independent Business. The data is categorized under Global Database’s United States – Table US.S017: NFIB Index of Small Business Optimism.

This statistic shows the results of a survey, conducted in 2013 among adult Americans, on whether they believe the glass is half full or half empty. 50 percent of respondents said they consider themselves optimists.

The optimism and pessimism of the American people

Optimism is defined as a mental attitude or worldview that favors a positive outcome, while pessimism favors a negative outcome or prediction. Depression in the United States is very common. In 2013, around 8.7 percent of U.S. adults aged between 18 and 25 reported that they had a major depression episode within the past year. Major depressive episodes in the United States are most common among American females. The number of prescription antidepressant drug use among women in the United States has increased by more than 10 percent between 1988 and 2012. Also in 2013, about one third of U.S. adults stated that they were happier than expected.

The general optimism and pessimism in a nation are often the result of its economic situation. The unemployment rate in the United States has been steadily decreasing every year since 2010; furthermore, it is expected to constantly decrease further until 2020. The prospering economy and increasing gross domestic product per capita in the United States is another source of optimism for the American people: The GDP per capita in the United States in 2014 was around 54,600 U.S. dollars. Moreover, it has been steadily increasing since 2010. In a survey conducted in July 2012, one third of Americans who defined themselves as lower-class stated that they were “not too happy” with their current lives. On the other hand, there was a larger percentage of people whom, according to themselves, belong to the upper class that stated that they were “very happy” with their current lives. In addition, upper- and middle-class American adults are more optimistic about the country’s long-term economic future in comparison to lower-class American adults.

Detailed Cost of revenue metrics and analytics for OP Mainnet, including historical data and trends.

United States SBOI: Credit Conditions: Relative Interest Rate Paid data was reported at 17.000 % in Oct 2018. This records an increase from the previous number of 16.000 % for Sep 2018. United States SBOI: Credit Conditions: Relative Interest Rate Paid data is updated monthly, averaging 2.000 % from Jan 1986 (Median) to Oct 2018, with 394 observations. The data reached an all-time high of 38.000 % in Mar 1989 and a record low of -28.000 % in Apr 1986. United States SBOI: Credit Conditions: Relative Interest Rate Paid data remains active status in CEIC and is reported by National Federation of Independent Business. The data is categorized under Global Database’s United States – Table US.S017: NFIB Index of Small Business Optimism.

United States SBOI: Credit Conditions: Actual Interest Rate Paid on ST Loans data was reported at 6.100 % in Nov 2018. This records a decrease from the previous number of 6.400 % for Oct 2018. United States SBOI: Credit Conditions: Actual Interest Rate Paid on ST Loans data is updated monthly, averaging 8.430 % from Jan 1986 (Median) to Nov 2018, with 395 observations. The data reached an all-time high of 12.920 % in May 1989 and a record low of 4.700 % in Nov 2015. United States SBOI: Credit Conditions: Actual Interest Rate Paid on ST Loans data remains active status in CEIC and is reported by National Federation of Independent Business. The data is categorized under Global Database’s United States – Table US.S017: NFIB Index of Small Business Optimism.

United States SBOI: sa: Most Pressing Problem: Survey Low: Fin. & Interest Rates data was reported at 0.000 % in Mar 2025. This stayed constant from the previous number of 0.000 % for Feb 2025. United States SBOI: sa: Most Pressing Problem: Survey Low: Fin. & Interest Rates data is updated monthly, averaging 1.000 % from Jan 2014 (Median) to Mar 2025, with 131 observations. The data reached an all-time high of 2.000 % in Jul 2019 and a record low of 0.000 % in Mar 2025. United States SBOI: sa: Most Pressing Problem: Survey Low: Fin. & Interest Rates data remains active status in CEIC and is reported by National Federation of Independent Business. The data is categorized under Global Database’s United States – Table US.S042: NFIB Index of Small Business Optimism. [COVID-19-IMPACT]

Optimism is a low-cost and lightning-fast Ethereum L2 blockchain.

United States SBOI: sa: Most Pressing Problem: Fin. & Interest Rates data was reported at 3.000 % in Mar 2025. This stayed constant from the previous number of 3.000 % for Feb 2025. United States SBOI: sa: Most Pressing Problem: Fin. & Interest Rates data is updated monthly, averaging 2.000 % from Jan 2014 (Median) to Mar 2025, with 131 observations. The data reached an all-time high of 6.000 % in May 2024 and a record low of 0.000 % in Feb 2022. United States SBOI: sa: Most Pressing Problem: Fin. & Interest Rates data remains active status in CEIC and is reported by National Federation of Independent Business. The data is categorized under Global Database’s United States – Table US.S042: NFIB Index of Small Business Optimism. [COVID-19-IMPACT]

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.

VistaGen's Future: Analyst Optimism Fuels (VTGN) Stock Forecasts

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

difference between optimist (greater learning rate for positive prediction errors) and unbiased concerning Reward prediction error representation

Collection description

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

group

Cognitive paradigm (task)

instrumental learning task

Map type

T

Current employment status (2); optimistic/pessimistic about investment (2); ability to maintain/increase current income over next twelve months (1); factors that could affect investment environment over next twelve months (4); how long been investing in financial markets (1); rate of return on portfolio (2); overall rate of return for investors (2); minimum rate of return on investments (2); good time/not a good time to invest in financial markets (1); value of stock market (1); Sectors offering best investment opportunities (12); economic conditions (2); rising stock and housing prices (4).

Since February 2023, The Information has asked readers each month whether they are more optimistic or more pessimistic about the outlook for 14 big technology companies than they were three months earlier. The results of those surveys have created a real-time record of sentiment about those companies, which we’ve compiled here. In this chart and the accompanying table, you can see the dip in confidence following the collapse of Silicon Valley Bank followed by generally rising optimism, largely around advances in artificial intelligence.

This chart shows the difference between the percentage of respondents who were optimistic about a company and the percentage who were pessimistic in each month. Click on a company, or hover over a line in the chart, to highlight that line.

This table also shows the difference between the percentage of respondents who were optimistic and the percentage who were pessimistic about the outlook for each company in each month. Click on the buttons to see the actual percentage who were optimistic or pessimistic. Each column can be sorted by the data that’s displayed. Note that the months are in reverse chronological order, with the most recent month in the first column.

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.

VN30 Rises on Investor Optimism, Bullish Forecasts

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

Nvidia stock is in correction territory, yet analysts remain optimistic about its long-term prospects, maintaining high price targets despite recent challenges.

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.

CeriBell's (CBLL) Forecast: Optimism Surrounds Future Growth Prospects.

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.

Nikkei 225 Forecast: Optimism Fuels Bullish Outlook for the Japanese Stock 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