Israel Bond Index: TASE: General:(CPI) Consumer Price IndexLinked data was reported at 347.940 31Dec1996=100 in Nov 2018. This records a decrease from the previous number of 350.640 31Dec1996=100 for Oct 2018. Israel Bond Index: TASE: General:(CPI) Consumer Price IndexLinked data is updated monthly, averaging 227.960 31Dec1996=100 from Jan 2000 (Median) to Nov 2018, with 227 observations. The data reached an all-time high of 354.310 31Dec1996=100 in Aug 2018 and a record low of 124.180 31Dec1996=100 in Feb 2000. Israel Bond Index: TASE: General:(CPI) Consumer Price IndexLinked data remains active status in CEIC and is reported by Tel Aviv Stock Exchange. The data is categorized under Global Database’s Israel – Table IL.Z002: Tel Aviv Stock Exchange: Bond Index.

Graph and download economic data for 20-Year 2-1/2% Treasury Inflation-Indexed Bond, Due 1/15/2029 (DTP20J29) from 2010-01-04 to 2025-08-15 about 20-year, TIPS, bonds, Treasury, interest rate, interest, real, rate, and USA.

View the CPI Property Group SA (CPIPGR) cfr deal profile

This study examines the association between the daily investments in government bonds with Consumer price index (CPI) for Pakistani space for the period beginning from July 2001 to September 2009. The findings significantly supports the proposition that consumer price index (CPI) has an association with the investments in government bonds and also the non-seasonal investments in government bonds for lag 1 also effects the investment in government bonds for the current period.

The average market yield on the United States Treasury's 10-year bond was **** percent during the second quarter of 2024. This rate was adjusted to reflect a constant maturity and also indexed to inflation, giving an idea of real returns for longer-term investments. The recent expected return was highest at the end of the end of the last quarter of 2024, and lowest in the second half of 2021, when it was negative.

View the CPI Property Group SA (CPIPGR) 2019-3 deal profile

Israel Bond Index: TASE: Corporate:(CPI) Consumer Price IndexLinked data was reported at 366.150 31Dec1996=100 in Nov 2018. This records a decrease from the previous number of 368.760 31Dec1996=100 for Oct 2018. Israel Bond Index: TASE: Corporate:(CPI) Consumer Price IndexLinked data is updated monthly, averaging 224.660 31Dec1996=100 from Jan 2000 (Median) to Nov 2018, with 227 observations. The data reached an all-time high of 371.460 31Dec1996=100 in Aug 2018 and a record low of 123.990 31Dec1996=100 in Feb 2000. Israel Bond Index: TASE: Corporate:(CPI) Consumer Price IndexLinked data remains active status in CEIC and is reported by Tel Aviv Stock Exchange. The data is categorized under Global Database’s Israel – Table IL.Z002: Tel Aviv Stock Exchange: Bond Index.

Graph and download economic data for 10-Year 0.125% Treasury Inflation-Indexed Bond, Due 01/15/2030 (DTP10J30) from 2020-02-20 to 2025-08-15 about TIPS, 10-year, bonds, Treasury, interest rate, interest, real, rate, and USA.

Graph and download economic data for 5-Year 0.125% Treasury Inflation-Indexed Bond, Due 4/15/2025 (DISCONTINUED) (DTP5A25) from 2020-06-30 to 2025-04-14 about fees, notes, TIPS, bonds, Treasury, 5-year, and USA.

Consumer price indexes (CPIs) are index numbers that measure changes in the prices of goods and services purchased or otherwise acquired by households, which households use directly, or indirectly, to satisfy their own needs and wants. In practice, most CPIs are calculated as weighted averages of the percentage price changes for a specified set, or ‘‘basket’’, of consumer products, the weights reflecting their relative importance in household consumption in some period. CPIs are widely used to index pensions and social security benefits. CPIs are also used to index other payments, such as interest payments or rents, or the prices of bonds. CPIs are also commonly used as a proxy for the general rate of inflation, even though they measure only consumer inflation. They are used by some governments or central banks to set inflation targets for purposes of monetary policy. The price data collected for CPI purposes can also be used to compile other indices, such as the price indices used to deflate household consumption expenditures in national accounts, or the purchasing power parities used to compare real levels of consumption in different countries.

In an effort to further coordinate and harmonize the collection of CPI data, the international organizations agreed that the International Monetary Fund (IMF) and the Organisation for Economic Cooperation and Development (OECD) would assume responsibility for the international collection and dissemination of national CPI data. Under this data collection initiative, countries are reporting the aggregate all items index; more detailed indexes and weights for 12 subgroups of consumption expenditure (according to the so-called COICOP-classification), and detailed metadata. These detailed data represent a valuable resource for data users throughout the world and this portal would not be possible without the ongoing cooperation of all reporting countries. In this effort, the OECD collects and validates the data for their member countries, including accession and key partner countries, whereas the IMF takes care of the collection of data for all other countries.

Ecuador Consumer Price Index (CPI): RC: NS: Bond Paper data was reported at 130.003 2004=100 in Dec 2014. This stayed constant from the previous number of 130.003 2004=100 for Nov 2014. Ecuador Consumer Price Index (CPI): RC: NS: Bond Paper data is updated monthly, averaging 123.406 2004=100 from Jan 2005 (Median) to Dec 2014, with 120 observations. The data reached an all-time high of 130.003 2004=100 in Dec 2014 and a record low of 103.867 2004=100 in Aug 2006. Ecuador Consumer Price Index (CPI): RC: NS: Bond Paper data remains active status in CEIC and is reported by National Institute of Statistics and Census. The data is categorized under Global Database’s Ecuador – Table EC.I016: Consumer Price Index: 2004=100.

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

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.

What happens to gold if CPI increases?

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.

What is the relationship between CPI and the 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

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.

What is cpi?

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

About this dataset

Context

The dataset illustrates the median household income in Bond County, spanning the years from 2010 to 2023, with all figures adjusted to 2023 inflation-adjusted dollars. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2023, the median household income for Bond County decreased by $11,378 (15.59%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $5,602 (7.68%) between 2010 and 2023.

Analyzing the trend in median household income between the years 2010 and 2023, spanning 13 annual cycles, we observed that median household income, when adjusted for 2023 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 5 years and declined for 8 years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 0223

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2023
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2023 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Bond County median household income. You can refer the same here

Graph and download economic data for 10-Year 0.875% Treasury Inflation-Indexed Note, Due 1/15/2029 (DTP10J29) from 2019-01-18 to 2025-08-15 about fees, notes, TIPS, 10-year, bonds, and Treasury.

The dataset contains All India Yearly External Debt from Handbook of Statistics on Indian Economy. IDBs : India Development Bonds FCCBs : Foreign Currency Convertible Bonds IFC(W) : International Finance Corporation (Washington) FC(B&O) : Foreign Currency (Banks & Others) Deposits

Note: 1. Other concessional multilateral Government borrowing refers to debt outstanding to Institutions like IFAD, OPEC & EEC (SAC). 2. Multilateral non-concessional Government/ public sector/ financial institutions other borrowings refers to debt outstanding against loans from ADB. 3. Securitised commercial borrowings (inclu. IDBs and FCCBs) includes net Investment by 100% Fll debt funds, Resurgent India Bonds (RIBs) and India Millenium Deposits(IMDs). 4. Rupee debt refers to debt owed to Russia denominated in Rupees and converted at current exchange rates, payable in exports. 5. Civillian's rupee debt includes Supplier’s credit from end-March 1990 onwards. 6. Short-term debt does not include suppliers’ credit of up to 180 days from 1994 till 2004. 7. Multilateral loans do not include revaluation of IBRD pooled loans and exchange rate adjustment under IDA loans for Pre-1971 credits. 8. Debt- service ratio from the year 1992 - 93 includes the revised private transfer contra-entry on account of gold and silver imports.

About this dataset

Context

The dataset illustrates the median household income in Bond County, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2021, the median household income for Bond County decreased by $12,095 (17.26%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 3 years and declined for 8 years.

https://i.neilsberg.com/ch/bond-county-il-median-household-income-trend.jpeg" alt="Bond County, IL median household income trend (2010-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2021
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Bond County median household income. You can refer the same here

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:CPI CAPITA PLC

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