China Shenzhen Stock Exchange: Index: Construction Index data was reported at 922.430 NA in 13 May 2025. This records a decrease from the previous number of 923.990 NA for 12 May 2025. China Shenzhen Stock Exchange: Index: Construction Index data is updated daily, averaging 1,201.355 NA from Jan 2012 (Median) to 13 May 2025, with 3240 observations. The data reached an all-time high of 3,595.800 NA in 12 Jun 2015 and a record low of 677.960 NA in 18 Sep 2024. China Shenzhen Stock Exchange: Index: Construction Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under High Frequency Database’s Financial and Futures Market – Table CN.EDI.SE: Shenzhen Stock Exchange.

Italy Index: Industrials: Construction & Materials data was reported at 26,675.443 19Dec2008=20000 in Nov 2018. This records a decrease from the previous number of 27,455.571 19Dec2008=20000 for Oct 2018. Italy Index: Industrials: Construction & Materials data is updated monthly, averaging 24,147.837 19Dec2008=20000 from Dec 2008 (Median) to Nov 2018, with 120 observations. The data reached an all-time high of 43,902.876 19Dec2008=20000 in Oct 2017 and a record low of 13,923.670 19Dec2008=20000 in Sep 2011. Italy Index: Industrials: Construction & Materials data remains active status in CEIC and is reported by Italian Stock Exchange. The data is categorized under Global Database’s Italy – Table IT.Z001: 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.

Will the Home Construction Index Build a Bullish Future?

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 Home Construction Continue to Build? 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

United Kingdom's main stock market index, the GB100, rose to 9256 points on September 10, 2025, gaining 0.14% from the previous session. Over the past month, the index has climbed 1.38% and is up 12.96% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on September of 2025.

Israel's main stock market index, the TA-125, fell to 3151 points on September 8, 2025, losing 0.17% from the previous session. Over the past month, the index has climbed 4.30% and is up 54.90% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Israel. Israel Stock Market (TA-125) - values, historical data, forecasts and news - updated on September of 2025.

Spain's main stock market index, the ES35, rose to 15002 points on September 8, 2025, gaining 1.02% from the previous session. Over the past month, the index has climbed 0.98% and is up 33.08% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Spain. Spain Stock Market Index (ES35) - values, historical data, forecasts and news - updated on September of 2025.

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.

Builders Momentum: Can BLDR Stock Sustain Growth Trajectory?

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

Predictions for the Dow Jones U.S. Select Home Construction index suggest a potential for further growth, primarily influenced by favorable housing market conditions. However, there are moderate risks associated with rising interest rates, potential economic slowdown, and supply chain disruptions that could impact the index's performance.

Builders Firstsource reported $16.17B in Market Capitalization this September of 2025, considering the latest stock price and the number of outstanding shares.Data for Builders Firstsource | BLDR - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Slovenia's main stock market index, the SBITOP, rose to 2521 points on September 9, 2025, gaining 0.64% from the previous session. Over the past month, the index has climbed 3.00% and is up 56.75% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Slovenia. Slovenia Stock Market (SBITOP) - values, historical data, forecasts and news - updated on September of 2025.

Ferrovial was the leading construction company in Spain as of October 11, 2024, with a market capitalization amounting to almost ** billion U.S. dollars. Grupo ACS followed as the second-largest construction company in the country, with a market capitalization of over ** billion U.S. dollars.

Website Builders Market is Segmented by Builder Type (PC Website Builders and Mobile Website Builders), Deployment (cloud and On-Premises), End User (Individuals and Businesses), Pricing Tier (Freemium, Subscription (Less Than USD (15 / Month)), and More), and by Geography. The Market Forecasts are Provided in Terms of Value (USD).

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.

BLDR Builders FirstSource Inc. Common Stock

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.

Builders FirstSource (BLDR) - Homebuilding Boom Fueling 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

As of December 2020, Wix was the leading website builder technology in Indonesia holding a market share of around **** percent. Wix is a cloud-based website builder that allows its users to create HTML5 websites and mobile sites by using drag and drop tools.

United States New York Stock Exchange: Index: Dow Jones US Select Sector: Home Construction Index data was reported at 16,597.330 NA in Apr 2025. This records a decrease from the previous number of 17,153.380 NA for Mar 2025. United States New York Stock Exchange: Index: Dow Jones US Select Sector: Home Construction Index data is updated monthly, averaging 7,091.900 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 22,747.730 NA in Sep 2024 and a record low of 3,670.900 NA in Aug 2013. United States New York Stock Exchange: Index: Dow Jones US Select Sector: Home Construction Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: Dow Jones: Monthly.

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.

Builders' Outlook: Stock Forecast Points to Potential Gains for (BLDR)

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

China's main stock market index, the SHANGHAI, rose to 3827 points on September 8, 2025, gaining 0.38% from the previous session. Over the past month, the index has climbed 4.92% and is up 39.85% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on September of 2025.

Taiwan TWSE: Equity Market Index: Building Material and Construction data was reported at 248.160 29Dec1986=100 in Oct 2018. This records a decrease from the previous number of 273.020 29Dec1986=100 for Sep 2018. Taiwan TWSE: Equity Market Index: Building Material and Construction data is updated monthly, averaging 278.715 29Dec1986=100 from Jan 1987 (Median) to Oct 2018, with 382 observations. The data reached an all-time high of 913.250 29Dec1986=100 in Aug 1989 and a record low of 56.520 29Dec1986=100 in Oct 2001. Taiwan TWSE: Equity Market Index: Building Material and Construction data remains active status in CEIC and is reported by Taiwan Stock Exchange Corporation. The data is categorized under Global Database’s Taiwan – Table TW.Z001: Taiwan Stock Exchange (TWSE): Indices.