Canada's main stock market index, the TSX, fell to 30943 points on December 2, 2025, losing 0.51% from the previous session. Over the past month, the index has climbed 2.21% and is up 20.70% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Canada. Canada Stock Market Index (TSX) - values, historical data, forecasts and news - updated on December of 2025.

The S&P/TSX Composite index (CAD) closed at ********* points at the end of 2024. This was an increase over the past year. What is the S&P/TSX Composite index? The S&P/TSX Composite index is a Canadian index that measures stocks on the Toronto Stock Exchange, one of the largest stock exchanges worldwide. A stock market index tracks the development of a group of stock prices. It allows to get a quick idea of economic climate in a given region. Canadian stock market The size of a stock exchange is basically the sum of market capitalizations of companies being traded on this stock exchange. The largest companies in terms of market capitalization in Canada in 2024 were the Royal Bank of Canada, and Toronto Dominion Bank. The total market capitalization of listed domestic companies in Canada equaled to **** trillion U.S. dollars in 2022.

This table contains 25 series, with data for years 1956 - present (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Toronto Stock Exchange Statistics (25 items: Standard and Poor's/Toronto Stock Exchange Composite Index; high; Standard and Poor's/Toronto Stock Exchange Composite Index; close; Toronto Stock Exchange; oil and gas; closing quotations; Standard and Poor's/Toronto Stock Exchange Composite Index; low ...).

Prices for Canada Stock Market Index (TSX) including live quotes, historical charts and news. Canada Stock Market Index (TSX) was last updated by Trading Economics this December 2 of 2025.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 800 companies listed on the Canadian Securities Exchange (XCNQ) in Canada. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Canada:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Canada:

S&P/TSX Composite Index: The primary stock market index in Canada, tracking the performance of domestic companies listed on the Toronto Stock Exchange (TSX). It provides a comprehensive view of the Canadian equity market.

Canadian Dollar (CAD): The official currency of Canada, used for transactions and trade within the country. The Canadian Dollar is also widely traded in international foreign exchange markets.

Bank of Canada: Canada's central bank responsible for monetary policy, currency issuance, and overall financial system stability. It plays a critical role in managing the country's economic and financial well-being.

Royal Bank of Canada (RBC): One of the largest and most prominent banks in Canada, offering a wide range of financial services to individuals, businesses, and institutions. RBC is a key player in the Canadian banking sector.

Canadian Government Bonds: Debt securities issued by the Canadian government to finance its operations and projects. These bonds are considered relatively safe investments and play a significant role in the country's capital markets.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Canada, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Canada ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Canada?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Canada exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct tran...

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:TSX Stock: Are We Headed for a Recession?

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

During the current period, tax preparation companies have navigated fluctuating economic conditions with varying success. The onset of COVID-19 triggered a decline in corporate profit, leading many businesses to cut back on outsourced tax services. Such financial pullbacks resulted in a dip in revenue, as companies either opted to utilize in-house tax teams or neglected additional tax services entirely. Regardless, as vaccination rollouts facilitated reopening economies in 2021, consumer spending soared, revitalizing corporate profit and demand for external tax preparers from individuals and businesses. Rising unemployment due to the cooling labor market brought on by high interest rates has recently reduced the number of taxpayers who can afford the industry’s services, causing revenue to slump in 2024. Overall, revenue for tax preparation service companies has grown at a CAGR of 2.9% over the past five years, reaching $14.5 billion in 2025. This includes a 0.9% rise in revenue in that year. Technological advancements have significantly transformed the tax preparation landscape. The advent and integration of artificial intelligence (AI) have streamlined processes, enhancing the efficiency of tax service providers. Specifically, AI-driven software has reduced time spent on tax preparation by automating data analysis, thereby enabling tax professionals to pivot toward more value-added services such as tax planning and customer relationship management. Over time, this will reduce wage costs and boost profit. Despite these advancements, there's been a notable rise in electronic filing, posing a threat to traditional tax preparers as more software companies market user-friendly tax solutions directly to consumers. However, major companies have adapted by incorporating these technological tools into their offerings, aiming to provide more comprehensive services. Looking ahead, tax preparation businesses are poised to experience moderate growth amid shifting economic conditions. As the US economy is expected to rebound gradually from current financial challenges, GDP and disposable income are projected to grow, fostering demand for professional tax services. Yet, ongoing competition from digital solutions, coupled with potential changes in tax legislation under the new administration, could shape the industry's trajectory. Overall, revenue for tax preparation service businesses in the US is forecast to creep upward at a CAGR of 1.1% in the next five years, reaching $15.3 billion in 2030.

Interest is charged if payment is not received by the due date. Remember: if the due date falls on a weekend or holiday, your payment is due the next working day.

The Ministry of Finance also applies interest to amounts the ministry owes to individuals and corporations.

Tax interest is compounded daily and interest rates are reset every 3 months.

Note: Provincial land tax interest rates are not reset every three months. Provincial land tax interest rates are summarized on the "https://www.ontario.ca/document/provincial-land-tax">provincial land tax webpage.

Note: Interest rates do not apply to the Estate Administration Tax Act, 1998.

Current interest rates (October 1, 2025 to December 31, 2025):

• 8% on taxes you owe to the ministry
• 2% on taxes you overpaid
• 5% on taxes or refunds you are eligible for as a result of a successful appeal or objection
• 6% on late International Fuel Tax Agreement payments
• 6% on International Fuel Tax Agreement refunds the ministry has not paid you within 90 days

You can download the dataset to view the historical tax interest rates.

Non-Resident Speculation Tax (NRST)

(1) Interest on tax you overpaid begins to accrue 40 business days after a complete NRST rebate or refund application is received by the Ministry of Finance to the date the rebate or refund is paid.

(2) On refunds you are eligible for as a result of a successful appeal or objection of a NRST refund/rebate disallowance, the interest rate is the same rate as though you had overpaid and will begin to accrue 40 business days after a complete NRST rebate or refund application is received by the Ministry of Finance to the date the rebate or refund is paid. Refunds as a result of a successful appeal or objection of NRST that was paid pursuant to a Notice of Assessment, interest will accrue at the higher appeals/objection rate, beginning to accrue from the date of payment to the date the rebate or refund is paid.

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 TSX Index Reach New Heights?

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

Peru Central Government: Revenue: Current: Taxes: Tax Refund data was reported at -1,369.040 PEN mn in Oct 2018. This records a decrease from the previous number of -1,357.794 PEN mn for Sep 2018. Peru Central Government: Revenue: Current: Taxes: Tax Refund data is updated monthly, averaging -369.785 PEN mn from Jan 1993 (Median) to Oct 2018, with 310 observations. The data reached an all-time high of -2.765 PEN mn in Aug 1993 and a record low of -2,105.908 PEN mn in Apr 2016. Peru Central Government: Revenue: Current: Taxes: Tax Refund data remains active status in CEIC and is reported by Central Reserve Bank of Peru. The data is categorized under Global Database’s Peru – Table PE.F003: Central Government: Revenue and Expenditure.

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.

TSX Index: A Bull Market in Disguise?

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

Cumulative Total Tax Returns Received: The number of original income tax and fringe benefits tax returns received by the ATO that relate to the current financial year.

Electronic: The subset of Tax Returns Received that were lodged through an electronic medium.

Paper: The subset of Tax Returns Received that were lodged via paper.

% of current year returns digitally submitted: Electronic / Cumulative Total Tax Returns Received

The dataset contains year-wise Number of Persons Filing Income-Tax Return (Return Filers) and Income-Tax Returns Filed (Including Revised Return) such as Individual, Hindu Undivided Family, Firm, Company, Association of Person, Local Authority, Artificial Juridical Person, Body of Individual

Note 1. returns_filers are Persons Filing Income-Tax Return (Return Filers) where as returns_filed denotes Income-Tax Returns Filed (Including Revised Return) 2.The category ‘Others’ includes Government 3. AOP & BOI category has been merged in one category i.e. “Other AOP/BOI”, accordingly data of return for AOP & BOI has been clubbed. 4. PAN Category- ‘Government’ has been changed to PAN Category –‘Others’ 5. In FY 2017-18 and earlier years, returns of two assessment years (current assessment year + belated returns of previous assessment year) could be filed. However, due to change in law, returns of only the current years can be filed from FY 2018-19 onwards. Hence, the marginal decline in returns filed in FY 2018-19

Description:

Explore the dynamic landscape of the Indian stock market with this extensive dataset featuring 4456 companies listed on both the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). Gain insights into each company's financial performance, quarterly and yearly profit and loss statements, balance sheets, cash flow data, and essential financial ratios. Dive deep into the intricacies of shareholding patterns, tracking the movements of promoters, foreign and domestic institutional investors, and the public.

This dataset is a rich resource for financial analysts, investors, and data enthusiasts. Perform thorough company evaluations, sector-wise comparisons, and predictive modeling. With figures presented in crore rupees, leverage the dataset for in-depth exploratory data analysis, time series forecasting, and machine learning applications. Stay tuned for updates as we enrich this dataset for a deeper understanding of the Indian stock market landscape. Unlock the potential of data-driven decision-making with this comprehensive repository of financial information.

Folder Structure:

• 4492 NSE & BSE Companies
  • Main directory containing data for 4456 NSE and BSE registered companies.
  • Company_name folder
    • Individual folders for each company allowing for easy organization and retrieval.
    • Company_name.csv
      • General company information.
    • Quarterly_Profit_Loss.csv
      • Quarterly financial data.
    • Yearly_Profit_Loss.csv
      • Annual financial data.
    • Yearly_Balance_Sheet.csv
      • Annual balance sheet information.
    • Yearly_Cash_flow.csv
      • Annual cash flow data.
    • Ratios.csv.csv
      • Financial ratios over time.
    • Quarterly_Shareholding_Pattern.csv
      • Quarterly shareholding pattern.
    • Yearly_Shareholding_Pattern.csv
      • Annual shareholding pattern.

File Explanation:

Company_name.csv

- `Company_name`: Name of the company.
- `Sector`: Industry sector of the company.
- `BSE`: Bombay Stock Exchange code.
- `NSE`: National Stock Exchange code.
- `Market Cap`: Market capitalization of the company.
- `Current Price`: Current stock price.
- `High/Low`: Highest and lowest stock prices.
- `Stock P/E`: Price to earnings ratio.
- `Book Value`: Book value per share.
- `Dividend Yield`: Dividend yield percentage.
- `ROCE`: Return on capital employed percentage.
- `ROE`: Return on equity percentage.
- `Face Value`: Face value of the stock.
- `Price to Sales`: Price to sales ratio.
- `Sales growth (1, 3, 5, 7, 10 years)`: Sales growth percentage over different time periods.
- `Profit growth (1, 3, 5, 7, 10 years)`: Profit growth percentage over different time periods.
- `EPS`: Earnings per share.
- `EPS last year`: Earnings per share in the last year.
- `Debt (1, 3, 5, 7, 10 years)`: Debt of the company over different time periods.

Quarterly_Profit_Loss.csv

 - `Sales`: Revenue generated by the company.
 - `Expenses`: Total expenses incurred.
 - `Operating Profit`: Profit from core operations.
 - `OPM %`: Operating Profit Margin percentage.
 - `Other Income`: Additional income sources.
 - `Interest`: Interest paid.
 - `Depreciation`: Depreciation of assets.
 - `Profit before tax`: Profit before tax.
 - `Tax %`: Tax percentage.
 - `Net Profit`: Net profit after tax.
 - `EPS in Rs`: Earnings per share.

Yearly_Profit_Loss.csv

- Same as Quarterly_Profit_Loss.csv, but on a yearly basis.

Yearly_Balance_Sheet.csv

- `Equity Capital`: Capital raised through equity.
- `Reserves`: Company's retained earnings.
- `Borrowings`: Company's borrowings.
- `Other Liabilities`: Other financial obligations.
- `Total Liabilities`: Sum of all liabilities.
- `Fixed Assets`: Company's long-term assets.
- `CWIP`: Capital Work in Progress.
- `Investments`: Company's investments.
- `Other Assets`: Other non-current assets.
- `Total Assets`: Sum of all assets.

Yearly_Cash_flow.csv

- `Cash from Operating Activity`: Cash generated from core business operations.
- `Cash from Investing Activity`: Cash from investments.
- `Cash from Financing Activity`: Cash from financing (borrowing, stock issuance, etc.).
- `Net Cash Flow`: Overall net cash flow.

Ratios.csv.csv

- `Debtor Days`: Number of days it takes to collect receivables.
- `Inventory Days`: Number of days inventory is held.
- `Days Payable`: Number of days a company takes to pay its bills.
- `Cash Conversion Cycle`: Time taken to convert sales into cash.
- `Wor...

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.

RY:TSX Royal Bank of Canada

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 table provides an overview, by tax year, of the filing and processing of non-resident corporation tax returns (INR-soc). As the final date for filing the return may vary, we usually resume the situation on 30 June of the year following that of the tax year because it should reflect the final situation. However, in recent years, we have noted that the management of declarations extends beyond that date. This is why we now give the final situation as at 31 December of the second year following the year of income (e.g. for the 2021 tax year, 2020 income: final situation as at 31 December 2022). We will subsequently update the figures for the current tax year for which we present the interim situation as at 31 December. Since the 2014 tax year, companies have been obliged to submit their return using the Biztax application. They can submit a paper declaration only if they do not have sufficient technical means (computer and internet). In this case, they must apply to the tax office for an exemption. They must renew this application every year. The number of declarations expected also includes: declarations which, after investigation, do not have to be lodged (and which are therefore closed administratively); taxpayers who are not taxable in Belgium but who are required to file a nil return. In these two categories, we find a large number of non-filing taxpayers. Audits in the Management teams begin as soon as the declarations are received and continue mainly during the following year. If the declaration is correct, it will be enlisted on the basis of the declared income. If the declaration is not correct, it will be subject to: a correction (if the correction is to the taxpayer's disadvantage) or a relief (if the correction is to the taxpayer's advantage). Following the coronavirus crisis in 2020, the General Tax Administration was forced to temporarily suspend contact with taxpayers. As a result, it has fallen behind in the treatment of non-applicants (which includes sending reminder letters followed by ex officio taxation or classification), both for the 2019 tax year and for the 2020 tax year. The General Administration of Taxation is a target group-oriented organisation made up of three administrations: the Individual Administration; the Administration for Small and Medium-sized Enterprises; the Administration of Large Enterprises. There are a few exceptions: Until 30 June 2020, the Public Administration also had the Eupen Multipurpose Centre, which dealt with all cases in the German-speaking region: individuals, small and medium-sized enterprises and large enterprises. This centre was abolished on 1 July 2020. The files are now processed by the P Liège and PME Liège centres according to the nature of the taxpayers concerned. The Small and Medium-sized Enterprises Administration also includes: the Brussels 4 control centre, which handles Members’ files; the Centre Étranger which, until 30 June 2020, dealt with all the files of non-residents (natural persons and companies). On July 1, 2020, the name "Centre Étranger" was changed to "Centre PME Matières Spécifiques". This centre is responsible for: withholding tax and withholding tax for all taxpayers and for all non-residents; tax on non-residents; VAT for non-residents who are subject to VAT; taxes treated as income taxes;

This table provides an overview, by tax year, of how corporate income tax returns (CITs) are filed and processed. As the final date for filing the return may vary, we usually resume the situation on 30 June of the year following that of the tax year because it should reflect the final situation. However, in recent years, we have found that the management of declarations extends beyond that date. This is why we now give the final situation as at 31 December of the second year following the year of income (e.g. for the 2021 tax year, 2020 income: final situation as at 31 December 2022). We will subsequently update the figures for the current tax year for which we present the interim situation as at 31 December. Since the 2014 tax year, companies have been obliged to submit their return using the Biztax application. They can submit a paper declaration only if they do not have sufficient technical means (computer and internet). In this case, they must apply to the tax office for an exemption. They must renew this application every year. Audits in the Management teams begin as soon as the declarations are received and continue mainly during the following year. If the declaration is correct, it will be enlisted on the basis of the declared income. If the declaration is not correct, it will be subject to: a correction (if the correction is to the taxpayer's disadvantage) or a relief (if the correction is to the taxpayer's advantage). Following the coronavirus crisis in 2020, the General Tax Administration was forced to temporarily suspend contact with taxpayers. As a result, it has fallen behind in the treatment of non-applicants (which includes sending reminder letters followed by ex officio taxation or classification), both for the 2019 tax year and for the 2020 tax year. The General Administration of Taxation is a target group-oriented organisation made up of three administrations: the Individual Administration; the Administration for Small and Medium-sized Enterprises; the Administration of Large Enterprises. There are a few exceptions: Until 30 June 2020, the Public Administration also had the Eupen Multipurpose Centre, which dealt with all cases in the German-speaking region: individuals, small and medium-sized enterprises and large enterprises. This centre was abolished on 1 July 2020. The files are now processed by the P Liège and PME Liège centres according to the nature of the taxpayers concerned. The Small and Medium-sized Enterprises Administration also includes: the Brussels 4 control centre, which handles Members’ files; the Centre Étranger which, until 30 June 2020, dealt with all the files of non-residents (natural persons and companies). On July 1, 2020, the name "Centre Étranger" was changed to "Centre PME Matières Spécifiques". This centre is responsible for: withholding tax and withholding tax for all taxpayers and for all non-residents; tax on non-residents; VAT for non-residents who are subject to VAT; taxes treated as income taxes; miscellaneous taxes. The Administration Particuliers now handles the files of non-residents - natural persons who are not subject to VAT. N.B.: The Eupen Multipurpose Centre processed the declarations entered in the accounts for the Individual Administration.

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.

TNX:TSX Stock: A Spark That Could Fizzle

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 table provides an overview, by taxation year, of the filing of corporate tax returns (CPIs) and their processing. Since the final date for filing the return may vary, we repeat the situation as of June 30 of the year following that of the tax year because it then reflects the final situation. We will subsequently update the current tax year figures for which we present the situation as at December 31. The return of returns via the Biztax application has been mandatory since the 2014 tax year. A company can enter a paper return only if it does not have sufficient technical means (computer and internet). In this case, she must apply for an exemption from the tax office. This application must be renewed each year. Verifications in management teams begin upon receipt of declarations and continue mainly during the following year. If the return is correct, it will be enrolled on the basis of the reported income. If the declaration is not correct, it will be subject to: a correction (if the correction is against the taxpayer) or relief (if the correction is in favour of the taxpayer). Following the coronavirus crisis in 2020, AGFisc was forced to temporarily suspend contact with taxpayers. As a result, it was delayed in the treatment of non-applicants (which includes sending reminder letters, followed by ex officio taxation or classification), both for the 2019 tax year and for the 2020 tax year. AGFisc is a target group oriented organisation composed of 3 administrations: the Private Administration (Adm. P); small and Medium-sized Enterprises Administration (Adm. SMEs); the Administration Grandes Entreprises (Adm. GE). There are a few exceptions: Until 30 June 2020, the Adm. P also had the Eupen Multipurpose Centre, which dealt with all the files in the German-speaking region: individuals, small and medium-sized enterprises and large enterprises. This centre was deleted on 1 July 2020. The files are now processed by the centres P Liège and PME Liège according to the nature of the taxpayers concerned. The SME Adm also includes: the Brussels 4 control centre, which handles the files of parliamentarians; the Centre Étranger, which processed, until 30 June 2020, all files of non-residents (natural persons and companies). On 1 July 2020, the name ‘Centre Étranger’ was changed to ‘Centre PMESpecific Materials’. The Centre is responsible for: professional withholding tax and withholding tax for all taxpayers and non-residents; tax on non-residents; VAT for non-residents who are subject to VAT; taxes treated as income taxes; various taxes. Adm. P is now dealing with the files of non-residents — natural persons who are not subject to VAT. N.B.: The Eupen Multipurpose Centre processed returns accounted for by the Individual Administration. Legend: 0 = null value. Empty cell = no possible value.

This table provides an overview, by tax year, of the filing and processing of non-resident - natural person (INR-pp) tax returns. As the final date for filing the return may vary, we usually resume the situation on 30 June of the year following that of the tax year because it should reflect the final situation. However, in recent years, we have found that the management of declarations extends beyond that date. This is why we now give the final situation as at 31 December of the second year following the year of income (e.g. for the 2021 tax year, 2020 income: final situation as at 31 December 2022). We will subsequently update the figures for the current tax year for which we present the interim situation as at 31 December. Audits in the Management teams begin as soon as the declarations are received and continue mainly during the following year. If the declaration is correct, it will be enlisted on the basis of the declared income. If the declaration is not correct, it will be subject to: a correction (if the correction is to the taxpayer's disadvantage) or a relief (if the correction is to the taxpayer's advantage). Following the coronavirus crisis in 2020, the General Tax Administration was forced to temporarily suspend contact with taxpayers. As a result, it has fallen behind in the treatment of non-applicants (which includes sending reminder letters followed by ex officio taxation or classification), both for the 2019 tax year and for the 2020 tax year. The General Administration of Taxation is a target group-oriented organisation made up of three administrations: the Individual Administration; the Administration for Small and Medium-sized Enterprises; the Administration of Large Enterprises. There are a few exceptions: Until 30 June 2020, the Public Administration also had the Eupen Multipurpose Centre, which dealt with all cases in the German-speaking region: individuals, small and medium-sized enterprises and large enterprises. This centre was abolished on 1 July 2020. The files are now processed by the P Liège and PME Liège centres according to the nature of the taxpayers concerned. The Small and Medium-sized Enterprises Administration also includes: the Brussels 4 control centre, which handles Members’ files; the Centre Étranger which, until 30 June 2020, dealt with all the files of non-residents (natural persons and companies). On July 1, 2020, the name "Centre Étranger" was changed to "Centre PME Matières Spécifiques". This centre is responsible for: withholding tax and withholding tax for all taxpayers and for all non-residents; tax on non-residents; VAT for non-residents who are subject to VAT; taxes treated as income taxes; miscellaneous taxes. The Administration Particuliers now handles the files of non-residents - natural persons who are not subject to VAT. N.B.: The Eupen Multipurpose Centre processed the declarations entered in the accounts for the Individual Administration. The Administration Grandes Entreprises does not process the files of natural persons. Legend: 0 = zero value.