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.

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

The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Indexes are available for the U.S. and various geographic areas. Average price data for select utility, automotive fuel, and food items are also available. Prices for the goods and services used to calculate the CPI are collected in 75 urban areas throughout the country and from about 23,000 retail and service establishments. Data on rents are collected from about 43,000 landlords or tenants. More information and details about the data provided can be found at http://www.bls.gov/cpi

Infant Formula Ingredients Market Outlook

The global infant formula ingredients market size is projected to grow from USD 13.5 billion in 2023 to USD 20.3 billion by 2032, reflecting a compound annual growth rate (CAGR) of 5.6% during this forecast period. This growth is driven by increasing awareness among parents regarding infant nutrition, rising birth rates in several developing regions, and heightened demand for convenient and reliable alternatives to breast milk. The market is experiencing significant expansion due to advancements in food technology, enabling the production of high-quality, nutritionally balanced formula ingredients that mimic natural breast milk more closely. As consumers become more health-conscious, there is a growing preference for formula ingredients that not only meet basic nutritional needs but also support immunity, growth, and cognitive development in infants.

A primary growth factor for the infant formula ingredients market is the increasing awareness and emphasis on infant health and nutrition. As scientific research continues to highlight the critical importance of early nutrition on long-term health outcomes, parents are becoming more discerning about the nutritional content of infant formulas. Manufacturers are responding by developing products with enhanced nutrient profiles, including added prebiotics and probiotics to support gut health, as well as omega fatty acids like DHA and ARA to aid brain development. This has led to a surge in demand for specialized ingredients that can fortify infant formulas with essential vitamins, minerals, and other bioactive compounds.

Additionally, the global increase in the female workforce is another significant driver for this market. As more women enter the workforce, the need for convenient feeding options rises, thereby increasing the demand for infant formula. Working mothers, who may have limited time for breastfeeding, often rely on formula as a primary or supplementary feeding method. This shift has prompted companies to innovate and diversify their product offerings to cater to varying dietary preferences and restrictions, such as lactose-free and hypoallergenic formulas, further bolstering market growth. The rise in dual-income households also enhances purchasing power, enabling families to spend more on high-quality nutritional products for their infants.

Moreover, rising urbanization and changing lifestyles are contributing to the steady growth of the infant formula ingredients market. Urban living often involves hectic schedules and limited access to breastfeeding support, making formula feeding a practical alternative for many parents. Furthermore, parents in urban areas tend to have better access to information, which increases their awareness of different formula options and ingredients. Consequently, there is a growing trend towards premium and organic infant formulas that promise enhanced safety and nutritional value, thus boosting the overall market demand.

Commercial Infant Formulas have become a cornerstone in the infant nutrition industry, offering a reliable alternative for parents who are unable or choose not to breastfeed. These formulas are meticulously designed to provide a balanced mix of essential nutrients that support the growth and development of infants. The commercial aspect of these formulas ensures that they are widely available and accessible, catering to the diverse needs of families across different regions. With advancements in nutritional science, manufacturers are continually enhancing the nutrient profiles of these formulas to closely mimic the composition of natural breast milk. This includes the incorporation of bioactive components such as prebiotics and probiotics, which play a crucial role in promoting gut health and boosting the immune system. As the demand for high-quality infant nutrition continues to rise, commercial infant formulas are expected to evolve, offering even more tailored solutions to meet the specific dietary requirements of infants.

Ingredient Type Analysis

In the infant formula ingredients market, carbohydrates, proteins, fats & oils, vitamins & minerals, prebiotics, and other nutrients play crucial roles in formulating balanced infant nutrition solutions. Carbohydrates, primarily sourced from lactose, maltodextrin, and corn syrup solids, serve as a major source of energy in infant formulas. The selection of carbohydrate sources is critical as it affects the formula's taste, digestibility, and glycemic index. Innovations in carboh

Key information about Algeria Algiers Stock Exchange

• Algeria Algiers Stock Exchange closed at 3,598.4 points in Feb 2025, compared with 3,614.3 points at the previous month end
• Algeria Equity Market Index: Month End: Algiers Stock Exchange data is updated monthly, available from Sep 1999 to Feb 2025, with an average number of 1,454.4 points
• The data reached an all-time high of 19,423.9 points in Feb 2000 and a record low of 369.7 points in Apr 2006

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 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

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

The real effective exchange rate (REER) – 42 trading partners – aims at assessing a country's price or cost competitiveness relative to its principal competitors in international markets. Changes in cost and price competitiveness depend not only on exchange rate movements but also on cost and price trends. The specific REER for the Macroeconomic Imbalances Procedure is deflated by the consumer price indices relative to a panel of 42 countries (double export weights are used to calculate REERs, reflecting not only competition in the home markets of the various competitors, but also competition in export markets elsewhere). A positive value means real appreciation. The data are presented as 3-year % change, and 1-year % change. The MIP scoreboard indicator is the percentage change over three years of REER based on consumer price index deflators relative to 42 trading partners. The formula is: [[(REER_HICP_42)t - (REER_HICP_42)t-3] / (REER_HICP_42)t-3]*100 The indicative thresholds are +/-5% for euro area and +/-11% for non-euro area countries. Data source: Directorate General for Economic and Financial Affairs (DG ECFIN)

The global market for mathematical calculation software is experiencing robust growth, driven by increasing demand across diverse sectors. The expanding adoption of sophisticated analytical techniques in scientific research, engineering, and finance is a key catalyst. The rising complexity of modern problems necessitates powerful software solutions capable of handling large datasets and intricate calculations. Furthermore, the growing integration of mathematical software into educational curricula at all levels is fueling market expansion. The market is segmented by application (scientific research, engineering, education, finance, others) and software type (numerical calculation, statistical, mathematical programming, others). While the precise market size in 2025 is unavailable, based on industry trends and growth patterns in similar software sectors, a reasonable estimate places the market value at approximately $15 billion. Assuming a conservative compound annual growth rate (CAGR) of 8% over the forecast period (2025-2033), the market is projected to reach approximately $30 billion by 2033. This growth will be further propelled by advancements in artificial intelligence (AI) and machine learning (ML), which are increasing the need for high-performance mathematical computation tools. However, market growth faces certain restraints. High initial investment costs associated with acquiring and implementing sophisticated software packages may hinder adoption, especially among smaller companies and educational institutions with limited budgets. Furthermore, the need for specialized expertise to effectively utilize these tools presents a barrier to entry for some users. Nevertheless, the ongoing development of user-friendly interfaces and the availability of cloud-based solutions are expected to mitigate these challenges to some extent. The competitive landscape is fragmented, with a mix of established players and emerging niche providers. Companies like MathWorks, Wolfram, and others are leading the market, while new entrants with specialized solutions are constantly emerging, resulting in a dynamic and innovative market. Geographical distribution shows strong growth in North America and Asia-Pacific regions, driven by robust technological infrastructure and a high concentration of research institutions and technology-driven industries.

The appraisal committee for real estate values in the city of Dortmund provides an online calculator as a free service to determine an estimated value for real estate on the basis of the real estate benchmarks. The result does not replace the market value determination by an expert opinion or the appraisal committee, but forms a good guideline for determining the value of your property.

Real estate benchmarks are average real estate values based on a "standard object" typical for the respective location. They are expertly derived from the purchase price collection and determined by decision of the expert committee on the reference date.

Values for garages, parking spaces and special rights of use are to be estimated separately according to their current value. Information on this can be found in the local land market report.

In addition, other influencing factors can play a role in determining the value, such as special local and structural conditions, the condition of the object, special installations, a hereditary building right, resale rights, building loads, pipeline rights, harmful soil contamination, etc.

It is applied on the basis of the conversion coefficients determined and adopted by the Expert Committee. The user enters the descriptive characteristics of his property independently and thus determines the estimated value of the property. In the case of special features such as exposed locations, villas, scrap real estate, new buildings or the like, it is unfortunately not possible to determine the estimated value with the help of the real estate value calculator. The responsibility for the proper application lies with the user.