The NYSE Health Care Index tracks the performance of the equity components on the New York Stock Exchange that offer goods and services in the health care industry. Between January 2004 and February 2025, the NYSE Health Care Index increased overall and reached a value of *********.

China Index: Shanghai Stock Exchange: Health Care data was reported at 5,933.270 31Dec2003=1000 in 14 May 2025. This records an increase from the previous number of 5,893.170 31Dec2003=1000 for 13 May 2025. China Index: Shanghai Stock Exchange: Health Care data is updated daily, averaging 5,950.950 31Dec2003=1000 from Jan 2005 (Median) to 14 May 2025, with 4943 observations. The data reached an all-time high of 11,615.656 31Dec2003=1000 in 01 Jul 2021 and a record low of 2,681.395 31Dec2003=1000 in 18 Jan 2012. China Index: Shanghai Stock Exchange: Health Care data remains active status in CEIC and is reported by China Securities Index Co., Ltd.. The data is categorized under China Premium Database’s Financial Market – Table CN.ZA: China Securities Index : Daily.

The NYSE U.S. Market Healthcare Sector Index tracks the performance of the U.S. domiciled equity components listed on the U.S. stock exchanges that offer goods and services in the healthcare sector. The statistics shows the monthly development of the NYSE U.S. Market Healthcare Sector Index from December 2015 to June 2023. The index increased overall throughout the period considered, reaching a value of ******** as of June 2023.

Norway Index: Oslo Bors Stock Exchange: Health Care: Health Care Equipments and Services data was reported at 959.370 29Dec1995=100 in Sep 2018. This records an increase from the previous number of 944.650 29Dec1995=100 for Aug 2018. Norway Index: Oslo Bors Stock Exchange: Health Care: Health Care Equipments and Services data is updated monthly, averaging 288.630 29Dec1995=100 from Aug 2001 (Median) to Sep 2018, with 206 observations. The data reached an all-time high of 979.920 29Dec1995=100 in Jul 2018 and a record low of 98.250 29Dec1995=100 in Mar 2003. Norway Index: Oslo Bors Stock Exchange: Health Care: Health Care Equipments and Services data remains active status in CEIC and is reported by Oslo Stock Exchange. The data is categorized under Global Database’s Norway – Table NO.Z001: Oslo Stock Exchange: Index.

Indonesia Stock Exchange: Index: IDX Sector Healthcare data was reported at 1,377.840 NA in Apr 2025. This records an increase from the previous number of 1,243.875 NA for Mar 2025. Indonesia Stock Exchange: Index: IDX Sector Healthcare data is updated monthly, averaging 1,443.264 NA from Mar 2021 (Median) to Apr 2025, with 50 observations. The data reached an all-time high of 1,588.800 NA in Sep 2024 and a record low of 1,243.875 NA in Mar 2025. Indonesia Stock Exchange: Index: IDX Sector Healthcare data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Indonesia – Table ID.EDI.SE: Indonesia Stock Exchange: Monthly.

Short-term: Dow Jones U.S. Select Health Care Providers index may experience a moderate gain as it approaches a resistance level. Medium-term: This index may continue to rise, albeit at a slower pace, as it enters a period of consolidation. Long-term: The index could potentially reach higher levels, but it is important to be aware of potential risks such as economic downturns or changes in healthcare policies.

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 Health Care Index Continue its Upward 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

Graph and download economic data for Equity Market Volatility Tracker: Healthcare Matters (EMVHEALTHCAREMAT) from Jan 1985 to Jul 2025 about healthcare, volatility, uncertainty, equity, health, and USA.

United States New York Stock Exchange: Index: S&P Health Care Select Sector data was reported at 1,419.450 NA in Apr 2025. This records a decrease from the previous number of 1,476.000 NA for Mar 2025. United States New York Stock Exchange: Index: S&P Health Care Select Sector data is updated monthly, averaging 921.250 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 1,586.510 NA in Aug 2024 and a record low of 493.270 NA in Aug 2013. United States New York Stock Exchange: Index: S&P Health Care Select Sector 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: S&P: Monthly.

United States New York Stock Exchange: Index: Dow Jones US Health Care Index data was reported at 1,476.740 NA in Apr 2025. This records a decrease from the previous number of 1,533.710 NA for Mar 2025. United States New York Stock Exchange: Index: Dow Jones US Health Care Index data is updated monthly, averaging 990.230 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 1,658.990 NA in Aug 2024 and a record low of 521.060 NA in Aug 2013. United States New York Stock Exchange: Index: Dow Jones US Health Care 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.

Data of S&P BSE Healthcare Index, Collected from Bombay stock exchange of India

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.

TA 35 Index: The Future of Healthcare?

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 Health Care Index Continue its Ascent?

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

New York Stock Exchange: Index: Dow Jones US Select Sector: Health Care Providers Index data was reported at 27,844.910 NA in Apr 2025. This records a decrease from the previous number of 29,279.610 NA for Mar 2025. New York Stock Exchange: Index: Dow Jones US Select Sector: Health Care Providers Index data is updated monthly, averaging 19,608.350 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 32,324.470 NA in Aug 2024 and a record low of 9,141.580 NA in Aug 2013. New York Stock Exchange: Index: Dow Jones US Select Sector: Health Care Providers 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.

DJ US Healthcare: Poised for Recovery?

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

Total-Assets Time Series for Worldwide Healthcare Trust Plc. Worldwide Healthcare Trust PLC is a closed ended equity mutual fund launched by Frostrow Capital LLP. The fund is managed by OrbiMed Capital LLC. It invests in public equity markets across the globe. The fund seeks to invest in stocks of companies operating in the healthcare sector, with an emphasis on pharmaceutical and biotechnology companies. It primarily invests in growth stocks of large-cap companies with a market capitalization of at least $5 billion. The fund employs fundamental analysis with a bottom-up stock picking approach to create its portfolio. It benchmarks the performance of its portfolio against the MSCI World Healthcare Index. The fund employs internal research to make its investments. It was formerly known as Finsbury Worldwide Pharmaceutical Trust plc. Worldwide Healthcare Trust PLC was formed in April 1995 and is domiciled in the United Kingdom.

New York Stock Exchange: Index: S&P Health Care Services Select Industry Index data was reported at 14,530.020 NA in Apr 2025. This records a decrease from the previous number of 14,868.690 NA for Mar 2025. New York Stock Exchange: Index: S&P Health Care Services Select Industry Index data is updated monthly, averaging 10,289.890 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 17,438.350 NA in Jun 2021 and a record low of 5,989.190 NA in Aug 2013. New York Stock Exchange: Index: S&P Health Care Services Select Industry 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: S&P: 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.

Dow Jones U.S. Select Medical Equipment: A Pulse on Healthcare Innovation?

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 Healthcare Innovation Driving the Select Medical Equipment 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

Hong Kong SAR (China) Hong Kong Stock Exchange: Index: Total Return: Hang Seng Healthcare Index data was reported at 3,663.950 NA in Apr 2025. This records an increase from the previous number of 3,621.340 NA for Mar 2025. Hong Kong SAR (China) Hong Kong Stock Exchange: Index: Total Return: Hang Seng Healthcare Index data is updated monthly, averaging 4,324.700 NA from Aug 2015 (Median) to Apr 2025, with 117 observations. The data reached an all-time high of 9,642.820 NA in Jun 2021 and a record low of 2,546.110 NA in Jun 2024. Hong Kong SAR (China) Hong Kong Stock Exchange: Index: Total Return: Hang Seng Healthcare Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.EDI.SE: Hong Kong Stock Exchange: Monthly.