The main stock market index of Philippines, the PSEi, fell to 6460 points on July 11, 2025, losing 0.05% from the previous session. Over the past month, the index has climbed 1.01%, though it remains 2.83% 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.

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

The Philippine Stock Exchange reported about **** million market accounts in 2023, reflecting an increase compared to the previous year. The number of stock market accounts in the Philippines has been gradually increasing in the past eight years. In the same year, about ** percent of the total stock market accounts in the country were online trading accounts.

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

The Philippines: Stock market return, percent: The latest value from 2021 is 7.82 percent, an increase from -19.62 percent in 2020. In comparison, the world average is 32.21 percent, based on data from 87 countries. Historically, the average for the Philippines from 1988 to 2021 is 9.06 percent. The minimum value, -30.47 percent, was reached in 1998 while the maximum of 60.82 percent was recorded in 1994.

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.

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 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]

Philippines Market Capitalization: PSE data was reported at 17,583,119.612 PHP mn in 2017. This records an increase from the previous number of 14,438,774.757 PHP mn for 2016. Philippines Market Capitalization: PSE data is updated yearly, averaging 1,170,054.044 PHP mn from Dec 1970 (Median) to 2017, with 48 observations. The data reached an all-time high of 17,583,119.612 PHP mn in 2017 and a record low of 4,614.805 PHP mn in 1971. Philippines Market Capitalization: PSE data remains active status in CEIC and is reported by Philippine Stock Exchange. The data is categorized under Global Database’s Philippines – Table PH.Z008: Philippines Stock Exchange: Annual Statistics.

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

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

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

Comprehensive list of blue chip stocks listed on the Philippine Stock Exchange for 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.

Content

This dataset contains the historical data of 303 publicly listed companies in the Philippines Stock Exchange from December 2011 to March 24, 2021. All data were scraped from https://ph.investing.com.

Scraping scripts can be found here: https://github.com/ShaneMaglangit/pse-stocks

The Philippine Stock Exchange noted an increase of about 24 percent of its online investor accounts in 2021 in comparison to the previous year - equivalent to about 1.16 million accounts. The number of online investor accounts has significantly increased over the past eight years.

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

In 2023, the total number of retail accounts in the Philippines' stock market amounted to approximately **** million. By comparison, the number of institutional accounts amounted to about ******.