Long term historical dataset of the NASDAQ Composite stock market index since 1971. Historical data is inflation-adjusted using the headline CPI and each data point represents the month-end closing value. The current month is updated on an hourly basis with today's latest value.

Key information about India P/E ratio

• India SENSEX recorded a daily P/E ratio of 21.540 on 26 Mar 2025, compared with 21.740 from the previous day.
• India SENSEX P/E ratio is updated daily, with historical data available from Dec 1988 to Mar 2025.
• The P/E ratio reached an all-time high of 36.210 in Feb 2021 and a record low of 15.670 in Mar 2020.
• BSE Limited provides daily P/E Ratio.

Key information about Thailand P/E ratio

• Thailand Approach 2 recorded a daily P/E ratio of 16.140 on 26 Mar 2025, compared with 16.210 from the previous day.
• Thailand Approach 2 P/E ratio is updated daily, with historical data available from Jan 2003 to Mar 2025.
• The P/E ratio reached an all-time high of 41.670 in Apr 2021 and a record low of 9.800 in Jul 2012.
• The Stock Exchange of Thailand provides daily P/E Ratio.

Hong Kong Exchanges reported 31.5 in PE Price to Earnings for its fiscal quarter ending in September of 2024. Data for Hong Kong Exchanges | 388 - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last March in 2025.

Key information about China P/E ratio

• China Shanghai Stock Exchange recorded a daily P/E ratio of 14.340 on 26 Mar 2025, compared with 14.320 from the previous day.
• China Shanghai Stock Exchange P/E ratio is updated daily, with historical data available from Apr 2001 to Mar 2025.
• The P/E ratio reached an all-time high of 27.970 in Apr 2010 and a record low of 9.590 in May 2014.
• Shanghai Stock Exchange provides daily P/E Ratio.

NZX reported 22.07 in PE Price to Earnings for its fiscal semester ending in June of 2024. Data for NZX - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last March in 2025.

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 March in 2025.

Key information about Indonesia P/E ratio

• Indonesia IDX recorded a monthly P/E ratio of 12.040 on Mar 2025, compared with 12.590 from the previous month.
• Indonesia IDX P/E ratio is updated monthly, with historical data available from Jan 1992 to Feb 2025.
• The P/E ratio reached an all-time high of 32.530 in Apr 1995 and a record low of 2.740 in Feb 1999.
• Indonesia Stock Exchange provides monthly P/E Ratio. Indonesia Stock Exchange does not provide month end data; thus monthly average is used instead.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

This is not going to be an article or Op-Ed about Michael Jordan. Since 2009 we've been in the longest bull-market in history, that's 11 years and counting. However a few metrics like the stock market P/E, the call to put ratio and of course the Shiller P/E suggest a great crash is coming in-between the levels of 1929 and the dot.com bubble. Mean reversion historically is inevitable and the Fed's printing money experiment could end in disaster for the stock market in late 2021 or 2022. You can read Jeremy Grantham's Last Dance article here. You are likely well aware of Michael Burry's predicament as well. It's easier for you just to skim through two related videos on this topic of a stock market crash. Michael Burry's Warning see this YouTube. Jeremy Grantham's Warning See this YouTube. Typically when there is a major event in the world, there is a crash and then a bear market and a recovery that takes many many months. In March, 2020 that's not what we saw since the Fed did some astonishing things that means a liquidity sloth and the risk of a major inflation event. The pandemic represented the quickest decline of at least 30% in the history of the benchmark S&P 500, but the recovery was not correlated to anything but Fed intervention. Since the pandemic clearly isn't disappearing and many sectors such as travel, business travel, tourism and supply chain disruptions appear significantly disrupted - the so-called economic recovery isn't so great. And there's this little problem at the heart of global capitalism today, the stock market just keeps going up. Crashes and corrections typically occur frequently in a normal market. But the Fed liquidity and irresponsible printing of money is creating a scenario where normal behavior isn't occurring on the markets. According to data provided by market analytics firm Yardeni Research, the benchmark index has undergone 38 declines of at least 10% since the beginning of 1950. Since March, 2020 we've barely seen a down month. September, 2020 was flat-ish. The S&P 500 has more than doubled since those lows. Look at the angle of the curve: The S&P 500 was 735 at the low in 2009, so in this bull market alone it has gone up 6x in valuation. That's not a normal cycle and it could mean we are due for an epic correction. I have to agree with the analysts who claim that the long, long bull market since 2009 has finally matured into a fully-fledged epic bubble. There is a complacency, buy-the dip frenzy and general meme environment to what BigTech can do in such an environment. The weight of Apple, Amazon, Alphabet, Microsoft, Facebook, Nvidia and Tesla together in the S&P and Nasdaq is approach a ridiculous weighting. When these stocks are seen both as growth, value and companies with unbeatable moats the entire dynamics of the stock market begin to break down. Check out FANG during the pandemic. BigTech is Seen as Bullet-Proof me valuations and a hysterical speculative behavior leads to even higher highs, even as 2020 offered many younger people an on-ramp into investing for the first time. Some analysts at JP Morgan are even saying that until retail investors stop charging into stocks, markets probably don’t have too much to worry about. Hedge funds with payment for order flows can predict exactly how these retail investors are behaving and monetize them. PFOF might even have to be banned by the SEC. The risk-on market theoretically just keeps going up until the Fed raises interest rates, which could be in 2023! For some context, we're more than 1.4 years removed from the bear-market bottom of the coronavirus crash and haven't had even a 5% correction in nine months. This is the most over-priced the market has likely ever been. At the night of the dot-com bubble the S&P 500 was only 1,400. Today it is 4,500, not so many years after. Clearly something is not quite right if you look at history and the P/E ratios. A market pumped with liquidity produces higher earnings with historically low interest rates, it's an environment where dangerous things can occur. In late 1997, as the S&P 500 passed its previous 1929 peak of 21x earnings, that seemed like a lot, but nothing compared to today. For some context, the S&P 500 Shiller P/E closed last week at 38.58, which is nearly a two-decade high. It's also well over double the average Shiller P/E of 16.84, dating back 151 years. So the stock market is likely around 2x over-valued. Try to think rationally about what this means for valuations today and your favorite stock prices, what should they be in historical terms? The S&P 500 is up 31% in the past year. It will likely hit 5,000 before a correction given the amount of added liquidity to the system and the QE the Fed is using that's like a huge abuse of MMT, or Modern Monetary Theory. This has also lent to bubbles in the housing market, crypto and even commodities like Gold with long-term global GDP meeting many headwinds in the years ahead due to a...

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 are the most successful trading algorithms? (NTAP 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

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.

Can we predict stock market using machine learning? (FZO 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

The main stock market index in Indonesia (JCI) decreased 608 points or 8.58% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Indonesia. Indonesia Stock Market (JCI) - values, historical data, forecasts and news - updated on March of 2025.

Key information about Sri Lanka P/E ratio

• Sri Lanka All Shares recorded a daily P/E ratio of 8.300 on 24 Mar 2025, compared with 8.210 from the previous day.
• Sri Lanka All Shares P/E ratio is updated daily, with historical data available from Aug 1998 to Mar 2025.
• The P/E ratio reached an all-time high of 29.530 in Feb 2011 and a record low of 4.520 in Nov 2022.
• Colombo Stock Exchange provides daily P/E Ratio.

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.

S&P 500: A Bull or a Bear?

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 South Korea P/E ratio

• South Korea KOSPI recorded a daily P/E ratio of 13.870 on 26 Mar 2025, compared with 13.720 from the previous day.
• South Korea KOSPI P/E ratio is updated daily, with historical data available from Apr 2000 to Mar 2025.
• The P/E ratio reached an all-time high of 33.350 in Apr 2021 and a record low of 9.260 in Sep 2022.
• Korea Exchange provides daily P/E Ratio.

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 Volatility King of the S&P 500 Index?

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 Saudi Arabia P/E ratio

• Saudi Arabia Tadawul recorded a daily P/E ratio of 16.460 on 26 Mar 2025, compared with 16.441 from the previous day.
• Saudi Arabia Tadawul P/E ratio is updated daily, with historical data available from Aug 2008 to Mar 2025.
• The P/E ratio reached an all-time high of 36.805 in Apr 2021 and a record low of 6.554 in Mar 2013.
• Saudi Exchange provides daily P/E Ratio.

Key information about Japan P/E ratio

• Japan Prime Market recorded a monthly P/E ratio of 19.900 on Mar 2025, compared with 20.700 from the previous month.
• Japan Prime Market P/E ratio is updated monthly, with historical data available from Apr 2022 to Feb 2025.
• The P/E ratio reached an all-time high of 25.800 in Apr 2022 and a record low of 18.400 in Jun 2022.
• Japan Exchange Group Inc. provides monthly P/E Ratio. Japan Exchange Group Inc. does not provide month end data; thus monthly average is used instead. On April 2022, the stock market was restructured into three new market segments (Prime Market, Standard Market, and Growth Market) replacing previous four market divisions: 1st Section, 2nd Section, Mothers, and JASDAQ (Standard and Growth).

Key information about Bangladesh P/E ratio

• Bangladesh DSE recorded a monthly P/E ratio of 9.510 on Mar 2025, compared with 9.500 from the previous month.
• Bangladesh DSE P/E ratio is updated monthly, with historical data available from Nov 2000 to Jan 2025.
• The P/E ratio reached an all-time high of 30.580 in Feb 2010 and a record low of 5.900 in Mar 2003.
• Dhaka Stock Exchange provides monthly P/E Ratio.