Canada TSX Venture Exchange (TSXV): Market Capitalization data was reported at 92,200.601 CAD mn in Mar 2025. This records an increase from the previous number of 90,047.721 CAD mn for Feb 2025. Canada TSX Venture Exchange (TSXV): Market Capitalization data is updated monthly, averaging 48,046.140 CAD mn from Dec 2012 (Median) to Mar 2025, with 148 observations. The data reached an all-time high of 101,903.220 CAD mn in Dec 2021 and a record low of 22,179.364 CAD mn in Jan 2016. Canada TSX Venture Exchange (TSXV): Market Capitalization data remains active status in CEIC and is reported by TMX Group Limited. The data is categorized under Global Database’s Canada – Table CA.Z002: TMX Group Limited: Market Capitalization. [COVID-19-IMPACT]

Net-Buyback-Yield Time Series for TMX Group Limited. TMX Group Limited operates exchanges, markets, and clearinghouses primarily for capital markets in Canada, the United States, the United Kingdom, Germany, and internationally. It operates in four segments: Global Solutions, Insights & Analytics; Capital Formation; Derivatives Trading & Clearing; and Equities and Fixed Income Trading & Clearing. The Global Solutions, Insights & Analytics segment delivers equities and index data, as well as integrated data sets for the proprietary and third party analytics to help clients in making trading and investment decisions; and provides solutions to European and global wholesale energy markets for price discovery, trade execution, post-trade transparency, and straight through processing. The Capital Formation segment operates Toronto Stock Exchange, a national stock exchange for the senior equities market; TSX Venture Exchange, a national stock exchange for the public venture equity market; TSX Trust that provides corporate trust, registrar, transfer agency, and foreign exchange services; and Newsfile, a news dissemination and regulatory filing provider. The Derivatives Trading & Clearing segment operates Montreal Exchange, a national derivatives exchange; Canadian Derivatives Clearing Corporation, a clearinghouse for options and futures contracts; BOX, a US equity operations market; and various over-the-counter products and fixed income repurchase agreements. The Equities and Fixed Income Trading & Clearing segment engages in the trading operations of Toronto Stock Exchange, TSX Venture Exchange, and TSX Alpha Exchange; CDS Clearing, an automated facility for the clearing and settlement of equities and fixed income transactions, and custody of securities; and provision of fixed income inter-dealer brokerage services. The company was formerly known as Maple Group Acquisition Corporation and changed its name to TMX Group Limited in August 2012. TMX Group Limited was incorporated in 2011 and is headquartered in Toronto, Canada.

Explore LSEG's Toronto Stock Exchange (TSX) Market Data, representing a broad range of businesses from Canada and abroad.

市值：TMX：多伦多证券交易所创业板（TSXV）在03-01-2025达92,200.601百万加拿大元，相较于02-01-2025的90,047.721百万加拿大元有所增长。市值：TMX：多伦多证券交易所创业板（TSXV）数据按月更新，12-01-2012至03-01-2025期间平均值为48,046.140百万加拿大元，共148份观测结果。该数据的历史最高值出现于12-01-2021，达101,903.220百万加拿大元，而历史最低值则出现于01-01-2016，为22,179.364百万加拿大元。CEIC提供的市值：TMX：多伦多证券交易所创业板（TSXV）数据处于定期更新的状态，数据来源于TMX Group Limited，数据归类于全球数据库的加拿大 – Table CA.Z002: TMX Group Limited: Market Capitalization。

Canada TSXV: Market Capitalization: Clean Technology & Renewable Energy data was reported at 1,578.238 CAD mn in Mar 2025. This records a decrease from the previous number of 1,628.153 CAD mn for Feb 2025. Canada TSXV: Market Capitalization: Clean Technology & Renewable Energy data is updated monthly, averaging 1,443.070 CAD mn from Dec 2012 (Median) to Mar 2025, with 148 observations. The data reached an all-time high of 5,183.139 CAD mn in Dec 2020 and a record low of 680.990 CAD mn in Jan 2016. Canada TSXV: Market Capitalization: Clean Technology & Renewable Energy data remains active status in CEIC and is reported by TMX Group Limited. The data is categorized under Global Database’s Canada – Table CA.Z002: TMX Group Limited: Market Capitalization. [COVID-19-IMPACT]

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.

IPA:TSXV Stock Forecast: A Buy For The Next 1 Year

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

Canada TSXV: Market Capitalization: Technology data was reported at 26,673.723 CAD mn in Mar 2025. This records an increase from the previous number of 26,026.506 CAD mn for Feb 2025. Canada TSXV: Market Capitalization: Technology data is updated monthly, averaging 4,260.152 CAD mn from Dec 2012 (Median) to Mar 2025, with 148 observations. The data reached an all-time high of 26,879.446 CAD mn in Jan 2025 and a record low of 1,222.870 CAD mn in Dec 2012. Canada TSXV: Market Capitalization: Technology data remains active status in CEIC and is reported by TMX Group Limited. The data is categorized under Global Database’s Canada – Table CA.Z002: TMX Group Limited: Market Capitalization. [COVID-19-IMPACT]

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.

PML:TSXV Stock: A Risky Investment, But One with a lot of Potential

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

Canada TSXV: Market Capitalization: Capital Pool Companies data was reported at 105.033 CAD mn in Mar 2025. This records a decrease from the previous number of 110.736 CAD mn for Feb 2025. Canada TSXV: Market Capitalization: Capital Pool Companies data is updated monthly, averaging 111.211 CAD mn from Dec 2012 (Median) to Mar 2025, with 148 observations. The data reached an all-time high of 490.432 CAD mn in Apr 2021 and a record low of 10.337 CAD mn in Feb 2017. Canada TSXV: Market Capitalization: Capital Pool Companies data remains active status in CEIC and is reported by TMX Group Limited. The data is categorized under Global Database’s Canada – Table CA.Z002: TMX Group Limited: Market Capitalization. [COVID-19-IMPACT]

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.

TK:TSXV Stock: Set to Take Off

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

Canada TSXV: Market Capitalization: Financial Services data was reported at 13,105.103 CAD mn in Mar 2025. This records a decrease from the previous number of 13,128.186 CAD mn for Feb 2025. Canada TSXV: Market Capitalization: Financial Services data is updated monthly, averaging 5,568.810 CAD mn from Dec 2012 (Median) to Mar 2025, with 148 observations. The data reached an all-time high of 14,193.368 CAD mn in Dec 2024 and a record low of 2,144.584 CAD mn in Dec 2012. Canada TSXV: Market Capitalization: Financial Services data remains active status in CEIC and is reported by TMX Group Limited. The data is categorized under Global Database’s Canada – Table CA.Z002: TMX Group Limited: Market Capitalization. [COVID-19-IMPACT]

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.

SLI:TSXV Stock: A Risky Investment, But One with a lot of Potential

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

Canada TSXV: Market Capitalization: Mining data was reported at 42,365.001 CAD mn in Mar 2025. This records an increase from the previous number of 40,077.553 CAD mn for Feb 2025. Canada TSXV: Market Capitalization: Mining data is updated monthly, averaging 20,438.587 CAD mn from Dec 2012 (Median) to Mar 2025, with 148 observations. The data reached an all-time high of 54,677.008 CAD mn in Mar 2022 and a record low of 7,476.190 CAD mn in Nov 2015. Canada TSXV: Market Capitalization: Mining data remains active status in CEIC and is reported by TMX Group Limited. The data is categorized under Global Database’s Canada – Table CA.Z002: TMX Group Limited: Market Capitalization. [COVID-19-IMPACT]

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.

LIO:TSXV Stock: A Bright Investment or a Bubble Waiting to Burst?

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

Canada TSXV: Market Capitalization: Real Estate data was reported at 660.188 CAD mn in Mar 2025. This records a decrease from the previous number of 677.390 CAD mn for Feb 2025. Canada TSXV: Market Capitalization: Real Estate data is updated monthly, averaging 1,154.186 CAD mn from Dec 2012 (Median) to Mar 2025, with 148 observations. The data reached an all-time high of 4,262.562 CAD mn in Dec 2021 and a record low of 620.749 CAD mn in Oct 2023. Canada TSXV: Market Capitalization: Real Estate data remains active status in CEIC and is reported by TMX Group Limited. The data is categorized under Global Database’s Canada – Table CA.Z002: TMX Group Limited: Market Capitalization. [COVID-19-IMPACT]

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.

ODV:TSXV Stock: A Risky Investment

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 Alberta Enterprise Corporation Act establishes the Alberta Enterprise Corporation with two objects: (1) to promote the development of knowledge-based industries (including, information technology, communications technology, life sciences technology, nanotechnology, and environmental technology) and a venture capital industry in Alberta, and (2) to make venture capital available to these industries. Venture capital is made available through investment by the Corporation in venture capital funds that meet criteria outlined in the Alberta Enterprise Corporation Regulation, the Corporation’s investment policy, and through the Corporation’s leadership of venture capital activities.

Canada TSXV: Market Capitalization: Industrial Products & Services data was reported at 979.341 CAD mn in Mar 2025. This records a decrease from the previous number of 1,030.684 CAD mn for Feb 2025. Canada TSXV: Market Capitalization: Industrial Products & Services data is updated monthly, averaging 1,422.651 CAD mn from May 2017 (Median) to Mar 2025, with 95 observations. The data reached an all-time high of 2,260.770 CAD mn in Jan 2021 and a record low of 720.699 CAD mn in Nov 2023. Canada TSXV: Market Capitalization: Industrial Products & Services data remains active status in CEIC and is reported by TMX Group Limited. The data is categorized under Global Database’s Canada – Table CA.Z002: TMX Group Limited: Market Capitalization. [COVID-19-IMPACT]

Invest Nova Scotia is Nova Scotia’s business development agency that works to promote economic growth and community economic development in the province by enabling business, innovation, sustainability and entrepreneurship. Early stage deep technology start-ups are a key focus area for Invest Nova Scotia. They are supported through venture capital, incubation facilities, expert advice, and other acceleration initiatives. Invest Nova Scotia makes equity investments through the Nova Scotia First Fund (NSFF), a venture capital fund backed by the Nova Scotia government. NSFF’s overall objective is to generate a return for Nova Scotia. This is measured by the total value of the investment portfolio (i.e., realized distributions and unrealized changes in value) to the total amount contributed by the Nova Scotia government (Modified TVPI ratio*). This dataset gives Invest Nova Scotia’s annual TVPI ratios. * Invest Nova Scotia’s TVPI ratios represent only the gross performance of the investments; they do not account for the costs of managing the portfolio, which are included in the organization’s operating budget.

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.

PTK:TSXV Stock: Where should i invest $1000 right now?

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