This statistic shows the percent change in 2019 in selected pharmaceutical and biotech stock indices worldwide. During this year, TOPIX Pharmaceutical Index in Japan increased by 21 percent. In general biopharma stock indices showed significant growth after a weak performance in the previous year.

Saudi Arabia Index: Tadawul: Pharma, Biotech and Live Science data was reported at 4,208.450 05Jan2017=5000 in Jun 2018. This records an increase from the previous number of 4,010.560 05Jan2017=5000 for May 2018. Saudi Arabia Index: Tadawul: Pharma, Biotech and Live Science data is updated monthly, averaging 4,356.865 05Jan2017=5000 from Jan 2016 (Median) to Jun 2018, with 30 observations. The data reached an all-time high of 5,382.590 05Jan2017=5000 in Dec 2016 and a record low of 3,759.900 05Jan2017=5000 in Nov 2017. Saudi Arabia Index: Tadawul: Pharma, Biotech and Live Science data remains active status in CEIC and is reported by Tadawul. The data is categorized under Global Database’s Saudi Arabia – Table SA.Z001: Tadawul 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.

Pharma Sector Outlook: Steady Growth Predicted for U.S. Select Pharmaceuticals 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

Sri Lanka CSE: Index: Chemicals & Pharmaceuticals data was reported at 5,188.540 NA in Oct 2018. This records an increase from the previous number of 4,972.570 NA for Sep 2018. Sri Lanka CSE: Index: Chemicals & Pharmaceuticals data is updated monthly, averaging 1,330.505 NA from Jan 1987 (Median) to Oct 2018, with 382 observations. The data reached an all-time high of 14,607.660 NA in May 2011 and a record low of 258.670 NA in Nov 1988. Sri Lanka CSE: Index: Chemicals & Pharmaceuticals data remains active status in CEIC and is reported by Colombo Stock Exchange. The data is categorized under Global Database’s Sri Lanka – Table LK.Z001: Colombo Stock Exchange: Index.

The Dow Jones U.S. Select Pharmaceuticals index is expected to experience moderate volatility in the near term, with potential for both gains and losses. The index may benefit from strong industry fundamentals and positive market sentiment. However, geopolitical uncertainties and macroeconomic factors could pose risks to its performance. Investors should exercise caution and monitor market conditions closely before making any investment decisions.

United States New York Stock Exchange: Index: Dow Jones US Pharmaceuticals Index data was reported at 899.400 NA in Apr 2025. This records a decrease from the previous number of 904.250 NA for Mar 2025. United States New York Stock Exchange: Index: Dow Jones US Pharmaceuticals Index data is updated monthly, averaging 549.590 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 1,004.200 NA in Aug 2024 and a record low of 281.970 NA in Jan 2012. United States New York Stock Exchange: Index: Dow Jones US Pharmaceuticals 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.

Analysis of ‘NIFTY-50 Stocks Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/iamsouravbanerjee/nifty50-stocks-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The NIFTY 50 is a benchmark Indian stock market index that represents the weighted average of 50 of the largest Indian companies listed on the National Stock Exchange. It is one of the two main stock indices used in India, the other being the BSE SENSEX.

Nifty 50 is owned and managed by NSE Indices (previously known as India Index Services & Products Limited), which is a wholly-owned subsidiary of the NSE Strategic Investment Corporation Limited. NSE Indices had a marketing and licensing agreement with Standard & Poor's for co-branding equity indices until 2013. The Nifty 50 index was launched on 22 April 1996, and is one of the many stock indices of Nifty.

The NIFTY 50 index has shaped up to be the largest single financial product in India, with an ecosystem consisting of exchange-traded funds (onshore and offshore), exchange-traded options at NSE, and futures and options abroad at the SGX. NIFTY 50 is the world's most actively traded contract. WFE, IOM, and FIA surveys endorse NSE's leadership position.

The NIFTY 50 index covers 13 sectors (as of 30 April 2021) of the Indian economy and offers investment managers exposure to the Indian market in one portfolio. Between 2008 & 2012, the NIFTY 50 index's share of NSE's market capitalization fell from 65% to 29% due to the rise of sectoral indices like NIFTY Bank, NIFTY IT, NIFTY Pharma, NIFTY SERV SECTOR, NIFTY Next 50, etc. The NIFTY 50 Index gives a weightage of 39.47% to financial services, 15.31% to Energy, 13.01% to IT, 12.38% to consumer goods, 6.11% to Automobiles a and 0% to the agricultural sector.

The NIFTY 50 index is a free-float market capitalization weighted index. The index was initially calculated on a full market capitalization methodology. On 26 June 2009, the computation was changed to a free-float methodology. The base period for the NIFTY 50 index is 3 November 1995, which marked the completion of one year of operations of the National Stock Exchange Equity Market Segment. The base value of the index has been set at 1000 and a base capital of ₹ 2.06 trillion.

Content

In this Dataset, we have records of all the NIFTY-50 stocks along with various parameters.

Important Note

• % change is calculated with respect to adjusted price on ex-date for Dividend, Bonus, Rights & Face Value Split.
• 52 weeks high & 52 week low prices are adjusted for Bonus, Split & Rights Corporate actions.
• 365 days % Change and 30 days % Change values are adjusted With respect to corporate actions.

Acknowledgements

For more, you can visit the website of the National Stock Exchange of India Limited (NSE): https://www1.nseindia.com/

--- Original source retains full ownership of the source dataset ---

Morocco Index: Casablanca Stock Exchange: Pharmaceutical Industry data was reported at 2,823.560 21Feb2005=1000 in Jun 2018. This records a decrease from the previous number of 2,847.950 21Feb2005=1000 for May 2018. Morocco Index: Casablanca Stock Exchange: Pharmaceutical Industry data is updated monthly, averaging 1,936.005 21Feb2005=1000 from Jan 2005 (Median) to Jun 2018, with 162 observations. The data reached an all-time high of 2,884.820 21Feb2005=1000 in Apr 2018 and a record low of 1,000.000 21Feb2005=1000 in Jan 2005. Morocco Index: Casablanca Stock Exchange: Pharmaceutical Industry data remains active status in CEIC and is reported by Casablanca Stock Exchange. The data is categorized under Global Database’s Morocco – Table MA.Z001: Casablanca 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.

Medicus Pharma (MDCX): M. Pharma Stock Expected to See Moderate 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

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.

Lantern Pharma's (LTRN) Gleaming Future or Fading Light?

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.

Pharmaceuticals Sector Expected to See Moderate Growth, Dow Jones U.S. Select Pharmaceuticals Index Forecasts

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 the Pharmaceuticals 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

In August 2021, ** percent of companies of the Star ** index at the Shanghai Stock Exchange in China belonged to the information technology industry. The index was the first index of the Star Market and it represented the performance of the board's ** biggest companies. Since the objective of the board was to foster the development of innovative companies, information technology and pharmaceutical industries accounted for the largest share in the Star ** index.

New York Stock Exchange: Index: Dow Jones US Select Sector: Pharmaceuticals Index data was reported at 11,256.810 NA in Apr 2025. This records a decrease from the previous number of 11,633.490 NA for Mar 2025. New York Stock Exchange: Index: Dow Jones US Select Sector: Pharmaceuticals Index data is updated monthly, averaging 8,753.450 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 11,981.300 NA in Aug 2024 and a record low of 5,613.400 NA in Aug 2013. New York Stock Exchange: Index: Dow Jones US Select Sector: Pharmaceuticals 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.

Is Dow Jones U.S. Select Pharmaceuticals 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

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.

K. Pharma Stock Forecast: Experts Predict Growth For (KAPA)

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

Norway Index: Oslo Bors Stock Exchange: Health Care: Pharmaceuticals and Biotechnology data was reported at 97.390 29Dec1995=100 in Jun 2018. This records a decrease from the previous number of 106.580 29Dec1995=100 for May 2018. Norway Index: Oslo Bors Stock Exchange: Health Care: Pharmaceuticals and Biotechnology data is updated monthly, averaging 44.260 29Dec1995=100 from Aug 2001 (Median) to Jun 2018, with 203 observations. The data reached an all-time high of 153.790 29Dec1995=100 in Nov 2016 and a record low of 15.680 29Dec1995=100 in Sep 2002. Norway Index: Oslo Bors Stock Exchange: Health Care: Pharmaceuticals and Biotechnology 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.

Astrazeneca was the leading pharmaceutical company in the United Kingdom as of March 7, 2024, with a market capitalization amounting to approximately ***** billion U.S. dollars. GlaxoSmithKline followed as the second largest pharma company in the country, with market capitalization of nearly **** billion U.S. dollars. Examining the development of the FTSE 100 Index, which was launched in January 1984 with a base level of 1,000, increased by more than sevenfold to date. What is the FTSE 100 index? The Financial Times Stock Exchange 100 Index, commonly known as the "Footsie", is the most widely recognized stock market index in the United Kingdom. It is made up of the 100 largest blue-chip companies on the London Stock Exchange. Companies from various sectors, such as healthcare, consumer goods, and energy, are included in the index, as are leading banks of the United Kingdom, such as HSBC, Lloyds Banking Group, and Barclays. Moreover, it can be seen as a reflection of the investment climate in the United Kingdom. What is not included in the FTSE 100 Index? Most notably, the FTSE 100 Index, like most indices, is not adjusted for inflation. While inflation in the United Kingdom has gone down dramatically since 2023, it might be useful to adjust the historic figures on the index when comparing historic data to current levels. This is especially important when the index seems to have increased by a few percentage points because inflation may have increased at a faster rate than stock prices.

Robustness tests: Alternative dependent variables using different sector indices returns.

National Stock Exchange of India Limited: Index: NIFTY Pharma data was reported at 21,650.650 NA in 15 May 2025. This records an increase from the previous number of 21,480.500 NA for 14 May 2025. National Stock Exchange of India Limited: Index: NIFTY Pharma data is updated daily, averaging 10,910.200 NA from Jan 2012 (Median) to 15 May 2025, with 3311 observations. The data reached an all-time high of 23,783.800 NA in 09 Oct 2024 and a record low of 4,567.600 NA in 02 Jan 2012. National Stock Exchange of India Limited: Index: NIFTY Pharma 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 IN.EDI.SE: National Stock Exchange of India Limited.