The main stock market index of Philippines, the PSEi, rose to 6526 points on July 14, 2025, gaining 1.03% from the previous session. Over the past month, the index has climbed 2.64%, though it remains 2.44% lower than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Philippines. Philippines Stock Market (PSEi) - values, historical data, forecasts and news - updated on July of 2025.

Prices for Philippines Stock Exchange PSEi Index including live quotes, historical charts and news. Philippines Stock Exchange PSEi Index was last updated by Trading Economics this July 13 of 2025.

The PSEi Composite index is expected to continue its upward trend in the coming months, supported by strong corporate earnings and economic growth. The index is expected to reach a new high in the next six months, although there are some risks to consider. These include geopolitical uncertainty and rising inflation. Overall, the upside potential for the PSEi Composite index outweighs the risks, and investors are advised to stay invested for the long term.

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.

When Will PSEi Composite Recover?

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 Philippines PSEi

• Philippines PSEi closed at 5,998.0 points in Feb 2025, compared with 5,862.6 points at the previous month end
• Philippines Equity Market Index: Month End: PSEi data is updated monthly, available from Jan 1987 to Feb 2025, with an average number of 2,806.2 points
• The data reached an all-time high of 8,764.0 points in Jan 2018 and a record low of 511.2 points in Mar 1987

• In the latest reports, PSEi recorded a monthly P/E ratio of 12.0 in Dec 2024

Philippines Dividend Yield Ratio: Index Level: PSEi data was reported at 3.445 % in Feb 2025. This records a decrease from the previous number of 3.535 % for Jan 2025. Philippines Dividend Yield Ratio: Index Level: PSEi data is updated monthly, averaging 2.225 % from Jul 2006 (Median) to Feb 2025, with 224 observations. The data reached an all-time high of 6.080 % in Oct 2008 and a record low of 1.529 % in Jan 2018. Philippines Dividend Yield Ratio: Index Level: PSEi data remains active status in CEIC and is reported by Philippine Stock Exchange. The data is categorized under Global Database’s Philippines – Table PH.Z004: Philippine Stock Exchange: PE Ratio, PB Ratio and Yield. [COVID-19-IMPACT]

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.

Will the PSEi Index Soar Higher?

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

Philippines PE Ratio: Index Level: PSEi data was reported at 11.064 Unit in Feb 2025. This records an increase from the previous number of 10.737 Unit for Jan 2025. Philippines PE Ratio: Index Level: PSEi data is updated monthly, averaging 17.168 Unit from Jul 2006 (Median) to Feb 2025, with 224 observations. The data reached an all-time high of 26.282 Unit in May 2021 and a record low of 9.230 Unit in Oct 2008. Philippines PE Ratio: Index Level: PSEi data remains active status in CEIC and is reported by Philippine Stock Exchange. The data is categorized under Global Database’s Philippines – Table PH.Z004: Philippine Stock Exchange: PE Ratio, PB Ratio and Yield. [COVID-19-IMPACT]

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.

Is the PSEi Index Poised for 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

Philippines Index: PSE: All Shares data was reported at 4,441.330 14Nov1996=1000 in Nov 2018. This records an increase from the previous number of 4,370.460 14Nov1996=1000 for Oct 2018. Philippines Index: PSE: All Shares data is updated monthly, averaging 1,668.750 14Nov1996=1000 from Nov 1996 (Median) to Nov 2018, with 265 observations. The data reached an all-time high of 5,124.830 14Nov1996=1000 in Jan 2018 and a record low of 400.470 14Nov1996=1000 in Aug 1998. Philippines Index: PSE: All Shares data remains active status in CEIC and is reported by Philippine Stock Exchange. The data is categorized under Global Database’s Philippines – Table PH.Z003: Philippines Stock Exchange: Index.

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.

PSEi Composite: Is it a Rocky Road Ahead?

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

Philippines Index: PSE: Property data was reported at 3,601.140 30Sep1994=1000 in Nov 2018. This records an increase from the previous number of 3,495.490 30Sep1994=1000 for Oct 2018. Philippines Index: PSE: Property data is updated monthly, averaging 1,152.325 30Sep1994=1000 from Oct 1994 (Median) to Nov 2018, with 290 observations. The data reached an all-time high of 3,978.190 30Sep1994=1000 in Dec 2017 and a record low of 370.050 30Sep1994=1000 in Oct 2000. Philippines Index: PSE: Property data remains active status in CEIC and is reported by Philippine Stock Exchange. The data is categorized under Global Database’s Philippines – Table PH.Z003: Philippines Stock Exchange: Index.

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.

PSEi Composite index seen cautiously optimistic.

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

Stock market return (%, year-on-year) in Philippines was reported at 7.8165 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Philippines - Stock market return (%, year-on-year) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Philippines Index: PSE: Services data was reported at 1,462.550 29Dec2005=1000 in Oct 2018. This records a decrease from the previous number of 1,494.970 29Dec2005=1000 for Sep 2018. Philippines Index: PSE: Services data is updated monthly, averaging 1,571.185 29Dec2005=1000 from Jan 2006 (Median) to Oct 2018, with 154 observations. The data reached an all-time high of 2,237.160 29Dec2005=1000 in Aug 2014 and a record low of 1,027.130 29Dec2005=1000 in Jan 2006. Philippines Index: PSE: Services data remains active status in CEIC and is reported by Philippine Stock Exchange. The data is categorized under Global Database’s Philippines – Table PH.Z003: Philippines Stock Exchange: Index.

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.

PSEi Composite Index Target Price Prediction

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.

Buy, sell or hold: PSEi Composite Index Stock Forecast

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

List of highest-yielding dividend stocks in the Philippine Stock Exchange for 2025

Czech Republic Index: PSE: Annual: PX 50 data was reported at 1,760.170 05Apr1994=1000 in 2024. This records an increase from the previous number of 1,414.020 05Apr1994=1000 for 2023. Czech Republic Index: PSE: Annual: PX 50 data is updated yearly, averaging 986.560 05Apr1994=1000 from Dec 1994 (Median) to 2024, with 31 observations. The data reached an all-time high of 1,815.100 05Apr1994=1000 in 2007 and a record low of 394.200 05Apr1994=1000 in 1998. Czech Republic Index: PSE: Annual: PX 50 data remains active status in CEIC and is reported by Prague Stock Exchange. The data is categorized under Global Database’s Czech Republic – Table CZ.Z001: Prague Stock Exchange: Index.

Philippines PB Ratio: Index Level: PSEi data was reported at 1.312 Unit in Feb 2025. This records an increase from the previous number of 1.274 Unit for Jan 2025. Philippines PB Ratio: Index Level: PSEi data is updated monthly, averaging 2.077 Unit from Jul 2006 (Median) to Feb 2025, with 224 observations. The data reached an all-time high of 3.160 Unit in Apr 2013 and a record low of 1.220 Unit in Jan 2009. Philippines PB Ratio: Index Level: PSEi data remains active status in CEIC and is reported by Philippine Stock Exchange. The data is categorized under Global Database’s Philippines – Table PH.Z004: Philippine Stock Exchange: PE Ratio, PB Ratio and Yield. [COVID-19-IMPACT]