Russia's main stock market index, the MOEX, rose to 2847 points on June 30, 2025, gaining 1.47% from the previous session. Over the past month, the index has climbed 0.63%, though it remains 10.62% lower than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Russia. Russia Stock Market Index MOEX CFD - values, historical data, forecasts and news - updated on July of 2025.

Key information about Russia RTS

• Russia RTS closed at 1,142.4 points in Feb 2025, compared with 949.5 points at the previous month end
• Russia Equity Market Index: Month End: RTS data is updated monthly, available from Sep 1995 to Feb 2025, with an average number of 1,055.6 points
• The data reached an all-time high of 2,459.9 points in May 2008 and a record low of 43.8 points in Sep 1998

Russia's main stock market index, the MOEX, rose to 2806 points on June 27, 2025, gaining 0.30% from the previous session. Over the past month, the index has climbed 0.74%, though it remains 10.95% lower than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Russia. Russia Stock Market Index MOEX CFD - values, historical data, forecasts and news - updated on June of 2025.

The MOEX index, the most prominent ruble-denominated index of stocks listed on the Moscow Stock Exchange, fell by nearly one third of its value between February 13 and February 20, 2022, following the Russian invasion of Ukraine. It has since fluctuated significantly and stood at 2,953.2 as of January 26, 2025. The MOEX index is considered the primary index for domestic investors in Russia. It contains the same components as the RTS index, however the latter is denominated in U.S. dollars and therefore preferred by many international investors.

Stock market index in Russia, March, 2025 The most recent value is 190.94 points as of March 2025, no change compared to the previous value of 190.94 points. Historically, the average for Russia from September 1997 to March 2025 is 90.43 points. The minimum of 1.51 points was recorded in August 1998, while the maximum of 250.66 points was reached in October 2021. | TheGlobalEconomy.com

Russia Equity Market Index: USD data was reported at 25.554 2010=100 in Apr 2025. This records a decrease from the previous number of 26.532 2010=100 for Mar 2025. Russia Equity Market Index: USD data is updated monthly, averaging 38.826 2010=100 from Sep 1995 (Median) to Apr 2025, with 356 observations. The data reached an all-time high of 196.583 2010=100 in May 2008 and a record low of 4.863 2010=100 in Jan 1999. Russia Equity Market Index: USD data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Russian Federation – Table RU.World Bank.GEM: Equity Market Index. Local equity market index valued in US$ terms

The statistic shows the annual development of the MOEX (RUB) Russia index from 2013 to 2023. MOEX (Moscow Interbank Currency Exchange) is the leading Russian Stock Exchange index, reflecting the performance of the 50 largest and most liquid companies traded on the Moscow Stock Exchange. The year-end value of IMOEX amounted to 2,883.04 in 2024 - below the value registered at the end of the previous year.

Russia Equity Market Index: Local Currency data was reported at 71.537 2010=100 in Apr 2025. This records a decrease from the previous number of 76.685 2010=100 for Mar 2025. Russia Equity Market Index: Local Currency data is updated monthly, averaging 68.584 2010=100 from Sep 1995 (Median) to Apr 2025, with 356 observations. The data reached an all-time high of 156.306 2010=100 in May 2008 and a record low of 3.360 2010=100 in Oct 1998. Russia Equity Market Index: Local Currency data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Russian Federation – Table RU.World Bank.GEM: Equity Market Index. Local equity market index valued in local currency unit (LCU) terms

Graph and download economic data for Financial Market: Share Prices for Russia (SPASTT01RUQ661N) from Q4 1997 to Q1 2025 about Russia and stock market.

In 2019, Gazprom accounted for over one quarter of the total contribution to the growth of the Russian stock market, or Russian Trading Index (RTS). The second leading contributor was Sberbank.

stock indexes in Russia. name, image, weighting method, type, date Foundation, Country, continent, Stock Market, Market capitalization, Website, legal entity

Key information about Russia Market Capitalization

• Russia Market Capitalization accounted for 78.427 USD bn in Feb 2025, compared with a percentage of 65.186 USD bn in the previous month
• Russia Market Capitalization is updated monthly, available from Apr 1997 to Feb 2025
• The data reached an all-time high of 294.008 USD bn in Oct 2021 and a record low of 7.925 USD bn in Sep 1998

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.

What is MOEX Russia Index stock 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.

Is MOEX Russia Index a Buy?

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

Moscow Exchange reported 7.46 in PE Price to Earnings for its fiscal quarter ending in June of 2024. Data for Moscow Exchange | MOEX - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last July in 2025.

El índice principal del mercado de valores de Rusia, el MOEX, subió a 2760 puntos el 23 de junio de 2025, aumentando un 0.31% con respecto a la sesión anterior. En el último mes, el índice ha subido un 2.22%, aunque sigue siendo un 11.20% más bajo que hace un año, según la negociación en un contrato por diferencia (CFD) que sigue este índice de referencia de Rusia. Los valores actuales, los datos históricos, las previsiones, estadísticas, gráficas y calendario económico - Rusia - Mercado de acciones.

Since Russia recognized the independence of the two separatist republics located in eastern Ukraine (February 21, 2022) and launched its invasion of the country (February 24, 2022), the stock prices of major Russian companies plummeted. Russian bank Sberbank lost over 99 percent of its market value.

Russia's central bank, the Central Bank of Russia (CBR), acknowledged that the banking sector had lost liquidity and increased interest rates from 9.5 to 20 percent. In addition, the government introduced capital controls by ordering every private company to sell currency to the Bank of Russia and prohibited residents from making foreign transfers. Customers of sanctioned banks were prevented from using Apple Pay, Google Pay and, Samsung Pay. Sberbank, Russia's largest bank, is leaving the European market as a result of pressure from Western sanctions. On February 28, the European Central Bank reported that Sberbank Europe and its subsidiaries in Croatia and Slovenia were at risk of bankruptcy as a result of a deterioration in its liquidity. On March 2, the London Stock Exchange suspended trading in global depository receipts (GDRs) of several Russian companies, including Rosneft, Sberbank, Gazprom, En+, and Lukoil, with immediate effect.

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.

How do you predict if a stock will go up or down? (MOEX Russia Index Stock 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 statistic shows the stock price development of selected petroleum companies from January 2, 2020 to April 15, 2024. After the Russian invasion of Ukraine in February 2022, oil prices increased sharply in the first quarter of 2022 since many countries depend on Russian oil. Petroleum companies highly benefited from inclined oil prices, and saw significant increases in their share prices.

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.

Saudi Arabia Seeks Russian Diesel as War in Ukraine Drives Up Prices

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