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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Exscientia Plc reported 7.53M in Cost of Sales for its fiscal quarter ending in September of 2024. Data for Exscientia Plc | EXAI - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last July in 2025.
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Exscientia Plc reported 630.77M in Market Capitalization this January of 2025, considering the latest stock price and the number of outstanding shares.Data for Exscientia Plc | EXAI - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last July in 2025.
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Exscientia Plc American Depositary Shares (EXAI) predictions are generally positive. Analysts anticipate continued growth in revenue and profitability, driven by the company's innovative drug discovery platform and strong pipeline of drug candidates. However, risks to consider include competition from larger pharmaceutical companies, regulatory challenges, and the potential for setbacks in clinical trials.
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License information was derived automatically
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Exscientia Plc reported 411.41M in Assets for its fiscal quarter ending in September of 2024. Data for Exscientia Plc | EXAI - Assets including historical, tables and charts were last updated by Trading Economics this last July in 2025.
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Exscientia Plc reported 21.39M in Debt for its fiscal quarter ending in June of 2024. Data for Exscientia Plc | EXAI - Debt including historical, tables and charts were last updated by Trading Economics this last July in 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Exscientia Plc reported 57.48M in Operating Expenses for its fiscal quarter ending in September of 2024. Data for Exscientia Plc | EXAI - Operating Expenses including historical, tables and charts were last updated by Trading Economics this last July in 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Exscientia Plc reported 3.87M in Interest Income for its fiscal quarter ending in June of 2024. Data for Exscientia Plc | EXAI - Interest Income including historical, tables and charts were last updated by Trading Economics this last July in 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Exscientia Plc reported 4.93M in Sales Revenues for its fiscal quarter ending in September of 2024. Data for Exscientia Plc | EXAI - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last July in 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Exscientia Plc reported 7.48M in Trade Creditors for its fiscal quarter ending in September of 2024. Data for Exscientia Plc | EXAI - Trade Creditors including historical, tables and charts were last updated by Trading Economics this last July in 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Exscientia Plc reported -32690000 in EBITDA for its fiscal quarter ending in June of 2024. Data for Exscientia Plc | EXAI - Ebitda including historical, tables and charts were last updated by Trading Economics this last July in 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Exscientia Plc reported -49400000 in EBIT for its fiscal quarter ending in June of 2024. Data for Exscientia Plc | EXAI - Ebit including historical, tables and charts were last updated by Trading Economics this last July in 2025.
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License information was derived automatically
Exscientia Plc reported 49.62M in Trade Debtors for its fiscal quarter ending in September of 2024. Data for Exscientia Plc | EXAI - Trade Debtors including historical, tables and charts were last updated by Trading Economics this last June in 2025.
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License information was derived automatically
Exscientia Plc reported -52130000 in Net Income for its fiscal quarter ending in September of 2024. Data for Exscientia Plc | EXAI - Net Income including historical, tables and charts were last updated by Trading Economics this last June in 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Exscientia Plc reported -0.4 in EPS Earnings Per Share for its fiscal quarter ending in September of 2024. Data for Exscientia Plc | EXAI - EPS Earnings Per Share including historical, tables and charts were last updated by Trading Economics this last July in 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Exscientia Plc reported 299.16M in Current Assets for its fiscal quarter ending in September of 2024. Data for Exscientia Plc | EXAI - Current Assets including historical, tables and charts were last updated by Trading Economics this last July in 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Exscientia Plc reported 64K in Ordinary Share Capital for its fiscal quarter ending in June of 2024. Data for Exscientia Plc | EXAI - Ordinary Share Capital including historical, tables and charts were last updated by Trading Economics this last July in 2025.
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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