In 2024, the Philippines’ inflation rate amounted to 3.21 percent. The Philippines are considered “newly industrialized”, but the economy relies on remittances from nationals overseas, and the services sector generates most of its GDP . Emerging and soon to develop?After switching from agriculture to services and manufacturing, the Philippines are now an emerging economy, i.e. the country has some characteristics of a developed nation but is not quite there yet. In order to transition into a developed nation, the Philippines must meet certain requirements, like being able to sustain their economic development, being very open to foreign investors, or maintaining a very high stability of the institutional framework (like law enforcement and the government). Only if these changes are irreversible can they be classified as a developed nation. The Philippines’ switch to servicesEver since the switch to services and manufacturing, employment in these areas has increased and the country is now among those with the highest employment in the tourism industry worldwide. This transition was not entirely voluntary but also due to decreasing government support, the liberalization of trade, and reform programs. Still, agriculture is important for the country: As of 2017, more than a quarter of Filipinos are still working in the agricultural sector, and urbanization has only increased very slightly over the last decade.

Philippines Bangko Sentral ng Pilipinas: Inflation Target: Lower Limit data was reported at 2.000 % in 2022. This stayed constant from the previous number of 2.000 % for 2021. Philippines Bangko Sentral ng Pilipinas: Inflation Target: Lower Limit data is updated yearly, averaging 3.000 % from Dec 2002 (Median) to 2022, with 21 observations. The data reached an all-time high of 5.000 % in 2005 and a record low of 2.000 % in 2022. Philippines Bangko Sentral ng Pilipinas: Inflation Target: Lower Limit data remains active status in CEIC and is reported by Bangko Sentral ng Pilipinas. The data is categorized under Global Database’s Philippines – Table PH.I001: Consumer Price Index: Inflation Target.

The benchmark interest rate in Philippines was last recorded at 5.25 percent. This dataset provides the latest reported value for - Philippines Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Franklin BSP Realty Rebounding with Recovery Promise? (FBRT)

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.

Franklin BSP Realty Trust sees potential gains. (FBRT)

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