A table showing standardized coefficients of the structural equation model for HWB indices.

A table showing descriptive statistics of HWB indices from 2006 to 2012.

Conceptual framework of the index system of human well-being (HWB).

The data provide event study market model calculation for 621 firms from a population of 634 firms to assess Indonesia equity market reaction on earnings announcement information content by using book-to-market as the proxy. The data include calculation of CAR, CAAR, Book-to-market value, stock prices, composite index, et cetera.

The Vegetation Health Index (VHI) illustrates the severity of drought based on the vegetation health and the influence of temperature on plant conditions. The VHI is a composite index and the elementary indicator used to compute the seasonal drought indicators in ASIS: Agricultural Stress Index (ASI), Drought Intensity and Weighted Mean Vegetation Health Index (Mean VHI).If the index is below 40, different levels of vegetation stress, losses of crop and pasture production might be expected; if the index is above 60 (favorable condition) plentiful production might be expected. VHI is very useful for an advanced prediction of crop losses.

Phenomenon Mapped: Vegetation Health Index

Units: None

Time Interval: 10-day

Time Extent: 1984-Present

Cell Size: 1 km

Pixel Type: 32-bit Signed Integer

Data Projection: WGS 1984

Mosaic Projection: WGS 1984 Web Mercator

Source: Food and Agriculture Organization of the United Nations

Update Cycle: 10-days + 5 days lagVHI combines both the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI). The TCI is calculated using a similar equation to the VCI, but relates the current temperature to the long-term maximum and minimum, as it is assumed that higher temperatures tend to cause a deterioration in vegetation conditions. A decrease in the VHI would, for example, indicate relatively poor vegetation conditions and warmer temperatures, signifying stressed vegetation conditions, and over a longer period would be indicative of drought.In ASIS, VHI is computed in two temporal granularities: dekadal and monthly. The dekadal/monthly VHI raster layer published is further updated in the following 5 dekads (improve data precision, remove cloud pixel etc.).Flags of raster file: 251=missing, 252=cloud, 253=snow, 254=sea, 255=backgroundExplore this and related data in this web applicationMore information please visit FAO GIEWS Earth Observation website.

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 accounting equation?

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 the asymmetric copula function, this paper analyzes the static and dynamic correlation between Shanghai Composite Index and Shenzhen Composite Index. Through the static analysis, it is found that the asymmetric copula function is better than Gumbel Copula in describing the distribution characteristics of the top tail dependence between the Shanghai Composite Index and the Shenzhen Composite Index, and the copula correlation coefficient definition based on the asymmetric copula function can well describe the asymmetric dependence between variables. In the time-varying analysis, the paper improves the traditional dynamic evolution equation of the tail-dependence coefficient. Through empirical analysis, the result shows that the improved dynamic evolution equation can better reflect the dynamic evolution process of the tail-dependence coefficient.

(1) Gender Inequality Index (GII) is compiled by the United Nations Development Programme (UNDP) to measure gender inequality in the areas of reproductive health, empowerment, and labor market. Our country calculates the index based on the UNDP formula.(2) Explanation: (1) GII is used to measure the difference in development achievements between the two genders, with a value between 0 and 1, where a smaller value is better. (2) Due to our country's non-membership in the United Nations and unique international situation, the index is calculated by our department according to the UNDP formula, incorporating our country's data. The calculation of the composite index for each year mainly uses the data year of various indicators adopted by UNDP. (3) In order to have the same standard for international comparison, the composite index and rankings, once published, will not be retrospectively adjusted.(3) Notes: (1) In 2011, UNDP adjusted the formula for the maternal mortality ratio in the Human Development Report, resulting in a significant decrease in GII values for each country, and the data for retrospective adjustments will not be re-ranked. (2) The original indicator "Labor force participation rate for ages 15-64" has been changed to "Labor force participation rate for ages 15 and above"; UNDP has not released the global GII ranking for 2016.

(1) Gender Gap Index (GGI) is compiled by the World Economic Forum (WEF) and consists of four sub-indices: "economic participation and opportunity," "educational attainment," "health and survival," and "political empowerment." It measures the gender differences in the allocation of social resources and opportunities. Our country calculates it according to the WEF formula. (2) Explanation: (1) GGI measures the gender equality gap, with a value between 0 and 1, where higher values are better. (2) Our country's data is calculated by the central authority according to the WEF formula. The calculation of the composite index for each year is based on the main data from the WEF's indicators. WEF sets equal standards for the female-to-male ratio for each indicator, with the exception of health life expectancy for females to males (1.06) and the sex ratio at birth (0.944), which are used as the baseline. Ratios exceeding the equal standard are replaced by the equal standard value. (3) In order to have the same benchmark for international comparison, the composite index and rankings will not be retroactively adjusted after their publication. (4) Since April 23, 2019, it is compiled by the Gender Equality Department of the Executive Yuan and can be found at the website https://www.gender.ey.gov.tw/gecdb/Stat_International_Node0.aspx?stZ7cAGjLH7DDUmC9hAf%2f4g%3d%3d.

The Vegetation Health Index (VHI) illustrates the severity of drought based on the vegetation health and the influence of temperature on plant conditions. The VHI is a composite index and the elementary indicator used to compute the seasonal drought indicators in ASIS: Agricultural Stress Index (ASI), Drought Intensity and Mean Vegetation Health Index (Mean VHI). VHI combines both the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI). The TCI is calculated using a similar equation to the VCI, but relates the current temperature to the long-term maximum and minimum , as it is assumed that higher temperatures tend to cause a deterioration in vegetation conditions. A decrease in the VHI would, for example, indicate relatively poor vegetation conditions and warmer temperatures, signifying stressed vegetation conditions, and over a longer period would be indicative of drought. In ASIS, VHI is computed in two modality: dekadal and monthly. The dekadal/monthly VHI raster layer published in Hand in Hand Geospatial platform is further updated in the following 5 dekads (improve data precision, remove cloud pixel etc.). Flags of raster file: 251=missing, 252=cloud, 253=snow, 254=sea, 255=background More information, please visit FAO GIEWS Earth Observation website: https://www.fao.org/giews/earthobservation/index.jsp?lang=en Recommended citation: © FAO - Agricultural Stress Index System (ASIS), http://www.fao.org/giews/earthobservation/, [Date accessed]

The Vegetation Health Index (VHI) illustrates the severity of drought based on the vegetation health and the influence of temperature on plant conditions. The VHI is a composite index and the elementary indicator used to compute the seasonal drought indicators in ASIS: Agricultural Stress Index (ASI), Drought Intensity and Mean Vegetation Health Index (Mean VHI). VHI combines both the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI). The TCI is calculated using a similar equation to the VCI, but relates the current temperature to the long-term maximum and minimum , as it is assumed that higher temperatures tend to cause a deterioration in vegetation conditions. A decrease in the VHI would, for example, indicate relatively poor vegetation conditions and warmer temperatures, signifying stressed vegetation conditions, and over a longer period would be indicative of drought. In ASIS, VHI is computed in two modality: dekadal and monthly. The dekadal/monthly VHI raster layer published in Hand in Hand Geospatial platform is further updated in the following 5 dekads (improve data precision, remove cloud pixel etc.). Flags of raster file: 251=missing, 252=cloud, 253=snow, 254=sea, 255=background More information, please visit FAO GIEWS Earth Observation website: https://www.fao.org/giews/earthobservation/index.jsp?lang=en Data license policy: Creative Commons Attribution- NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO) Recommended citation: © FAO - Agricultural Stress Index System (ASIS), http://www.fao.org/giews/earthobservation/, [Date accessed]

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.

Liberty's (FWONA) Formula for Success?

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.

Liberty Formula One (FWONA) Race to New Highs?

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.

Liberty Formula One Stock: Analysts Predict Continued Growth for (FWONK)

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 dataset contains the statewide composite patient safety and Adverse Events indicator (PSI) rate used to determine the “Incidence of measurable hospital-acquired conditions” rate for the Let’s Get Healthy California Initiative. PSI rates may not be comparable across years as significant changes were made to composition, definition, and calculation of PSI over time. The current composite PSI includes the following component indicators: pressure ulcer, iatrogenic pneumothorax, in-hospital fall-associated fracture, postoperative hemorrhage or hematoma, postoperative acute kidney injury requiring dialysis, postoperative respiratory failure, perioperative pulmonary embolism or deep vein thrombosis, postoperative sepsis, postoperative wound dehiscence, abdominopelvic accidental puncture or laceration.

Abstract

Comparison of economic performance over time is a key factor in economic analysis and a fundamental requirement for policy-making. Short-term indicators play an important role in this context by providing such comparison indicators. Among the short-term indicators, the Index of Industrial Production (IIP) has historically been one of the most well-known and well-used indicators. The all India IIP is a composite indicator that measures the short-term changes in the volume of production of a basket of industrial products during a given period with respect to that in a chosen base period. It is compiled and published monthly by the Central Statistics Office (CSO) with a time lag of six weeks from the reference month.

Geographic coverage

Industrial Coverage: Although UNSD guidelines state that IIP is to be compiled for activities in ISIC Rev. 4 Sections B, C, D and E, i.e. (i) Mining and quarrying, (ii) Manufacturing, (iii) Electricity, Gas, Steam and Air-conditioning supply and (iv) Water supply, Sewerage, Waste management and Remediation activities, due to constraints of the data availability and other resources, the index is being compiled with (i) Mining, (ii) Manufacturing and (iii) Electricity as scope of All India IIP. In the current base year (i.e. 2011-12), the index covers 839 items clubbed into 407 item groups under three sectors i.e. Mining (29 items clubbed into 1 item group), Manufacturing (809 items clubbed into 405 item groups) and Electricity (1 item) with weights of 14.37%, 77.63% and 7.99% respectively.

The mining sector covers 29 items under different headings viz. Fuel Minerals, Metallic Minerals and Non-Metallic Minerals. This sector also includes Crude Petroleum, Natural Gas, Coal and Lignite. The manufacturing sector covers 809 items under different groups e.g. Food products, Beverages, Textiles, Chemicals and chemical products etc. The Electricity sector is treated as a single item.

Product Coverage: Within an industry the products are covered on the basis of the concepts of Primary (Main) Product as well as Secondary (By) Product. All those items which represent at least 80% of the output within each industry group, i.e., 3-digit industry of NIC-2008 (based on ISIC 4) have been included in the Item basket. Essential products like tea, coffee, salt and sugar have been included. The over-riding criteria for finalization of item basket have been the regular monthly flow of production data from the source agencies/collection authorities.

Analysis unit

Frame for coverage of units is decided by the source agencies which collect data from the factories. For compilation of IIP both large and medium factories are covered for collection of data by the source agencies.

Mode of data collection

The sample size for data collection is decided by the source agencies. Generally, efforts are made to cover all the major units.

statistical techniques :

Procedures for Non-Response: In India, the Index of Industrial Production is based on the responded production as well as estimated production for non-responding units. The production estimates for the non-responding units are developed using various methods including: repetition of last available data; taking the average production data for the last few months; using previous year's growth rate; etc. The appropriate estimation procedure is decided by the source agencies themselves in consultation with CSO. Treatment of Missing Production: The index is compiled on the basis of the data on a fixed number of items collected from the source agencies which in turn collect the data from different factories and estimate the data on their own, as per the requirements. Selection of Replacement Items: Replacement of items is not done at present. Introducing New Units and Products: New units/ new products are included only at the time of the revision of base year.

Other statistical procedures : The production figures, if not reported by all the units in the current month due to any reason, are estimated for the current month and revised subsequently in the next month, and finally in the third month on the basis of which the final indices for a month are calculated.

Nature of Weights: The weights for the three sectors (mining, manufacturing, and electricity) are based on share of the sector in total domestic production in the base year. The overall weight of the manufacturing sector is apportioned to the industry groups at the 2-digit, 3-digit- and 4-digit level of the National Industrial Classification (NIC) 2008, on the basis of the Gross Value Added (GVA). The weighting diagram for the current series of IIP is prepared on the basis of GVA up to the 2-digit, 3 and 4 digit level of NIC based on the results of ASI 2011- 12. At the final level (i.e. 5 digit level of NIC), weights to items have been distributed on the basis of Gross Value of Output (GVO). The weights of selected items within an industry group are apportioned on the basis of the value of output.

Period of Current Index Weights: The current index weights are based on the value of production of the industries during the base year period viz. April, 2011 to March 2012 as reported in the Annual Survey of Industries for the year 2011-12. The same weights are used until the revision of the base year is done.

Frequency of Weight Updates: The weights are revised with every revision of the base year. The base year was revised to 2011-12 from 2004-05 in May 2017. Efforts would be made to revise the base year once in every five years as per UNSD's recommendations (the previous base years of the index were 2004-05, 1993-94, 1980-81, 1970, 1956, 1951 and 1946).

• Computation of lowest level indices: The lowest level, for which an index is prepared, is the item group. It is compiled as the ratio of production quantity in the current month with respect to its average monthly production quantity in the base year.

• Aggregation: The IIP is calculated using the Laspeyres formula as a weighted arithmetic average of production relatives. The index is primarily quantity based, although for some item groups the quantity relatives are obtained by price deflation.

The index at group level/ 2-digit level of NIC is compiled by using the Laspeyeres' formula, i.e. I = Uppercase sigma(Wi*Ri)/ Uppercase sigm(Wi) where Ri is the production relative and Wi is the weight of an item.

The index is prepared for each two-digit level of NIC. Also the index is prepared on the basis of the following use-based classification: Primary Goods, Capital Goods, Intermediate Goods, Infrastructure/ Construction Goods, Durable Consumer Goods and Non-Durable Consumer Goods.

• Alignment of Value of Weights and Base Period: No alignment of the weights is required as the weights as well as the base year production relate to the same reference period viz. April, 2011 to March 2012.

-- Linking of Re-weighted Index to Historical Index: Whenever there is change in the base year, the new series can be linked with the old series by preparing linked series. For the common period, the index series are available with both old weights & new weights for linking the two series.

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.

Liberty Formula One Stock Forecast: LMC Sees Positive Momentum for (FWONA)

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

Selected technical indicators and their formulas (Type 1).

Tables showing internal consistency and reliability test for HWB dimension of basic material for good life, dimension of security, dimension of health, dimension of good social relations, and for dimension of freedom of choice and action.

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.

ETY: A Winning Formula for Income and Growth?

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