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:
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.
'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.
'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
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-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
Dividend
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-dividend_all-annual: Cash dividends on all(common and preferred) stocks - Annual Only
Note
Unit : 1 thousand
If you append "-annual" to an item name, it returns annual data.
market
fund_center
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
19800101
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 funds price_volume
Summary
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
20000101
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 stocks cax
Summary
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
20000101
20 14 * * 1-5
Item List
Adjustment Factors
adjust_factor: Adjustment factor of Korean stocks
dividend_factor: Dividend yield of Korean stocks investor_activity
Summary
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
20000101
20 14 * * 1-5
Daily
Item List
Selling Amounts
sell_amt_0101: Selling amount of stocks by the institutions
sell_amt_0102: Selling amount of stocks by the insurance sector
sell_amt_0103: Selling amount of stocks by the investment trust sector
sell_amt_0104: Selling amount of stocks by the banking sector
sell_amt_0105: Selling amount of stocks by the merchant and savings banks sector
sell_amt_0106: Selling amount of stocks by the pension funds and mutual aid associations sector
sell_amt_0107: Selling amount of stocks by the private equity funds sector
sell_amt_0200: Selling amount of stocks by the aggregated foreign sectors
sell_amt_0201: Selling amount of stocks by the foreign investor sector
sell_amt_0202: Selling amount of stocks by the other foreign investor sector
sell_amt_0300: Selling amount of stocks by the individual sector
sell_amt_0400: Selling amount of stocks by the other corporations and government/local authorities sector
sell_amt_0900: Total selling amount of stocks by all investors
Buying Amounts
buy_amt_0101: Buying amount of stocks by the institutions&x20;
buy_amt_0102: Buying amount of stocks by the insurance sector
buy_amt_0103: Buying amount of stocks by the investment trust sector
buy_amt_0104: Buying amount of stocks by the banking sector
buy_amt_0105: Buying amount of stocks by the merchant and savings banks sector
buy_amt_0106: Buying amount of stocks by the pension funds and mutual aid associations sector
buy_amt_0107: Buying amount of stocks by the private equity funds sector
buy_amt_0200: Buying amount of stocks by the aggregated foreign sectors
buy_amt_0201: Buying amount of stocks by the foreign investor sector
buy_amt_0202: Buying amount of stocks by the other foreign investor sector
buy_amt_0300: Buying amount of stocks by the individual sector
buy_amt_0400: Buying amount of stocks by the other corporations and government/local authorities sector
buy_amt_0900: Total buying amount of stocks by all investors
Net Buying Amounts
net_buy_amt_0101: Net buying amount of stocks by the institutions&x20;
net_buy_amt_0102: Net buying amount of stocks by the insurance sector
net_buy_amt_0103: Net buying amount of stocks by the investment trust sector
net_buy_amt_0104: Net buying amount of stocks by the banking sector
net_buy_amt_0105: Net buying amount of stocks by the merchant and savings banks sector
net_buy_amt_0106: Net buying amount of stocks by the pension funds and mutual aid associations sector
net_buy_amt_0107: Net buying amount of stocks by the private equity funds sector
net_buy_amt_0200: Net buying amount of stocks by the aggregated foreign sectors
net_buy_amt_0201: Net buying amount of stocks by the foreign investor sector
net_buy_amt_0202: Net buying amount of stocks by the other foreign investor sector
net_buy_amt_0300: Net buying amount of stocks by the individual sector
net_buy_amt_0400: Net buying amount of stocks by the other corporations and government/local authorities sector
net_buy_amt_0900: Total net buying amount of stocks by all investors
universe
Summary
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
20180401
00 20 * * *
Item List
venture: List of Venture Companies status
Summary
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
20000101
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 market credit
Summary
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
20000101
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 market calendar
Summary
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
20000101
20 14 * * 1-5
Item List
Expiration Dates
expiry_dates-spread: Spread maturity date information
expiry_dates-future: Futures expiration date information capital
Summary
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
20000101
20 14 * * 1-5
Item List
krx-spot-listed_date_of_merger_spac: Listing date of SPAC mergers in the Korean stock market
edge
narr
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
20100101
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'] disclosure
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("buyback_trust", unpivot=True)Metadata
20100101
00 17 * * *
Item List
Corporate Actions
buyback = report on decision to acquire treasury shares
buyback_trust = report on decision to sign a trust agreement for acquiring treasury shares
buyback_disposal = report on decision to dispose of treasury shares
bonus_issue = report on decision for bonus (free) issue
rights_issue = report on decision for rights issue (paid-in capital increase)
br_issue = report on decision for combined paid-in/bonus issue
reduction = report on decision for capital reduction
cb = report on decision to issue convertible bonds
eb = report on decision to issue exchangeable bonds
bw = report on decision to issue bonds with warrants
coco = report on decision to issue contingent convertible bonds
merge = report on decision for company merger
division = report on decision for company division
division_merge = report on decision for divisional merger
major_stock = report on ownership status of securities by executives or major shareholder theme
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
20180101
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 ews
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
20050102
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 keyword
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
20160101
20 12 * * *
Daily
Item List
krx-spot-trend: Daily keyword trends in the Korean stock market emp_exc
Summary
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
20171113
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
Data Format
Report Value Table Format
Index:
pit date(Point in time; Data feeding date)Columns: Korean stock ticker
Values:
{ticker}: item value
Index Mapping
idx_r(Report Type Code):11013: Q1 Report11012: Semiannual Report11014: Q3 Report11011: Annual Report
idx_y(Report Year):The year of the report
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
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
fundamental_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
20000101
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. descriptor
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
20000101
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
consensus
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
20000101
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 somemoney
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
20100101
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
marketregime
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
20110103
5 17 * * 1-5
20110103
0 21 * * 1-5
20110103
30 10 * * *
20110103
30 2 * * 2-6
20110103
30 21 * * 0-4
Monthly
20110103
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 index economy
Summary
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
20000101
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
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
19831230
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
19840102
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
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 cmMetadata
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
Note
We provide Financial CM converted into USD. According to Compustat’s policy, balance sheet items are converted using a 12-month exchange rate, while income statement and cash flow items are converted using the monthly exchange rate.
Unit : 1 million
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
19831230
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
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
19831230
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 cax
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('raw', 20200101, 20251031)
cf.get_df('content.spglobal.compustat.cax.us-stock-total_return_factor.1d')Metadata
19831230
30 5 * * 2-6
Item List
content.spglobal.compustat.cax.us-stock-total_return_factor : Total return coefficient of U.S. stocks
content.spglobal.compustat.cax.us-stock-adjust_factor : Adjustment factor of U.S. stocks universe
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
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
narr
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
20100101
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'] ews
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
20040101
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) llm
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
20230901
00 3 * * 2-6
Item List
asset: Unstructured information related to assets using LLM. dkg
Summary
This document provides information about the US stock market risk-related knowledge graph data.
Example code
from finter.data import ContentFactory
cf = ContentFactory('raw', 20200101, 20251102)
cf.get_df('content.handa.unstructured.dkg.kg_risk_us.1d')Metadata
20100101
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.
Value: indicates how many conditions are satisfied.
Example code
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("us-brad_gerstner")Metadata
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.
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
19820531
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
Note
The paper calculate factor using annual data, but Finterlabs provides factors calculated using quarterly data. Therefore, we do not provide factors for items that do not have quarterly data available.
For factors calculated using me_company(such as _me, _mev and etc.), their column is provided as gvkeyiid.
In the paper, me_company was used on a monthly basis, but we provide factors calculated using daily me_company.
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
20110103
30 6 * * 2-6
Monthly
Item List
hmm_wo_us_etf: Analysis of HMM-based market system excluding US ETFs (Monthly)
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
19620102
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
20180101
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
VN STOCK
financial
balance_sheet
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
20140101
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 stocks cash_flow
Summary
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
20140101
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. income_statement
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
20140101
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 ratio
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
20140101
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
20080101
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
20140101
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
20080101
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
ID STOCK
financial
Summary This document provides an overview of the financial data items available for the Indonesia stock universe, including income statements, balance sheets, and cash flow statements.
Summary
This document provides an overview of the balance sheet data items available for the Indonesia stock universe.
Users can choose from 2 types of data frames by adjusting the ‘mode’ parameter:
1. Default behavior: In this case, the values in the data frame become dictionaries possibly including multiple keys and values.
2. 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 2025-05-30, provided with 90 days delayed.
Example code
from finter.data import ContentFactory
cf = ContentFactory("id_stock", 20250101, 20250401)
df = cf.get_df("assets", mode='unpivot')Metadata
19700101
10 22 * * 1-5
Item List
Balance Sheet Items
assets: Total resources owned by the company with future economic value.
current_liabilities: Obligations due within one year or operating cycle.
inventories: Goods held for sale or used in production.
equity: Residual ownership interest after deducting liabilities from assets.
goodwill: Premium paid over fair value in acquisitions, representing intangible value.
current_inventories: Inventory expected to be sold within one year.
non_current_assets: Long-term assets not expected to convert to cash within one year.
current_assets: Assets expected to convert to cash within one year.
non_current_liabilities: Long-term obligations due beyond one year.
treasury_stocks: Company's own shares repurchased and held by the company.
investments: Holdings in securities, subsidiaries, or other investment instruments.
cash: Cash on hand and demand deposits readily available.
liabilities: Total financial obligations owed to creditors.
property_plant_and_equipment: Tangible fixed assets used in operations (PP&E).
common_stocks: Equity shares representing basic ownership in the company.
payments_for_acquisition_of_intangible_assets: Cash outflows for purchasing intangible assets like patents, trademarks, licenses, or software. cash_flow
Summary
This document provides an overview of the cash flow data items available for the Indonesia stock universe.
Users can choose from 2 types of data frames by adjusting the ‘mode’ parameter:
1. Default behavior: In this case, the values in the data frame become dictionaries possibly including multiple keys and values.
2. 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 2025-05-30, provided with 90 days delayed.
Example code
from finter.data import ContentFactory
cf = ContentFactory("id_stock", 20250101, 20250401)
df = cf.get_df("cash_flows_from_operating_activities", mode='unpivot')Metadata
19700101
10 22 * * 1-5
Item List
Cash Flow Items
cash_flows_from_operating_activities: Net cash generated or used by core business operations.
cash_flows_from_investing_activities: Net cash from buying/selling long-term assets and investments.
payments_for_acquisition_of_property_plant_and_equipment: Cash outflows for purchasing fixed assets (PP&E).
cash_flows_from_financing_activities: Net cash from debt, equity, and dividend transactions.
cash_and_cash_equivalents_cash_flows: Net change in cash and highly liquid short-term investments.
payments_for_acquisition_of_intangible_assets: Cash outflows for purchasing intangible assets like patents or licenses. income_statement
Summary
This document provides an overview of the income statement data items available for the Indonesia stock universe.
Users can choose from 2 types of data frames by adjusting the ‘mode’ parameter:
1. Default behavior: In this case, the values in the data frame become dictionaries possibly including multiple keys and values.
2. 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 2025-05-30, provided with 90 days delayed.
Example code
from finter.data import ContentFactory
cf = ContentFactory("id_stock", 20250101, 20250401)
df = cf.get_df("interest_income", mode='unpivot')Metadata
19700101
10 22 * * 1-5
Item List
Income Statement Items
interest_income: Interest earned from loans, deposits, and interest-bearing assets.
depreciation_and_amortisation_expenses: Non-cash allocation of asset costs over their useful lives.
profit_from_operation: Profit from core business operations before financing and taxes.
finance_income: Income from financing activities including interest and lease income.
net_investment_income: Net returns from investments after deducting related expenses.
non_operating_income: Revenue from activities outside primary business operations.
gross_profit: Revenue minus cost of goods sold.
cost_of_sales_and_revenue: Direct costs of producing goods or services sold.
ratio
Summary
The data consists of various financial ratios related to Indonesian stocks, including metrics such as price-to-earnings ratio, free cash flow per share, cash ratio, gross profit margin, operating profit margin, asset turnover, price-to-sales ratio, and net profit margin. Each item is associated with a description indicating its relevance to the financial income statement.
Users can choose from 2 types of data frames by adjusting the ‘mode’ parameter:
1. Default behavior: In this case, the values in the data frame become dictionaries possibly including multiple keys and values.
2. 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 2025-08-08, provided with 90 days delayed.
Example code
from finter.data import ContentFactory
cf = ContentFactory("id_stock", 20250101, 20250401)
df = cf.get_df("per", mode='unpivot')Metadata
19700101
10 22 * * 1-5
1d
Item List
asset_turnover: Ratio measuring how efficiently assets generate revenue (Revenue/Total Assets).
cash_ratio: Liquidity ratio showing ability to cover current liabilities with cash (Cash/Current Liabilities).
free_cashflow_per_share: Free cash flow divided by shares outstanding.
gross_profit_margin: Percentage of revenue remaining after cost of sales (Gross Profit/Revenue).
net_profit_margin: Percentage of revenue retained as net profit after all expenses.
operating_profit_margin: Percentage of revenue from operations before financing and taxes.
per: Price-to-Earnings Ratio, valuation metric (Stock Price/Earnings Per Share).
psr: Price-to-Sales Ratio, valuation metric (Market Cap/Revenue).
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
20000101
Item List
gics: GICS classification of Indonesian stocks price_volume
Summary
This document provides an overview of the Indonesia stock data catalog, including various price and volume metrics.
Example code
from finter.data import ContentFactory
cf = ContentFactory("id_stock", 20200101, 20200201)
df = cf.get_df("price_close")
Metadata
19700101
10 22 * * 1-5
Item List
Price Metrics
price_open: indonesia stock price_open
price_close: indonesia stock price_close
price_high: indonesia stock price_high
price_low: indonesia stock price_low
Volume Metrics
volume_sum: indonesia stock volume
Adjustment Factors
adjust_factor: adjust_factor calculated by stock_split event
total_return_factor: total_return_factor calculated by stock_split * dividend_factor
Stock Information
id-stock-stock_outstanding: IDN stock listed shares.&x20;
&x20;-> [CAUTION] 20250101 ~ 20250807 IS FFILL DATA. IT WILL BE FIXED LATER Attributes
Summary
This document provides information on the IDN stock data catalog, including sector codes and sharia compliance for stocks.
Example code
from finter.data import ContentFactory
cf = ContentFactory('id_stock', 20200101, 20251105)
cf.get_df('sector_code')Metadata
19700101
10 22 * * 1-5
Item List
Sector Information
id-stock-sector_code: IDN stock sector_code. sector_name is on IdTable. [CAUTION] 20250101 ~ 20250807 IS FFILL DATA. IT WILL BE FIXED LATER
Sharia Compliance
from finter.data import ContentFactory
cf = ContentFactory('id_stock', 20200101, 20251105)
cf.get_df('sharia')id-stock-sharia: IDN stock whether sharia or not;&x20;
history (~ 20241231) : ISSI constituent stocks
live (20250807 ~) : daily stock_master data
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
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.
.GITBOOK
Last updated