Content Model Catalog

KR STOCK

financial

financial

Summary

This dataset provides PIT (point-in-time) financial statements for KR stocks, including various financial metrics and indicators. The data’s index indicates the date the data was included in the database.

Users can choose from 3 types of data frames by adjusting the preprocess_type parameter. Note that using preprocess_type may slow down data retrieval due to additional transformation logic, potentially taking several seconds per CM:

  1. None: In this case, the values in the data frame become dictionaries possibly including multiple keys and values. If you want to know all values (including original, restatement, etc.), this option can be a solution.

  2. 'default': In this case, the values in the data frame become the most recent values at the point in time, not including the fiscal date.

  3. 'unpivot': In this case, there will be 4 columns (id, pit, fiscal, value). Each row contains information about the announced data’s announcing date, fiscal quarter, and value.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("krx-spot-total_assets", preprocess_type='default')

Metadata

valid from
delivery schedule
time zone
data frequency

20000101

20 14 * * 1-5

Asia/Seoul

1d

Item List

Balance Sheet

  • krx-spot-advance_from_customers: Information on advance payments from customers for Korean stocks

  • krx-spot-capital_stock: Capital information of Korean stocks

  • krx-spot-cash_and_cash_equivalent: Information on cash and cash equivalents of Korean stocks

  • krx-spot-common_stock: Information on common stocks in Korea

  • krx-spot-construction_in_progress: Construction in progress of Korean stocks

  • krx-spot-convertible_bonds: Convertible bonds of Korean stocks

  • krx-spot-current_assets: Current assets of Korean stocks

  • krx-spot-current_financial_assets: Information on liquid financial assets of Korean stocks

  • krx-spot-current_income_tax_liabilities: Current corporate tax liabilities of Korean stocks

  • krx-spot-current_liabilities: Information on current liabilities of Korean stocks

  • krx-spot-current_portion_of_long_term_debt: Information on the liquidity of long-term debt in Korean stocks

  • krx-spot-current_provisions_for_employee_benefits: Current Employee Benefits Provision for Korean Stocks

  • krx-spot-deferred_tax_assets: Information on deferred corporate tax assets of Korean stocks

  • krx-spot-deferred_tax_liabilities: Information on deferred corporate tax liabilities of Korean stocks

  • krx-spot-finished_goods: Products of Korean stocks

  • krx-spot-goodwill: Information on goodwill in Korean stocks

  • krx-spot-intangible_assets: Information on intangible assets of Korean stocks

  • krx-spot-inventory: Information on inventory assets of Korean stocks

  • krx-spot-invested_capital: Information on investment capital in Korean stocks

  • krx-spot-investment_in_properties: Investment real estate of Korean stocks

  • krx-spot-land: Land of Korean stocks

  • krx-spot-liabilities_included_in_disposal_groups_classified_as_held_for_sale: Liabilities included in the group of assets held for sale classified as intended for sale of Korean stocks

  • krx-spot-listed_shares_comm: The number of listed common stocks in the Korean stock market

  • krx-spot-listed_shares_pref: The number of listed preferred stocks in the Korean stock market

  • krx-spot-loans: Loan information of the Korean stock market

  • krx-spot-long_term_borrowings: Information on long-term borrowings of Korean stocks

  • krx-spot-long_term_financial_instrument: Information on long-term financial products of Korean stocks

  • krx-spot-long_term_financial_liabilities: Information on long-term financial liabilities of Korean stocks

  • krx-spot-long_term_provisions: Information on long-term provisions for Korean stocks

  • krx-spot-non_controlling_interests_equity: Information on non-controlling interests in Korean stocks

  • krx-spot-non_current_biological_assets: Information on non-current biological assets of Korean stocks

  • krx-spot-non_current_liabilities: Information on non-current liabilities of Korean stocks

  • krx-spot-non_current_provisions_for_employee_benefits: Non-current employee benefits provision for Korean stocks

  • krx-spot-other_current_assets: Other current assets of Korean stocks

  • krx-spot-other_current_liabilities: Other current liabilities of Korean stocks

  • krx-spot-other_non_current_assets: Information on other non-current assets of Korean stocks

  • krx-spot-other_non_current_liabilities: Information on other non-current liabilities of Korean stocks

  • krx-spot-other_payables: Information on other liabilities of Korean stocks

  • krx-spot-owners_of_parent_equity: Information on the ownership equity of controlling companies in Korean stocks

  • krx-spot-paid_in_capital_in_excess_of_par_value: Paid-in capital in excess of par value of Korean stocks

  • krx-spot-paid_in_capital_increase: Information on paid-in capital increase of Korean stocks

  • krx-spot-prepaid_expenses: Information on prepaid expenses for Korean stocks

  • krx-spot-preferred_stock: Information on preferred stocks in Korea

  • krx-spot-property_plant_and_equipment: Information on tangible assets of Korean stocks

  • krx-spot-receivables: Information on receivables of Korean stocks

  • krx-spot-retained_earnings: Information on retained earnings of Korean stocks

  • krx-spot-short_term_bonds: Information on short-term bonds in the Korean stock market

  • krx-spot-short_term_borrowings: Information on short-term borrowings of Korean stocks

  • krx-spot-short_term_financial_liabilities: Information on short-term financial liabilities of Korean stocks

  • krx-spot-short_term_provisions: Information on short-term provisions for Korean stocks

  • krx-spot-total_assets: Total assets of Korean stocks

  • krx-spot-total_equity: Total capital information of Korean stocks

  • krx-spot-total_liabilities: Total liabilities of Korean stocks

  • krx-spot-trade_and_other_current_payables: Accounts payable and other current liabilities of Korean stocks

  • krx-spot-trade_payables: Information on accounts payable for Korean stocks

  • krx-spot-trade_receivables: Information on accounts receivable of Korean stocks

  • krx-spot-unearned_income: Information on forward earnings of Korean stocks

Income Statement

  • krx-spot-advertising_expenses: Information on advertising expenditure for Korean stocks

  • krx-spot-amortization: Information on amortization costs of Korean stocks

  • krx-spot-cost_of_sales: Cost of goods sold information for Korean stocks

  • krx-spot-discontinued_operation_income: Operating income from discontinued operations in Korean stocks

  • krx-spot-ebitda: Information on EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) of Korean stocks

  • krx-spot-financial_income: Financial return information of Korean stocks

  • krx-spot-financing_expenses: Financial costs of the Korean stock market

  • krx-spot-gross_profit: Information on gross profit of Korean stocks

  • krx-spot-impairment_losses_on_intangible_assets: Information on impairment losses of intangible assets in Korean stocks

  • krx-spot-impairment_losses_on_property_plant_and_equipment: Impairment loss of tangible assets in Korean stocks

  • krx-spot-income_before_income_taxes_expenses: Information on pre-tax profit of Korean stocks

  • krx-spot-income_taxes_expenses: Corporate tax expenses of Korean stocks

  • krx-spot-interest_expenses: Interest expense information of Korean stocks

  • krx-spot-interest_income: Information on interest income from Korean stocks

  • krx-spot-net_income: Information on the net income of Korean stocks

  • krx-spot-net_income_attributed_to_non_controlling_interest: Net income attributable to non-controlling interests in Korean stocks

  • krx-spot-net_sales: Information on the net sales of Korean stocks

  • krx-spot-number_of_employees: Information on the number of employees in Korean stocks

  • krx-spot-ongoing_operating_income: Information on continuing operating profit of Korean stocks

  • krx-spot-operating_income: Operating profit information of Korean stocks

  • krx-spot-owners_of_parent_net_income: Information on the net income attributable to the controlling shareholders of Korean stocks

  • krx-spot-owners_of_parent_ongoing_operating_income_or_loss_per_share: Information on the earnings per share of controlling shareholders in Korean stocks

  • krx-spot-purchase_of_treasury_stock: Information on share buybacks of Korean stocks

  • krx-spot-research_and_development: Information on research and development expenses of Korean stocks

  • krx-spot-salaries_and_wages: Salary information of Korean stocks

  • krx-spot-sales: Sales revenue information of Korean stocks

  • krx-spot-sales_of_treasury_stock: Profit from the disposal of treasury shares in Korean stocks

  • krx-spot-selling_general_administrative_expenses: Information on selling and administrative expenses of Korean stocks

  • krx-spot-is_depreciation: Information on depreciation expenses in the income statement of Korean stocks

Cashflow Statement

  • krx-spot-capex: Information on capital expenditures (CAPEX) of Korean stocks

  • krx-spot-cash_payout_ratio: Cash dividend payout ratio of the Korean stock market

  • krx-spot-cashflow_from_financial_activities: Cash flow information resulting from financial activities of Korean stocks

  • krx-spot-cashflow_from_investing_activities: Cash flow information resulting from investment activities in Korean stocks

  • krx-spot-cashflow_from_operating_activities: Cash flow information from operating activities of Korean stocks

  • krx-spot-cf_depreciation: Information on depreciation expenses in the cash flow statement of Korean stocks

  • krx-spot-dividends_paid: Information on dividend payments for Korean stocks

  • krx-spot-fcf1: Information on Free Cash Flow (FCF1) of Korean stocks

  • krx-spot-fcf2: Information on Free Cash Flow (FCF2) of Korean stocks

Ratio

  • krx-spot-adj_bps: Adjusted Book Value per Share of Korean Stocks

  • krx-spot-dividend: Dividend information of Korean stocks

  • krx-spot-dividend_comm_cash: Cash dividends on common stocks in Korea

  • krx-spot-dividend_pref_cash: Cash dividends on preferred stocks in South Korea

  • krx-spot-enterprise_value: Information on the corporate value of Korean stocks

  • krx-spot-eps: Earnings per Share of Korean Stocks

  • krx-spot-ex_dividend_date: Ex-dividend date of the Korean stock market

  • krx-spot-fiscal_end: Information on the fiscal year-end date of Korean stocks

  • krx-spot-payout_ratio: Dividend policy of the Korean stock market

  • krx-spot-pretax_income: Pre-tax profit of Korean stocks

  • krx-spot-profit_from_continuing_operations: Information on continuing operating profit of Korean stocks

  • krx-spot-roic: Return on Invested Capital (ROIC) in the Korean stock market

market

Summary This document provides an overview of the Korean fund data available in the data catalog, including various metrics related to fund performance and characteristics.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("krx-fund-nav")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

30 10 * * 1-5

Item List Fund Performance Metrics

  • krx-fund-nav: Net Asset Value of Korean Funds

  • krx-fund-return: The return rate of Korean funds

  • krx-fund-excess_return: Excess return of Korean funds

  • krx-fund-sharpe: Sharpe ratio of Korean funds

Fund Characteristics

  • krx-fund-base_tax_price: Taxation benchmark price of Korean funds

  • krx-fund-duration: Duration of Korean funds

  • krx-fund-fund_grade: Rating of Korean Funds

  • krx-fund-preserve: Principal preservation rate of Korean funds

  • krx-fund-aum: The size of managed assets in Korean funds

  • krx-fund-base_price: The benchmark price of Korean fundsSummary This document provides an overview of the price and volume data available for Korean stocks, including various metrics related to trading and market capitalization.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("price_close")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

20 14 * * 1-5

Item List Price Data

  • price_close: Closing price of Korean stocks

  • price_open: The market price of Korean stocks

  • price_high: High price of Korean stocks

  • price_low: Low prices of Korean stocks

  • price_base: The reference price of Korean stocks

Volume Data

  • turnover: Trading volume of Korean stocks

  • turnover_all: Total trading volume of Korean stocks

  • short_selling_volume: Short selling trading volume of Korean stocks

  • short_selling_turnover: Short selling transaction volume of Korean stocks

  • slb_balance_volume: The balance of margin trading in Korean stocks

  • slb_repay: Repayment volume of securities lending in the Korean stock market

  • slb_new_loan: New lending volume of securities lending in the Korean stock market

  • volume_sum: Trading volume of Korean stocks

Market Capitalization Data

  • mkt_cap: Market capitalization of Korean stocks

  • mkt_cap_all: Total market capitalization of Korean stocks

  • mkt_cap_index: Market capitalization index of Korean stocks

  • foreigner_shares_ratio: Foreign ownership ratio of Korean stocks

  • foreigner_shares: Number of shares held by foreigners in Korean stocks

  • listed_shares: The number of listed shares in the Korean stock market

  • listed_shares_to_be: Expected number of listed shares in the Korean stock market

  • free_float: The number of floating shares in Korean stocks

Margin Trading Data

  • slb_balance_amount: The balance amount of margin trading in Korean stocksSummary This document provides information on the Korean stock data catalog, specifically focusing on adjustment and dividend factors.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("adjust_factor")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

20 14 * * 1-5

Item List

Adjustment Factors

  • adjust_factor: Adjustment factor of Korean stocks

  • dividend_factor: Dividend yield of Korean stocksSummary This document provides an overview of investor activity data in the Korean stock market, including daily buying and selling amounts segmented by investor type and sector.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("buy_amt_0400")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

20 14 * * 1-5

Daily

Item List Selling Amounts

  • sell_amt_0400: Daily selling amount of the individual sector in the Korean stock market

  • sell_amt_0202: Daily selling amount of the national sector in the Korean stock market

  • sell_frgn_qty: Daily selling volume of foreign investors in the Korean stock market

  • sell_amt_0107: Daily selling amount of the other financial sector in the Korean stock market

  • sell_amt_0104: Daily selling amount of the private equity sector in the Korean stock market

  • sell_amt_0101: Daily selling amount in the securities sector in the Korean stock market

  • sell_amt_0900: Investor selling amount as of 9 AM in the Korean stock market

  • sell_amt_0106: Daily selling amount of the financial sector in the Korean stock market

  • sell_amt_0105: Daily selling amount of the banking sector in the Korean stock market

  • sell_amt_0200: Daily selling amount in sectors such as pension funds in the Korean stock market

  • sell_amt_0201: Daily selling amount of the pension fund sector in the Korean stock market

  • sell_amt_all: Daily selling amount of all investors in the Korean stock market

  • sell_amt_0103: Daily selling amount of the trust sector in the Korean stock market

  • sell_amt_0300: Daily selling amount of the other corporations sector in the Korean stock market

  • sell_indvsl_qty: Daily selling volume of individual investors in the Korean stock market

  • sell_indvsl_amt: Daily selling amount of individual investors in the Korean stock market

  • sell_frgn_amt: Daily selling amount of foreign investors in the Korean stock market

  • sell_insti_amt: Daily selling amount of institutional investors in the Korean stock market

  • sell_insti_qty: Daily selling volume of institutional investors in the Korean stock market

Buying Amounts

  • buy_amt_0400: Daily purchase amount of the individual sector in the Korean stock market

  • buy_amt_0202: Daily purchase amount of the national sector in the Korean stock market

  • buy_frgn_amt: Daily purchase amount of foreign investors in the Korean stock market

  • buy_insti_qty: Daily purchase volume of institutional investors in the Korean stock market

  • buy_amt_0900: Investor purchase amount as of 9 AM in the Korean stock market

  • buy_amt_0300: Daily purchase amount in the other corporation sector of the Korean stock market

  • buy_amt_0107: Daily purchase amount in the other financial sector in the Korean stock market

  • buy_amt_0101: Daily purchase amount in the securities sector in the Korean stock market

  • buy_amt_0102: Daily purchase amount in the insurance sector in the Korean stock market

  • buy_amt_0100: Daily purchase amount in the financial investment sector in the Korean stock market

  • buy_amt_0105: Daily purchase amount in the banking sector in the Korean stock market

  • buy_amt_0104: Daily purchase amount in the private equity sector of the Korean stock market

  • buy_amt_0201: Daily purchase amount of the pension fund sector in the Korean stock market

  • buy_amt_all: Daily purchase amount of all investors in the Korean stock market

  • buy_indvsl_qty: Daily purchase quantity of individual investors in the Korean stock market

  • buy_indvsl_amt: Daily purchase amount of individual investors in the Korean stock market

  • buy_frgn_qty: Daily purchase quantity of foreign investors in the Korean stock market

  • buy_amt_0103: Daily purchase amount of the trust sector in the Korean stock market

Net Buying Amounts

  • net_buy_amt_0101: Daily net buying amount in the securities sector of the Korean stock market

  • net_buy_amt: Daily net purchase amount by investor type in the Korean stock market

  • net_buy_amt_0100: Daily net purchase amount in the financial investment sector in the Korean stock market

  • net_buy_amt_0102: Daily net buying amount in the insurance sector in the Korean stock market

  • net_buy_amt_0103: Daily net purchase amount of the trust sector in the Korean stock market

  • net_buy_amt_0104: Daily net buying amount in the private equity sector in the Korean stock market

  • net_buy_amt_0105: Daily net buying amount in the banking sector in the Korean stock market

  • net_buy_amt_0106: Daily net buying amount in the financial sector of the Korean stock market

  • net_buy_amt_0107: Daily net buying amount in the other financial sector of the Korean stock market

  • net_buy_amt_0200: Daily net purchase amount in the Korean stock market for sectors such as pension funds

  • net_buy_amt_0201: Daily net purchase amount of the pension fund sector in the Korean stock market

  • net_buy_amt_0202: Daily net buying amount of the national sector in the Korean stock market

  • net_buy_amt_0300: Daily net buying amount in the other corporations sector of the Korean stock market

  • net_buy_amt_0400: Daily net buying amount of the individual sector in the Korean stock market

  • net_buy_amt_all: The daily net buying amount of all investors in the Korean stock market

  • net_buy_amt_0900: Daily net buying amount of foreign investors in the Korean stock marketSummary This document provides information about the data catalog for the Korean stock universe, specifically focusing on venture companies.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("venture")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

00 20 * * *

Item List

  • venture: List of Venture CompaniesSummary This document provides an overview of the status indicators for the Korean stock market, including various metrics related to stock performance and market conditions.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("overheating")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

20 14 * * 1-5

Daily

Item List Market Status Indicators

  • overheating: Indicators of overheating in the Korean stock market

  • unreliable: Indicator of the lack of reliability in Korean stocks

  • illiquid: Indicators of liquidity shortage in the Korean stock market

  • abnormal: Indicators of abnormal conditions in the Korean stock market

  • alert: Warning status indicator of Korean stocks

  • caution: Daily information on stocks of interest in the Korean stock market

Stock Management Information

  • administration: Daily information on managed stocks in the Korean stock market

  • liquidation: Daily information on liquidation stocks in the Korean stock market

  • suspension: Daily information on suspended stocks in the Korean stock market

  • borrowing: Loan status indicators of Korean stocks

  • list_yn: Indicator of the listing status of Korean stocks

  • market: Daily information on the status of the Korean stock marketSummary This document provides an overview of the credit data items available in the Korean stock market, including their descriptions and usage examples.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("fin_bal_cnt")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

40 23 * * 0-4

Daily

Item List Margin Trading Data

  • fin_bal_cnt: Daily margin trading balance count in the Korean stock market

  • fin_pay_cnt: Daily margin trading repayment count in the Korean stock market

  • fin_pay_amt: Daily margin repayment amount in the Korean stock market

  • fin_bal_amt: Daily margin trading balance amount in the Korean stock market

  • fin_new_cnt: Daily new margin trading cases in the Korean stock market

  • fin_new_amt: Daily new margin trading amount in the Korean stock market

  • fin_bal_rt: Daily margin trading balance rate of the Korean stock market

  • fin_giv_rt: Daily margin lending rate of the Korean stock market

Short Selling Data

  • lend_new_cnt: Daily new short selling cases in the Korean stock market

  • lend_new_amt: Daily new amount of large shareholders in the Korean stock market

  • lend_pay_cnt: Daily short selling repayment count in the Korean stock market

  • lend_pay_amt: Daily short selling repayment amount in the Korean stock market

  • lend_bal_cnt: Daily short selling balance in the Korean stock market

  • lend_bal_amt: Daily short selling balance amount in the Korean stock market

  • lend_bal_rt: Daily short selling balance ratio of the Korean stock market

  • lend_giv_rt: Daily lending rate of major stocks in the Korean stock marketSummary This data catalog provides information on expiration dates for futures and spread maturity in the Korean stock market.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("expiry_dates-spread")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

20 14 * * 1-5

Item List

Expiration Dates

  • expiry_dates-spread: Spread maturity date information

  • expiry_dates-future: Futures expiration date informationSummary This document provides information about the listing date of SPAC mergers in the Korean stock market.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("krx-spot-listed_date_of_merger_spac")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

20 14 * * 1-5

Item List

  • krx-spot-listed_date_of_merger_spac: Listing date of SPAC mergers in the Korean stock market

edge

Summary The data consists of summaries of major English business news and key news, categorized by topic. Each item includes a description that highlights the themes covered, with examples of topics such as 'edge', 'lee', and 'mortality'.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("topenbiznews")

Metadata

valid from
delivery schedule
time zone
data frequency

00 6 * * *

1m

Item List

  • topenbiznews: Summary of major English business news; columns are topic. e.g. 0:['edge', 'lee', 'rambunctious', 'inflect', 'ontroerend', 'obliqueness', 'mortality', 'pounce', 'merry', 'len']

  • topnews: Summary of Key News; columns are topic. e.g. 0:['edge', 'lee', 'rambunctious', 'inflect', 'ontroerend', 'obliqueness', 'mortality', 'pounce', 'merry', 'len']

Summary This document provides an overview of the disclosure data related to KOSDAQ and KOSPI listed companies, including various financial metrics and corporate actions.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("kosdaq-sales_profit_structure_change-mandatory-operating_profit_pct_change")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

00 17 * * *

Item List KOSDAQ Listed Companies

  • kosdaq-sales_profit_structure_change-mandatory-operating_profit_pct_change: KOSDAQ listed companies' revenue or profit structure change disclosure operating profit change rate

  • kosdaq-sales_profit_structure_change-mandatory-net_income_pct_change: The fluctuation rate of net income for KOSDAQ-listed companies

  • kosdaq-sales_profit_structure_change-mandatory-sales: Disclosure of revenue or changes in profit and loss structure of KOSDAQ listed companies

  • kosdaq-sales_profit_structure_change-mandatory-is_large: Whether the KOSDAQ-listed company is a large corporation

  • kosdaq-sales_profit_structure_change-mandatory-is_affiliate_or_subsidary: Whether it is an affiliate or subsidiary of a KOSDAQ-listed company

  • kosdaq-sales_profit_structure_change-mandatory-is_operation: Whether the KOSDAQ-listed companies are engaged in business activities

  • kosdaq-sales_profit_structure_change-mandatory-is_non_operation: Whether the KOSDAQ listed companies have non-operating activities

  • kosdaq-sales_profit_structure_change-mandatory-continuing_operating_income_before_tax_pct_change: KOSDAQ listed companies' revenue or changes in profit and loss structure disclosure before tax expense on continuing operations profit margin variation rate

  • kosdaq-sales_profit_structure_change-mandatory-continuing_operating_income_before_tax: Disclosure of revenue or changes in profit structure of KOSDAQ listed companies before corporate tax expenses on continuing operations

  • kosdaq-sales_profit_structure_change-mandatory-total_equity: Total capital of KOSDAQ listed companies

  • kosdaq-sales_profit_structure_change-mandatory-total_liability: Total liabilities of KOSDAQ listed companies

  • kosdaq-sales_profit_structure_change-mandatory-total_asset: Total assets of KOSDAQ listed companies

  • kosdaq-sales_profit_structure_change-mandatory-net_income: Disclosure of revenue or profit structure changes for KOSDAQ listed companies and current net income

  • kosdaq-sales_profit_structure_change-mandatory-sales_pct_change: KOSDAQ listed companies' revenue or profit structure change disclosure revenue change rate

  • kosdaq-sales_profit_structure_change-mandatory-rcept_no: KOSDAQ listed companies' revenue or profit structure change disclosure receipt number

KOSPI Listed Companies

  • kospi-sales_profit_structure_change-mandatory-operating_profit_pct_change: KOSPI listed companies' revenue or profit structure change disclosure operating profit change rate

  • kospi-sales_profit_structure_change-mandatory_correction-rcept_no: Correction registration number for the disclosure of changes in the revenue and profit structure of KOSPI listed companies

  • kospi-sales_profit_structure_change-mandatory-sales: Disclosure of revenue or changes in profit and loss structure of KOSPI listed companies

  • kospi-sales_profit_structure_change-mandatory-is_large: Whether the KOSPI listed companies are large corporations

  • kospi-sales_profit_structure_change-mandatory-is_affiliate_or_subsidary: Disclosure of revenue or profit structure changes of KOSPI listed companies regarding whether they are affiliated companies or subsidiaries

  • kospi-sales_profit_structure_change-mandatory-is_operation: Disclosure of revenue or changes in profit structure of KOSPI listed companies regarding operational activities

  • kospi-sales_profit_structure_change-mandatory-is_non_operation: Disclosure of changes in revenue or profit structure of KOSPI listed companies regarding non-operating activities

  • kospi-sales_profit_structure_change-mandatory-continuing_operating_income_before_tax_pct_change: Changes in revenue or profit structure disclosures of KOSPI listed companies, and the change rate of earnings before tax.

  • kospi-sales_profit_structure_change-mandatory-continuing_operating_income_before_tax: Disclosure of revenue or profit structure changes for KOSPI listed companies before corporate tax expenses on continuing operations

  • kospi-sales_profit_structure_change-mandatory-total_equity: Total capital of KOSPI listed companies

  • kospi-sales_profit_structure_change-mandatory-total_liability: Total liabilities of KOSPI listed companies

  • kospi-sales_profit_structure_change-mandatory-total_asset: Total assets of KOSPI listed companies

  • kospi-sales_profit_structure_change-mandatory-net_income: Disclosure of revenue or profit structure changes of KOSPI listed companies and current net income

  • kospi-sales_profit_structure_change-mandatory-rcept_no: KOSPI listed companies' revenue or profit structure change disclosure receipt number

Corporate Actions

  • buyback_trust: Disclosure of the trust agreement for treasury shares of Korean companies

  • buyback: Disclosure of share buybacks by Korean companies

  • buyback_disposal: Disclosure of treasury stock disposal by Korean companies

  • division_merge: Disclosure of Split Mergers of Korean Companies

  • division: Disclosure of corporate spin-off in Korea

  • merge: Merger announcement of Korean companies

  • reduction: Korean company capital reduction announcement

  • bonus_issue: Announcement of free capital increase by Korean companies

  • rights_issue: Public announcement of capital increase by Korean companies

  • cb: Announcement of convertible bond issuance by Korean companies

  • eb: Announcement of the issuance of exchangeable bonds by Korean companies

  • br_issue: Disclosure of Stock Issuance by Korean Companies

  • coco: Announcement of the issuance of contingent convertible bonds by Korean companies Summary This document provides an overview of the Korean stock theme data catalog, including various metrics and scores related to Korean stock companies and themes.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("theme-z_score")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

10 22 * * *

Item List Theme Metrics

  • theme-z_score: Z-score of the Korean stock theme

  • theme-ma: Moving average of the Korean stock theme

  • theme-score: Score of Korean stock themes

Business Metrics

  • business-ma: Business moving average of Korean stock companies

  • business-score: Business score of Korean stock companies

  • business-z_score: Business Z-score of Korean stock companies

Company Keyword Metrics

  • company-keyword_last_upt_dt: Final update date of keyword information for Korean stock companies

  • company-keyword_score: Keyword-related scores of Korean stock companies

  • company-keyword_total_sales: Total revenue related to keywords of Korean stock companies

  • company-keyword_sales_ratio: Revenue ratio related to keywords of Korean stock companies

  • company-keyword_pearson: Daily keyword Pearson correlation coefficient of Korean stocks

Item Metrics

  • item-z_score: Z-score of Korean stock items

  • item-score: Score of Korean stock items

  • item-ma: Moving average of Korean stock items Summary The data consists of various sentiment and early warning system indicators for the Korean stock market, including daily sentiment indices, principal component analysis indicators, and multiple versions of weekly and monthly early warning system indicators. These indicators are designed to provide insights into market sentiment and potential future trends, with some specifically comparing the Korean market to the S&P 500.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("korea_tone_SP500")

Metadata

valid from
delivery schedule
time zone
data frequency

30 21 * * * 30 23 * * * 30 22 * * * 30 21 * * 0-4

1d, 1w, 1m

Item List

  • korea_EWS: Monthly early warning system indicators for the Korean stock market

  • korea_EWS: Weekly early warning system indicators for the Korean stock market

  • korea_EWS_003: Weekly Early Warning System Indicators for the Korean Stock Market (Version 003)

  • korea_EWS_003_dcut: Weekly early warning system indicators for the Korean stock market (Version 003, data cutoff applied)

  • korea_EWS_003_inferenceonly: Weekly early warning system indicators for the Korean stock market (Version 003, inference only applied)

  • korea_EWS_003_inferenceonly_dcut: Weekly early warning system indicators for the Korean stock market (Version 003, inference only applied, data cutoff)

  • korea_EWS_003_sp1: Weekly Early Warning System Indicator for the Korean Stock Market (Version 003, Special Variation 1)

  • korea_EWS_003_sp1_dcut: Weekly early warning system indicators for the Korean stock market (Version 003, Special Variant 1, Data Cutoff)

  • korea_EWS_003_sp2: Weekly Early Warning System Indicators for the Korean Stock Market (Version 003, Special Variation 2)

  • korea_EWS_003_sp2_dcut: Weekly early warning system indicators for the Korean stock market (Version 003, Special Variant 2, Data Cutoff)

  • korea_EWS_003_sp3: Weekly Early Warning System Indicators for the Korean Stock Market (Version 003, Special Variant 3)

  • korea_EWS_003_sp3_dcut: Weekly early warning system indicators for the Korean stock market (Version 003, Special Variant 3, Data Cutoff)

  • korea_EWS_003_sp4: Weekly Early Warning System Indicators for the Korean Stock Market (Version 003, Special Variant 4)

  • korea_EWS_003_sp4_dcut: Weekly early warning system indicators for the Korean stock market (Version 003, Special Variant 4, Data Cutoff)

  • korea_EWS_SP500: Monthly early warning system indicators for the Korean stock market compared to the S&P 500

  • korea_tone: Daily Sentiment Index for the Korean Stock Market

  • korea_tone_SP500: Daily sentiment index of the Korean stock market compared to the S&P 500

  • korea_tone_neutral: Daily Neutral Sentiment Index for the Korean Stock Market

  • pca_proxy: Daily principal component analysis proxy indicator for the Korean stock market

  • pca_sentiment: Daily sentiment indicators based on principal component analysis of the Korean stock market

Summary This data catalog provides daily keyword trends in the Korean stock market.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("krx-spot-trend")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

20 12 * * *

Daily

Item List

  • krx-spot-trend: Daily keyword trends in the Korean stock marketSummary This data catalog provides information on employee and executive demographics within the Korean stock universe, focusing on gender distribution and salary details.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("exc_tot_woman")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

20 18 * * 5

Item List Executive Data

  • exc_tot_woman: Total number of female executives

  • exc_tot_man: Total number of male executives

  • exc_tot_inside_director: Total number of internal directors

  • exc_tot_outside_director: Total number of outside directors

  • exc_eq_ceo_chairman: Whether the CEO and the Chairman of the Board are the same person

Employee Data Female Employees

  • emp_woman_fulltime: Number of female regular employees

  • emp_woman_parttime: Number of female non-regular employees

  • emp_woman_avg_year_of_service: Average tenure of female employees

  • emp_woman_tot_year_salary: Total annual salary of female employees

Male Employees

  • emp_man_fulltime: Number of male full-time employees

  • emp_man_parttime: Number of male non-regular employees

  • emp_man_avg_year_of_service: Average tenure of male employees

  • emp_man_tot_year_salary: Annual total salary of male employees Summary The data consists of various metrics related to Foreign Currency Securities Custody and Settlement, including the total amounts, buy amounts, sell amounts, and net buy amounts of foreign securities. Each metric is accompanied by a description detailing its purpose and the adjustments made for publication timing.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("sum_frsec_amt_shift_2")

Metadata

valid from
delivery schedule
time zone
data frequency

20100104

0 23 * * 1-5

1d

Item List

  • sum_frsec_amt: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foreign Securities Amount.

  • sum_frsec_amt_shift_2: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foreign Securities Amount. Shifted two weekdays to incorporate the gap between the reference and publication dates.

  • sum_frsec_buy_amt: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foregin Security Buy Amount.

  • sum_frsec_net_buy_amt: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foregin Security Net Buy Amount.

  • sum_frsec_sell_amt: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foregin Security Sell Amount.

  • sum_frsec_tot_amt: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foregin Security Total Amount.

factor

Summary This document provides a comprehensive overview of various financial metrics and factors related to Korean stocks, including investment strategies and performance indicators.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("kr-gross_profit_to_assets")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

20 14 * * 1-5

Item List Investment Strategy Factors

  • kr-glenn_greenberg: Factors of Korean stocks based on Glen Greenberg's investment strategy

  • kr-cathie_wood: Factors of Korean stocks based on Cathie Wood's investment strategy

  • kr-james_oshaughnessy: Factors of Korean stocks based on James O'Shaughnessy's investment strategy

  • kr-benjamin_graham: Factors of Korean stocks based on Benjamin Graham's investment strategy

  • kr-alex_sacerdote: Factors of Korean stocks based on Alex Sasserdo's investment strategy

  • kr-ron_baron: Factors of Korean stocks based on Ron Baron’s investment strategy

  • kr-warren_buffet: Factors of Korean stocks based on Warren Buffett's investment strategy

  • kr-bill_ackman: Factors of Korean stocks based on Bill Ackman's investment strategy

  • kr-david_dreman: Korean stock factors based on David Dreman's investment strategy

  • kr-charles_munger: Factors of Korean stocks based on Charles Munger's investment strategy

  • kr-brad_gerstner: Korean stock factors based on Brad Gerstner's investment strategy

  • kr-colin_moran: Korean stock factors based on Colin Moran's investment strategy

  • kr-william_oneil: Korean stock factors based on William O'Neil's investment strategy

Profitability Metrics

  • kr-gross_profit_to_assets: The ratio of gross profit to total assets of Korean stocks

  • kr-op_12mf_3m: The 3-month change rate of the 12-month forecast of operating profit for Korean stocks

  • kr-op_fy1_1m: The one-month change rate of the operating profit forecast for Korean stocks one year later

  • kr-op_yoy: The growth rate of operating profit of Korean stocks compared to the previous year

  • kr-return_on_asset_4q: The cumulative return on total assets for Korean stocks in the fourth quarter

  • kr-return_on_equity_4q: Cumulative return on equity for Korean stocks in the fourth quarter

  • kr-net_profit_margin_before_XI: Net profit margin excluding special items for Korean stocks

  • kr-operating_profit_margin_after_DP: Operating profit margin after depreciation of Korean stocks

Valuation Ratios

  • kr-p_fcf_24mf: Expected price/free cash flow ratio of Korean stocks over the next 24 months

  • kr-p_fcf_12mf: 12-month forward P/FCF of Korean stocks

  • kr-pbr_12mf: 12-month forward PBR of Korean stocks

  • kr-pcr_12mf: 12-month forward PCR of Korean stocks

  • kr-ev_ebitda_12mf: 12-month forward EV/EBITDA of Korean stocks

  • kr-ev_ebitda_24mf: 24-month forward EV/EBITDA of Korean stocks

Investment Ratios

  • kr-dividend_yield_fy1: The dividend yield for Korean stocks in the next fiscal year

  • kr-dividend_yield_fy0: Dividend yield for the current fiscal year of Korean stocks

  • kr-payout_ratio_3y: The 3-year dividend payout ratio of Korean stocks

  • kr-payout_ratio_4q: Dividend payout ratio of Korean stocks in the fourth quarter

Market Performance Metrics

  • kr-sales_fy1_3m: The 3-month change rate of the 1-year revenue forecast for Korean stocks

  • kr-sales_fy2_3m: The 3-month change rate of the 2-year revenue forecast for Korean stocks

  • kr-sales_yoy: The revenue growth rate of Korean stocks compared to the previous year

  • kr-sales_12mf_3m: The 3-month change rate of the 12-month forward revenue forecast for Korean stocks

Investment Activity Metrics

  • kr-net_invest_ratio_20d: 20-day net investment ratio of Korean stocks

  • kr-net_invest_ratio_10d: 10-day net investment ratio of Korean stocks

  • kr-net_invest_sum_10d: Total net purchases by investor type in the Korean stock market over the past 10 days

  • kr-net_invest_sum_20d: Total net purchases by investor type in the Korean stock market over the past 20 days

Risk Metrics

  • kr-beta_756d: 756-day beta of Korean stocks

  • kr-beta_1260d: 1260-day beta of Korean stocks

  • kr-beta_yield_10y_60m: 60-month beta regarding the 10-year government bond yield of Korean stocks

  • kr-beta_yield_5y_60m: 60-month beta related to the 5-year government bond yield of Korean stocks

Other Metrics

  • kr-interest_coverage_ratio: Interest Coverage Ratio of Korean Stocks

  • kr-leverage_ratio: Debt ratio of Korean stocks

  • kr-asset_turnover: Asset turnover ratio of Korean stocks

  • kr-capex_growth_12mf: 12-month leading capital investment growth rate of Korean stocks

  • kr-capex_to_assets: The ratio of capital expenditures to total assets of Korean stocks

This structure provides a clear and organized way to navigate through the various metrics available for Korean stocks, making it easier for users to find the information they need.Summary This document provides an overview of various financial metrics related to Korean stocks, including their definitions and usage.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("operating-profits-to-assets")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

20 14 * * 1-5

Item List

Company Age Metrics

  • age-found: The number of years elapsed since the establishment of a company in the Korean stock market.

  • age-listed: Years elapsed since the listing of Korean stocks.

Profitability Metrics

  • operating-profits-to-assets: Operating profit margin relative to assets of Korean stocks.

  • profit-margin: Profitability of Korean stocks.

  • return-on-assets: Return on Assets (ROA) of Korean stocks.

  • return-on-equity: Return on Equity (ROE) of Korean stocks.

  • operating-profits-lagged-assets: Operating profit compared to delayed assets of Korean stocks.

  • operating-profits-lagged-equity: Operating profit relative to delayed equity in Korean stocks.

  • operating-cashflow-to-price: Operating cash flow to stock price ratio of Korean stocks.

  • cash-based-operating-profits-to-assets: Cash-based operating profit ratio relative to assets of Korean stocks.

Investment Metrics

  • change-short-term-investments: Changes in short-term investment assets of Korean stocks.

  • change-long-term-investments: Changes in long-term investment assets in Korean stocks.

  • investment-to-assets: The ratio of total assets to investment in Korean stocks.

  • investment-growth-1-year: 1-Year Investment Growth Rate of Korean Stocks.

  • investment-growth-2-year: 2-Year Investment Growth Rate of Korean Stocks.

  • investment-growth-3-year: 3-Year Investment Growth Rate of Korean Stocks.

Asset and Market Metrics

  • gross-profits-to-assets: Total return on assets ratio of Korean stocks.

  • assets-to-market: Asset to Market Value Ratio of Korean Stocks.

  • asset-liquidity-to-assets: The ratio of total assets to asset liquidity of Korean stocks.

  • asset-liquidity-to-market: Market capitalization ratio compared to asset liquidity of Korean stocks.

Volatility and Risk Metrics

  • total-volatility: Total volatility of Korean stocks.

  • idiosyncratic-volatility-per-capm: Idiosyncratic volatility based on the CAPM model of Korean stocks.

  • idiosyncratic-volatility-per-ff3: Idiosyncratic volatility based on the Fama-French three-factor model of Korean stocks.

  • market-beta: Market beta of Korean stocks.

  • frazzini-pedersen-beta: Fama-French Beta of Korean Stocks.

Financial Ratios

  • debt-to-market: Debt to Market Value Ratio of Korean Stocks.

  • book-to-market: The ratio of book value to market value of Korean stocks.

  • book-leverage: Book leverage of Korean stocks.

  • capital-turnover: Capital turnover rate of Korean stocks.

  • sales-to-price: Sales to Price Ratio of Korean Stocks.

Growth and Change Metrics

  • sales-growth: Revenue growth rate of Korean stocks.

  • sales-growth-quarter: Quarterly revenue growth rate of Korean stocks.

  • change-return-on-assets: Changes in the quarterly return on assets (ROA) of Korean stocks.

  • change-return-on-equity: Changes in the Return on Equity (ROE) of Korean stocks.

Other Metrics

  • ohlson-o-score: Ohlson O-Score of Korean stocks (bankruptcy probability indicator).

  • altman-z-score: Altman Z-Score of Korean Stocks (Financial Health Indicator).

  • fundamental-score: Basic score of Korean stocks.

  • enterprise-multiple: Valuation multiples of Korean stocks.

  • maximum-five-daily-return: The maximum daily return of Korean stocks over 5 days.

  • maximum-ten-daily-return: The maximum daily return of Korean stocks over 10 days.

  • 52-week-high: 52-week high of Korean stocks.

analysis

Summary This data catalog provides consensus data for Korean stocks, including earnings per share (EPS), net profit, cash flows, and other financial metrics.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("krx-spot-eps_q1_up")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

20 14 * * 1-5

Item List EPS and Earnings Consensus

  • krx-spot-eps2_up: Adjustment of EPS2 for Korean stocks

  • krx-spot-eps_q2_revision_ratio_1m: Consensus on the 1-month 2nd quarter EPS revision ratio for Korean stocks

  • krx-spot-eps2_down: Adjustment of EPS2 downward for Korean stocks

  • krx-spot-new_eps_q1: New Q1 EPS forecast for Korean stocks

  • krx-spot-new_eps1: New EPS1 consensus for Korean stocks

  • krx-spot-eps_q2_down: Downgraded consensus for Q2 EPS of Korean stocks

  • krx-spot-eps1_down: Downward consensus on EPS1 of Korean stocks

  • krx-spot-eps2_stay: The maintenance of EPS2 for Korean stocks

  • krx-spot-eps_q1_up: Upward consensus on Q1 EPS of Korean stocks

  • krx-spot-new_eps2: New EPS2 forecast for Korean stocks

  • krx-spot-eps_q2_up: Upward consensus on Q2 EPS of Korean stocks

  • krx-spot-eps_q1_stay: Consensus for maintaining Q1 EPS of Korean stocks

  • krx-spot-eps_q2_stay: Consensus on maintaining Q2 EPS for Korean stocks

  • krx-spot-eps1_revision_ratio_3m: Consensus on the 3-month EPS1 adjustment ratio for Korean stocks

  • krx-spot-eps_q1_revision_ratio_1m: Consensus on the EPS adjustment ratio for Korean stocks for the 1-month 1st quarter

  • krx-spot-eps_q1_revision_ratio_3m: The 3-month revision rate of the Q1 EPS forecast for Korean stocks

  • krx-spot-eps2_revision_ratio_1m: Consensus on the 1-month EPS2 adjustment ratio for Korean stocks

  • krx-spot-eps2_revision_ratio_3m: Consensus on the 3-month EPS2 adjustment ratio for Korean stocks

  • krx-spot-changed_ratio_in_eps2: The change rate of EPS2 in Korean stocks

  • krx-spot-changed_ratio_in_eps1: The change rate of EPS1 in Korean stocks

Financial Metrics

  • krx-spot-net_debt_a: Net debt of Korean stocks

  • krx-spot-financing_cash_flows_a: Annual financial cash flow consensus of Korean stocks

  • krx-spot-investing_cash_flows_a: Consensus on annual investment cash flow of Korean stocks

  • krx-spot-operating_profit_a: Annual operating profit consensus of Korean stocks

  • krx-spot-operating_profit_q: Consensus on quarterly operating profit of Korean stocks

  • krx-spot-net_profit_a: Annual net profit consensus of Korean stocks

  • krx-spot-net_profit_q: Consensus on quarterly net profit of Korean stocks

  • krx-spot-sales_a: Annual revenue consensus of Korean stocks

  • krx-spot-sales_q: Quarterly revenue consensus of Korean stocks

  • krx-spot-ebitda_a: Annual EBITDA consensus of Korean stocks

  • krx-spot-evebitda_a: Annual EV/EBITDA consensus of Korean stocks

  • krx-spot-fcf_a: Consensus on annual free cash flow of Korean stocks

  • krx-spot-capex_a: Annual capital expenditure consensus for Korean stocks

  • krx-spot-total_assets_a: Annual total asset consensus of Korean stocks

  • krx-spot-owners_of_parent_equity_a: Annual controlling shareholder equity consensus of Korean stocks

  • krx-spot-owners_of_parent_net_profit_a: Annual controlling shareholder net income consensus of Korean stocks

  • krx-spot-owners_of_parent_net_profit_q: Consensus on quarterly controlling shareholder net income of Korean stocks

Dividends and Ratings

  • krx-spot-cash_dividend_a: Annual cash dividend consensus of Korean stocks

  • krx-spot-dividend_yield_a: Consensus on the annual dividend yield of Korean stocks

  • krx-spot-rating: Consensus on investment opinions for Korean stocks

  • krx-spot-rating_revision_ratio_1m: Consensus on the revision rate of investment opinions for Korean stocks over one month

  • krx-spot-tp: Consensus target price for Korean stocks

  • krx-spot-tp_revision_ratio_1m: Consensus on the revision ratio of the 1-month target price for Korean stocks

  • krx-spot-disparate_ratio_tp: Consensus on the target price deviation rate of Korean stocks

Fiscal Information

  • krx-spot-fiscal: Information on the fiscal year of Korean stocks Summary The data consists of a collection of stock recommendations categorized under 'somemoney', focusing on various segments of the KOSDAQ and KOSPI markets, including small-cap, mid-cap, and large-cap stocks. Each recommendation is described as a new approach or a specific category, indicating a targeted strategy for investment in these stock markets.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("recommend-n-kosdaq_s1")

Metadata

valid from
delivery schedule
time zone
data frequency

30 14 * * 1-5

1d

Item List

  • recommend-kosdaq_l1: Recommendation for large-cap stocks in KOSDAQ

  • recommend-kosdaq_m1: Recommendation for mid-cap stocks on KOSDAQ

  • recommend-kospi_l1: Recommendation for KOSPI Large-cap Stocks

  • recommend-kospi_m1: Recommendation for KOSPI mid-cap stocks

  • recommend-n-kosdaq_l1: Recommendation for KOSDAQ Large-cap Stocks (New Approach)

  • recommend-n-kosdaq_m1: Recommendation for KOSDAQ mid-cap stocks (new approach)

  • recommend-n-kosdaq_s1: Recommendation for KOSDAQ small-cap stocks (new approach)

  • recommend-n-kospi_l1: Recommendation for KOSPI Large-cap Stocks (New Approach)

  • recommend-n-kospi_m1: Recommendation for KOSPI Mid-Cap Stocks (New Approach)

macro

Summary This document provides an overview of market regime data items available for the Korean stock market, including economic cycle indicators and analyses based on Hidden Markov Models (HMM).

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("hmm")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

5 17 * * 1-5

0 21 * * 1-5

30 10 * * *

30 2 * * 2-6

30 21 * * 0-4

Monthly

30 10 * * *

Item List OECD Economic Cycle Indicators

  • oecd_world: OECD Global Economic Cycle Indicators

  • oecd_china: OECD China Economic Cycle Indicators

  • oecd_usa: OECD U.S. Economic Cycle Indicators

HMM-Based Market Regime Analysis

  • hmm: Analysis of the market system based on HMM (Hidden Markov Model) in the Korean stock market

  • hmm_wo_krx_etf: Analysis of market system based on HMM excluding Korean ETFs

  • hmm_wo_us_etf: Analysis of market system based on HMM excluding US ETFs

  • hmm_sp500: HMM-based market regime analysis of the S&P 500 index

  • hmm_wo_krx_etf (Monthly): Analysis of HMM-based market system excluding Korean ETFs (Monthly)

KOSPI 200 Market Status

  • k200_ms4: Analysis of the 4-stage market status of the KOSPI 200 indexSummary This document provides information on various economic data items related to the Korean economy, including currency exchange rates, interest rates, and economic indices.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("currency")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

20 14 * * 1-5

Daily

Item List

Currency Data

  • currency: Daily exchange rate information related to the Korean economy

  • interest_rate: Daily interest rate information related to the Korean economy

Index Data

  • index_close: Daily closing prices of major economic indices in Korea

  • index_mkt_cap: Daily market capitalization of major economic indices in Korea

KR ETF

market

Summary This document provides information about the cumulative fundflow data for KR ETFs.

Example code

from finter.data import ContentFactory
cf = ContentFactory("kr_etf", 20200101, 20200201)
df = cf.get_df("fundflow_cumsum")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

20000104

00 16 * * 1-5

Item List

  • fundflow_cumsum: Cumulative fundflow for KR ETFs.

US ETF

market

price_volume

Summary

This document provides an overview of the US ETF price and volume data items available in the data catalog.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_etf", 20200101, 20200201)
df = cf.get_df("price_close")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

30 5 * * 2-6

Daily

Item List

Price Data

  • price_open: Daily opening price of US ETFs

  • price_close: Daily closing price of US ETFs

  • price_high: Daily high of US ETFs

  • price_low: Daily low of US ETFs

  • indicated_annual_dividend: Annual expected dividends of U.S. ETFs

Volume Data

  • trading_volume: Daily trading volume of US ETFs

  • amount: Trading volume of US ETFs

  • shares_outstanding: Daily issuance of shares for U.S. ETFs

Performance Metrics

  • eps: Earnings Per Share (EPS) of US ETFs

  • adr_ratio: ADR ratio of US ETFs

  • ctoc: Rate of change in closing price compared to the closing price of US ETFs

  • ctoc_total: Rate of change in closing price compared to the cumulative closing price of U.S. ETFs cax

Summary

This document provides information on U.S. ETF data items, including total return and adjustment factors.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_etf", 20200101, 20200201)
df = cf.get_df("total_return_factor")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

30 5 * * 2-6

Item List

Total Return Factors

  • total_return_factor: Total return coefficient of U.S. ETFs

Adjustment Factors

  • adjust_factor: Adjustment factor of US ETFs Summary This data catalog provides information on cumulative fund flows for US ETFs.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_etf", 20200101, 20200201)
df = cf.get_df("fundflow_cumsum")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

20000104

30 06 * * 2-6

Item List

  • fundflow_cumsum: Cumulative fundflow for US ETFs.

US STOCK

financial

financial

Summary

This dataset provides PIT (point-in-time) financial statements for US stocks, including various financial metrics and indicators. The data’s index indicates the date the data was included in the database.

Users can choose from 3 types of data frames by adjusting the ‘mode’ parameter:

1. Default behavior: In this case, the values in the data frame become the most recent values at the point in time, not including the fiscal date.

2. Original: In this case, the values in the data frame become dictionaries possibly including multiple keys and values. If you are considering pre-announced data revisions, this option can be a solution.

3. Unpivot: In this case, there will be 4 columns (‘id’, ‘pit’, ‘fiscal’, ‘value’). Each row contains information about the announced data’s announcing date, fiscal quarter, and value.

Since the collection of PIT data started recently, data before 2023-08-09, is the same as the research data, not the PIT data.

If you want to load quarterly period end cm,

This dataset provides 90 days delayed financial statements for US stocks, including various financial metrics and indicators.

Most US companies disclose their IR materials within 90 days after the end of the quarter, but for some that do not, there may be a look-ahead bias. Therefore, it is not recommended to use this data for modeling purposes, but rather for research purposes.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("pit-atq") pit cm
df = cf.get_df("atq") period end cm

Metadata

valid from
delivery schedule
time zone
data frequency

1982-01-31

30 5 * * 2-6

US/Eastern

1d

Item List

Balance Sheet

Current Assets

  • accoq: Quarterly liquid assets of U.S. stocks

  • actq: Quarterly total current assets of U.S. stocks

  • apoq: Prepaid expenses and other current assets of U.S. stocks

  • caq: Current assets of U.S. stocks

  • cheq: Cash and cash equivalents of U.S. stocks

  • chq: Cash items of U.S. stocks

  • chsq: Short-term investment items in U.S. stocks

  • csh12q: Quarterly data on cash and cash equivalents for U.S. stocks over the past 12 months

  • cshiq: Quarterly data on cash and short-term investments in U.S. stocks

  • cibegniq: Basic inventory items of U.S. stocks

  • cicurrq: Current inventory items of U.S. stocks

  • capcstq: Capitalized software cost items of U.S. stocks

  • capsftq: Capitalized software costs for U.S. stocks

  • aul3q: Unused credit limit items of U.S. stocks

  • autxrq: Tax refund items for U.S. stocks related to automobiles

  • artfsq: Accounts receivable financial items of U.S. stocks

  • acchgq: Quarterly changes in accounts receivable for U.S. stocks

Non-Current Assets

  • adpacq: Quarterly accumulated depreciation of U.S. stocks

  • anoq: Quarterly non-operating assets of U.S. stocks

  • aoq: Other asset items of U.S. stocks

  • aotq: Total asset items of U.S. stocks

  • atq: Total asset items of U.S. stocks

  • dfpacq: Quarterly data on the acquisition costs of deferred policy for U.S. stocks

  • aqpl1q: Acquisition of Preferred Stocks in U.S. Stocks

  • deracq: Quarterly data on derivative assets of U.S. stocks

  • ivaoq: Other investments in U.S. stocks

  • ivltq: Long-term investment in U.S. stocks

  • gdwlq: Quarterly goodwill of U.S. stocks

  • intanq: Total intangible assets of U.S. stocks

Current Liabilities

  • acoq: Quarterly current liabilities of U.S. stocks

  • acoxq: Quarterly current liabilities and other debts of U.S. stocks

  • apq: Accounts payable for U.S. stocks

  • cltq: Quarterly data on total current liabilities of U.S. stocks

  • dlcq: Quarterly data on current liabilities of U.S. stocks

  • dd1q: Quarterly data on liquidity among long-term liabilities of U.S. stocks

  • pclq: Current liabilities of U.S. stocks

  • ulcoq: Current liabilities of U.S. stocks

Non-Current Liabilities

  • aol2q: Other liabilities (non-current) of U.S. stocks

  • derhedglq: Quarterly data on hedge derivatives liabilities of U.S. stocks

  • derlcq: Quarterly data on liquid derivative liabilities of U.S. stocks

  • derlltq: Quarterly data on long-term derivative liabilities of U.S. stocks

  • dlttq: Quarterly data on the total long-term debt of U.S. stocks

  • rllq: Quarterly long-term debt of U.S. stocks

  • pllq: Long-term debt of U.S. stocks

  • ltq: Total long-term debt of U.S. stocks

Shareholders’ Equity

  • ceqq: Common stock capital item of U.S. stocks

  • cstkq: Quarterly data of common stocks in the United States

  • cshprq: Quarterly data of preferred stocks in the United States

  • pstkq: Quarterly preferred stock capital of U.S. stocks

  • seqq: Quarterly shareholder equity of U.S. stocks

  • lseq: Shareholder equity of U.S. stocks

  • ucapsq: Capital surplus of U.S. stocks

Accumulated Other Comprehensive Income

  • acomincq: Quarterly cumulative other comprehensive income of U.S. stocks

  • aociderglq: Other comprehensive income items related to deferred corporate tax liabilities of U.S. stocks

  • aociotherq: Other items in the other comprehensive income of U.S. stocks

  • aocipenq: Other comprehensive income items related to pensions for U.S. stocks

  • aocisecglq: Other comprehensive income items related to securities of U.S. stocks

  • ciotherq: Other comprehensive income items of U.S. stocks

Other Balance Sheet Items

  • ancq: Quarterly net current assets of U.S. stocks

  • aqaq: Acquisition asset items of U.S. stocks

  • aqdq: Acquisition debt items of U.S. stocks

  • aqpq: Acquisition cost item of U.S. stocks

  • dpactq: Quarterly data on the total accumulated depreciation of U.S. stocks

  • glaq: Quarterly total assets of U.S. stocks

  • gldq: Quarterly total liabilities of U.S. stocks

  • glpq: Total debt and preferred equity of U.S. stocks

  • lsq: Total liabilities and shareholders' equity of U.S. stocks

Income Statement

Revenue

  • istq: Total revenue of U.S. stocks

  • saleq: Quarterly revenue of U.S. stocks

  • salq: Quarterly revenue of U.S. stocks (alternative)

  • prcq: Quarterly revenue of U.S. stocks

  • revtq: Quarterly total revenue of U.S. stocks

  • xsq: Quarterly revenue of U.S. stocks

Cost of Goods Sold & Operating Expenses

  • cogsq: Quarterly data on the cost of goods sold for U.S. stocks

  • prcpq: Quarterly cost of goods sold for U.S. stocks

  • rectoq: Quarterly total operating expenses of U.S. stocks

  • finxoprq: Quarterly financial accounts of U.S. stocks - Operating expenses

  • iobdq: Other operating and administrative expenses of U.S. stocks

  • isgtq: Selling, general and administrative expenses of U.S. stocks

  • xcomq: Quarterly selling and administrative expenses of U.S. stocks

  • xsg aq: Quarterly selling and administrative expenses of U.S. stocks

  • ssnpq: Selling and general administrative expenses of U.S. stocks

  • msa q: Selling and administrative expenses of U.S. stocks

  • saaq: Quarterly selling and administrative expenses of U.S. stocks

  • scq: Quarterly Selling Expenses of U.S. Stocks (Alternative)

  • scoq: Quarterly selling expenses of U.S. stocks

  • sctq: Quarterly total selling expenses of U.S. stocks

Operating Income

  • arcedq: Quarterly operating income items of U.S. stocks

  • arceq: Current earnings item of U.S. stocks

  • oproq: Operating profit of U.S. stocks

  • xoiq: Quarterly operating profit of U.S. stocks

  • xoprq: Quarterly operating profit of U.S. stocks

  • xoptq: Quarterly operating profit of U.S. stocks

  • spiopq: Operating profit of U.S. stocks

Non-Operating Income and Expenses

  • ioiq: Non-operating income (expenses) of U.S. stocks

  • nopioq: Non-operating income and expenses of U.S. stocks

  • nopiq: Non-operating income from U.S. stocks

  • ioreq: Other operating income from U.S. stocks

  • xoreq: Quarterly non-operating income from U.S. stocks

  • xobdq: Quarterly non-operating expenses of U.S. stocks

Earnings Before Tax (EBT) and Net Income

  • ibq: Quarterly basic earnings of U.S. stocks

  • ibmiiq: Net income before minority interest deductions for U.S. stocks

  • niq: Net income of U.S. stocks

  • npq: Net income of U.S. stocks

  • pncpq: Quarterly net income of U.S. stocks

  • pncq: Quarterly net income of U.S. stocks

  • xiq: Quarterly net income of U.S. stocks

  • nitq: Net income after corporate tax deduction for U.S. stocks

  • esubq: Quarterly special items and net income before discontinued operations of U.S. stocks

Depreciation, Amortization, and Related

  • dpq: Quarterly data on depreciation and amortization of U.S. stocks

  • amq: Quarterly amortization expenses of U.S. stocks

  • wddq: Quarterly depreciation expense of U.S. stocks

  • wdpq: Quarterly depreciation expense of U.S. stocks

  • rrdq: Quarterly depreciation expense of U.S. stocks

  • xagtq: Quarterly amortization of intangible assets for U.S. stocks

  • wdaq: Quarterly depreciation and amortization before working capital for U.S. stocks

  • wdepsq: Quarterly depreciation and amortization expenses of U.S. stocks

  • oiadpq: Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA) of U.S. Stocks

  • oibdpq: Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA) of U.S. Stocks

Interest & Taxes

  • intcq: Interest expense of U.S. stocks

  • spidq: Interest and related costs of U.S. stocks

  • xintq: Quarterly interest expenses of U.S. stocks

  • finxintq: Quarterly financial accounts of U.S. stocks - Interest expenses

  • tieq: Total interest expense of U.S. stocks

  • txpq: Corporate tax payment on U.S. stocks

  • txtq: Total corporate tax on U.S. stocks

  • iptiq: Corporate tax reserves for U.S. stocks

Special Items & Adjustments

  • spiq: Special items of U.S. stocks

  • xioq: Quarterly special items of U.S. stocks

  • uspiq: Quarterly special items of U.S. stocks

  • spcedq: Quarterly special items of U.S. stocks (diluted)

  • spcedpq: Quarterly special items of U.S. stocks (diluted, per share)

Cashflow

Operating Cashflow

  • cfoq: Cash flow items resulting from operating activities of U.S. stocks

  • cshopq: Quarterly data on cash flows from operating activities of U.S. stocks

  • ffoq: Cash flow from operating activities for U.S. stocks on a quarterly basis

  • spioaq: Interest payments resulting from the operating activities of U.S. stocks

Investing Cashflow

  • capr1q: Capital expenditures for U.S. stocks in the first quarter

  • capr2q: Capital expenditures for U.S. stocks in the second quarter

  • capr3q: Capital expenditures of U.S. stocks for the third quarter

  • caprtq: Total capital expenditures for U.S. stocks

  • rcpq: Quarterly capital expenditures of U.S. stocks

  • spcepq: Capital expenditures per share of U.S. stocks

  • spceq: Capital expenditures of U.S. stocks

Financing Cashflow

  • cfbdq: Cash inflow item due to the issuance of bonds in U.S. stocks

  • cfereq: Cash outflow items due to stock buybacks of U.S. stocks

  • cfpdoq: Cash outflow item due to the payment of preferred stock dividends in U.S. stocks

  • udvpq: Dividend payments of U.S. stocks

  • updvpq: Quarterly dividend payments of U.S. stocks

  • dvpq: Quarterly data on dividend payments of U.S. stocks

  • dvpdpq: Quarterly data on preferred stock dividends of U.S. stocks

  • dvrreq: Quarterly data on dividend reinvestment of U.S. stocks

  • rrpq: Quarterly share repurchase amount of U.S. stocks

Other / Summary Cashflow

  • cshfdq: Quarterly cash flow data of U.S. stocks

  • cshfd12: Quarterly data of 12-month cash flow for U.S. stocks

Ratio

Profitability & Efficiency Ratios

  • eroq: Quarterly operating profit margin of U.S. stocks

  • xoproq: Quarterly operating profit margin of U.S. stocks

  • ratiq: Quarterly operating income ratio compared to interest expenses of U.S. stocks

Return Ratios

  • eqrtq: Quarterly Return on Equity of U.S. Stocks

Capital Structure Ratios

  • setaq: Quarterly shareholder equity to total assets ratio of U.S. stocks

  • seta12: Annual shareholder equity to total assets ratio of U.S. stocks

  • setdq: Quarterly shareholder equity to debt ratio of U.S. stocks

  • setd12: Annual shareholder equity to debt ratio of U.S. stocks

  • setpq: Quarterly shareholder equity to price ratio of U.S. stocks

market

classification

Summary

This document provides information about the GICS classification of U.S. stocks available in the data catalog.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("gics")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

30 5 * * 2-6

Item List

  • gics: GICS classification of U.S. stocks top_foreign_invest

Summary

The data encompasses various metrics related to the SEIBRO Foreign Currency Securities Custody and Settlement, including sums of foreign security sell amounts, total amounts, net buy amounts, and buy amounts. These metrics are designed to track and analyze foreign securities transactions over time, with some values adjusted to account for publication delays.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("us_sum_frsec_sell_amt")

Metadata

valid from
delivery schedule
time zone
data frequency

20100104

0 23 * * 1-5

1d

Item List

  • us_sum_frsec_amt: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foreign Securities Amount.

  • us_sum_frsec_amt_shift_2: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foreign Securities Amount. Shifted two weekdays to incorporate the gap between the reference and publication dates.

  • us_sum_frsec_buy_amt: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foregin Security Buy Amount.

  • us_sum_frsec_net_buy_amt: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foregin Security Net Buy Amount.

  • us_sum_frsec_sell_amt: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foregin Security Sell Amount.

  • us_sum_frsec_tot_amt: SEIBRO Foreign Currency Securities Custody and Settlement. Sum of Foregin Security Total Amount. price_volume

Summary

This document provides an overview of U.S. stock price and volume data items available in the data catalog.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("price_close")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

30 5 * * 2-6

Daily

Item List

Price Data

  • price_open: Daily opening price of U.S. stocks

  • price_close: Daily closing price of U.S. stocks

  • price_high: Daily high of U.S. stocks

  • price_low: Daily low of U.S. stocks

  • ctoc: Rate of change in closing price compared to the closing price of U.S. stocks

  • ctoc_total: Rate of change in closing price compared to the cumulative closing price of U.S. stocks

Volume Data

  • trading_volume: Daily trading volume of U.S. stocks

  • amount: Trading volume of U.S. stocks

  • shares_outstanding: Daily number of shares issued for U.S. stocks

Financial Metrics

  • eps: Earnings per Share of U.S. Stocks

  • mkt_cap: Market capitalization of U.S. stocks

  • indicated_annual_dividend: Annual projected dividends of U.S. stocks

  • adr_ratio: ADR ratio of U.S. stocks Summary This document provides information on U.S. stock data items available in the data catalog, including total return and adjustment factors.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("us-stock-total_return_factor")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

30 5 * * 2-6

Item List

  • us-stock-total_return_factor: Total return coefficient of U.S. stocks

  • us-stock-adjust_factor: Adjustment factor of U.S. stocksuniverse

Summary

The data consists of various categories of US stock market constituents, including those filtered by Shariah compliance, which excludes stocks with Islamic-prohibited GICS and limits total debt to market cap ratios below 33%. Additionally, it includes general constituent stocks of the US market and specific indices such as the NDX and SPX, with mechanisms for generating daily forward-filled data aligned to monthly indices.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("shariah_constituent")

Metadata

valid from
delivery schedule
time zone
data frequency

19980401 20000101

30 5 * * 2-6 0 9 * * 2-6

1d

Item List

  • constituent: Constituent stocks of the US stock market

  • ndx_constituent: Generates a daily forward-filled non-NA mask aligned to monthly first valid indices for ndx_data, saved as content.spglobal.compustat.universe.us-stock-ndx_constituent.1d.

  • shariah_constituent: quantit_universe shariah filter cm; Excluding Islamic-prohibited GICS, total_debt / market cap < 33%

  • spx_constituent: Generates a daily forward-filled non-NA mask aligned to monthly first valid indices for spx_data, saved as content.spglobal.compustat.universe.us-stock-spx_constituent.1d.

  • spx_shariah_constituent: spx shariah filter cm; Excluding Islamic-prohibited GICS, total_debt / market cap < 33%

edge

Summary The data encompasses summaries of major news topics in the United States, specifically focusing on general news and business news. Each summary is organized by topic and includes a list of relevant keywords associated with the news items.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("topnews_us")

Metadata

valid from
delivery schedule
time zone
data frequency

00 20 * * *

1m

Item List

  • topenbiznews_us: Summary of major English business news in the United States; columns are topic. e.g. 0:['edge', 'lee', 'rambunctious', 'inflect', 'ontroerend', 'obliqueness', 'mortality', 'pounce', 'merry', 'len'],

  • topnews_us: Summary of Major News in the United States; columns are topic. e.g. 0:['edge', 'lee', 'rambunctious', 'inflect', 'ontroerend', 'obliqueness', 'mortality', 'pounce', 'merry', 'len']

Summary The data consists of various sentiment indices and tone metrics related to the U.S. stock market, including averages, totals, and percentages derived from raw sentiment analysis and business news. These indices are designed to provide insights into market sentiment and trends, utilizing models such as SARIMA and moving averages for analysis.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("raw_sentiment-1_2-mean_sum")

Metadata

valid from
delivery schedule
time zone
data frequency

0 9 * * * 30 21 * * *

1d, 1w, 1m

Item List

  • SP500_EWS: S&P 500 Early Warning System (Monthly)

  • SP500_EWS: S&P 500 Early Warning System (Weekly)

  • SP500_EWS_UI_append: Monthly UI Additional Sentiment Index for S&P 500

  • SP500_EWS_UI_append: Weekly UI Additional Sentiment Index for S&P 500

  • SP500_EWS_biz_news: Monthly Business News Sentiment Index for S&P 500

  • SP500_EWS_biz_news: Weekly Business News Sentiment Index for S&P 500

  • SP500_EWS_compustat: S&P 500 Early Warning System (Based on Compustat, Weekly)

  • SP500_EWS_compustat: S&P 500 Early Warning System (Compustat-based, Monthly)

  • SP500_EWS_origin: S&P 500 Early Warning System Original (Monthly)

  • SP500_EWS_origin: S&P 500 Early Warning System Original (Weekly)

  • SP500_EWS_origin_biz_news: Original monthly business news sentiment index for the S&P 500

  • SP500_EWS_origin_biz_news: Original weekly business news sentiment index for the S&P 500

  • SP500_EWS_origin_compustat: S&P 500 Early Warning System Original (Compustat-based, Monthly)

  • SP500_EWS_origin_compustat: S&P 500 Early Warning System Original (Compustat-based, Weekly)

  • SP500_EWS_origins_biz_news: Original value of the weekly business news sentiment index for the S&P 500

  • SP500_EWS_origins_biz_news: Original values of the monthly business news sentiment index for the S&P 500

  • SP500_EWSs_biz_news: Monthly Business News Sentiment Index for S&P 500

  • SP500_EWSs_biz_news: Weekly Business News Sentiment Index for S&P 500

  • post_sarima_sent-1_1-sum_per: Percentage of the sentiment index for the US stock market after applying the SARIMA model (alternative version)

  • post_sarima_sent-1_1-sum_sum: Total sentiment index of the US stock market after applying the SARIMA model (alternative version)

  • post_sarima_sent-1_2-sum_per: Percentage of sentiment index in the US stock market after applying the SARIMA model

  • post_sarima_sent-1_2-sum_sum: Total sentiment index of the US stock market after applying the SARIMA model

  • post_sarima_sent_biz_news-1_1-sum_per: The percentage sum of sentiment analysis results after applying the SARIMA model to business news about the US stock market

  • post_sarima_sent_biz_news-1_1-sum_sum: The total results of sentiment analysis after applying the SARIMA model to business news about the US stock market

  • post_sarima_sent_biz_news-1_2-sum_per: The percentage sum of the results of the second type of sentiment analysis after applying the SARIMA model to business news about the US stock market

  • post_sarima_sent_biz_news-1_2-sum_sum: The total of the second type sentiment analysis results after applying the SARIMA model to business news about the US stock market

  • raw_sentiment-1_1-mean_per: Average percentage of the U.S. stock market sentiment index

  • raw_sentiment-1_1-mean_sum: Average Total of the U.S. Stock Market Sentiment Index

  • raw_sentiment-1_1-sum_per: Percentage of the U.S. Stock Market Sentiment Index

  • raw_sentiment-1_1-sum_sum: Total Sentiment Index of the U.S. Stock Market

  • raw_sentiment-1_2-mean_per: Average Percentage of the U.S. Stock Market Sentiment Index (Alternative Version)

  • raw_sentiment-1_2-mean_sum: Average Total of the U.S. Stock Market Sentiment Index (Alternative Version)

  • raw_sentiment-1_2-sum_per: U.S. Stock Market Sentiment Index Percentage (Alternative Version)

  • raw_sentiment-1_2-sum_sum: Total Sentiment Index of the U.S. Stock Market (Alternative Version)

  • raw_sentiment_biz_news-1_1-mean_per: Average percentage of raw sentiment analysis results of business news regarding the US stock market

  • raw_sentiment_biz_news-1_1-mean_sum: The average sum of raw sentiment analysis results of business news regarding the U.S. stock market

  • raw_sentiment_biz_news-1_1-sum_per: The percentage sum of raw sentiment analysis results of business news regarding the US stock market

  • raw_sentiment_biz_news-1_1-sum_sum: The total of raw sentiment analysis results of business news regarding the U.S. stock market

  • raw_sentiment_biz_news-1_2-mean_per: The average percentage of the second type of raw sentiment analysis results for business news related to the U.S. stock market

  • raw_sentiment_biz_news-1_2-mean_sum: The average total of the second type of raw sentiment analysis results for business news related to the U.S. stock market

  • raw_sentiment_biz_news-1_2-sum_per: The second type percentage total of raw sentiment analysis results for business news about the U.S. stock market

  • raw_sentiment_biz_news-1_2-sum_sum: The total of the second type of raw sentiment analysis results for business news regarding the U.S. stock market

  • us_tone_m3: U.S. Stock Market Tone Index (3-Month Moving Average)

  • us_tone_m3_EWS: U.S. Stock Market Tone Index Early Warning System (3-Month Moving Average, Monthly)

  • us_tone_m3_EWS_compustat: U.S. Stock Market Tone Index Early Warning System (3-Month Moving Average, Compustat-Based, Monthly)

  • us_tone_m3_exp: U.S. Stock Market Tone Index Smoothing (3-Month Moving Average)

  • us_tone_m3_exp_EWS: U.S. Stock Market Tone Index Smoothing Early Warning System (3-Month Moving Average, Monthly)

  • us_tone_m3_exp_EWS_compustat: U.S. Stock Market Tone Index Smoothing Early Warning System (3-Month Moving Average, Compustat-Based, Monthly)

Summary This data catalog provides unstructured information related to assets using LLM for the US stock universe.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("asset")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

00 3 * * 2-6

Item List

  • asset: Unstructured information related to assets using LLM.Summary This document provides information about the US stock market risk-related knowledge graph data.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("kg_risk_us")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

Item List

  • kg_risk_us: Knowledge graph information on risks in the U.S. market

factor

guru_factor

Summary

This document provides an overview of various US stock strategies based on different investment criteria set by renowned investors.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("us-brad_gerstner")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

20000101

0 15 * * 1-5

Item List

Strategies Based on Specific Investors

  • us-brad_gerstner: A US stock strategy based on Brad Gerstner's criteria: Revenue growth with annual revenue growth > 20%, margin expansion with annual gross margin growth > 5%, cash flow with annual FCF growth > 10%, EPS growth with annual EPS growth > 10%, and valuation with P/E < 15.

  • us-charlie_munger: A US stock strategy based on Charlie Munger's criteria: Buy and Hold with ROIC > 15%, margin of safety with P/E < 10 & P/B < 1.5, moat holding quality stocks with ROE > 15% & annual revenue growth < 15%, cash strategy with debt-to-equity ratio < 0.5 & ROE > 15%, and risk management with current ratio > 2.

  • us-david_dreman: A US stock strategy based on David Dreman's criteria: PER in the bottom 20% or PBR in the bottom 20%, market cap within the top 500, annual net income growth > 7%, ROE in the top one-third among the top 500 by market cap, current ratio > 2, and debt-to-equity ratio < 1.

  • us-alex_sacerdote: A US stock strategy based on Alex Sacerdote's criteria: Growth with annual revenue growth > 20% or net income growth > 30%, innovation with R&D expenditure > 10%, market leadership with EPS growth > 20% or ROE > 10%, macro trends with debt-to-equity ratio < 0.5, and valuation with P/E < 15 or PSR < 2 or EV/EBITDA < 10 or P/CF < 10.

  • us-james_oshaughnessy: A US stock strategy based on James O'Shaughnessy's criteria: ROIC > 13% & P/E < 20, P/B < 2, debt-to-equity ratio > 1.5 or FCF > 0, dividend yield > 2%, annual EPS growth > 20%, dividend yield > 4% & debt-to-equity ratio < 1, 12-month price momentum > 0 & annual EPS growth > 20%.

  • us-glenn_welling: A US stock strategy based on Glenn Welling's criteria: Activist strategy with EV/EBITDA < 8, operational improvement with ROIC < 10%, spinoff strategy with P/B < 1.5, event-driven strategy with P/S < 1.5, activist focus with P/B < 1, and growth strategy with PEG < 1.

  • us-benjamin: A US stock strategy based on Benjamin Graham's criteria: Current ratio > 200%, net current assets > long-term debt, EPS growth > 3%, PER < 15, PBR*PER < 22, and debt-to-equity ratio < 1.

  • us-colin_moran: A US stock strategy based on Colin Moran's criteria: High-quality investments with a focus on consistent earnings growth, low leverage with debt-to-equity ratio < 0.5, strong ROE > 15%, valuation with P/E < 20 or P/B < 2, and sustainable free cash flow (FCF > 0).

  • us-warren_buffet: A US stock strategy based on Warren Buffett's criteria: ROE > 15%, long-term debt-to-equity ratio < 1, current ratio > 1.5, FCF > 0, PER < 17, P/B < 1.5, debt-to-equity ratio > 1.5, and EPS growth > 10%.

  • us-bill_ackman: A US stock strategy based on Bill Ackman's criteria: ROIC > 13% & P/E < 20, activist strategy with P/E < 20 & P/B < 2, debt-to-equity ratio > 1.5 or FCF > 0, and dividend yield > 2%.

  • benjamin_graham: No description.

  • us-glenn_greenberg: A US stock strategy based on Glenn Greenberg's criteria: Low valuation with P/E < 15, high efficiency with ROIC > 15%, strong financials with debt-to-equity ratio < 0.5, margin expansion with gross margin growth > 3%, and strong cash flow with FCF > 5% of market cap.

  • us-peter_lynch: A US stock strategy based on Peter Lynch's criteria: PER < 40, PEG < 1.8, inventory-to-sales ratio < 5%, debt-to-equity ratio < 0.8, ROE > 5%, ROA > 1%, and dividend yield > 3%.

  • us-cathie_wood: A US stock strategy based on Cathie Wood's criteria: Innovative companies with PEG < 2, technological transition with PSR < 20, disruptive companies with revenue growth > 20%, and risk management with current ratio > 2.

  • us-william_oneil: A US stock strategy based on William O'Neil's criteria: Current quarterly EPS growth > 18%, annual EPS growth > 18%, ROE > 17%, recent stock price > 85% of the 52-week high, and annual stock price growth in the top 20%.

  • us-ron_baron: A US stock strategy based on Ron Baron's criteria: Long-term investment with ROE > 15%, valuation strategy with PSR < 1.5 or P/E < 20 or EV/EBITDA < 10 or P/FCF < 15, growth potential with annual revenue growth > 25%, and innovation with R&D expenditure > 10% of revenue.

If you would like to use the data, please contact the admin.

fundamental_factor

Summary

The dataset contains various financial metrics related to U.S. stocks, focusing on growth rates, ratios, and performance indicators over different time frames, such as 1-year and 3-year periods. Key metrics include growth rates for sales, net income, operating assets, and liabilities, as well as ratios like EBITDA to market capitalization, return on equity, and cash to total assets, providing insights into the financial health and performance trends of U.S. stocks.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("us-stock_pit-ncol_gr3a")

Metadata

valid from
delivery schedule
time zone
data frequency

30 5 * * 2-6

1d

Item List

• at_gr3 : Asset Growth 3yr

• sale_gr3 : Sales Growth 3yr

• ca_gr3 : Current Asset Growth 3yr

• nca_gr3 : Non-Current Asset Growth 3yr

• lt_gr3 : Total Liabilities Growth 3yr

• cl_gr3 : Current Liabilities Growth 3yr

• ncl_gr3 : Non-Current Liabilities Growth 3yr

• be_gr3 : Book Equity Growth 3yr

• pstk_gr3 : Preferred Stock Growth 3yr

• debt_gr3 : Total Debt Growth 3yr

• cogs_gr3 : Cost of Goods Sold Growth 3yr

• sga_gr3 : Selling, General, and Administrative Expenses Growth 3yr

• opex_gr3 : Operating Expenses Growth 3yr

Growth - Changed Scaled by Total Assets

• gp_gr1a : Gross Profit Change 1yr (Scaled by Total Assets)

• ocf_gr1a : Operating Cash Flow Change 1yr (Scaled by Total Assets)

• cash_gr1a : Cash and Short-Term Investments Change 1yr (Scaled by Total Assets)

• inv_gr1a : Inventory Change 1yr (Scaled by Total Assets)

• rec_gr1a : Receivables Change 1yr (Scaled by Total Assets)

• ppeg_gr1a : Property, Plant, and Equipment Gross Change 1yr

• intan_gr1a : Intangible Assets Change 1yr

• debtst_gr1a : Short-Term Debt Change 1yr

• ap_gr1a : Accounts Payable Change 1yr

• txp_gr1a : Income Tax Payable Change 1yr

• debtlt_gr1a : Long-Term Debt Change 1yr

• txditc_gr1a : Deferred Taxes and Investment Credit Change 1yr

• coa_gr1a : Current Operating Assets Change 1yr

• col_gr1a : Current Operating Liabilities Change 1yr

• cowc_gr1a : Current Operating Working Capital Change 1yr

• ncoa_gr1a : Non-Current Operating Assets Change 1yr

• ncol_gr1a : Non-Current Operating Liabilities Change 1yr

• nncoa_gr1a : Net Non-Current Operating Assets Change 1yr

• oa_gr1a : Operating Assets Change 1yr

• ol_gr1a : Operating Liabilities Change 1yr

• noa_gr1a : Net Operating Assets Change 1yr

• fna_gr1a : Financial Assets Change 1yr

• fnl_gr1a : Financial Liabilities Change 1yr

• nfna_gr1a : Net Financial Assets Change 1yr

• ebitda_gr1a : Operating Profit before Depreciation Change 1yr

• ebit_gr1a : Operating Profit after Depreciation Change 1yr

• ope_gr1a : Operating Earnings to Equity Change 1yr

• ni_gr1a : Net Income Change 1yr

• dp_gr1a : Depreciation and Amortization Change 1yr

• nwc_gr1a : Net Working Capital Change 1yr

• nix_gr1a : Net Income Including Extraordinary Items Change 1yr

• tax_gr1a : Effective Tax Rate Change 1yr

• div_gr1a : Dividend Payout Ratio Change 1yr

• gp_gr3a : Gross Profit Change 3yr

• ocf_gr3a : Operating Cash Flow Change 3yr

• cash_gr3a : Cash and Short-Term Investments Change 3yr

• inv_gr3a : Inventory Change 3yr

• rec_gr3a : Receivables Change 3yr

• ppeg_gr3a : Property, Plant, and Equipment Gross Change 3yr

• lti_gr3a : Investment and Advances Change 3yr

• intan_gr3a : Intangible Assets Change 3yr

• debst_gr3a : Short-Term Debt Change 3yr

• ap_gr3a : Accounts Payable Change 3yr

• txp_gr3a : Income Tax Payable Change 3yr

• debtlt_gr3a : Long-Term Debt Change 3yr

• txditc_gr3a : Deferred Taxes and Investment Credit Change 3yr

• coa_gr3a : Current Operating Assets Change 3yr

• col_gr3a : Current Operating Liabilities Change 3yr

• cowc_gr3a : Current Operating Working Capital Change 3yr

• ncoa_gr3a : Non-Current Operating Assets Change 3yr

• nncoa_gr3a : Net Non-Current Operating Assets Change 3yr

• oa_gr3a : Operating Assets Change 3yr

• ol_gr3a : Operating Liabilities Change 3yr

• noa_gr3a : Net Operating Assets Change 3yr

• fna_gr3a : Financial Assets Change 3yr

• fnl_gr3a : Financial Liabilities Change 3yr

• nfna_gr3a : Net Financial Assets Change 3yr

• ebitda_gr3a : Operating Profit before Depreciation Change 3yr

• ebit_gr3a : Operating Profit after Depreciation Change 3yr

• ope_gr3a : Operating Earnings to Equity Change 3yr

• ni_gr3a : Net Income Change 3yr

• dp_gr3a : Depreciation and Amortization Change 3yr

• nwc_gr3a : Net Working Capital Change 3yr

• inv_gr3a : Inventory Change 3yr

• ncol_gr3a : Non-Current Operating Liabilities Change 3yr

• nix_gr3a : Net Income Including Extraordinary Items Change 3yr

• tax_gr3a : Effective Tax Rate Change 3yr

• div_gr3a : Dividend Payout Ratio Change 3yr

Investment

• rd_at : R&D scaled by Assets

Non-Recurring Items

• spi_at : Special Items scaled by Assets

• xido_at : Extraordinary Items and Discontinued Operations scaled by Assets

• nri_at : Non-Recurring Items scaled by Assets

Profit Margins

• gp_sale : Gross Profit Margin

• ebitda_sale : Operating Profit Margin before Depreciation

• ebit_sale : Operating Profit Margin after Depreciation

• pi_sale : Pretax Profit Margin

• nix_sale : Net Profit Margin

• ocf_sale : Operating Cash Flow Margin

Return on Assets

• gp_at : Gross Profit scaled by Assets

• ebitda_at : Operating Profit before Depreciation scaled by Assets

• ebit_at : Operating Profit after Depreciation scaled by Assets

• fi_at : Firm Income scaled by Assets

• cop_at : Cash Based Operating Profitability scaled by Assets

Return on Book Equity

• ope_be : Operating Profit to Equity scaled by BE

• ni_be : Net Income scaled by BE

• nix_be : Net Income Including Extraordinary Items scaled by BE

• ocf_be : Operating Cash Flow scaled by BE

Return on Invested Capital

• gp_bev : Gross Profit scaled by BEV

• ebitda_bev : Operating Profit before Depreciation scaled by BEV

• ebit_bev : Operating Profit after Depreciation scaled by BEV

• fi_bev : Firm Income scaled by BEV

• cop_bev : Cash Based Operating Profitability scaled by BEV

Return on Physical Capital

• gp_ppen : Gross Profit scaled by PPEN

• ebitda_ppen : Operating Profit before Depreciation scaled by PPEN

Equity Payout

• div_at : Total Dividends scaled by Assets

Accurals

• oaccruals_at : Operating Accruals

• oaccruals_ni : Percent Operating Accruals

• taccruals_at : Total Accruals

• taccruals_ni : Percent Total Accruals

• noa_at : Net Operating Asset to Total Assets

Capitalization/Leverage Ratios

• be_bev : Common Equity scaled by BEV

• debt_bev : Total Debt scaled by BEV

• cash_bev : Cash and Short-Term Investments scaled by BEV

• pstk_bev : Preferred Stock scaled by BEV

• debtlt_bev : Long-Term Debt scaled by BEV

• debtst_bev : Short-Term Debt scaled by BEV

Capitalization/Leverage Ratios (Columns gvkeyiid)

• debt_mev : Total Debt scaled by MEV

• pstk_mev : Preferred Stock scaled by MEV

• debtlt_mev : Long-Term Debt scaled by MEV

• debtst_mev : Short-Term Debt scaled by MEV

Financial Soundness Ratios

• int_debt : Interest scaled by Total Debt

• int_debtlt : Interest scaled by Long-Term Debt

• ebitda_debt : Operating Profit before Depreciation scaled by Total Debt

• profit_cl : Profit before D&A scaled by Current Liabilities

• ocf_cl : Operating Cash Flow scaled by Current Liabilities

• ocf_debt : Operating Cash Flow scaled by Total Debt

• cash_lt : Cash Balance scaled by Total Liabilities

• inv_act : Inventory scaled by Current Assets

• rec_act : Receivables scaled by Current Assets

• debtst_debt : Short-Term Debt scaled by Total Debt

• cl_lt : Current Liabilities scaled by Total Liabilities

• debtlt_debt : Long-Term Debt scaled by Total Debt

• opex_at : Operating Leverage

• lt_ppen : Total Liabilities scaled by Total Tangible Assets

• debtlt_be : Long-Term Debt to Book Equity

• nwc_at : Working Capital scaled by Assets

Solvency Ratios

• debt_at : Debt-to-Assets

• ebit_int : Interest Coverage Ratio

Liquidity Ratios

• inv_days : Days Inventory Outstanding

• rec_days : Days Sales Outstanding

• ap_days : Days Accounts Payable Outstanding

• cash_conversion : Cash Conversion Cycle

• cash_cl : Cash Ratio

• caliq_cl : Quick Ratio

• ca_cl : Current Ratio

Activity/Efficiency Ratios

• inv_turnover : Inventory Turnover

• at_turnover : Asset Turnover

• rec_turnover : Receivables Turnover

• ap_turnover : Account Payables Turnover

Miscellaneous

• sale_bev : Sales scaled by BEV

• rd_sale : R&D scaled by Sales

• sale_be : Sales scaled by Total Stockholders’ Equity

• div_ni : Dividend Payout Ratio

• sale_nwc : Sales scaled by Working Capital

• tax_pi : Effective Tax Rate

Balance Sheet Fundamental to Market Equity

• be_me : Book Equity scaled by Market Equity

• at_me : Total Assets scaled by Market Equity

• cash_me : Cash and Short-Term Investments scaled by Market Equity

Income Fundamentals to Market Equity

• gp_me : Gross Profit scaled by ME

• ebitda_me : Operating Profit before Depreciation scaled by ME

• ebit_me : Operating Profit after Depreciation scaled by ME

• ope_me : Operating Earnings to Equity scaled by ME

• ni_me : Net Income scaled by ME

• sale_me : Sales scaled by ME

• ocf_me : Operating Cash Flow scaled by ME

• nix_me : Net Income Including Extraordinary Items scaled by ME

• cop_me : Cash Based Operating Profitability scaled by ME

• rd_me : R&D scaled by ME

Balance Sheet Fundamentals to Market Enterprise Value

• be_mev : Book Equity scaled by MEV

• at_mev : Total Assets scaled by MEV

• cash_mev : Cash and Short-Term Investments scaled by MEV

• bev_mev : Book Enterprise Value scaled by MEV

• ppen_mev : Property, Plant, and Equipment Net scaled by MEV

Equity Payout/Issuance to Market Equity

• div_me : Total Dividends scaled by ME

Income Fundamentals to Market Enterprise Value

• gp_mev : Gross Profit scaled by MEV

• ebitda_mev : Operating Profit before Depreciation scaled by MEV

• ebit_mev : Operating Profit after Depreciation scaled by MEV

• sale_mev : Sales scaled by MEV

• ocf_mev : Operating Cash Flow scaled by MEV

• cop_mev : Cash Based Operating Profitability scaled by MEV

New Variables not in HXZ

• niq_saleq_std : Net Income to Sales Quarterly Volatility

• ni_at : Net Income scaled by Assets

• ocf_at : Operating Cash Flow scaled by Assets

• ocf_at_chg1 : Operating Cash Flow to Assets 1 yr Change

• roeq_be_std : Quarterly ROE Volatility

• roe_be_std : ROE Volatility

• gpoa_ch5 : Gross Product to Assets 5 yr Change

• roe_ch5 : ROE 5 yr Change

• roa_ch5 : ROA 5 yr Change

• cfoa_ch5 : Operating Cash Flow to Assets 5 yr Change

• gmar_ch5 : Gross Product to Sales 5 yr Change

New Variables from HXZ

• cash_at : Cash and Short Term Investments scaled by Assets

• ni_inc8q : Number of Consecutive Earnings Increases

• ppeinv_gr1a : Change in Property, Plant and Equipment Less Inventories scaled by lagged Assets

• lnoa_gr1a : Change in Long-Term NOA scaled by average Assets

• sti_gr1a : Change in Short-Term Investments scaled by Assets

• niq_be : Quarterly Income scaled by BE

• niq_be_chg1 : Change in Quarterly Income scaled by BE

• niq_at : Quarterly Income scaled by AT

• niq_at_chg1 : Change in Quarterly Income scaled by AT

• saleq_gr1 : Quarterly Sales Growth

• rd5_at : R&D Capital-to-Assets

• dsale_dinv : Change Sales minus Change Inventory

• dsale_drec : Change Sales minus Change Receivables

• dgp_dsale : Change Gross Profit minus Change Sales

• dsale_dsga : Change Sales minus Change SG&A

• saleq_su : Earnings Surprise

• niq_su : Revenue Surprise

• inv_gr1 : Inventory Change 1 yr

• be_gr1a : Book Equity Change 1 yr scaled by Assets

• op_at : Ball Operating Profit to Assets

• pi_nix : Earnings before Tax and Extraordinary Items to Net Income Including Extraordinary Items

• op_atl1 : Ball Operating Profit scaled by lagged Assets

• ope_bel1 : Operating Profit scaled by lagged Book Equity

• gp_atl1 : Gross Profit scaled by lagged Assets

• cop_atl1 : Cash Based Operating Profitability scaled by lagged Assets

• at_be : Book Leverage

• ocfq_saleq_std : Operating Cash Flow to Sales Quarterly Volatility

• aliq_at : Liquidity scaled by lagged Assets

• tangibility : Tangibility

• o_score : Ohlson O-Score

• earnings_variability : Earnings Variability

• ni_ar1 : 1 yr lagged Net Income to Assets

• ni_ivol : Net Income Idiosyncratic Volatility

New Variables from HXZ (Columns gvkeyiid)

• debt_me : Total Debt scaled by ME

• netdebt_me : Net Debt scaled by ME

• aliq_mat : Liquidity scaled by lagged Market Assets

• eq_dur : Equity Duration

• z_score : Altman Z-Score

• kz_index : Kaplan-Zingales Index

Reference and Formula

Jensen, Theis Ingerslev, Bryan Kelly, and Lasse Heje Pedersen. "Is there a replication crisis in finance?." The Journal of Finance 78.5 (2023): 2465-2518.

Jensen, Theis Ingerslev, Bryan Kelly, and Lasse Heje Pedersen. "Global Factor Data Documentation." (2021).

analysis

analysis

Summary

This document provides information about the HMM-based market system analysis excluding US ETFs.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("hmm_wo_us_etf")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

30 6 * * 2-6

Monthly

Item List

  • hmm_wo_us_etf: Analysis of HMM-based market system excluding US ETFs (Monthly)

If you would like to use the data, please contact the admin.

macro

macro

Summary

The data set includes various economic indicators related to the U.S. stock market, such as the 30-year Treasury yield, Producer Price Index, and Real Gross Domestic Product (GDP) Growth Rate. It also features metrics like the Consumer Price Index, unemployment rate estimates, and housing market statistics, providing a comprehensive overview of the current economic landscape.

Example code

from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("live-dgs30")

Metadata

valid from
delivery schedule
time zone
data frequency

00 10 * * *

1d

Item List

Macroeconomic Indicators

  • live-a261rx1q020sbea: U.S. real GDP growth rate

  • live-gdp: Real-time nominal GDP estimate

  • live-gdpc1: Real-time real (inflation-adjusted) GDP estimate

  • live-cfnai: Chicago Fed index summarizing overall economic activity

  • live-indpro: Real-time index of U.S. industrial production

  • live-usalolitoaastsam: U.S. leading economic indicator estimate

  • live-oecdlolitoaastsam: OECD leading indicator of global economic trends

  • live-chnlolitoaastsam: China’s leading economic indicator estimate

  • live-gacdfsa066msfrbphi: Philadelphia Fed’s U.S. GDP growth projection

  • live-gacdisa066msfrbny: New York Fed’s GDP forecast (e.g. Nowcast)

  • live-usepuindxd: Index measuring uncertainty about U.S. economic policy

  • live-usrec: Indicator signaling current U.S. recession periods

  • live-recprousm156n: Estimated probability of U.S. recession

Labor Market

  • live-payems: Total number of non-farm payroll employees

  • live-icsa: Initial claims for unemployment insurance

  • live-unrate: Real-time unemployment rate estimate

  • live-awhaeman: Real-time estimate of average hourly wages

  • live-ulcnfb: Unit labor cost for the non-farm business sector

  • live-jtsjol: Total number of job openings

Inflation & Prices

  • live-cpiaucsl: Real-time Consumer Price Index (CPI)

  • live-cpilfesl: Core CPI (excludes food and energy)

  • live-pcepi: PCE Price Index (used by the Fed for inflation targeting)

  • live-pcepilfe: Core PCE Price Index

  • live-ppiaco: Producer Price Index (PPI) for all commodities

  • live-ppifis: PPI for final demand

Monetary Policy & Interest Rates

  • live-fedfunds: Real-time Federal Funds Rate estimate

  • live-dgs1mo ~ live-dgs30: U.S. Treasury yields (1 month to 30 years)

  • live-dfii10: 10-year Treasury Inflation-Protected Securities (TIPS) yield

  • live-t10yie: 10-year breakeven inflation rate

  • live-t5yie: 5-year breakeven inflation rate

  • live-t10y2y: Yield spread between 10Y and 2Y Treasury bonds

  • live-daaa: Yield on Moody’s Aaa-rated corporate bonds

  • live-dbaa: Yield on Moody’s Baa-rated corporate bonds

  • live-aaa10y: Spread between Aaa bonds and 10Y Treasuries

  • live-tedrate: Spread between 3M LIBOR and 3M Treasury yield

Financial Markets

  • live-sp500: S&P 500 stock index

  • live-vixcls: CBOE Volatility Index (market fear gauge)

  • live-stlfsi2: St. Louis Fed Financial Stress Index

  • live-bamlh0a0hym2: High-yield corporate bond spread (ICE BofA)

  • live-bamlhyh0a0hym2triv: High-yield bond total return index (ICE BofA)

Housing & Real Estate

  • live-houst: Monthly housing starts

  • live-hsn1f: New single-family home sales

  • live-permit: Construction permits for new housing units

  • live-csushpinsa: S&P/Case-Shiller U.S. home price index

  • live-comrepusq159n: Commercial real estate price index

  • live-mortgage30us: 30-year fixed mortgage rate

  • live-wshotsl: Inventory of homes for sale

Consumer Activity

  • live-dspic96: Real disposable personal income

  • live-rpi: Real personal income

  • live-pcec96: Real personal consumption expenditures

  • live-rsafs: Retail sales

  • live-mich: University of Michigan consumer expectations index

  • live-umcsent: University of Michigan consumer sentiment index

Business Activity

  • live-dgorder: New manufacturing orders

  • live-businv: Total business inventories

  • live-mdsp: Manufacturing inventory-to-sales ratio

  • live-cdsp: Retail inventory-to-sales ratio

  • live-whlslrimsa: Wholesale inventories

  • live-ttlcons: Total construction spending

Trade & Commodities

  • live-boptexp: Total exports

  • live-boptimp: Total imports (or import revenue)

  • live-dcoilwtico: WTI crude oil price

  • live-pcoppusdm: Copper price in U.S. dollars

Money Supply & Liquidity

  • live-m1sl: M1 money supply (cash, demand deposits)

  • live-m2sl: M2 money supply (M1 + savings, time deposits)

  • live-m2real: Real M2 (adjusted for inflation)

Credit & Lending

  • live-wdtgal: Bank lending standards (loan officer survey)

  • live-wlodll: Bank loan demand (loan officer survey)

Capacity & Utilization

  • live-tcu: Capacity utilization rate in manufacturing

VN ETF

market

market

Summary

This document provides information on Vietnam ETF data items, including their adjustment factors and total return coefficients.

Example code

from finter.data import ContentFactory
cf = ContentFactory("vn_etf", 20200101, 20200201)
df = cf.get_df("vnm-etf-adjust_factor")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

00 22 * * 1-5

Item List

  • vnm-etf-adjust_factor: Adjustment factor of Vietnam ETF

  • vnm-etf-total_return_factor: Total return coefficient of Vietnam ETF

If you would like to use the data, please contact the admin.

VN STOCK

financial

Summary This document provides an overview of the balance sheet data for Vietnamese stocks, including various financial metrics.

Example code

from finter.data import ContentFactory
cf = ContentFactory("vn_stock", 20200101, 20200201)
df = cf.get_df("total_assets")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

00 17 * * *

Item List Assets

  • long_term_assets: Non-current assets of Vietnamese stocks

  • total_assets: Total assets of Vietnamese stocks

  • current_assets: Liquid assets of Vietnamese stocks

  • fixed_assets: Fixed assets of Vietnamese stocks

  • inventories_net: Net inventory assets of Vietnamese stocks

  • cash_and_cash_equivalents: Cash and cash equivalents of Vietnamese stocks

Liabilities

  • total_liabilities: Total debt of Vietnamese stocks

  • current_liabilities: Current liabilities of Vietnamese stocks

  • long_term_liabilities: Non-current liabilities of Vietnamese stocks

  • short_term_loans: Short-term borrowings of Vietnamese stocks

  • long_term_loans: Long-term loan information for Vietnamese stocks

  • trade_accounts_payable: Accounts payable for Vietnamese stocks

Equity

  • owners_equity: Information on the equity of Vietnamese stocks

  • paid_in_capital: Information on paid-in capital of Vietnamese stocks

  • retained_earnings: Information on retained earnings of Vietnamese stocks

Other

  • total_resources: Total resource information of Vietnamese stocksSummary This document provides an overview of cash flow data items related to Vietnamese stocks, including their descriptions and usage examples.

Example code

from finter.data import ContentFactory
cf = ContentFactory("vn_stock", 20200101, 20200201)
df = cf.get_df("net_profit_before_tax")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

00 17 * * *

Item List Cash Flow Information

  • net_profit_before_tax: Pre-tax net income information of Vietnamese stocks

  • net_cash_inflows_outflows_from_operating_activities: Information on net cash flow from operating activities of Vietnamese stocks

  • net_cash_inflows_outflows_from_financing_activities: Information on net cash flow from financing activities of Vietnamese stocks

  • net_cash_inflows_outflows_from_investing_activities: Information on net cash flow resulting from investment activities in Vietnamese stocks

  • net_increase_in_cash_and_cash_equivalents: Information on the net increase of cash and cash equivalents in Vietnamese stocks

  • cash_and_cash_equivalents_at_the_beginning_of_period: Information on the basic cash and cash equivalents of Vietnamese stocks

  • cash_and_cash_equivalents_at_the_end_of_period: Information on cash and cash equivalents at the end of the Vietnamese stocks

Investment and Asset Information

  • purchases_of_fixed_assets_and_other_long_term_assets: Information on the purchase of fixed assets and other long-term assets of Vietnamese stocks

  • proceeds_from_disposal_of_fixed_assets: Information on the disposal income of fixed assets in Vietnamese stocks

  • profit_loss_from_liquidating_fixed_assets: Information on the profit and loss from the disposal of fixed assets in Vietnamese stocks

  • investments_in_other_entities: Investment information on other companies in Vietnamese stocks

  • proceeds_from_divestment_in_other_entities: Information on the return on investment from other companies in Vietnamese stocks

Income and Expense Information

  • dividends_and_interest_received: Information on dividends and interest income from Vietnamese stocks

  • interest_income_and_dividend: Information on interest income and dividends from Vietnamese stocks

  • interest_expense: Interest expense information of Vietnamese stocks

  • loan_interests_already_paid: Information on interest payments for loans on Vietnamese stocks

  • payments_for_corporate_income_tax: Information on corporate tax payments for Vietnamese stocks

  • business_income_tax_paid: Information on corporate tax payments for Vietnamese stocks

  • payments_to_employees: Information on employee salary payments for Vietnamese stocks

  • payments_to_suppliers: Information on supplier payments for Vietnamese stocks

  • payments_for_share_returns_and_repurchases: Information on share buybacks and redemption expenditures of Vietnamese stocks

  • other_payments_on_operating_activities: Information on other operating expenses of Vietnamese stocks

Cash Flow Adjustments

  • increase_decrease_in_inventories: Information on the changes in inventory assets of Vietnamese stocks

  • increase_decrease_in_prepaid_expenses: Information on the changes in prepaid expenses for Vietnamese stocks

  • increase_decrease_in_payables: Information on the increase and decrease of accounts payable for Vietnamese stocks

  • increase_decrease_in_receivables: Information on the changes in accounts receivable of Vietnamese stocks

  • effect_of_foreign_exchange_differences: Information on the effects of exchange rate fluctuations on Vietnamese stocks

  • unrealised_foreign_exchange_gain_loss: Information on unrealized foreign exchange gains and losses of Vietnamese stocks

Other Financial Information

  • other_receipts_from_operating_activities: Information on other operating income from Vietnamese stocks

  • other_disbursements: Other expenditure information for Vietnamese stocks

  • other_gains: Other income information of Vietnamese stocks

  • gains_from_sales_of_goods_and_service_provisons_and_other_gains: Revenue from the sale of products and provision of services related to Vietnamese stocks and other revenue information

  • profit_loss_from_investing_activities: Profit and loss information on investment activities in Vietnamese stocks

  • collection_of_loans_proceeds_from_sales_of_debt_instruments: Information on loan recovery and debt product sales revenue from Vietnamese stocks

  • finance_lease_principal_payments: Information on principal repayment of financial leases for Vietnamese stocks

  • provisions: Information on provisions for Vietnamese stocks

This structured list provides a comprehensive overview of the cash flow data items available for Vietnamese stocks, making it easier for users to identify and utilize the relevant data. Summary The data encompasses various financial metrics related to Vietnamese stocks, including revenue sources such as sales, interest expenses, and insurance premiums, as well as expenses like general administrative and selling costs. It also includes profitability indicators such as net profit after tax, gross profit, and earnings per share, providing a comprehensive overview of the income statement for the Vietnamese stock market.

Example code

from finter.data import ContentFactory
cf = ContentFactory("vn_stock", 20200101, 20200201)
df = cf.get_df("sales_deductions")

Metadata

valid from
delivery schedule
time zone
data frequency

00 17 * * *

1d

Item List

  • business_tax_current: Current corporate tax on Vietnamese stocks

  • business_tax_deferred: Deferred corporate tax on Vietnamese stocks

  • cogs: Cost of goods sold for Vietnamese stocks

  • eps_basic: Basic Earnings Per Share of Vietnamese Stocks

  • eps_diluted: Diluted earnings per share of Vietnamese stocks

  • expenses_banking_activities: Banking activity costs of Vietnamese stocks

  • financial_expense: Financial costs of Vietnamese stocks

  • financial_income: Financial returns of Vietnamese stocks

  • general_admin_expense: General administrative expenses of Vietnamese stocks

  • gross_insurance_operating_profit: Total profit from insurance operations in Vietnamese stocks

  • gross_profit: Gross profit of Vietnamese stocks

  • gross_written_premium: Total insurance premium of Vietnamese stocks

  • income_banking_activities: Banking activity income from Vietnamese stocks

  • income_reinsurance_assumed: The profitability of reinsurance in Vietnamese stocks

  • income_reinsurance_ceded: The revenue from the issuance insurance of Vietnamese stocks

  • increase_unearned_premium_reserve: Increase in unearned premium reserves for Vietnamese stocks

  • increase_unearned_premium_reserve_direct: Increase in direct unearned premium reserves for Vietnamese stocks

  • interest_expenses: Interest expenses of Vietnamese stocks

  • investment_income: Investment returns on Vietnamese stocks

  • minority_interest: Minority shareholder equity in Vietnamese stocks

  • net_operating_income_banking: Net operating income of banks in Vietnam

  • net_other_income_exp: Net other income or expenses from Vietnamese stocks

  • net_profit_after_tax: Net income after tax of Vietnamese stocks

  • net_profit_bank_operation: Net operating profit of banks in Vietnam

  • net_profit_before_tax: Pre-tax net income of Vietnamese stocks

  • net_profit_insurance_operation: Net profit from insurance operations of Vietnamese stocks

  • net_profit_parent_company: Net income of controlling companies in Vietnam stocks

  • net_revenue_insurance_premium: Net premium income from Vietnamese stocks

  • net_sales: Net sales of Vietnamese stocks

  • net_sales_insurance_business: Net sales of the insurance business in Vietnam

  • operating_profit_loss: Operating profit or loss of Vietnamese stocks

  • other_expenses: Other expenses of Vietnamese stocks

  • other_income: Other income from Vietnamese stocks

  • provision_credit_losses: Vietnamese stock allowance for doubtful accounts

  • reinsurance_premium_assumed: Vietnamese stock reinsurance premium

  • reinsurance_premium_ceded: Vietnamese stock issuance insurance premium

  • revenue_brokerage: Brokerage commission revenue from Vietnamese stocks

  • revenue_insurance_premium: Insurance premium income from Vietnamese stocks

  • revenue_investment_advisory: Revenue from investment advisory fees for Vietnamese stocks

  • revenue_issuance_agency: Revenue from issuance agency fees for Vietnamese stocks

  • revenue_securities_custody: Custody fee income from Vietnamese stocks

  • revenue_underwriting: Acquisition commission revenue from Vietnamese stocks

  • sales: Revenue of Vietnamese stocks

  • sales_deductions: Revenue deduction for Vietnamese stocks

  • selling_expenses: Selling costs of Vietnamese stocks

  • tax_expense: Total tax cost of Vietnamese stocks

Summary The data encompasses various financial ratios and metrics related to Vietnamese stocks, including earnings per share (EPS), price-to-earnings ratios, dividend yields, and market capitalization. It also includes growth rates, cash flow metrics, and valuation ratios, providing a comprehensive overview of the financial performance and valuation of stocks within the Vietnamese market.

Example code

from finter.data import ContentFactory
cf = ContentFactory("vn_stock", 20200101, 20200201)
df = cf.get_df("EPS_growth_QoQ")

Metadata

valid from
delivery schedule
time zone
data frequency

00 17 * * *

1d

Item List

  • BV_per_Share: Book value per share of Vietnamese stocks

  • Beta_2_years: 2-Year Beta Coefficient of Vietnamese Stocks

  • Beta_6_months: 6-month beta coefficient of Vietnamese stocks

  • Cash_Flow_per_Share: Cash flow per share of Vietnamese stocks

  • Correlation_between_price_and_market: The correlation between Vietnamese stock prices and the market

  • Dividend_Yield_Paid: The actual paid dividend yield of Vietnamese stocks

  • Dividend_Yield_Plan: Planned dividend yield of Vietnamese stocks

  • Dividend_payout_ratio: Dividend policy of Vietnamese stocks

  • Dividend_per_share: Dividend per share of Vietnamese stocks

  • Dividend_yield_ratio: Dividend yield of Vietnamese stocks

  • EBITDA_per_share: EBITDA per share of Vietnamese stocks

  • EPS: Earnings Per Share of Vietnamese Stocks

  • EPS_Forecast: EPS forecast for Vietnamese stocks

  • EPS_diluted: Diluted earnings per share of Vietnamese stocks

  • EPS_diluted_finacial_statement: Diluted earnings per share of Vietnamese stocks

  • EPS_finacial_statement: Earnings per Share based on the financial statements of Vietnamese stocks

  • EPS_growth_QoQ: EPS growth rate of Vietnamese stocks compared to the previous quarter

  • EPS_growth_YoY: Year-over-year EPS growth rate of Vietnamese stocks

  • EPS_preother_income: Earnings per share excluding other income from Vietnamese stocks

  • EV: Corporate value of Vietnamese stocks

  • EV_EBIT: Vietnamese stock enterprise value to EBIT ratio

  • EV_EBITDA: Vietnamese stock enterprise value to EBITDA ratio

  • EV_Sales: The ratio of enterprise value to revenue of Vietnamese stocks

  • Foreign_investor_sector: Information on foreign investor sectors in Vietnamese stocks

  • Free_cash_flow_to_firm: Free cash flow of Vietnamese stocks

  • Freeloat_Shares: The number of outstanding shares of Vietnamese stocks

  • Graham_Number: Graham number of Vietnamese stocks

  • Listing_Volume: Number of listed shares in Vietnam stocks

  • Market_Cap: Market capitalization of Vietnamese stocks

  • Median_P_S_Value: The median price-to-sales ratio of Vietnamese stocks

  • Outstanding_Shares: Number of issued shares of Vietnamese stocks

  • PEG: PEG ratio of Vietnamese stocks

  • PEG_percentage: PEG ratio (%) of Vietnamese stocks

  • PE_preother_income: Price-to-earnings ratio excluding other income of Vietnamese stocks

  • P_B: Price-to-Book Ratio of Vietnamese Stocks

  • P_CFO: Price-to-Cash-Flow Ratio of Vietnamese Stocks

  • P_Cash_Flow: Price-to-Cash-Flow Ratio of Vietnamese Stocks

  • P_DIV: The price-to-dividend ratio of Vietnamese stocks

  • P_E: Price-to-earnings ratio of Vietnamese stocks

  • P_E_Forecast: Forecasted Price-to-Earnings Ratio of Vietnamese Stocks

  • P_E_diluted: Diluted price-to-earnings ratio of Vietnamese stocks

  • P_FCFE: The price-to-free cash flow to equity ratio (based on FCFE) of Vietnamese stocks

  • P_FCFF: The price-to-free cash flow ratio (based on FCFF) of Vietnamese stocks

  • P_S: Price-to-Sales Ratio of Vietnamese Stocks

  • P_Tangible_Book: Price-to-Asset Ratio of Vietnamese Stocks

  • Peter_Lynch_Value: Peter Lynch's value of Vietnamese stocks

  • Return_of_market_in_2_years: 2-Year Return of the Vietnamese Market

  • Return_of_stock_in_2_years: 2-Year Return on Vietnamese Stocks

  • Sales_per_Share: Revenue per share of Vietnamese stocks

  • Tangible_BV_per_Share: Book value per share of Vietnamese stocks for tangible assets

  • Trading_value_compared_to_market_capitalization: The trading value ratio compared to the market capitalization of Vietnamese stocks

  • Trading_value_compared_to_the_market: The trading value ratio of Vietnamese stocks compared to the market

  • Weighted_Average_Diluted_Outstanding_Shares: Weighted average diluted shares outstanding of Vietnamese stocks

  • Weighted_Average_Outstanding_Shares: Weighted average number of shares issued in Vietnam

  • Weighted_Average_Outstanding_Shares_in_the_period: Weighted average number of shares outstanding during the period for Vietnamese stocks

market

classification

Summary

This document provides information about the GICS industry classification of Vietnamese stocks.

Example code

from finter.data import ContentFactory
cf = ContentFactory("vn_stock", 20200101, 20200201)
df = cf.get_df("gics")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

00 22 * * 1-5

Item List

  • fiintek-gics: GICS industry classification of Vietnamese stocks price_volume

Summary

This document provides an overview of the Vietnamese stock price and volume data available in the data catalog.

Example code

from finter.data import ContentFactory
cf = ContentFactory("vn_stock", 20200101, 20200201)
df = cf.get_df("OpenPrice")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

00 22 * * 1-5

Item List

Price Data

  • OpenPrice: The market price of Vietnamese stocks

  • ClosePrice: Closing price of Vietnamese stocks

  • HighestPrice: High price of Vietnamese stocks

  • LowestPrice: Low prices of Vietnamese stocks

  • AveragePrice: Average prices of Vietnamese stocks

Volume Data

  • TotalVolume: volume of Vietnamese stocks

cax

Summary

This document provides information on Vietnamese stock data, including adjustment factors and total return coefficients.

Example code

from finter.data import ContentFactory
cf = ContentFactory("vn_stock", 20200101, 20200201)
df = cf.get_df("vnm-stock-adjust_factor")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

00 22 * * 1-5

Item List

Adjustment Factors

  • vnm-stock-adjust_factor: Adjustment factor of Vietnamese stocks

Total Return Factors

  • vnm-stock-total_return_factor: Total return coefficient of Vietnamese stocks

If you would like to use the data, please contact the admin.

ID STOCK

market

market

Summary

This document provides information about the GICS classification of Indonesian stocks.

Example code

from finter.data import ContentFactory
cf = ContentFactory("id_stock", 20200101, 20200201)
df = cf.get_df("gics")

Metadata

Valid From
Delivery Schedule
Time Zone
Data Frequency

Item List

  • gics: GICS classification of Indonesian stocks

COMMON

macro

macro

Summary

The data consists of a collection of financial metrics related to the yield curve, specifically including discount factors, zero-coupon rates, and forward rates. These items are updated weekly and have been valid since January 1, 1961, with a future start date set for June 7, 2025.

Example code

from finter.data import ContentFactory
cf = ContentFactory("common", 20200101, 20200201)
df = cf.get_df("disc_factors")

Metadata

valid from
delivery schedule
time zone
data frequency

19610101

15 22 * * *

1d

Item List

  • disc_factors: https://www.federalreserve.gov/data/yield-curve-tables/feds200628_1.html : updated weekly pit. disc_factors.

  • fwd_rates: https://www.federalreserve.gov/data/yield-curve-tables/feds200628_1.html : updated weekly pit. fwd_rates.

  • zeros: https://www.federalreserve.gov/data/yield-curve-tables/feds200628_1.html : updated weekly pit. zeros.

If you would like to use the data, please contact the admin.

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