Household Saving Rate in the United States decreased to 4.50 percent in May from 4.90 percent in April of 2025. This dataset provides - United States Personal Savings Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Foreign Direct Investment in the United States increased by 66726 USD Million in the first quarter of 2025. This dataset provides - United States Foreign Direct Investment - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides values for FOREIGN DIRECT INVESTMENT reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Techsalerator’s Business Funding Data for North America is an extensive and insightful resource designed for businesses, investors, and financial analysts who need a deep understanding of the Asian funding landscape. This dataset meticulously captures and categorizes critical information about the funding activities of companies across the continent, providing valuable insights into the financial health and investment trends within various sectors.

What the Dataset Includes: Funding Rounds: Detailed records of funding rounds for companies in North America, including the size of the round, the date it occurred, and the stages of investment (Seed, Series A, Series B, etc.).

Investment Sources: Information on the sources of investment, such as venture capital firms, private equity investors, angel investors, and corporate investors.

Financial Milestones: Key financial achievements and benchmarks reached by companies, including valuation increases, revenue milestones, and profitability metrics.

Sector-Specific Data: Insights into how different sectors are performing, with data segmented by industry verticals such as technology, healthcare, finance, and consumer goods.

Geographic Breakdown: An overview of funding trends and activities specific to each North America country, allowing users to identify regional patterns and opportunities.

EU Countries Included in the Dataset: Antigua and Barbuda Bahamas Barbados Belize Canada Costa Rica Cuba Dominica Dominican Republic El Salvador Grenada Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Trinidad and Tobago United States

Benefits of the Dataset: Informed Decision-Making: Investors and analysts can use the data to make well-informed investment decisions by understanding funding trends and financial health across different regions and sectors. Strategic Planning: Businesses can leverage the insights to identify potential investors, benchmark against industry peers, and plan their funding strategies effectively. Market Analysis: The dataset helps in analyzing market dynamics, identifying emerging sectors, and spotting investment opportunities across North America. Techsalerator’s Business Funding Data for North America is a vital tool for anyone involved in the financial and investment sectors, offering a granular view of the funding landscape and enabling more strategic and data-driven decisions.

This description provides a more detailed view of what the dataset offers and highlights the relevance and benefits for various stakeholders.

There are few centralized information systems on the evolution and composition of public investment expenditure in Latin America, a critical aspect for monitoring and evaluating investment priorities. The Database of Public Investment Expenditure in Latin America (BDD-GIPAL), available for 16 countries in the region, provides cross-classifications of expenditures (economic, institutional, and functional) for the period 2000-2016. Analysis of BDD-GIPAL helps answer three key questions: How much is invested? Who invests? And in what is it invested? Public investment in the region increased from 2.8% to 3.9% of GDP (2002-2006 vs. 2012-2016); however, this growth was driven by only five countries. Some countries in the region have delegated greater responsibility for public investment spending to subnational governments. In four countries, subnational governments account for over 50% of total public investment expenditure. Nearly 50% of public investment spending in the region has been directed toward transportation infrastructure and housing and community services. In the current context of fiscal constraints across the region, BDD-GIPAL serves as a valuable resource for policymakers and society at large to analyze the prioritization and quality of public investment expenditure.

Techsalerator’s Business Funding Data for Latin America is an extensive and insightful resource designed for businesses, investors, and financial analysts who need a deep understanding of the Latin America funding landscape. This dataset meticulously captures and categorizes critical information about the funding activities of companies across the continent, providing valuable insights into the financial health and investment trends within various sectors.

What the Dataset Includes: Funding Rounds: Detailed records of funding rounds for companies in Latin America, including the size of the round, the date it occurred, and the stages of investment (Seed, Series A, Series B, etc.).

Investment Sources: Information on the sources of investment, such as venture capital firms, private equity investors, angel investors, and corporate investors.

Financial Milestones: Key financial achievements and benchmarks reached by companies, including valuation increases, revenue milestones, and profitability metrics.

Sector-Specific Data: Insights into how different sectors are performing, with data segmented by industry verticals such as technology, healthcare, finance, and consumer goods.

Geographic Breakdown: An overview of funding trends and activities specific to each Asian country, allowing users to identify regional patterns and opportunities.

Latam Countries Included in the Dataset: Argentina Bolivia Brazil Chile Colombia Ecuador Guyana Paraguay Peru Suriname Uruguay Venezuela Central America: Belize Costa Rica El Salvador Guatemala Honduras Nicaragua Panama

Benefits of the Dataset: Informed Decision-Making: Investors and analysts can use the data to make well-informed investment decisions by understanding funding trends and financial health across different regions and sectors. Strategic Planning: Businesses can leverage the insights to identify potential investors, benchmark against industry peers, and plan their funding strategies effectively. Market Analysis: The dataset helps in analyzing market dynamics, identifying emerging sectors, and spotting investment opportunities across Latin America. Techsalerator’s Business Funding Data for Latin America is a vital tool for anyone involved in the financial and investment sectors, offering a granular view of the funding landscape and enabling more strategic and data-driven decisions.

This description provides a more detailed view of what the dataset offers and highlights the relevance and benefits for various stakeholders.

The real options approach often assumes that investment projects last indefinitely, which is an unrealistic assumption. When projects live finitely, valuation techniques from American option pricing are required. This article presents a method for pricing American options based on the first-passage approach to the problem. The key is to correct the error associated with the price obtained from a rough first approximation. The procedure leads to a significant reduction in error corresponding to the initial approximation. As a particular case of the method proposed, we derive a closed-form approximation of the option price. The existence of a closed-form approximating formula (that does not involve iterative methods) keeps the computational cost low. In terms of accuracy, the method can be compared to much more sophisticated methods. A tight lower bound (given in closed form) is also provided. The method is fast, accurate, flexible, and easy to implement. A spreadsheet suffices for practical implementation.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to May 2025 about savings, personal, rate, and USA.

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.

JPMorgan American Investment Trust (JAM): Questioning the Future

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

A novel dataset for bankruptcy prediction related to American public companies listed on the New York Stock Exchange and NASDAQ is provided. The dataset comprises accounting data from 8,262 distinct companies recorded during the period spanning from 1999 to 2018.

According to the Security Exchange Commission (SEC), a company in the American market is deemed bankrupt under two circumstances. Firstly, if the firm's management files for Chapter 11 of the Bankruptcy Code, indicating an intention to "reorganize" its business. In this case, the company's management continues to oversee day-to-day operations, but significant business decisions necessitate approval from a bankruptcy court. Secondly, if the firm's management files for Chapter 7 of the Bankruptcy Code, indicating a complete cessation of operations and the company going out of business entirely.

In this dataset, the fiscal year prior to the filing of bankruptcy under either Chapter 11 or Chapter 7 is labeled as "Bankruptcy" (1) for the subsequent year. Conversely, if the company does not experience these bankruptcy events, it is considered to be operating normally (0). The dataset is complete, without any missing values, synthetic entries, or imputed added values.

The resulting dataset comprises a total of 78,682 observations of firm-year combinations. To facilitate model training and evaluation, the dataset is divided into three subsets based on time periods. The training set consists of data from 1999 to 2011, the validation set comprises data from 2012 to 2014, and the test set encompasses the years 2015 to 2018. The test set serves as a means to assess the predictive capability of models in real-world scenarios involving unseen cases.

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.

AEL^A American Equity Investment Life Holding Company Depositary Shares each representing a 1/1000th interest in a share of 5.95% Fixed-Rate Reset Non-Cumulative Preferred Stock Series A

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.

Can American Strategic Investment (NYC) Continue Its Market Outperformance?

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 Institute for the Integration of Latin America and the Caribbean (INTAL), part of the Integration and Trade Sector of the Inter-American Development Bank (IDB), conducted the second edition of a survey targeting firms in Latin America and the Caribbean (LAC) that export both within the region and to extraregional markets. This dataset contains the inputs used to analyze 405 firms, providing insights into how they navigated the second year of the pandemic. It examines the evolution of their exports, the challenges posed by the specific context of the pandemic, the measures they have taken, the public support policies they have received, and their outlook for the future. Additionally, the dataset includes methodological appendices and a sample of the survey.

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Alternative Data Market Size 2025-2029

The alternative data market size is forecast to increase by USD 60.32 billion, at a CAGR of 52.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increased availability and diversity of data sources. This expanding data landscape is fueling the rise of alternative data-driven investment strategies across various industries. However, the market faces challenges related to data quality and standardization. As companies increasingly rely on alternative data to inform business decisions, ensuring data accuracy and consistency becomes paramount. Addressing these challenges requires robust data management systems and collaboration between data providers and consumers to establish industry-wide standards. Companies that effectively navigate these dynamics can capitalize on the wealth of opportunities presented by alternative data, driving innovation and competitive advantage.

What will be the Size of the Alternative Data Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, with new applications and technologies shaping its dynamics. Predictive analytics and deep learning are increasingly being integrated into business intelligence systems, enabling more accurate risk management and sales forecasting. Data aggregation from various sources, including social media and web scraping, enriches datasets for more comprehensive quantitative analysis. Data governance and metadata management are crucial for maintaining data accuracy and ensuring data security. Real-time analytics and cloud computing facilitate decision support systems, while data lineage and data timeliness are essential for effective portfolio management. Unstructured data, such as sentiment analysis and natural language processing, provide valuable insights for various sectors. Machine learning algorithms and execution algorithms are revolutionizing trading strategies, from proprietary trading to high-frequency trading. Data cleansing and data validation are essential for maintaining data quality and relevance. Standard deviation and regression analysis are essential tools for financial modeling and risk management. Data enrichment and data warehousing are crucial for data consistency and completeness, allowing for more effective customer segmentation and sales forecasting. Data security and fraud detection are ongoing concerns, with advancements in technology continually addressing new threats. The market's continuous dynamism is reflected in its integration of various technologies and applications. From data mining and data visualization to supply chain optimization and pricing optimization, the market's evolution is driven by the ongoing unfolding of market activities and evolving patterns.

How is this Alternative Data Industry segmented?

The alternative data industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeCredit and debit card transactionsSocial mediaMobile application usageWeb scrapped dataOthersEnd-userBFSIIT and telecommunicationRetailOthersGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalyUKAPACChinaIndiaJapanRest of World (ROW)

By Type Insights

The credit and debit card transactions segment is estimated to witness significant growth during the forecast period.Alternative data derived from card and debit card transactions plays a pivotal role in business intelligence, offering valuable insights into consumer spending behaviors. This data is essential for market analysts, financial institutions, and businesses aiming to optimize strategies and enhance customer experiences. Two primary categories exist within this data segment: credit card transactions and debit card transactions. Credit card transactions reveal consumers' discretionary spending patterns, luxury purchases, and credit management abilities. By analyzing this data through quantitative methods, such as regression analysis and time series analysis, businesses can gain a deeper understanding of consumer preferences and trends. Debit card transactions, on the other hand, provide insights into essential spending habits, budgeting strategies, and daily expenses. This data is crucial for understanding consumers' practical needs and lifestyle choices. Machine learning algorithms, such as deep learning and predictive analytics, can be employed to uncover patterns and trends in debit card transactions, enabling businesses to tailor their offerings and services accordingly. Data governance, data security, and data accuracy are critical considerations when dealing with sensitive financial d

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.

British American Tobacco (BTI) - A Smokin' Hot Investment or a Fading Flame?

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 CA: Foreign Direct Investment Position: Outward: Total: American Samoa data was reported at 0.000 CAD mn in 2023. This stayed constant from the previous number of 0.000 CAD mn for 2022. Canada CA: Foreign Direct Investment Position: Outward: Total: American Samoa data is updated yearly, averaging 0.000 CAD mn from Dec 2016 (Median) to 2023, with 8 observations. The data reached an all-time high of 0.000 CAD mn in 2023 and a record low of 0.000 CAD mn in 2023. Canada CA: Foreign Direct Investment Position: Outward: Total: American Samoa data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Canada – Table CA.OECD.FDI: Foreign Direct Investment Position: by Region and Country: OECD Member: Annual. Reverse investment:Reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) cannot be identified but is believed to be extremely rare. Netting of reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. In the case of Canada, any extension of loans by the DIE to its parent is netted out from inward and outward transactions and positions, regardless of the DIE's equity ownership in its parent. Treatment of debt transactions and positions between fellow enterprises: asset/liability basis. FDI transactions and positions by partner country and by industry include resident Special Purpose Entities (SPEs), which cannot yet be reported separately. Valuation method used for listed inward and outward equity positions: Own funds at book values. Valuation method used for unlisted inward and outward equity positions: Own funds at book values. Valuation method used for inward and outward debt positions: Book value .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered except in outward FDI positions. Collective investment institutions are covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the resident direct investor. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Canada CA: Foreign Direct Investment Position: Outward: USD: Total: ODA Recipients - America data was reported at 99.442 USD bn in 2023. This records an increase from the previous number of 85.254 USD bn for 2022. Canada CA: Foreign Direct Investment Position: Outward: USD: Total: ODA Recipients - America data is updated yearly, averaging 78.644 USD bn from Dec 2016 (Median) to 2023, with 8 observations. The data reached an all-time high of 99.442 USD bn in 2023 and a record low of 56.949 USD bn in 2016. Canada CA: Foreign Direct Investment Position: Outward: USD: Total: ODA Recipients - America data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Canada – Table CA.OECD.FDI: Foreign Direct Investment Position: USD: by Region and Country: OECD Member: Annual. Reverse investment:Reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) cannot be identified but is believed to be extremely rare. Netting of reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. In the case of Canada, any extension of loans by the DIE to its parent is netted out from inward and outward transactions and positions, regardless of the DIE's equity ownership in its parent. Treatment of debt transactions and positions between fellow enterprises: asset/liability basis. FDI transactions and positions by partner country and by industry include resident Special Purpose Entities (SPEs), which cannot yet be reported separately. Valuation method used for listed inward and outward equity positions: Own funds at book values. Valuation method used for unlisted inward and outward equity positions: Own funds at book values. Valuation method used for inward and outward debt positions: Book value .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered except in outward FDI positions. Collective investment institutions are covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the resident direct investor. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.; Countries from AMERICA recipients of Offical Development Assistance (ODA), 30 countries: Chile, Mexico , Antigua and Barbuda, Cuba, Dominica, Dominican Republic, Grenada, Haiti, Jamaica, Montserrat, Saint Lucia, Saint Vincent and the Grenadines, Belize, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, Panama, Argentina, Bolivia, Brazil, Colombia, Ecuador, Guyana, Paraguay, Peru, Suriname, Uruguay, Venezuela

Canada CA: Foreign Direct Investment Position: Inward: USD: Total: America data was reported at 610.285 USD bn in 2023. This records an increase from the previous number of 568.389 USD bn for 2022. Canada CA: Foreign Direct Investment Position: Inward: USD: Total: America data is updated yearly, averaging 401.353 USD bn from Dec 2014 (Median) to 2023, with 10 observations. The data reached an all-time high of 610.285 USD bn in 2023 and a record low of 327.026 USD bn in 2015. Canada CA: Foreign Direct Investment Position: Inward: USD: Total: America data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Canada – Table CA.OECD.FDI: Foreign Direct Investment Position: USD: by Region and Country: OECD Member: Annual. Reverse investment:Reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) cannot be identified but is believed to be extremely rare. Netting of reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. In the case of Canada, any extension of loans by the DIE to its parent is netted out from inward and outward transactions and positions, regardless of the DIE's equity ownership in its parent. Treatment of debt transactions and positions between fellow enterprises: asset/liability basis. FDI transactions and positions by partner country and by industry include resident Special Purpose Entities (SPEs), which cannot yet be reported separately. Valuation method used for listed inward and outward equity positions: Own funds at book values. Valuation method used for unlisted inward and outward equity positions: Own funds at book values. Valuation method used for inward and outward debt positions: Book value .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered except in outward FDI positions. Collective investment institutions are covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the resident direct investor. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Canada CA: Foreign Direct Investment Position: Outward: USD: Total: America data was reported at 1,154.714 USD bn in 2023. This records an increase from the previous number of 1,062.571 USD bn for 2022. Canada CA: Foreign Direct Investment Position: Outward: USD: Total: America data is updated yearly, averaging 702.142 USD bn from Dec 2011 (Median) to 2023, with 11 observations. The data reached an all-time high of 1,154.714 USD bn in 2023 and a record low of 428.809 USD bn in 2011. Canada CA: Foreign Direct Investment Position: Outward: USD: Total: America data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Canada – Table CA.OECD.FDI: Foreign Direct Investment Position: USD: by Region and Country: OECD Member: Annual. Reverse investment:Reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) cannot be identified but is believed to be extremely rare. Netting of reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. In the case of Canada, any extension of loans by the DIE to its parent is netted out from inward and outward transactions and positions, regardless of the DIE's equity ownership in its parent. Treatment of debt transactions and positions between fellow enterprises: asset/liability basis. FDI transactions and positions by partner country and by industry include resident Special Purpose Entities (SPEs), which cannot yet be reported separately. Valuation method used for listed inward and outward equity positions: Own funds at book values. Valuation method used for unlisted inward and outward equity positions: Own funds at book values. Valuation method used for inward and outward debt positions: Book value .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered except in outward FDI positions. Collective investment institutions are covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the resident direct investor. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Canada CA: Foreign Direct Investment Position: Outward: USD: Total: Northern America data was reported at 813.151 USD bn in 2023. This records an increase from the previous number of 745.691 USD bn for 2022. Canada CA: Foreign Direct Investment Position: Outward: USD: Total: Northern America data is updated yearly, averaging 489.888 USD bn from Dec 2011 (Median) to 2023, with 11 observations. The data reached an all-time high of 813.151 USD bn in 2023 and a record low of 266.839 USD bn in 2011. Canada CA: Foreign Direct Investment Position: Outward: USD: Total: Northern America data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Canada – Table CA.OECD.FDI: Foreign Direct Investment Position: USD: by Region and Country: OECD Member: Annual. Reverse investment:Reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) cannot be identified but is believed to be extremely rare. Netting of reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. In the case of Canada, any extension of loans by the DIE to its parent is netted out from inward and outward transactions and positions, regardless of the DIE's equity ownership in its parent. Treatment of debt transactions and positions between fellow enterprises: asset/liability basis. FDI transactions and positions by partner country and by industry include resident Special Purpose Entities (SPEs), which cannot yet be reported separately. Valuation method used for listed inward and outward equity positions: Own funds at book values. Valuation method used for unlisted inward and outward equity positions: Own funds at book values. Valuation method used for inward and outward debt positions: Book value .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered except in outward FDI positions. Collective investment institutions are covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the resident direct investor. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.