Argentina Treasury Bonds: Parity: USD: Discount data was reported at 49.000 % in Aug 2020. This records an increase from the previous number of 48.700 % for Jul 2020. Argentina Treasury Bonds: Parity: USD: Discount data is updated monthly, averaging 95.460 % from Dec 2005 (Median) to Aug 2020, with 177 observations. The data reached an all-time high of 161.700 % in Nov 2015 and a record low of 29.130 % in Mar 2009. Argentina Treasury Bonds: Parity: USD: Discount data remains active status in CEIC and is reported by Central Bank of Argentina. The data is categorized under Global Database’s Argentina – Table AR.M009: Treasury Securities: Yield and Spread (Discontinued).

As of July 18, 2025, the major economy with the highest yield on 10-year government bonds was Turkey, with a yield of ** percent. This is due to the risks investors take when investing in Turkey, notably due to high inflation rates potentially eradicating any profits made when using a foreign currency to investing in securities denominated in Turkish lira. Of the major developed economies, United Kingdom had one the highest yield on 10-year government bonds at this time with **** percent, while Switzerland had the lowest at **** percent. How does inflation influence the yields of government bonds? Inflation reduces purchasing power over time. Due to this, investors seek higher returns to offset the anticipated decrease in purchasing power resulting from rapid price rises. In countries with high inflation, government bond yields often incorporate investor expectations and risk premiums, resulting in comparatively higher rates offered by these bonds. Why are government bond rates significant? Government bond rates are an important indicator of financial markets, serving as a benchmark for borrowing costs, interest rates, and investor sentiment. They affect the cost of government borrowing, influence the price of various financial instruments, and serve as a reflection of expectations regarding inflation and economic growth. For instance, in financial analysis and investing, people often use the 10-year U.S. government bond rates as a proxy for the longer-term risk-free rate.

Argentina Treasury Bonds Parity: BONAR 24: USD data was reported at 46.500 % in Aug 2020. This records an increase from the previous number of 43.200 % for Jul 2020. Argentina Treasury Bonds Parity: BONAR 24: USD data is updated monthly, averaging 104.850 % from Apr 2016 (Median) to Aug 2020, with 52 observations. The data reached an all-time high of 117.500 % in May 2017 and a record low of 27.600 % in Apr 2020. Argentina Treasury Bonds Parity: BONAR 24: USD data remains active status in CEIC and is reported by Central Bank of Argentina. The data is categorized under Global Database’s Argentina – Table AR.M009: Treasury Securities: Yield and Spread (Discontinued).

The Emerging Markets Bond Index (EMBI), commonly known as "riesgo país" in Spanish speaking countries, is a weighted financial benchmark that measures the interest rates paid each day by a selected portfolio of government bonds from emerging countries. It is measured in base points, which reflect the difference between the return rates paid by emerging countries' government bonds and those offered by U.S. Treasury bills. This difference is defined as "spread". Which Latin American country has the highest risk bonds? As of September 19, 2024, Venezuela was the Latin American country with the greatest financial risk and highest expected returns of government bonds, with an EMBI spread of around 254 percent. This means that the annual interest rates paid by Venezuela's sovereign debt titles were estimated to be exponentially higher than those offered by the U.S. Treasury. On the other hand, Brazil's EMBI reached 207 index points at the end of August 2023. In 2023, Venezuela also had the highest average EMBI in Latin America, exceeding 40,000 base points. The impact of COVID-19 on emerging market bonds The economic crisis spawned by the coronavirus pandemic heavily affected the financial market's estimated risks of emerging governmental bonds. For instance, as of June 30, 2020, Argentina's EMBI spread had increased more than four percentage points in comparison to January 30, 2020. All the Latin American economies measured saw a significant increase of the EMBI spread in the first half of the year.

Argentina Treasury Bonds: Parity: ARS: Discount data was reported at 101.900 % in Aug 2020. This records an increase from the previous number of 86.200 % for Jul 2020. Argentina Treasury Bonds: Parity: ARS: Discount data is updated monthly, averaging 73.685 % from Nov 2005 (Median) to Aug 2020, with 178 observations. The data reached an all-time high of 120.600 % in Dec 2015 and a record low of 24.920 % in Mar 2009. Argentina Treasury Bonds: Parity: ARS: Discount data remains active status in CEIC and is reported by Central Bank of Argentina. The data is categorized under Global Database’s Argentina – Table AR.M009: Treasury Securities: Yield and Spread (Discontinued).

The benchmark interest rate in Argentina was last recorded at 29 percent. This dataset provides the latest reported value for - Argentina Money Market Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Amounts outstanding of debt securities issued in international markets by residents of Argentina of general government (nationality of All countries excluding residents of all issuers), all currencies, Total all currencies, original maturity of total (all maturities), remaining maturity of total (all maturities), all interest rates

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.

Banco Macro: (BMA) Navigating Argentine Economic Winds

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

Key information about Argentina Policy Rate

• Argentina cash rate (Interest Rate: Financial Institution Deposit: 30 Days) was set at 27.74 % in Feb 2025, compared with 28.72 % in the previous Jan 2025.
• Argentina Policy Rate averaged 17.09 % and is updated monthly, available from Jan 1985 to Feb 2025.
• The data reached an all-time high of 1,010.56 % in May 1989 and a record low of 0.00 % in Jul 1990.

• In the latest reports, Argentina Short Term Interest Rate: Month End: Argentina: Up to 15 Days Interbank Call Rate (BAIBAR) was reported at 31.80 % pa in Jul 2021.
• Argentina Exchange Rate against USD averaged 254.03 (USD/ARS) in Jun 2023.
• Its Real Effective Exchange Rate was 50.18 in Feb 2025.

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.

BBAR Banco BBVA Argentina S.A. ADS

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.

Telecom Argentina SA (TEO): A Glimmer of Hope?

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.

Cresud Warrant (CRESW): Window into Argentine Agriculture?

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.

Galicia Financial: Analysts Bullish on Argentine Bank's Growth Potential (GGAL)

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

Serie diaria del rendimiento (yield) del Emerging Markets Bond Index (EMBI) de JP Morgan para Argentina, Brasil y el índice Global. A diferencia del EMBI spread, que mide el diferencial con respecto a los bonos del Tesoro de EE.UU., el EMBI yield representa el rendimiento total de los bonos soberanos emitidos por estos países en mercados internacionales. Este conjunto de datos permite analizar la evolución del costo absoluto de financiamiento externo para los principales socios comerciales de Uruguay y para el conjunto de mercados emergentes. Es un indicador clave para evaluar las condiciones financieras regionales, la sostenibilidad de la deuda soberana y las expectativas de los inversores sobre el desempeño económico futuro de estas economías.

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.

Loma Negra: Argentina's Cement Giant (LOMA)

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

（停止更新）长期国库债券：产次：美元：贴现率在08-01-2020达49.000%，相较于07-01-2020的48.700%有所增长。（停止更新）长期国库债券：产次：美元：贴现率数据按月更新，12-01-2005至08-01-2020期间平均值为95.460%，共177份观测结果。该数据的历史最高值出现于11-01-2015，达161.700%，而历史最低值则出现于03-01-2009，为29.130%。CEIC提供的（停止更新）长期国库债券：产次：美元：贴现率数据处于定期更新的状态，数据来源于Banco Central de la Republica Argentina，数据归类于全球数据库的阿根廷 – Table AR.M009: Treasury Securities: Yield and Spread (Discontinued)。

（停止更新）长期国库券平价：BONAR 24：美元在08-01-2020达46.500%，相较于07-01-2020的43.200%有所增长。（停止更新）长期国库券平价：BONAR 24：美元数据按月更新，04-01-2016至08-01-2020期间平均值为104.850%，共52份观测结果。该数据的历史最高值出现于05-01-2017，达117.500%，而历史最低值则出现于04-01-2020，为27.600%。CEIC提供的（停止更新）长期国库券平价：BONAR 24：美元数据处于定期更新的状态，数据来源于Banco Central de la Republica Argentina，数据归类于全球数据库的阿根廷 – Table AR.M009: Treasury Securities: Yield and Spread (Discontinued)。