United States CSI: Expected Inflation: Next 5 Yrs: Same data was reported at 1.000 % in May 2018. This records a decrease from the previous number of 2.000 % for Apr 2018. United States CSI: Expected Inflation: Next 5 Yrs: Same data is updated monthly, averaging 3.000 % from Feb 1979 (Median) to May 2018, with 380 observations. The data reached an all-time high of 16.000 % in Sep 1981 and a record low of 0.000 % in Jan 1997. United States CSI: Expected Inflation: Next 5 Yrs: Same data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The questions were: 'What about the outlook for prices over the next 5 to 10 years? Do you think prices will be higher, to go up, on the average, during the next 12 months?' and 'By about what percent per year do you expect prices to go up or down, on the average, during the next 5 to 10 years?'

Unemployment Rate in Australia remained unchanged at 4.10 percent in February. This dataset provides - Australia Unemployment Rate at 5.8% in December - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States CSI: Expected Inflation: Next Yr data was reported at 2.900 % in Jul 2018. This records a decrease from the previous number of 3.000 % for Jun 2018. United States CSI: Expected Inflation: Next Yr data is updated monthly, averaging 3.000 % from Jan 1978 (Median) to Jul 2018, with 487 observations. The data reached an all-time high of 10.400 % in Jan 1980 and a record low of 0.400 % in Nov 2001. United States CSI: Expected Inflation: Next Yr data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The question was: No one can say for sure, but what do you think will happen to interest rates for borrowing money during the next 12 months -- will they go up, stay the same, or go down?

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.

S&P 500: A Bull or a Bear?

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: Expected Inflation: Next 5 Yrs: Up by 10-14% data was reported at 3.000 % in May 2018. This records a decrease from the previous number of 4.000 % for Apr 2018. United States CSI: Expected Inflation: Next 5 Yrs: Up by 10-14% data is updated monthly, averaging 6.000 % from Feb 1979 (Median) to May 2018, with 380 observations. The data reached an all-time high of 24.000 % in Feb 1980 and a record low of 1.000 % in Jan 2018. United States CSI: Expected Inflation: Next 5 Yrs: Up by 10-14% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The questions were: 'What about the outlook for prices over the next 5 to 10 years? Do you think prices will be higher, to go up, on the average, during the next 12 months?' and 'By about what percent per year do you expect prices to go up or down, on the average, during the next 5 to 10 years?'

United States CSI: Expected Inflation: Next Yr: 75th Percentile data was reported at 4.700 % in May 2018. This records an increase from the previous number of 4.600 % for Apr 2018. United States CSI: Expected Inflation: Next Yr: 75th Percentile data is updated monthly, averaging 5.100 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 15.600 % in Jan 1980 and a record low of 3.100 % in Nov 2001. United States CSI: Expected Inflation: Next Yr: 75th Percentile data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The questions were: 'During the next 12 months, do you think that prices in general will go up, or go down, or stay where they are now?' and 'By what percent do you expect prices to go up, on the average, during the next 12 months?'

United States CSI: Expected Inflation: Next Yr: Up by 10-14% data was reported at 6.000 % in May 2018. This records a decrease from the previous number of 7.000 % for Apr 2018. United States CSI: Expected Inflation: Next Yr: Up by 10-14% data is updated monthly, averaging 8.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 31.000 % in Feb 1980 and a record low of 3.000 % in Feb 1998. United States CSI: Expected Inflation: Next Yr: Up by 10-14% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The questions were: 'During the next 12 months, do you think that prices in general will go up, or go down, or stay where they are now?' and 'By what percent do you expect prices to go up, on the average, during the next 12 months?'

United States CSI: Government Economic Policy data was reported at 97.000 1966=100 in May 2018. This records a decrease from the previous number of 102.000 1966=100 for Apr 2018. United States CSI: Government Economic Policy data is updated monthly, averaging 89.000 1966=100 from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 143.000 1966=100 in Feb 1999 and a record low of 48.000 1966=100 in Aug 2011. United States CSI: Government Economic Policy data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The question was: As to the economic policy of the government -- I mean steps taken to fight inflation or unemployment -- would you say the government is going a good job, only fair, or a poor job?

United States CSI: Expected Unemployment: Next Yr: More data was reported at 24.000 % in May 2018. This records a decrease from the previous number of 25.000 % for Apr 2018. United States CSI: Expected Unemployment: Next Yr: More data is updated monthly, averaging 31.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 72.000 % in May 1980 and a record low of 13.000 % in May 1983. United States CSI: Expected Unemployment: Next Yr: More data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The question was: How about people out of work during the coming 12 months -- do you think there will be more unemployment than now, about the same, or less?

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.

How Do You Pick a Stock? (LON:ONT 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

Unemployment Rate in the United Kingdom remained unchanged at 4.40 percent in January. This dataset provides the latest reported value for - United Kingdom Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Unemployment Rate in South Africa decreased to 31.90 percent in the fourth quarter of 2024 from 32.10 percent in the third quarter of 2024. This dataset provides - South Africa Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Inflation Rate in Malaysia decreased to 1.50 percent in February from 1.70 percent in January of 2025. This dataset provides - Malaysia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar 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.

Retractable Roller Coaster Ride (RVP): Ready for a Wild Ride?

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