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.

Dow Jones New Zealand 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

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.

United States Index: Dow Jones: Industrial Average data was reported at 25,538.460 26May1896=40.94 in Nov 2018. This records an increase from the previous number of 25,115.760 26May1896=40.94 for Oct 2018. United States Index: Dow Jones: Industrial Average data is updated monthly, averaging 1,546.670 26May1896=40.94 from Jan 1953 (Median) to Nov 2018, with 791 observations. The data reached an all-time high of 26,458.310 26May1896=40.94 in Sep 2018 and a record low of 261.220 26May1896=40.94 in Aug 1953. United States Index: Dow Jones: Industrial Average data remains active status in CEIC and is reported by Dow Jones. The data is categorized under Global Database’s United States – Table US.Z015: Dow Jones: Indexes.

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.

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.

Tech Sector Outlook: Bullish Trend Expected for Dow Jones U.S. Technology 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

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.

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 Dow Jones U.S. Oil & Gas 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

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.

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.

Dow Jones Shanghai index: Analysts predict moderate gains 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.

When should you buy or sell a stock? (Dow Jones New Zealand 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

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.

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).

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

The main stock market index in Romania (BET) increased 718 points or 4.29% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Romania. Romania Stock Market (BET) - values, historical data, forecasts and news - updated on March of 2025.

Key information about Malaysia P/E ratio

• Malaysia FTSE Composite Index recorded a monthly P/E ratio of 17.270 on Mar 2025, compared with 19.480 from the previous month.
• Malaysia FTSE Composite Index P/E ratio is updated monthly, with historical data available from Jul 2009 to Feb 2025.
• The P/E ratio reached an all-time high of 29.800 in Apr 2023 and a record low of 12.150 in Sep 2022.
• Bursa Malaysia provides monthly P/E Ratio.

Key information about Egypt EGP

• Egypt EGP closed at 30,610.4 points in Feb 2025, compared with 30,010.6 points at the previous month end
• Egypt Equity Market Index: Month End: EGX 30: EGP data is updated monthly, available from Jan 1998 to Feb 2025, with an average number of 6,725.5 points
• The data reached an all-time high of 31,587.0 points in Sep 2024 and a record low of 448.0 points in Jan 2002

• In the latest reports, EGX recorded a monthly P/E ratio of 16.5 in Oct 2024

Key information about Spain IBEX 35

• Spain IBEX 35 closed at 13,347.3 points in Feb 2025, compared with 12,368.9 points at the previous month end
• Spain Equity Market Index: Month End: IBEX 35 data is updated monthly, available from Dec 1989 to Feb 2025, with an average number of 8,851.5 points
• The data reached an all-time high of 15,890.5 points in Oct 2007 and a record low of 2,032.1 points in Sep 1992

• In the latest reports, Bolsas y Mercados Españoles (BME) recorded a monthly P/E ratio of 13.1 in Jan 2025

Key information about Mauritius SEMDEX Official Market

• Mauritius SEMDEX Official Market closed at 2,529.7 points in Feb 2025, compared with 2,510.2 points at the previous month end
• Mauritius Equity Market Index: Month End: SEMDEX Official Market data is updated monthly, available from Nov 1997 to Feb 2025, with an average number of 1,731.6 points
• The data reached an all-time high of 2,529.7 points in Feb 2025 and a record low of 340.9 points in Dec 2001

• In the latest reports, SEM Official Market recorded a yearly P/E ratio of 6.5 in Dec 2023