# Content Model Catalog

## Content Model Catalog

## KR STOCK

### Market

#### Price Volume

A concise reference for accessing South Korean stock market price and volume data.

**Example code**

```python
from finter.data import ContentFactory

cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("price_close")
```

**Metadata**

| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
|            |                   |           |                |

**Item List**

**Price Data**

* `price_close`: Closing price
* `price_base`: Base price
* `price_open`: Opening price
* `price_high`: Highest price
* `price_low`: Lowest price

**Volume Data**

* `volume_sum`: Total volume
* `turnover`: Turnover
* `turnover_all`: Total turnover

**Shares Data**

* `listed_shares`: Number of listed shares
* `listed_shares_to_be`: Number of shares to be listed
* `treasury_stock`: Treasury stock
* `foreigner_shares`: Number of shares held by foreigners
* `foreigner_shares_ratio`: Ratio of shares held by foreigners

**Market Capitalization**

* `mkt_cap`: Market capitalization
* `mkt_cap_all`: Total market capitalization
* `mkt_cap_index`: Market capitalization index
* `free_float`: Free float shares

**Short Selling Data**

* `short_selling_volume`: Short selling volume
* `short_selling_turnover`: Short selling turnover

**Securities Lending and Borrowing (SLB)**

* `slb_new_loan`: New SLB loans
* `slb_repay`: SLB repayments
* `slb_balance_volume`: SLB balance volume
* `slb_balance_amount`: SLB balance amount

#### Price Volume v2

A concise reference for accessing South Korean stock market price and volume data.

**Example code**

```python
from finter.data import ContentFactory

cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("kr-spot-price_close")
```

**Item List**

**Price Data**

* `kr-spot-price_close`: Closing price
* `kr-spot-price_base`: Base price
* `kr-spot-price_open`: Opening price
* `kr-spot-price_high`: Highest price
* `kr-spot-price_low`: Lowest price

**Volume Data**

* `kr-spot-volume_sum`: Total volume
* `kr-spot-turnover`: Turnover
* `kr-spot-turnover_all`: Total turnover

**Shares Data**

* `kr-spot-listed_shares`: Number of listed shares
* `kr-spot-listed_shares_to_be`: Number of shares to be listed
* `kr-spot-treasury_stock`: Treasury stock
* `kr-spot-foreigner_shares`: Number of shares held by foreigners
* `kr-spot-foreigner_shares_ratio`: Ratio of shares held by foreigners

**Market Capitalization**

* `kr-spot-mkt_cap`: Market capitalization
* `kr-spot-mkt_cap_all`: Total market capitalization
* `kr-spot-mkt_cap_index`: Market capitalization index
* `kr-spot-free_float`: Free float shares

#### Investor Activity

A comprehensive guide to accessing investor activity data for the Korean stock market, detailing quantities and amounts of buying and selling by different investor types.

**Example code**

```python
from finter.data import ContentFactory

cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("buy_insti_qty")
```

**Metadata**

| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 2000-01-04 | "20 23 \* \* 1-5" | KST       | 1d delay       |

**Item List**

**Institutional Investors**

* `buy_insti_qty`: Quantity of stocks bought by institutional investors.
* `buy_insti_amt`: Amount of money spent by institutional investors on buying stocks.
* `sell_insti_qty`: Quantity of stocks sold by institutional investors.
* `sell_insti_amt`: Amount of money received by institutional investors from selling stocks.

**Foreign Investors**

* `buy_frgn_qty`: Quantity of stocks bought by foreign investors.
* `buy_frgn_amt`: Amount of money spent by foreign investors on buying stocks.
* `sell_frgn_qty`: Quantity of stocks sold by foreign investors.
* `sell_frgn_amt`: Amount of money received by foreign investors from selling stocks.

**Individual Investors**

* `buy_indvsl_qty`: Quantity of stocks bought by individual investors.
* `buy_indvsl_amt`: Amount of money spent by individual investors on buying stocks.
* `sell_indvsl_qty`: Quantity of stocks sold by individual investors.
* `sell_indvsl_amt`: Amount of money received by individual investors from selling stocks.

**Net Buy Amounts**

* <mark style="color:red;">**DO NOT USE, Data Under Maintenance Due to Issues**</mark>
* `net_buy_amt`: Net amount of money spent on buying stocks (buy amount - sell amount).
* `net_buy_amt_all`: Total net amount of money spent on buying stocks by all investors.

**Total Buy and Sell Amounts**

* <mark style="color:red;">**DO NOT USE, Data Under Maintenance Due to Issues**</mark>
* `buy_amt_all`: Total amount of money spent on buying stocks by all investors.
* `sell_amt_all`: Total amount of money received from selling stocks by all investors.

**Net Buy Amounts by Investor Code**

* `net_buy_amt_0100`: Net buy amount for institutional investors.
* `buy_amt_0100`: Buy amount for institutional investors.
* `sell_amt_0100`: Sell amount for institutional investors.
* `net_buy_amt_0101`: Net buy amount for securities and futures companies.
* `buy_amt_0101`: Buy amount for securities and futures companies.
* `sell_amt_0101`: Sell amount for securities and futures companies.
* `net_buy_amt_0102`: Net buy amount for insurance companies.
* `buy_amt_0102`: Buy amount for insurance companies.
* `sell_amt_0102`: Sell amount for insurance companies.
* `net_buy_amt_0103`: Net buy amount for investment trust companies.
* `buy_amt_0103`: Buy amount for investment trust companies.
* `sell_amt_0103`: Sell amount for investment trust companies.
* `net_buy_amt_0104`: Net buy amount for banks.
* `buy_amt_0104`: Buy amount for banks.
* `sell_amt_0104`: Sell amount for banks.
* `net_buy_amt_0105`: Net buy amount for savings banks.
* `buy_amt_0105`: Buy amount for savings banks.
* `sell_amt_0105`: Sell amount for savings banks.
* `net_buy_amt_0106`: Net buy amount for pension funds.
* `buy_amt_0106`: Buy amount for pension funds.
* `sell_amt_0106`: Sell amount for pension funds.
* `net_buy_amt_0107`: Net buy amount for private equity funds.
* `buy_amt_0107`: Buy amount for private equity funds.
* `sell_amt_0107`: Sell amount for private equity funds.

**Net Buy Amounts by Investor Type**

* `net_buy_amt_0200`: Net buy amount for all foreign investors.
* `buy_amt_0200`: Buy amount for all foreign investors.
* `sell_amt_0200`: Sell amount for all foreign investors.
* `net_buy_amt_0201`: Net buy amount for foreign individual investors.
* `buy_amt_0201`: Buy amount for foreign individual investors.
* `sell_amt_0201`: Sell amount for foreign individual investors.
* `net_buy_amt_0202`: Net buy amount for other foreign investors.
* `buy_amt_0202`: Buy amount for other foreign investors.
* `sell_amt_0202`: Sell amount for other foreign investors.
* `net_buy_amt_0300`: Net buy amount for individual investors.
* `buy_amt_0300`: Buy amount for individual investors.
* `sell_amt_0300`: Sell amount for individual investors.
* `net_buy_amt_0400`: Net buy amount for other corporations and governments.
* `buy_amt_0400`: Buy amount for other corporations and governments.
* `sell_amt_0400`: Sell amount for other corporations and governments.
* `net_buy_amt_0900`: Net buy amount for all investor types.
* `buy_amt_0900`: Buy amount for all investor types.
* `sell_amt_0900`: Sell amount for all investor types.

#### Credit

Data catalog for Korean stock market credit data, including various counts, amounts, and rates related to financial balances and lending.

**Example code**

```python
from finter.data import ContentFactory

cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("fin_bal_cnt")
```

**Metadata**

| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
|            |                   |           |                |

**Item List**

**Financial Balances**

* `fin_bal_cnt`: Financial balance count
* `fin_bal_amt`: Financial balance amount
* `fin_bal_rt`: Financial balance rate

**Financial Transactions**

* `fin_new_cnt`: New financial transaction count
* `fin_new_amt`: New financial transaction amount
* `fin_pay_cnt`: Financial payment count
* `fin_pay_amt`: Financial payment amount
* `fin_giv_rt`: Financial given rate

**Lending Balances**

* `lend_bal_cnt`: Lending balance count
* `lend_bal_amt`: Lending balance amount
* `lend_bal_rt`: Lending balance rate

**Lending Transactions**

* `lend_new_cnt`: New lending transaction count
* `lend_new_amt`: New lending transaction amount
* `lend_pay_cnt`: Lending payment count
* `lend_pay_amt`: Lending payment amount
* `lend_giv_rt`: Lending given rate

### Financial

#### Financial v2 Statements

Access financial statement data for Korean stocks with daily and annual frequency.

**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:

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

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

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

**Example code**

```python
from finter.data import ContentFactory

cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("kr-spot-total_assets")

last_value_df = cf.get_df("kr-spot-total_assets", preprocess_type='default')
```

**Metadata**

| Valid From | Delivery Schedule   | Time Zone  | Data Frequency |
| ---------- | ------------------- | ---------- | -------------- |
| 20000101   | UTC 20 14 \* \* 1-5 | Asia/Seoul | 1d             |

**Item List**

**Balance Sheet Items**

* Total Assets:
  * kr-spot-total\_assets
  * 'kr-spot-total\_assets-annual'
* Current Assets:
  * 'kr-spot-current\_assets'
  * 'kr-spot-current\_assets-annual'
  * 'kr-spot-inventory'
  * 'kr-spot-inventory-annual'
  * 'kr-spot-trade\_receivables'
  * 'kr-spot-trade\_receivables-annual'
  * 'kr-spot-receivables'
  * 'kr-spot-receivables-annual'
  * 'kr-spot-cash\_and\_cash\_equivalent'
  * 'kr-spot-cash\_and\_cash\_equivalent-annual'
  * 'kr-spot-other\_current\_assets'
  * 'kr-spot-other\_current\_assets-annual'
  * 'kr-spot-current\_financial\_assets'
  * 'kr-spot-current\_financial\_assets-annual'
  * 'kr-spot-prepaid\_expenses'
  * 'kr-spot-prepaid\_expenses-annual'
* Non-Current Assets:
  * 'kr-spot-property\_plant\_and\_equipment'
  * 'kr-spot-property\_plant\_and\_equipment-annual'
  * 'kr-spot-intangible\_assets'
  * 'kr-spot-intangible\_assets-annual'
  * 'kr-spot-goodwill'
  * 'kr-spot-goodwill-annual'
  * 'kr-spot-deferred\_tax\_assets'
  * 'kr-spot-deferred\_tax\_assets-annual'
  * 'kr-spot-non\_current\_biological\_assets'
  * 'kr-spot-non\_current\_biological\_assets-annual'
  * 'kr-spot-other\_non\_current\_assets'
  * 'kr-spot-other\_non\_current\_assets-annual'
  * 'kr-spot-long\_term\_financial\_instrument'
  * 'kr-spot-long\_term\_financial\_instrument-annual'
  * 'kr-spot-investment\_in\_properties'
  * 'kr-spot-investment\_in\_properties-annual'
  * 'kr-spot-land'
  * 'kr-spot-land-annual'
  * 'kr-spot-construction\_in\_progress'
  * 'kr-spot-construction\_in\_progress-annual'
* Liabilities:
  * 'kr-spot-current\_liabilities'
  * 'kr-spot-current\_liabilities-annual'
  * 'kr-spot-trade\_payables'
  * 'kr-spot-trade\_payables-annual'
  * 'kr-spot-other\_payables'
  * 'kr-spot-other\_payables-annual'
  * 'kr-spot-short\_term\_provisions'
  * 'kr-spot-short\_term\_provisions-annual'
  * 'kr-spot-current\_portion\_of\_long\_term\_debt'
  * 'kr-spot-current\_portion\_of\_long\_term\_debt-annual'
  * 'kr-spot-short\_term\_bonds'
  * 'kr-spot-short\_term\_bonds-annual'
  * 'kr-spot-short\_term\_borrowings'
  * 'kr-spot-short\_term\_borrowings-annual'
  * 'kr-spot-short\_term\_financial\_liabilities'
  * 'kr-spot-short\_term\_financial\_liabilities-annual'
  * 'kr-spot-current\_income\_tax\_liabilities'
  * 'kr-spot-current\_income\_tax\_liabilities-annual'
  * 'kr-spot-current\_provisions\_for\_employee\_benefits'
  * 'kr-spot-current\_provisions\_for\_employee\_benefits-annual'
  * 'kr-spot-other\_current\_liabilities'
  * 'kr-spot-other\_current\_liabilities-annual'
  * 'kr-spot-trade\_and\_other\_current\_payables'
  * 'kr-spot-trade\_and\_other\_current\_payables-annual'
  * 'kr-spot-liabilities\_included\_in\_disposal\_groups\_classified\_as\_held\_for\_sale'
  * 'kr-spot-liabilities\_included\_in\_disposal\_groups\_classified\_as\_held\_for\_sale-annual'
  * 'kr-spot-advance\_from\_customers'
  * 'kr-spot -advance\_from\_customers-annual'
  * 'kr-spot-non\_current\_liabilities'
  * 'kr-spot-non\_current\_liabilities-annual'
  * 'kr-spot-long\_term\_borrowings'
  * 'kr-spot-long\_term\_borrowings-annual'
  * 'kr-spot-long\_term\_provisions'
  * 'kr-spot-long\_term\_provisions-annual'
  * 'kr-spot-deferred\_tax\_liabilities'
  * 'kr-spot-deferred\_tax\_liabilities-annual'
  * 'kr-spot-long\_term\_financial\_liabilities'
  * 'kr-spot-long\_term\_financial\_liabilities-annual'
  * 'kr-spot-non\_current\_provisions\_for\_employee\_benefits'
  * 'kr-spot-non\_current\_provisions\_for\_employee\_benefits-annual'
  * 'kr-spot-other\_non\_current\_liabilities'
  * 'kr-spot-other\_non\_current\_liabilities-annual'
  * 'kr-spot-convertible\_bonds'
  * 'kr-spot-convertible\_bonds-annual'
  * 'kr-spot-total\_liabilities'
  * 'kr-spot-total\_liabilities-annual'
* Equity:
  * 'kr-spot-common\_stock'
  * 'kr-spot-common\_stock-annual'
  * 'kr-spot-preferred\_stock'
  * 'kr-spot-preferred\_stock-annual'
  * 'kr-spot-paid\_in\_capital\_in\_excess\_of\_par\_value'
  * 'kr-spot-paid\_in\_capital\_in\_excess\_of\_par\_value-annual'
  * 'kr-spot-retained\_earnings'
  * 'kr-spot-retained\_earnings-annual'
  * 'kr-spot-total\_equity'
  * 'kr-spot-total\_equity-annual'
  * 'kr-spot-non\_controlling\_interests\_equity'
  * 'kr-spot-non\_controlling\_interests\_equity-annual'
  * 'kr-spot-owners\_of\_parent\_equity'
  * 'kr-spot-owners\_of\_parent\_equity-annual'
  * 'kr-spot-capital\_stock'
  * 'kr-spot-capital\_stock-annual'

**Income Statement Items**

* Revenue:
  * 'kr-spot-net\_sales'
  * 'kr-spot-net\_sales-annual'
  * 'kr-spot-sales'
  * 'kr-spot-sales-annual'
* Cost of Revenue:
  * 'kr-spot-cost\_of\_sales'
  * 'kr-spot-cost\_of\_sales-annual'
* Gross Profit:
  * 'kr-spot-gross\_profit'
  * 'kr-spot-gross\_profit-annual'
* Operating Expenses:
  * 'kr-spot-selling\_general\_administrative\_expenses'
  * 'kr-spot-selling\_general\_administrative\_expenses-annual'
  * 'kr-spot-research\_and\_development'
  * 'kr-spot-research\_and\_development-annual'
  * 'kr-spot-advertising\_expenses'
  * 'kr-spot-advertising\_expenses-annual'
  * 'kr-spot-salaries\_and\_wages'
  * 'kr-spot-salaries\_and\_wages-annual'
  * 'kr-spot-accured\_expenses'
  * 'kr-spot-accured\_expenses-annual'
* Non-Operating Income/Expenses:
  * 'kr-spot-interest\_income'
  * 'kr-spot-interest\_income-annual'
  * 'kr-spot-interest\_expenses'
  * 'kr-spot-interest\_expenses-annual'
  * 'kr-spot-financing\_expenses'
  * 'kr-spot-financing\_expenses-annual'
  * 'kr-spot-financial\_income'
  * 'kr-spot-financial\_income-annual'
* Depreciation/Amortization:
  * 'kr-spot-is\_depreciation'
  * 'kr-spot-is\_depreciation-annual'
  * 'kr-spot-cf\_depreciation'
  * 'kr-spot-cf\_depreciation-annual'
  * 'kr-spot-amortization'
  * 'kr-spot-amortization-annual'
* Pretax Income:
  * 'kr-spot-income\_before\_income\_taxes\_expenses'
  * 'kr-spot-income\_before\_income\_taxes\_expenses-annual'
  * 'kr-spot-pretax\_income'
  * 'kr-spot-pretax\_income-annual'
* Income Tax Expense:
  * 'krx-spot-income\_taxes\_expenses'
  * 'krx-spot-income\_taxes\_expenses-annual'
* Net Income:
  * 'kr-spot-net\_income'
  * 'kr-spot-net\_income-annual'
  * 'kr-spot-owners\_of\_parent\_net\_income'
  * 'kr-spot-owners\_of\_parent\_net\_income-annual'
  * 'kr-spot-net\_income\_attributed\_to\_non\_controlling\_interest'
  * 'kr-spot-net\_income\_attributed\_to\_non\_controlling\_interest-annual'
  * 'kr-spot-discontinued\_operation\_income'
  * 'kr-spot-discontinued\_operation\_income-annual'
* EBITDA:
  * 'kr-spot-ebitda'
  * 'kr-spot-ebitda-annual'
* Operating Income:
  * 'kr-spot-operating\_income'
  * 'kr-spot-operating\_income-annual'
  * 'kr-spot-ongoing\_operating\_income'
  * 'kr-spot-ongoing\_operating\_income-annual'
* Other Items:
  * 'kr-spot-owners\_of\_parent\_ongoing\_operating\_income\_or\_loss\_per\_share'
  * 'kr-spot-owners\_of\_parent\_ongoing\_operating\_income\_or\_loss\_per\_share-annual'
  * 'kr-spot-eps'
  * 'kr-spot-eps-annual'
  * 'kr-spot-adj\_bps'
  * 'kr-spot-adj\_bps-annual'
  * 'kr-spot-profit\_from\_continuing\_operations'
  * 'kr-spot-profit\_from\_continuing\_operations-annual'
  * 'kr-spot-impairment\_losses\_on\_intangible\_assets'
  * 'kr-spot-impairment\_losses\_on\_intangible\_assets-annual'
  * 'kr-spot-impairment\_losses\_on\_property\_plant\_and\_equipment'
  * 'kr-spot-impairment\_losses\_on\_property\_plant\_and\_equipment-annual'

**Cash Flow Statement Items**

* Operating Cash Flows:
  * 'kr-spot-cashflow\_from\_operating\_activities'
  * 'kr-spot-cashflow\_from\_operating\_activities-annual'
* Investing Cash Flows:
  * 'kr-spot-cashflow\_from\_investing\_activities'
  * 'kr-spot-cashflow\_from\_investing\_activities-annual'
  * 'kr-spot-capex'
  * 'kr-spot-capex-annual'
* Financing Cash Flows:
  * 'kr-spot-cashflow\_from\_financial\_activities'
  * 'kr-spot-cashflow\_from\_financial\_activities-annual'
  * 'kr-spot-dividends\_paid'
  * 'kr-spot-dividends\_paid-annual'
  * 'kr-spot-paid\_in\_capital\_increase'
  * 'kr-spot-paid\_in\_capital\_increase-annual'
  * 'kr-spot-purchase\_of\_treasury\_stock'
  * 'kr-spot-purchase\_of\_treasury\_stock-annual'
  * 'kr-spot-sales\_of\_treasury\_stock'
  * 'kr-spot-sales\_of\_treasury\_stock-annual'
  * 'kr-spot-dividend\_comm\_cash'
  * 'kr-spot-dividend\_comm\_cash-annual'
  * 'kr-spot-dividend\_pref\_cash'
  * 'kr -spot-dividend\_pref\_cash-annual'
* Free Cash Flow:
  * 'kr-spot-fcf1'
  * 'kr-spot-fcf1-annual'
  * 'kr-spot-fcf2'
  * 'kr-spot-fcf2-annual'

**Other Financial Metrics**

* 'kr-spot-enterprise\_value'
* 'kr-spot-enterprise\_value-annual'
* 'kr-spot-invested\_capital'
* 'kr-spot-invested\_capital-annual'
* 'kr-spot-roic'
* 'kr-spot-roic-annual'
* 'kr-spot-dividend'
* 'kr-spot-dividend-annual'
* 'kr -spot-dividend\_all-annual'
* 'kr-spot-payout\_ratio'
* 'kr-spot-payout\_ratio-annual'
* 'kr-spot-cash\_payout\_ratio'
* 'kr-spot-cash\_payout\_ratio-annual'
* 'kr-spot-ex\_dividend\_date'
* 'kr-spot-ex\_dividend\_date-annual'
* 'kr-spot-scheduled\_dividend\_payment\_date'
* 'kr-spot-scheduled\_dividend\_payment\_date-annual'
* 'kr-spot-loans'
* 'kr-spot-loans-annual'

**Other Items**

* 'kr-spot-fiscal\_end'
* 'kr-spot-finished\_goods'
* 'kr-spot-finished\_goods-annual'
* 'kr-spot-number\_of\_employees'
* 'kr-spot-number\_of\_employees-annual'
* 'kr-spot-unearned\_income'
* 'kr-spot-unearned\_income-annual'
* 'kr-spot-common\_shares\_fy\_end-annual'
* 'kr-spot-preferred\_shares\_fy\_end-annual'
* 'kr-spot-shares\_fy\_end-annual'
* 'kr-spot-listed\_shares\_comm'
* 'kr-spot-listed\_shares\_pref'
* 'kr-spot-listed\_shares\_comm-annual'
* 'kr-spot-listed\_shares\_pref-annual'
* 'kr-spot-fst\_pub\_dt'

#### Financial Statements

Access financial statement data for Korean stocks with daily and annual frequency.

**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:

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

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

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

**Example code**

```python
from finter.data import ContentFactory

cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("krx-spot-total_assets")

last_value_df = cf.get_df("krx-spot-total_assets", preprocess_type='default')
```

**Metadata**

| Valid From | Delivery Schedule   | Time Zone  | Data Frequency |
| ---------- | ------------------- | ---------- | -------------- |
| 20000101   | UTC 20 14 \* \* 1-5 | Asia/Seoul | 1d             |

**Item List**

**Balance Sheet Items**

* Total Assets:
  * 'krx-spot-total\_assets'
  * 'krx-spot-total\_assets-annual'
* Current Assets:
  * 'krx-spot-current\_assets'
  * 'krx-spot-current\_assets-annual'
  * 'krx-spot-inventory'
  * 'krx-spot-inventory-annual'
  * 'krx-spot-trade\_receivables'
  * 'krx-spot-trade\_receivables-annual'
  * 'krx-spot-receivables'
  * 'krx-spot-receivables-annual'
  * 'krx-spot-cash\_and\_cash\_equivalent'
  * 'krx-spot-cash\_and\_cash\_equivalent-annual'
  * 'krx-spot-other\_current\_assets'
  * 'krx-spot-other\_current\_assets-annual'
  * 'krx-spot-current\_financial\_assets'
  * 'krx-spot-current\_financial\_assets-annual'
  * 'krx-spot-prepaid\_expenses'
  * 'krx-spot-prepaid\_expenses-annual'
* Non-Current Assets:
  * 'krx-spot-property\_plant\_and\_equipment'
  * 'krx-spot-property\_plant\_and\_equipment-annual'
  * 'krx-spot-intangible\_assets'
  * 'krx-spot-intangible\_assets-annual'
  * 'krx-spot-goodwill'
  * 'krx-spot-goodwill-annual'
  * 'krx-spot-deferred\_tax\_assets'
  * 'krx-spot-deferred\_tax\_assets-annual'
  * 'krx-spot-non\_current\_biological\_assets'
  * 'krx-spot-non\_current\_biological\_assets-annual'
  * 'krx-spot-other\_non\_current\_assets'
  * 'krx-spot-other\_non\_current\_assets-annual'
  * 'krx-spot-long\_term\_financial\_instrument'
  * 'krx-spot-long\_term\_financial\_instrument-annual'
  * 'krx-spot-investment\_in\_properties'
  * 'krx-spot-investment\_in\_properties-annual'
  * 'krx-spot-land'
  * 'krx-spot-land-annual'
  * 'krx-spot-construction\_in\_progress'
  * 'krx-spot-construction\_in\_progress-annual'
* Liabilities:
  * 'krx-spot-current\_liabilities'
  * 'krx-spot-current\_liabilities-annual'
  * 'krx-spot-trade\_payables'
  * 'krx-spot-trade\_payables-annual'
  * 'krx-spot-other\_payables'
  * 'krx-spot-other\_payables-annual'
  * 'krx-spot-short\_term\_provisions'
  * 'krx-spot-short\_term\_provisions-annual'
  * 'krx-spot-current\_portion\_of\_long\_term\_debt'
  * 'krx-spot-current\_portion\_of\_long\_term\_debt-annual'
  * 'krx-spot-short\_term\_bonds'
  * 'krx-spot-short\_term\_bonds-annual'
  * 'krx-spot-short\_term\_borrowings'
  * 'krx-spot-short\_term\_borrowings-annual'
  * 'krx-spot-short\_term\_financial\_liabilities'
  * 'krx-spot-short\_term\_financial\_liabilities-annual'
  * 'krx-spot-current\_income\_tax\_liabilities'
  * 'krx-spot-current\_income\_tax\_liabilities-annual'
  * 'krx-spot-current\_provisions\_for\_employee\_benefits'
  * 'krx-spot-current\_provisions\_for\_employee\_benefits-annual'
  * 'krx-spot-other\_current\_liabilities'
  * 'krx-spot-other\_current\_liabilities-annual'
  * 'krx-spot-trade\_and\_other\_current\_payables'
  * 'krx-spot-trade\_and\_other\_current\_payables-annual'
  * 'krx-spot-liabilities\_included\_in\_disposal\_groups\_classified\_as\_held\_for\_sale'
  * 'krx-spot-liabilities\_included\_in\_disposal\_groups\_classified\_as\_held\_for\_sale-annual'
  * 'krx-spot-advance\_from\_customers'
  * 'krx-spot -advance\_from\_customers-annual'
  * 'krx-spot-non\_current\_liabilities'
  * 'krx-spot-non\_current\_liabilities-annual'
  * 'krx-spot-long\_term\_borrowings'
  * 'krx-spot-long\_term\_borrowings-annual'
  * 'krx-spot-long\_term\_provisions'
  * 'krx-spot-long\_term\_provisions-annual'
  * 'krx-spot-deferred\_tax\_liabilities'
  * 'krx-spot-deferred\_tax\_liabilities-annual'
  * 'krx-spot-long\_term\_financial\_liabilities'
  * 'krx-spot-long\_term\_financial\_liabilities-annual'
  * 'krx-spot-non\_current\_provisions\_for\_employee\_benefits'
  * 'krx-spot-non\_current\_provisions\_for\_employee\_benefits-annual'
  * 'krx-spot-other\_non\_current\_liabilities'
  * 'krx-spot-other\_non\_current\_liabilities-annual'
  * 'krx-spot-convertible\_bonds'
  * 'krx-spot-convertible\_bonds-annual'
  * 'krx-spot-total\_liabilities'
  * 'krx-spot-total\_liabilities-annual'
* Equity:
  * 'krx-spot-common\_stock'
  * 'krx-spot-common\_stock-annual'
  * 'krx-spot-preferred\_stock'
  * 'krx-spot-preferred\_stock-annual'
  * 'krx-spot-paid\_in\_capital\_in\_excess\_of\_par\_value'
  * 'krx-spot-paid\_in\_capital\_in\_excess\_of\_par\_value-annual'
  * 'krx-spot-retained\_earnings'
  * 'krx-spot-retained\_earnings-annual'
  * 'krx-spot-total\_equity'
  * 'krx-spot-total\_equity-annual'
  * 'krx-spot-non\_controlling\_interests\_equity'
  * 'krx-spot-non\_controlling\_interests\_equity-annual'
  * 'krx-spot-owners\_of\_parent\_equity'
  * 'krx-spot-owners\_of\_parent\_equity-annual'
  * 'krx-spot-capital\_stock'
  * 'krx-spot-capital\_stock-annual'

**Income Statement Items**

* Revenue:
  * 'krx-spot-net\_sales'
  * 'krx-spot-net\_sales-annual'
  * 'krx-spot-sales'
  * 'krx-spot-sales-annual'
* Cost of Revenue:
  * 'krx-spot-cost\_of\_sales'
  * 'krx-spot-cost\_of\_sales-annual'
* Gross Profit:
  * 'krx-spot-gross\_profit'
  * 'krx-spot-gross\_profit-annual'
* Operating Expenses:
  * 'krx-spot-selling\_general\_administrative\_expenses'
  * 'krx-spot-selling\_general\_administrative\_expenses-annual'
  * 'krx-spot-research\_and\_development'
  * 'krx-spot-research\_and\_development-annual'
  * 'krx-spot-advertising\_expenses'
  * 'krx-spot-advertising\_expenses-annual'
  * 'krx-spot-salaries\_and\_wages'
  * 'krx-spot-salaries\_and\_wages-annual'
  * 'krx-spot-accured\_expenses'
  * 'krx-spot-accured\_expenses-annual'
* Non-Operating Income/Expenses:
  * 'krx-spot-interest\_income'
  * 'krx-spot-interest\_income-annual'
  * 'krx-spot-interest\_expenses'
  * 'krx-spot-interest\_expenses-annual'
  * 'krx-spot-financing\_expenses'
  * 'krx-spot-financing\_expenses-annual'
  * 'krx-spot-financial\_income'
  * 'krx-spot-financial\_income-annual'
* Depreciation/Amortization:
  * 'krx-spot-depreciation'
  * 'krx-spot-is\_depreciation'
  * 'krx-spot-is\_depreciation-annual'
  * 'krx-spot-cf\_depreciation'
  * 'krx-spot-cf\_depreciation-annual'
  * 'krx-spot-amortization'
  * 'krx-spot-amortization-annual'
* Pretax Income:
  * 'krx-spot-income\_before\_income\_taxes\_expenses'
  * 'krx-spot-income\_before\_income\_taxes\_expenses-annual'
  * 'krx-spot-pretax\_income'
  * 'krx-spot-pretax\_income-annual'
* Income Tax Expense:
  * 'krx-spot-income\_taxes\_expenses'
  * 'krx-spot-income\_taxes\_expenses-annual'
* Net Income:
  * 'krx-spot-net\_income'
  * 'krx-spot-net\_income-annual'
  * 'krx-spot-owners\_of\_parent\_net\_income'
  * 'krx-spot-owners\_of\_parent\_net\_income-annual'
  * 'krx-spot-net\_income\_attributed\_to\_non\_controlling\_interest'
  * 'krx-spot-net\_income\_attributed\_to\_non\_controlling\_interest-annual'
  * 'krx-spot-discontinued\_operation\_income'
  * 'krx-spot-discontinued\_operation\_income-annual'
* EBITDA:
  * 'krx-spot-ebitda'
  * 'krx-spot-ebitda-annual'
* Operating Income:
  * 'krx-spot-operating\_income'
  * 'krx-spot-operating\_income-annual'
  * 'krx-spot-ongoing\_operating\_income'
  * 'krx-spot-ongoing\_operating\_income-annual'
* Other Items:
  * 'krx-spot-owners\_of\_parent\_ongoing\_operating\_income\_or\_loss\_per\_share'
  * 'krx-spot-owners\_of\_parent\_ongoing\_operating\_income\_or\_loss\_per\_share-annual'
  * 'krx-spot-eps'
  * 'krx-spot-eps-annual'
  * 'krx-spot-adj\_bps'
  * 'krx-spot-adj\_bps-annual'
  * 'krx-spot-profit\_from\_continuing\_operations'
  * 'krx-spot-profit\_from\_continuing\_operations-annual'
  * 'krx-spot-impairment\_losses\_on\_intangible\_assets'
  * 'krx-spot-impairment\_losses\_on\_intangible\_assets-annual'
  * 'krx-spot-impairment\_losses\_on\_property\_plant\_and\_equipment'
  * 'krx-spot-impairment\_losses\_on\_property\_plant\_and\_equipment-annual'

**Cash Flow Statement Items**

* Operating Cash Flows:
  * 'krx-spot-cashflow\_from\_operating\_activities'
  * 'krx-spot-cashflow\_from\_operating\_activities-annual'
* Investing Cash Flows:
  * 'krx-spot-cashflow\_from\_investing\_activities'
  * 'krx-spot-cashflow\_from\_investing\_activities-annual'
  * 'krx-spot-capex'
  * 'krx-spot-capex-annual'
* Financing Cash Flows:
  * 'krx-spot-cashflow\_from\_financial\_activities'
  * 'krx-spot-cashflow\_from\_financial\_activities-annual'
  * 'krx-spot-dividends\_paid'
  * 'krx-spot-dividends\_paid-annual'
  * 'krx-spot-paid\_in\_capital\_increase'
  * 'krx-spot-paid\_in\_capital\_increase-annual'
  * 'krx-spot-purchase\_of\_treasury\_stock'
  * 'krx-spot-purchase\_of\_treasury\_stock-annual'
  * 'krx-spot-sales\_of\_treasury\_stock'
  * 'krx-spot-sales\_of\_treasury\_stock-annual'
  * 'krx-spot-dividend\_comm\_cash'
  * 'krx-spot-dividend\_comm\_cash-annual'
  * 'krx-spot-dividend\_pref\_cash'
  * 'krx -spot-dividend\_pref\_cash-annual'
* Free Cash Flow:
  * 'krx-spot-fcf1'
  * 'krx-spot-fcf1-annual'
  * 'krx-spot-fcf2'
  * 'krx-spot-fcf2-annual'

**Other Financial Metrics**

* 'krx-spot-enterprise\_value'
* 'krx-spot-enterprise\_value-annual'
* 'krx-spot-invested\_capital'
* 'krx-spot-invested\_capital-annual'
* 'krx-spot-roic'
* 'krx-spot-roic-annual'
* 'krx-spot-dividend'
* 'krx-spot-dividend-annual'
* 'krx -spot-dividend\_all-annual'
* 'krx-spot-payout\_ratio'
* 'krx-spot-payout\_ratio-annual'
* 'krx-spot-cash\_payout\_ratio'
* 'krx-spot-cash\_payout\_ratio-annual'
* 'krx-spot-ex\_dividend\_date'
* 'krx-spot-ex\_dividend\_date-annual'
* 'krx-spot-scheduled\_dividend\_payment\_date'
* 'krx-spot-scheduled\_dividend\_payment\_date-annual'
* 'krx-spot-loans'
* 'krx-spot-loans-annual'

**Other Items**

* 'krx-spot-fiscal\_end'
* 'krx-spot-finished\_goods'
* 'krx-spot-finished\_goods-annual'
* 'krx-spot-number\_of\_employees'
* 'krx-spot-number\_of\_employees-annual'
* 'krx-spot-unearned\_income'
* 'krx-spot-unearned\_income-annual'
* 'krx-spot-common\_shares\_fy\_end-annual'
* 'krx-spot-preferred\_shares\_fy\_end-annual'
* 'krx-spot-shares\_fy\_end-annual'
* 'krx-spot-listed\_shares\_comm'
* 'krx-spot-listed\_shares\_pref'
* 'krx-spot-listed\_shares\_comm-annual'
* 'krx-spot-listed\_shares\_pref-annual'

#### Analyst Consensus

Access financial analyst consensus data for Korean stocks, including earnings, assets, cash flows, and analyst ratings.

**Example code**

```python
from finter.data import ContentFactory

cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("krx-spot-eps_q1_up")
```

**Metadata**

| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
|            |                   |           |                |

**Item List**

**Earnings Estimates**

* krx-spot-eps\_q1\_up
* krx-spot-eps\_q1\_down
* krx-spot-eps\_q1\_stay
* krx-spot-eps\_q2\_up
* krx-spot-eps\_q2\_down
* krx-spot-eps\_q2\_stay
* krx-spot-eps1\_up
* krx-spot-eps1\_down
* krx-spot-eps1\_stay
* krx-spot-new\_eps1
* krx-spot-eps2\_up
* krx-spot-eps2\_down
* krx-spot-eps2\_stay
* krx-spot-new\_eps2
* krx-spot-new\_eps\_q1
* krx-spot-new\_eps\_q2

**Earnings Revisions**

* krx-spot-eps1\_revision\_ratio\_1m
* krx-spot-eps2\_revision\_ratio\_1m
* krx-spot-eps\_q1\_revision\_ratio\_1m
* krx-spot-eps\_q2\_revision\_ratio\_1m
* krx-spot-eps1\_revision\_ratio\_3m
* krx-spot-eps2\_revision\_ratio\_3m
* krx-spot-eps\_q1\_revision\_ratio\_3m
* krx-spot-eps\_q2\_revision\_ratio\_3m
* krx-spot-changed\_ratio\_in\_eps1
* krx-spot-changed\_ratio\_in\_eps2

**Valuation and Profitability**

* krx-spot-evebitda\_a
* krx-spot-fcf\_a
* krx-spot-sales\_a
* krx-spot-sales\_q
* krx-spot-operating\_profit\_q
* krx-spot-operating\_profit\_a
* krx-spot-net\_profit\_q
* krx-spot-net\_profit\_a
* krx-spot-ebitda\_a
* krx-spot-owners\_of\_parent\_equity\_a
* krx-spot-owners\_of\_parent\_net\_profit\_a
* krx-spot-owners\_of\_parent\_net\_profit\_q

**Assets and Investments**

* krx-spot-total\_assets\_a
* krx-spot-capex\_a
* krx-spot-operating\_cash\_flows\_a
* krx-spot-investing\_cash\_flows\_a
* krx-spot-financing\_cash\_flows\_a
* krx-spot-net\_debt\_a

**Dividends and Book Value**

* krx-spot-dividend\_yield\_a
* krx-spot-cash\_dividend\_a
* krx-spot-book\_value\_a

**Analyst Ratings and Target Prices**

* krx-spot-rating
* krx-spot-tp
* krx-spot-tp\_revision\_ratio\_1m
* krx-spot-rating\_revision\_ratio\_1m
* krx-spot-disparate\_ratio\_tp

**Fiscal Data**

* krx-spot-fiscal

### Quantitative

#### Descriptor

A comprehensive data catalog for KR STOCK quantitative descriptors, providing a wide range of financial metrics for analysis.

**Example code**

```python
from finter.data import ContentFactory

cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("rnd-expense-to-market-quarter")
```

**Metadata**

| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
|            |                   |           |                |

**Item List**

**Financial Ratios**

* Quarterly Metrics
  * `rnd-expense-to-market-quarter`
  * `rnd-expenses-to-sales-quarter`
  * `operating-leverage-quarter`
  * `tangibility-quarter`
  * `asset-liquidity-to-assets-quarter`
  * `asset-liquidity-to-market-quarter`
  * `investment-to-assets-quarter`
  * `return-on-equity-quarter`
  * `return-on-assets-quarter`
  * `change-return-on-equity-quarter`
  * `change-return-on-assets-quarter`
  * `return-on-net-operating-assets-quarter`
  * `asset-turnover-quarter`
  * `profit-margin-quarter`
  * `capital-turnover-quarter`
  * `gross-profits-lagged-assets-quarter`
  * `operating-profits-lagged-equity-quarter`
  * `operating-profits-lagged-assets-quarter`
  * `cash-based-operating-profits-lagged-assets-quarter`
  * `fundamental-score-quarter`
  * `ohlson-o-score-quarter`
  * `altman-z-score-quarter`
  * `taxable-income-to-book-income-quarter`
  * `book-leverage-quarter`
  * `sales-growth-quarter`
  * `book-to-market-quarter`
  * `debt-to-market-quarter`
  * `assets-to-market-quarter`
  * `earnings-to-price-quarter`
  * `cashflow-to-price-quarter`
  * `operating-cashflow-to-price-quarter`
  * `sales-to-price-quarter`
  * `net-debt-to-price-quarter`
  * `enterprise-book-to-price-quarter`
  * `enterprise-multiple-quarter`
* Annual Metrics
  * `rnd-expense-to-market`
  * `rnd-expenses-to-sales`
  * `rnd-capital-to-assets`
  * `operating-leverage`
  * `hiring-rate`
  * `percent-change-sales-minus-inventory`
  * `percent-change-sales-minus-accounts-receivables`
  * `percent-change-gross-margin-minus-sales`
  * `percent-change-sales-minus-sga`
  * `labor-force-efficiency`
  * `tangibility`
  * `asset-liquidity-to-assets`
  * `asset-liquidity-to-market`
  * `abnormal-corporate-investment`
  * `investment-to-assets`
  * `change-ppe-inventory-to-assets`
  * `net-operating-assets`
  * `change-net-operating-assets`
  * `change-long-term-net-operating-assets`
  * `investment-growth-1-year`
  * `investment-growth-2-year`
  * `investment-growth-3-year`
  * `composite-equity-issuance`
  * `composite-debt-issuance`
  * `inventory-growth`
  * `inventory-changes`
  * `operating-accruals`
  * `percent-operating-accruals`
  * `change-current-operating-assets`
  * `change-current-operating-liabilities`
  * `change-net-non-cash-working-capital`
  * `change-non-current-operating-assets`
  * `change-non-current-operating-liabilities`
  * `change-net-non-current-operating-assets`
  * `change-short-term-investments`
  * `change-long-term-investments`
  * `change-financial-liabilities`
  * `change-net-financial-assets`
  * `change-book-equity`
  * `return-on-equity`
  * `return-on-assets`
  * `change-return-on-equity`
  * `change-return-on-assets`
  * `return-on-net-operating-assets`
  * `asset-turnover`
  * `profit-margin`
  * `capital-turnover`
  * `gross-profits-to-assets`
  * `gross-profits-lagged-assets`
  * `operating-profits-to-equity`
  * `operating-profits-lagged-equity`
  * `operating-profits-to-assets`
  * `operating-profits-lagged-assets`
  * `cash-based-operating-profits-to-assets`
  * `cash-based-operating-profits-lagged-assets`
  * `fundamental-score`
  * `ohlson-o-score`
  * `altman-z-score`
  * `taxable-income-to-book-income`
  * `book-leverage`
  * `book-to-market`
  * `book-to-market-june-end`
  * `debt-to-market`
  * `assets-to-market`
  * `earnings-to-price`
  * `cashflow-to-price`
  * `operating-cashflow-to-price`
  * `sales-to-price`
  * `sales-growth`
  * `sales-growth-rank-5-year`
  * `net-debt-to-price`
  * `enterprise-book-to-price`
  * `enterprise-multiple`
  * `net-stock-issues`

**Market Dynamics**

* `total-volatility`
* `total-skewness`
* `maximum-five-daily-return`
* `maximum-ten-daily-return`
* `share-turnover`
* `dollar-trading-volume`
* `absolute-return-to-volume`
* `coefficient-variation-of-share-turnover`
* `coefficient-variation-of-dollar-trading-volume`
* `short-term-reversal`

**Risk Factors**

* `market-beta`
* `dimson-beta`
* `frazzini-pedersen-beta`
* `idiosyncratic-volatility-per-ff3`
* `idiosyncratic-volatility-per-capm`
* `idiosyncratic-skewness-per-ff3`
* `idiosyncratic-skewness-per-capm`

**Company Fundamentals**

* `advertising-expenses-to-market`
* `growth-advertising-expense`
* `6-month-momentum`
* `11-month-momentum`
* `52-week-high`

**Company Background**

* `age-found`
* `age-listed`

#### Factor

**Factor (1)**

**Item List**

**Valuation Ratios**

* Price-to-Book Ratio (PBR)
  * `kr-pbr_12mf`: Price-to-book ratio for the next 12 months forecast.
  * `kr-pbr_12mf-z`: Z-score of the price-to-book ratio for the next 12 months forecast.
  * `kr-pbr_12mf_rel3y`: Price-to-book ratio for the next 12 months relative to the past 3 years.
  * `kr-pbr_12mf_rel3y-z`: Z-score of the price-to-book ratio for the next 12 months relative to the past 3 years.
  * `kr-pbr_18mf`: Price-to-book ratio for the next 18 months forecast.
  * `kr-pbr_18mf-z`: Z-score of the price-to-book ratio for the next 18 months forecast.
  * `kr-pbr_18mf_rel3y`: Price-to-book ratio for the next 18 months relative to the past 3 years.
  * `kr-pbr_18mf_rel3y-z`: Z-score of the price-to-book ratio for the next 18 months relative to the past 3 years.
  * `kr-pbr_24mf`: Price-to-book ratio for the next 24 months forecast.
  * `kr-pbr_24mf-z`: Z-score of the price-to-book ratio for the next 24 months forecast.
  * `kr-pbr_24mf_rel3y`: Price-to-book ratio for the next 24 months relative to the past 3 years.
  * `kr-pbr_24mf_rel3y-z`: Z-score of the price-to-book ratio for the next 24 months relative to the past 3 years.
* Price-to-Sales Ratio (PSR)
  * `kr-psr_12mf`: Price-to-sales ratio for the next 12 months forecast.
  * `kr-psr_12mf-z`: Z-score of the price-to-sales ratio for the next 12 months forecast.
  * `kr-psr_12mf_rel3y`: Price-to-sales ratio for the next 12 months relative to the past 3 years.
  * `kr-psr_12mf_rel3y-z`: Z-score of the price-to-sales ratio for the next 12 months relative to the past 3 years.
  * `kr-psr_18mf`: Price-to-sales ratio for the next 18 months forecast.
  * `kr-psr_18mf-z`: Z-score of the price-to-sales ratio for the next 18 months forecast.
  * `kr-psr_18mf_rel3y`: Price-to-sales ratio for the next 18 months relative to the past 3 years.
  * `kr-psr_18mf_rel3y-z`: Z-score of the price-to-sales ratio for the next 18 months relative to the past 3 years.
  * `kr-psr_24mf`: Price-to-sales ratio for the next 24 months forecast.
  * `kr-psr_24mf-z`: Z-score of the price-to-sales ratio for the next 24 months forecast.
  * `kr-psr_24mf_rel3y`: Price-to-sales ratio for the next 24 months relative to the past 3 years.
  * `kr-psr_24mf_rel3y-z`: Z-score of the price-to-sales ratio for the next 24 months relative to the past 3 years.
* Price-to-Free Cash Flow (P/FCF)
  * `kr-p_fcf_12mf`: Price-to-free cash flow ratio for the next 12 months forecast.
  * `kr-p_fcf_12mf-z`: Z-score of the price-to-free cash flow ratio for the next 12 months forecast.
  * `kr-p_fcf_12mf_rel3y`: Price-to-free cash flow ratio for the next 12 months relative to the past 3 years.
  * `kr-p_fcf_12mf_rel3y-z`: Z-score of the price-to-free cash flow ratio for the next 12 months relative to the past 3 years.
  * `kr-p_fcf1_4q`: Price-to-free cash flow ratio for the last 4 quarters.
  * `kr-p_fcf1_4q-z`: Z-score of the price-to-free cash flow ratio for the last 4 quarters.
  * `kr-p_fcf1_4q_rel3y`: Price-to-free cash flow ratio for the last 4 quarters relative to the past 3 years.
  * `kr-p_fcf1_4q_rel3y-z`: Z-score of the price-to-free cash flow ratio for the last 4 quarters relative to the past 3 years.
  * `kr-p_fcf2_4q`: Alternative calculation of the price-to-free cash flow ratio for the last 4 quarters.
  * `kr-p_fcf2_4q-z`: Z-score of the alternative calculation of the price-to-free cash flow ratio for the last 4 quarters.
  * `kr-p_fcf2_4q_rel3y`: Alternative calculation of the price-to-free cash flow ratio for the last 4 quarters relative to the past 3 years.
  * `kr-p_fcf2_4q_rel3y-z`: Z-score of the alternative calculation of the price-to-free cash flow ratio for the last 4 quarters relative to the past 3 years.
* Price-to-Earnings Ratio (PER)
  * `kr-per_12mf`: Price-to-earnings ratio for the next 12 months forecast.
  * `kr-per_12mf-z`: Z-score of the price-to-earnings ratio for the next 12 months forecast.
  * `kr-per_12mf_rel3y`: Price-to-earnings ratio for the next 12 months relative to the past 3 years.
  * `kr-per_12mf_rel3y-z`: Z-score of the price-to-earnings ratio for the next 12 months relative to the past 3 years.
  * `kr-per_18mf`: Price-to-earnings ratio for the next 18 months forecast.
  * `kr-per_18mf-z`: Z-score of the price-to-earnings ratio for the next 18 months forecast.
  * `kr-per_18mf_rel3y`: Price-to-earnings ratio for the next 18 months relative to the past 3 years.
  * `kr-per_18mf_rel3y-z`: Z-score of the price-to-earnings ratio for the next 18 months relative to the past 3 years.
  * `kr-per_24mf`: Price-to-earnings ratio for the next 24 months forecast.
  * `kr-per_24mf-z`: Z-score of the price-to-earnings ratio for the next 24 months forecast.
  * `kr-per_24mf_rel3y`: Price-to-earnings ratio for the next 24 months relative to the past 3 years.
  * `kr-per_24mf_rel3y-z`: Z-score of the price-to-earnings ratio for the next 24 months relative to the past 3 years.
* EV/EBITDA
  * `kr-ev_ebitda_18mf`: Enterprise value to EBITDA ratio for the next 18 months forecast.
  * `kr-ev_ebitda_18mf-z`: Z-score of the enterprise value to EBITDA ratio for the next 18 months forecast.
  * `kr-ev_ebitda_18mf_rel3y`: Enterprise value to EBITDA ratio for the next 18 months relative to the past 3 years.
  * `kr-ev_ebitda_18mf_rel3y-z`: Z-score of the enterprise value to EBITDA ratio for the next 18 months relative to the past 3 years.
  * `kr-ev_ebitda_24mf`: Enterprise value to EBITDA ratio for the next 24 months forecast.
  * `kr-ev_ebitda_24mf-z`: Z-score of the enterprise value to EBITDA ratio for the next 24 months forecast.
  * `kr-ev_ebitda_24mf_rel3y`: Enterprise value to EBITDA ratio for the next 24 months relative to the past 3 years.
  * `kr-ev_ebitda_24mf_rel3y-z`: Z-score of the enterprise value to EBITDA ratio for the next 24 months relative to the past 3 years.
* Price-to-Cash Flow Ratio (PCR)
  * `kr-pcr_12mf`: Price-to-cash flow ratio for the next 12 months forecast.
  * `kr-pcr_12mf-z`: Z-score of the price-to-cash flow ratio for the next 12 months forecast.
  * `kr-pcr_12mf_rel3y`: Price-to-cash flow ratio for the next 12 months relative to the past 3 years.
  * `kr-pcr_12mf_rel3y-z`: Z-score of the price-to-cash flow ratio for the next 12 months relative to the past 3 years.
  * `kr-pcr_18mf`: Price-to-cash flow ratio for the next 18 months forecast.
  * `kr-pcr_18mf-z`: Z-score of the price-to-cash flow ratio for the next 18 months forecast.
  * `kr-pcr_18mf_rel3y`: Price-to-cash flow ratio for the next 18 months relative to the past 3 years.
  * `kr-pcr_18mf_rel3y-z`: Z-score of the price-to-cash flow ratio for the next 18 months relative to the past 3 years.
  * `kr-pcr_24mf`: Price-to-cash flow ratio for the next 24 months forecast.
  * `kr-pcr_24mf-z`: Z-score of the price-to-cash flow ratio for the next 24 months forecast.
  * `kr-pcr_24mf_rel3y`: Price-to-cash flow ratio for the next 24 months relative to the past 3 years.
  * `kr-pcr_24mf_rel3y-z`: Z-score of the price-to-cash flow ratio for the next 24 months relative to the past 3 years.

**Profitability Ratios**

* Operating Profit Margin
  * `kr-operating_profit_margin_12mf`: Operating profit margin for the next 12 months forecast.
  * `kr-operating_profit_margin_12mf-z`: Z-score of the operating profit margin for the next 12 months forecast.
* Net Profit Margin
  * `kr-net_profit_margin_12mf`: Net profit margin for the next 12 months forecast.
  * `kr-net_profit_margin_12mf-z`: Z-score of the net profit margin for the next 12 months forecast.
* Return on Equity (ROE)
  * `kr-return_on_equity_12mf`: Return on equity for the next 12 months forecast.
  * `kr-return_on_equity_12mf-z`: Z-score of the return on equity for the next 12 months forecast.
* Return on Asset (ROA)
  * `kr-return_on_asset_12mf`: Return on assets for the next 12 months forecast.
  * `kr-return_on_asset_12mf-z`: Z-score of the return on assets for the next 12 months forecast.

**Investment Ratios**

* Dividend Yield
  * `kr-dividend_yield_12mf`: Dividend yield for the next 12 months forecast.
  * `kr-dividend_yield_12mf-z`: Z-score of the dividend yield for the next 12 months forecast.
  * `kr-dividend_yield_12mf_rel3y`: Dividend yield for the next 12 months relative to the past 3 years.
  * `kr-dividend_yield_12mf_rel3y-z`: Z-score of the dividend yield for the next 12 months relative to the past 3 years.
  * `kr-dividend_yield_fy0`: Dividend yield for the current fiscal year.
  * `kr-dividend_yield_fy0-z`: Z-score of the dividend yield for the current fiscal year.
  * `kr-dividend_yield_fy0_rel3y`: Dividend yield for the current fiscal year relative to the past 3 years.
  * `kr-dividend_yield_fy0_rel3y-z`: Z-score of the dividend yield for the current fiscal year relative to the past 3 years.
  * `kr-dividend_yield_fy1`: Dividend yield for the next fiscal year.
  * `kr-dividend_yield_fy1-z`: Z-score of the dividend yield for the next fiscal year.
  * `kr-dividend_yield_fy1_rel3y`: Dividend yield for the next fiscal year relative to the past 3 years.
  * `kr-dividend_yield_fy1_rel3y-z`: Z-score of the dividend yield for the next fiscal year relative to the past 3 years.
* Dividend Per Share (DPS) Change
  * `kr-dps_fy1_1m_chg`: Change in dividend per share for the next fiscal year over the last month.
  * `kr-dps_fy1_1m_chg-z`: Z-score of the change in dividend per share for the next fiscal year over the last month.
  * `kr-dps_fy1_3m_chg`: Change in dividend per share for the next fiscal year over the last three months.
  * `kr-dps_fy1_3m_chg-z`: Z-score of the change in dividend per share for the next fiscal year over the last three months.
  * `kr-dps_fy2_1m_chg`: Change in dividend per share for the second next fiscal year over the last month.
  * `kr-dps_fy2_1m_chg-z`: Z-score of the change in dividend per share for the second next fiscal year over the last month.
  * `kr-dps_fy2_3m_chg`: Change in dividend per share for the second next fiscal year over the last three months.
  * `kr-dps_fy2_3m_chg-z`: Z-score of the change in dividend per share for the second next fiscal year over the last three months.

**Market Ratios**

* Implied Volatility
  * `kr-ivol_mkt_252d`: Implied volatility in the market over the last 252 days.
  * `kr-ivol_mkt_252d-z`: Z-score of the implied volatility in the market over the last 252 days.
  * `kr-ivol_ff_21d`: Implied volatility from a fast formula over the last 21 days.
  * `kr-ivol_ff_21d-z`: Z-score of the implied volatility from a fast formula over the last 21 days.
  * `kr-ivol_ff_63d`: Implied volatility from a fast formula over the last 63 days.
  * `kr-ivol_ff_63d-z`: Z-score of the implied volatility from a fast formula over the last 63 days.
  * `kr-ivol_ff_126d`: Implied volatility from a fast formula over the last 126 days.
  * `kr-ivol_ff_126d-z`: Z-score of the implied volatility from a fast formula over the last 126 days.
  * `kr-ivol_ff_252d`: Implied volatility from a fast formula over the last 252 days.
  * `kr-ivol_ff_252d-z`: Z-score of the implied volatility from a fast formula over the last 252 days.
* Beta
  * `kr-beta_yield_3y_60m`: Beta calculated using a 60-month window, based on the last 3 years.
  * `kr-beta_yield_3y_60m-z`: Z-score of the beta calculated using a 60-month window, based on the last 3 years.
  * `kr-beta_yield_5y_60m`: Beta calculated using a 60-month window, based on the last 5 years.
  * `kr-beta_yield_5y_60m-z`: Z-score of the beta calculated using a 60-month window, based on the last 5 years.
  * `kr-beta_yield_10y_60m`: Beta calculated using a 60-month window, based on the last 10 years.
  * `kr-beta_yield_10y_60m-z`: Z-score of the beta calculated using a 60-month window, based on the last 10 years.
* Residual Yield
  * `kr-resid_yield_3y_60m`: Residual yield calculated using a 60-month window, based on the last 3 years.
  * `kr-resid_yield_3y_60m-z`: Z-score of the residual yield calculated using a 60-month window, based on the last 3 years.
  * `kr-resid_us_1260d`: Residual yield for the US market over the last 1260 days.
  * `kr-resid_us_1260d-z`: Z-score of the residual yield for the US market over the last 1260 days.

**Credit and Investment Intensity Ratios**

* Credit Ratio
  * `kr-credit_ratio_5d`: Credit ratio over the last 5 days.
  * `kr-credit_ratio_5d-z`: Z-score of the credit ratio over the last 5 days.
  * `kr-credit_ratio_10d`: Credit ratio over the last 10 days.
  * `kr-credit_ratio_10d-z`: Z-score of the credit ratio over the last 10 days.
  * `kr-credit_ratio_20d`: Credit ratio over the last 20 days.
  * `kr-credit_ratio_20d-z`: Z-score of the credit ratio over the last 20 days.
  * `kr-credit_ratio_60d`: Credit ratio over the last 60 days.
  * `kr-credit_ratio_60d-z`: Z-score of the credit ratio over the last 60 days.
* Securities Lending and Borrowing (SLB) Intensity
  * `kr-slb_intensity_5d`: Securities lending and borrowing intensity over the last 5 days.
  * `kr-slb_intensity_5d-z`: Z-score of the securities lending and borrowing intensity over the last 5 days.
  * `kr-slb_intensity_10d`: Securities lending and borrowing intensity over the last 10 days.
  * `kr-slb_intensity_10d-z`: Z-score of the securities lending and borrowing intensity over the last 10 days.
  * `kr-slb_intensity_20d`: Securities lending and borrowing intensity over the last 20 days.
  * `kr-slb_intensity_20d-z`: Z-score of the securities lending and borrowing intensity over the last 20 days.
  * `kr-slb_intensity_60d`: Securities lending and borrowing intensity over the last 60 days.
  * `kr-slb_intensity_60d-z`: Z-score of the securities lending and borrowing intensity over the last 60 days.
* Net Investment Ratios
  * `kr-net_inst_10d`: Net investment by institutions over the last 10 days.
  * `kr-net_inst_10d-z`: Z-score of the net investment by institutions over the last 10 days.
  * `kr-net_inst_20d`: Net investment by institutions over the last 20 days.
  * `kr-net_inst_20d-z`: Z-score of the net investment by institutions over the last 20 days.
  * `kr-net_fore_10d`: Net foreign investment over the last 10 days.
  * `kr-net_fore_10d-z`: Z-score of the net foreign investment over the last 10 days.
  * `kr-net_fore_20d`: Net foreign investment over the last 20 days.
  * `kr-net_fore_20d-z`: Z-score of the net foreign investment over the last 20 days.
  * `kr-net_retail_10d`: Net retail investment over the last 10 days.
  * `kr-net_retail_10d-z`: Z-score of the net retail investment over the last 10 days.
  * `kr-net_retail_20d`: Net retail investment over the last 20 days.
  * `kr-net_retail_20d-z`: Z-score of the net retail investment over the last 20 days.
  * `kr-net_invest_or_10d`: Other net investment over the last 10 days.
  * `kr-net_invest_or_10d-z`: Z-score of other net investment over the last 10 days.
  * `kr-net_invest_or_20d`: Other net investment over the last 20 days.
  * `kr-net_invest_or_20d-z`: Z-score of other net investment over the last 20 days.
  * `kr-net_invest_and_10d`: Another category of net investment over the last 10 days.
  * `kr-net_invest_and_10d-z`: Z-score of another category of net investment over the last 10 days.
  * `kr-net_invest_and_20d`: Another category of net investment over the last 20 days.
  * `kr-net_invest_and_20d-z`: Z-score of another category of net investment over the last 20 days.
  * `kr-net_invest_sum_10d`: Sum of net investments over the last 10 days.
  * `kr-net_invest_sum_10d-z`: Z-score of the sum of net investments over the last 10 days.
  * `kr-net_invest_sum_20d`: Sum of net investments over the last 20 days.
  * `kr-net_invest_sum_20d-z`: Z-score of the sum of net investments over the last 20 days.
* Investment Ratio by Investor Type
  * `kr-inst_invest_ratio_5d`: Investment ratio by institutions over the last 5 days.
  * `kr-inst_invest_ratio_5d-z`: Z-score of the investment ratio by institutions over the last 5 days.
  * `kr-inst_invest_ratio_10d`: Investment ratio by institutions over the last 10 days.
  * `kr-inst_invest_ratio_10d-z`: Z-score of the investment ratio by institutions over the last 10 days.
  * `kr-inst_invest_ratio_20d`: Investment ratio by institutions over the last 20 days.
  * `kr-inst_invest_ratio_20d-z`: Z-score of the investment ratio by institutions over the last 20 days.
  * `kr-inst_invest_ratio_60d`: Investment ratio by institutions over the last 60 days.
  * `kr-inst_invest_ratio_60d-z`: Z-score of the investment ratio by institutions over the last 60 days.
  * `kr-fore_invest_ratio_5d`: Investment ratio by foreign investors over the last 5 days.
  * `kr-fore_invest_ratio_5d-z`: Z-score of the investment ratio by foreign investors over the last 5 days.
  * `kr-fore_invest_ratio_10d`: Investment ratio by foreign investors over the last 10 days.
  * `kr-fore_invest_ratio_10d-z`: Z-score of the investment ratio by foreign investors over the last 10 days.
  * `kr-fore_invest_ratio_20d`: Investment ratio by foreign investors over the last 20 days.
  * `kr-fore_invest_ratio_20d-z`: Z-score of the investment ratio by foreign investors over the last 20 days.
  * `kr-fore_invest_ratio_60d`: Investment ratio by foreign investors over the last 60 days.
  * `kr-fore_invest_ratio_60d-z`: Z-score of the investment ratio by foreign investors over the last 60 days.
  * `kr-retail_invest_ratio_5d`: Investment ratio by retail investors over the last 5 days.
  * `kr-retail_invest_ratio_5d-z`: Z-score of the investment ratio by retail investors over the last 5 days.
  * `kr-retail_invest_ratio_10d`: Investment ratio by retail investors over the last 10 days.
  * `kr-retail_invest_ratio_10d-z`: Z-score of the investment ratio by retail investors over the last 10 days.
  * `kr-retail_invest_ratio_20d`: Investment ratio by retail investors over the last 20 days.
  * `kr-retail_invest_ratio_20d-z`: Z-score of the investment ratio by retail investors over the last 20 days.
  * `kr-retail_invest_ratio_60d`: Investment ratio by retail investors over the last 60 days.
  * `kr-retail_invest_ratio_60d-z`: Z-score of the investment ratio by retail investors over the last 60 days.
  * `kr-net_invest_ratio_5d`: Net investment ratio over the last 5 days.
  * `kr-net_invest_ratio_5d-z`: Z-score of the net investment ratio over the last 5 days.
  * `kr-net_invest_ratio_10d`: Net investment ratio over the last 10 days.
  * `kr-net_invest_ratio_10d-z`: Z-score of the net investment ratio over the last 10 days.
  * `kr-net_invest_ratio_20d`: Net investment ratio over the last 20 days.
  * `kr-net_invest_ratio_20d-z`: Z-score of the net investment ratio over the last 20 days.
  * `kr-net_invest_ratio_60d`: Net investment ratio over the last 60 days.
  * `kr-net_invest_ratio_60d-z`: Z-score of the net investment ratio over the last 60 days.
* Growth Ratios
  * `kr-np_yoy`: Year-over-year growth in net profit.
  * `kr-np_yoy-z`: Z-score of the year-over-year growth in net profit.
  * `kr-roe_delta`: Change in return on equity from the previous period.
  * `kr-roe_delta-z`: Z-score of the change in return on equity from the previous period.
  * `kr-sales_yoy`: Year-over-year growth in sales.
  * `kr-sales_yoy-z`: Z-score of the year-over-year growth in sales.
  * `kr-op_yoy`: Year-over-year growth in operating profit.
  * `kr-op_yoy-z`: Z-score of the year-over-year growth in operating profit.
* Capital Expenditure Ratios
  * `kr-capex_to_asset_12mf`: Capital expenditure to asset ratio for the next 12 months forecast.
  * `kr-capex_to_asset_12mf-z`: Z-score of the capital expenditure to asset ratio for the next 12 months forecast.
  * `kr-capex_growth_12mf`: Growth in capital expenditure over the next 12 months forecast.
  * `kr-capex_growth_12mf-z`: Z-score of the growth in capital expenditure over the next 12 months forecast.

**Factor (2)**

**Item List**

**Sales**

* 12-Month Forward
  * Monthly
    * `kr-sales_12mf_1m` - Represents the forecasted sales for the next 12 months on a monthly basis.
    * `kr-sales_12mf_1m-z` - Z-score of the 12-month forward sales on a monthly basis.
  * Quarterly
    * `kr-sales_12mf_3m` - Represents the forecasted sales for the next 12 months on a quarterly basis.
    * `kr-sales_12mf_3m-z` - Z-score of the 12-month forward sales on a quarterly basis.
* 18-Month Forward
  * Monthly
    * `kr-sales_18mf_1m` - Represents the forecasted sales for the next 18 months on a monthly basis.
    * `kr-sales_18mf_1m-z` - Z-score of the 18-month forward sales on a monthly basis.
  * Quarterly
    * `kr-sales_18mf_3m` - Represents the forecasted sales for the next 18 months on a quarterly basis.
    * `kr-sales_18mf_3m-z` - Z-score of the 18-month forward sales on a quarterly basis.
* 24-Month Forward
  * Monthly
    * `kr-sales_24mf_1m` - Represents the forecasted sales for the next 24 months on a monthly basis.
    * `kr-sales_24mf_1m-z` - Z-score of the 24-month forward sales on a monthly basis.
  * Quarterly
    * `kr-sales_24mf_3m` - Represents the forecasted sales for the next 24 months on a quarterly basis.
    * `kr-sales_24mf_3m-z` - Z-score of the 24-month forward sales on a quarterly basis.
* Fiscal Quarters
  * FQ1
    * Monthly
      * `kr-sales_fq1_1m` - Represents the forecasted sales for the first fiscal quarter on a monthly basis.
      * `kr-sales_fq1_1m-z` - Z-score of the first fiscal quarter sales on a monthly basis.
    * Quarterly
      * `kr-sales_fq1_3m` - Represents the forecasted sales for the first fiscal quarter on a quarterly basis.
      * `kr-sales_fq1_3m-z` - Z-score of the first fiscal quarter sales on a quarterly basis.
  * FQ2
    * Monthly
      * `kr-sales_fq2_1m` - Represents the forecasted sales for the second fiscal quarter on a monthly basis.
      * `kr-sales_fq2_1m-z` - Z-score of the second fiscal quarter sales on a monthly basis.
    * Quarterly
      * `kr-sales_fq2_3m` - Represents the forecasted sales for the second fiscal quarter on a quarterly basis.
      * `kr-sales_fq2_3m-z` - Z-score of the second fiscal quarter sales on a quarterly basis.
* Fiscal Years
  * FY1
    * Monthly
      * `kr-sales_fy1_1m` - Represents the forecasted sales for the first fiscal year on a monthly basis.
      * `kr-sales_fy1_1m-z` - Z-score of the first fiscal year sales on a monthly basis.
    * Quarterly
      * `kr-sales_fy1_3m` - Represents the forecasted sales for the first fiscal year on a quarterly basis.
      * `kr-sales_fy1_3m-z` - Z-score of the first fiscal year sales on a quarterly basis.
  * FY2
    * Monthly
      * `kr-sales_fy2_1m` - Represents the forecasted sales for the second fiscal year on a monthly basis.
      * `kr-sales_fy2_1m-z` - Z-score of the second fiscal year sales on a monthly basis.
    * Quarterly
      * `kr-sales_fy2_3m` - Represents the forecasted sales for the second fiscal year on a quarterly basis.
      * `kr-sales_fy2_3m-z` - Z-score of the second fiscal year sales on a quarterly basis.

**Operating Profit**

* 12-Month Forward
  * Monthly
    * `kr-op_12mf_1m` - Represents the forecasted operating profit for the next 12 months on a monthly basis.
    * `kr-op_12mf_1m-z` - Z-score of the 12-month forward operating profit on a monthly basis.
  * Quarterly
    * `kr-op_12mf_3m` - Represents the forecasted operating profit for the next 12 months on a quarterly basis.
    * `kr-op_12mf_3m-z` - Z-score of the 12-month forward operating profit on a quarterly basis.
* 18-Month Forward
  * Monthly
    * `kr-op_18mf_1m` - Represents the forecasted operating profit for the next 18 months on a monthly basis.
    * `kr-op_18mf_1m-z` - Z-score of the 18-month forward operating profit on a monthly basis.
  * Quarterly
    * `kr-op_18mf_3m` - Represents the forecasted operating profit for the next 18 months on a quarterly basis.
    * `kr-op_18mf_3m-z` - Z-score of the 18-month forward operating profit on a quarterly basis.
* 24-Month Forward
  * Monthly
    * `kr-op_24mf_1m` - Represents the forecasted operating profit for the next 24 months on a monthly basis.
    * `kr-op_24mf_1m-z` - Z-score of the 24-month forward operating profit on a monthly basis.
  * Quarterly
    * `kr-op_24mf_3m` - Represents the forecasted operating profit for the next 24 months on a quarterly basis.
    * `kr-op_24mf_3m-z` - Z-score of the 24-month forward operating profit on a quarterly basis.
* Fiscal Quarters
  * FQ1
    * Monthly
      * `kr-op_fq1_1m` - Represents the forecasted operating profit for the first fiscal quarter on a monthly basis.
      * `kr-op_fq1_1m-z` - Z-score of the first fiscal quarter operating profit on a monthly basis.
    * Quarterly
      * `kr-op_fq1_3m` - Represents the forecasted operating profit for the first fiscal quarter on a quarterly basis.
      * `kr-op_fq1_3m-z` - Z-score of the first fiscal quarter operating profit on a quarterly basis.
  * FQ2
    * Monthly
      * `kr-op_fq2_1m` - Represents the forecasted operating profit for the second fiscal quarter on a monthly basis.
      * `kr-op_fq2_1m-z` - Z-score of the second fiscal quarter operating profit on a monthly basis.
    * Quarterly
      * `kr-op_fq2_3m` - Represents the forecasted operating profit for the second fiscal quarter on a quarterly basis.
      * `kr-op_fq2_3m-z` - Z-score of the second fiscal quarter operating profit on a quarterly basis.
* Fiscal Years
  * FY1
    * Monthly
      * `kr-op_fy1_1m` - Represents the forecasted operating profit for the first fiscal year on a monthly basis.
      * `kr-op_fy1_1m-z` - Z-score of the first fiscal year operating profit on a monthly basis.
    * Quarterly
      * `kr-op_fy1_3m` - Represents the forecasted operating profit for the first fiscal year on a quarterly basis.
      * `kr-op_fy1_3m-z` - Z-score of the first fiscal year operating profit on a quarterly basis.
  * FY2
    * Monthly
      * `kr-op_fy2_1m` - Represents the forecasted operating profit for the second fiscal year on a monthly basis.
      * `kr-op_fy2_1m-z` - Z-score of the second fiscal year operating profit on a monthly basis.
    * Quarterly
      * `kr-op_fy2_3m` - Represents the forecasted operating profit for the second fiscal year on a quarterly basis.
      * `kr-op_fy2_3m-z` - Z-score of the second fiscal year operating profit on a quarterly basis.
  * Adjusted FY1
    * Monthly
      * `kr-op_fy1_1m_adj` - Represents the adjusted forecasted operating profit for the first fiscal year on a monthly basis.
      * `kr-op_fy1_1m_adj-z` - Z-score of the adjusted first fiscal year operating profit on a monthly basis.
  * Adjusted FY2
    * Monthly
      * `kr-op_fy2_1m_adj` - Represents the adjusted forecasted operating profit for the second fiscal year on a monthly basis.
      * `kr-op_fy2_1m_adj-z` - Z-score of the adjusted second fiscal year operating profit on a monthly basis.

**Net Profit**

* 12-Month Forward
  * Monthly
    * `kr-np_12mf_1m` - Represents the forecasted net profit for the next 12 months on a monthly basis.
    * `kr-np_12mf_1m-z` - Z-score of the 12-month forward net profit on a monthly basis.
  * Quarterly
    * `kr-np_12mf_3m` - Represents the forecasted net profit for the next 12 months on a quarterly basis.
    * `kr-np_12mf_3m-z` - Z-score of the 12-month forward net profit on a quarterly basis.
* 18-Month Forward
  * Monthly
    * `kr-np_18mf_1m` - Represents the forecasted net profit for the next 18 months on a monthly basis.
    * `kr-np_18mf_1m-z` - Z-score of the 18-month forward net profit on a monthly basis.
  * Quarterly
    * `kr-np_18mf_3m` - Represents the forecasted net profit for the next 18 months on a quarterly basis.
    * `kr-np_18mf_3m-z` - Z-score of the 18-month forward net profit on a quarterly basis.
* 24-Month Forward
  * Monthly
    * `kr-np_24mf_1m` - Represents the forecasted net profit for the next 24 months on a monthly basis.
    * `kr-np_24mf_1m-z` - Z-score of the 24-month forward net profit on a monthly basis.
  * Quarterly
    * `kr-np_24mf_3m` - Represents the forecasted net profit for the next 24 months on a quarterly basis.
    * `kr-np_24mf_3m-z` - Z-score of the 24-month forward net profit on a quarterly basis.
* Fiscal Quarters
  * FQ1
    * Monthly
      * `kr-np_fq1_1m` - Represents the forecasted net profit for the first fiscal quarter on a monthly basis.
      * `kr-np_fq1_1m-z` - Z-score of the first fiscal quarter net profit on a monthly basis.
    * Quarterly
      * `kr-np_fq1_3m` - Represents the forecasted net profit for the first fiscal quarter on a quarterly basis.
      * `kr-np_fq1_3m-z` - Z-score of the first fiscal quarter net profit on a quarterly basis.
  * FQ2
    * Monthly
      * `kr-np_fq2_1m` - Represents the forecasted net profit for the second fiscal quarter on a monthly basis.
      * `kr-np_fq2_1m-z` - Z-score of the second fiscal quarter net profit on a monthly basis.
    * Quarterly
      * `kr-np_fq2_3m` - Represents the forecasted net profit for the second fiscal quarter on a quarterly basis.
      * `kr-np_fq2_3m-z` - Z-score of the second fiscal quarter net profit on a quarterly basis.
* Fiscal Years
  * FY1
    * Monthly
      * `kr-np_fy1_1m` - Represents the forecasted net profit for the first fiscal year on a monthly basis.
      * `kr-np_fy1_1m-z` - Z-score of the first fiscal year net profit on a monthly basis.
    * Quarterly
      * `kr-np_fy1_3m` - Represents the forecasted net profit for the first fiscal year on a quarterly basis.
      * `kr-np_fy1_3m-z` - Z-score of the first fiscal year net profit on a quarterly basis.
  * FY2
    * Monthly
      * `kr-np_fy2_1m` - Represents the forecasted net profit for the second fiscal year on a monthly basis.
      * `kr-np_fy2_1m-z` - Z-score of the second fiscal year net profit on a monthly basis.
    * Quarterly
      * `kr-np_fy2_3m` - Represents the forecasted net profit for the second fiscal year on a quarterly basis.
      * `kr-np_fy2_3m-z` - Z-score of the second fiscal year net profit on a quarterly basis.

**Dividend Yield**

* 18-Month Forward
  * `kr-dividend_yield_18mf` - Represents the forecasted dividend yield for the next 18 months.
  * `kr-dividend_yield_18mf-z` - Z-score of the 18-month forward dividend yield.
  * Relative 3-Year
    * `kr-dividend_yield_18mf_rel3y` - Represents the 18-month forward dividend yield relative to the past 3 years.
    * `kr-dividend_yield_18mf_rel3y-z` - Z-score of the 18-month forward dividend yield relative to the past 3 years.
* 24-Month Forward
  * `kr-dividend_yield_24mf` - Represents the forecasted dividend yield for the next 24 months.
  * `kr-dividend_yield_24mf-z` - Z-score of the 24-month forward dividend yield.
  * Relative 3-Year
    * `kr-dividend_yield_24mf_rel3y` - Represents the 24-month forward dividend yield relative to the past 3 years.
    * `kr-dividend_yield_24mf_rel3y-z` - Z-score of the 24-month forward dividend yield relative to the past 3 years.

**Profit Surprises**

* Net Profit Surprise
  * Current Quarter
    * `kr-net_profit_surprise_fq0` - Represents the net profit surprise for the current quarter.
    * `kr-net_profit_surprise_fq0-z` - Z-score of the net profit surprise for the current quarter.
  * Adjusted
    * `kr-net_profit_surprise_adj_fq0` - Represents the adjusted net profit surprise for the current quarter.
    * `kr-net_profit_surprise_adj_fq0-z` - Z-score of the adjusted net profit surprise for the current quarter.
* Operating Profit Surprise
  * Current Quarter
    * `kr-operating_profit_surprise_fq0` - Represents the operating profit surprise for the current quarter.
    * `kr-operating_profit_surprise_fq0-z` - Z-score of the operating profit surprise for the current quarter.
  * Adjusted
    * `kr-operating_profit_surprise_adj_fq0` - Represents the adjusted operating profit surprise for the current quarter.
    * `kr-operating_profit_surprise_adj_fq0-z` - Z-score of the adjusted operating profit surprise for the current quarter.

**Profit Growth**

* Net Profit Growth
  * Current Quarter
    * `kr-net_profit_growth_fq0` - Represents the net profit growth for the current quarter.
    * `kr-net_profit_growth_fq0-z` - Z-score of the net profit growth for the current quarter.
* Operating Profit Growth
  * Current Quarter
    * `kr-operating_profit_growth_fq0` - Represents the operating profit growth for the current quarter.
    * `kr-operating_profit_growth_fq0-z` - Z-score of the operating profit growth for the current quarter.

**Target Price Change**

* 21-Day Change
  * `kr-change_of_target_price_21d` - Represents the change in target price over the past 21 days.
  * `kr-change_of_target_price_21d-z` - Z-score of the change in target price over the past 21 days.

**Wage to Asset Ratio**

* `kr-wage_to_asset` - Represents the ratio of wages to total assets.
* `kr-wage_to_asset-z` - Z-score of the ratio of wages to total assets.

**Price to Free Cash Flow**

* 18-Month Forward
  * `kr-p_fcf_18mf` - Represents the forecasted price to free cash flow ratio for the next 18 months.
  * `kr-p_fcf_18mf-z` - Z-score of the 18-month forward price to free cash flow ratio.
  * Relative 3-Year
    * `kr-p_fcf_18mf_rel3y` - Represents the 18-month forward price to free cash flow ratio relative to the past 3 years.
    * `kr-p_fcf_18mf_rel3y-z` - Z-score of the 18-month forward price to free cash flow ratio relative to the past 3 years.
* 24-Month Forward
  * `kr-p_fcf_24mf` - Represents the forecasted price to free cash flow ratio for the next 24 months.
  * `kr-p_fcf_24mf-z` - Z-score of the 24-month forward price to free cash flow ratio.
  * Relative 3-Year
    * `kr-p_fcf_24mf_rel3y` - Represents the 24-month forward price to free cash flow ratio relative to the past 3 years.
    * `kr-p_fcf_24mf_rel3y-z` - Z-score of the 24-month forward price to free cash flow ratio relative to the past 3 years.

**Fama-French Factors**

* `kr-ff3_daily` - Represents the daily Fama-French three-factor model coefficients.
* `kr-ff3_monthly` - Represents the monthly Fama-French three-factor model coefficients.

**Earnings Per Share (EPS)**

* Fiscal Years
  * FY1
    * Monthly
      * `kr-eps_fy1_1m` - Represents the forecasted EPS for the first fiscal year on a monthly basis.
      * `kr-eps_fy1_1m-z` - Z-score of the first fiscal year EPS on a monthly basis.
  * FY2
    * Monthly
      * `kr-eps_fy2_1m` - Represents the forecasted EPS for the second fiscal year on a monthly basis.
      * `kr-eps_fy2_1m-z` - Z-score of the second fiscal year EPS on a monthly basis.
* Fiscal Quarters
  * FQ1
    * Monthly
      * `kr-eps_fq1_1m` - Represents the forecasted EPS for the first fiscal quarter on a monthly basis.
      * `kr-eps_fq1_1m-z` - Z-score of the first fiscal quarter EPS on a monthly basis.
  * FQ2
    * Monthly
      * `kr-eps_fq2_1m` - Represents the forecasted EPS for the second fiscal quarter on a monthly basis.
      * `kr-eps_fq2_1m-z` - Z-score of the second fiscal quarter EPS on a monthly basis.
* Adjusted EPS
  * FY1
    * Monthly
      * `kr-eps_fy1_1m_adj` - Represents the adjusted forecasted EPS for the first fiscal year on a monthly basis.
      * `kr-eps_fy1_1m_adj-z` - Z-score of the adjusted first fiscal year EPS on a monthly basis.
  * FY2
    * Monthly
      * `kr-eps_fy2_1m_adj` - Represents the adjusted forecasted EPS for the second fiscal year on a monthly basis.
      * `kr-eps_fy2_1m_adj-z` - Z-score of the adjusted second fiscal year EPS on a monthly basis.

**Return Ratios**

* `kr-r126d_r21d` - Represents the return ratio of 126 days to 21 days.
* `kr-r126d_r21d-z` - Z-score of the return ratio of 126 days to 21 days.

**Trading Volume**

* Logarithm of 12-Month Trading Volume
  * `kr-log_tvol_12m` - Represents the logarithm of the trading volume over the past 12 months.
  * `kr-log_tvol_12m-z` - Z-score of the logarithm of the trading volume over the past 12 months.

**Factor Scores (Z-Scores)**

* `kr-size-z` - Z-score representing the size factor.
* `kr-value-z` - Z-score representing the value factor.
* `kr-momentum-z` - Z-score representing the momentum factor.
* `kr-quality-z` - Z-score representing the quality factor.
* `kr-volatility-z` - Z-score representing the volatility factor.
* `kr-beta-z` - Z-score representing the beta factor.
* `kr-dividend-z` - Z-score representing the dividend factor.
* `kr-surprise-z` - Z-score representing the surprise factor.
* `kr-liquidity-z` - Z-score representing the liquidity factor.

**Investment Strategies**

* `kr-benjamin_graham` - Represents the investment strategy based on Benjamin Graham's principles.
* `kr-peter_lynch` - Represents the investment strategy based on Peter Lynch's principles.
* `kr-warren_buffet` - Represents the investment strategy based on Warren Buffet's principles.
* `kr-william_oneil` - Represents the investment strategy based on William O'Neil's principles.
* `kr-david_dreman` - Represents the investment strategy based on David Dreman's principles.
* `kr-charles_munger` - Represents the investment strategy based on Charles Munger's principles.
* `kr-bill_ackman` - Represents the investment strategy based on Bill Ackman's principles.
* `kr-james_oshaughnessy` - Represents the investment strategy based on James O'Shaughnessy's principles.
* `kr-glenn_welling` - Represents the investment strategy based on Glenn Welling's principles.
* `kr-ron_baron` - Represents the investment strategy based on Ron Baron's principles.
* `kr-cathie_wood` - Represents the investment strategy based on Cathie Wood's principles.
* `kr-alex_sacerdote` - Represents the investment strategy based on Alex Sacerdote's principles.
* `kr-glenn_greenberg` - Represents the investment strategy based on Glenn Greenberg's principles.
* `kr-brad_gerstner` - Represents the investment strategy based on Brad Gerstner's principles.
* `kr-colin_moran` - Represents the investment strategy based on Colin Moran's principles.

**Debt Ratios**

* Net Debt to Equity
  * `kr-net_debt_to_equity` - Represents the net debt to equity ratio.
  * `kr-net_debt_to_equity-z` - Z-score of the net debt to equity ratio.

#### Regime

This document provides information on accessing the market regime data for the Korean stock market, suitable for quantitative analysis.

**Example code**

```python
from finter.data import ContentFactory

cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("hmm")
```

**Metadata**

| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
|            |                   |           |                |

**Item List**

**Hidden Markov Models (HMM)**

* `hmm`: Standard HMM for KR STOCK
* `hmm_sp500`: HMM based on S\&P 500 index
* `hmm_wo_us_etf`: HMM without US ETFs
* `hmm_wo_krx_etf`: HMM without KRX-listed ETFs

**OECD Economic Indicators**

* `oecd_usa`: OECD indicators for the USA
* `oecd_world`: Global OECD indicators
* `oecd_china`: OECD indicators for China

**Market Sentiment**

* `k200_ms4`: Market sentiment for KOSPI 200

### Event

#### Company Status

This document provides information on how to access and use the company status event data for the Korean stock market from our data catalog.

**Example code**

```python
from finter.data import ContentFactory

cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("overheating")
```

**Metadata**

| Valid From | Delivery Schedule   | Time Zone  | Data Frequency |
| ---------- | ------------------- | ---------- | -------------- |
| 2000-01-01 | KST 20 23 \* \* 1-5 | Asia/Seoul | 1d             |

**Item List**

**Company Status**

* `market`: General market-related status updates. (1: KS, 2: KQ)
* `liquidation`: Information about liquidation trading (1: applicable, 0: normal).
* `unreliable`: Data on unreliable public disclosure companies (1: applicable, 0: not applicable).
* `abnormal`: Details on abnormal surge stocks (1: applicable, 0: not applicable).
* `overheating`: Information on short-term overheating stocks (0: not applicable, 1: designation notice, 2: designated, 3: designation extended).
* `borrowing`: Data on borrowing short-selling overheating stocks (1: applicable, 0: not applicable).
* `illiquid`: Information on low liquidity stocks (1: applicable, 0: not applicable).
* `caution`: Alerts and warnings issued for particular stocks (1: caution, 0: normal).
* `alert`: Investment warning classification for stocks (1: applicable, 0: not applicable).
* `administration`: Information on administrative decisions affecting stocks (1: administrative, 0: normal).
* `suspension`: Notices of temporary halts in trading for certain stocks (1: suspended, 0: normal).
* `list_yn` : Indicates whether the stock is listed (1: listed, NaN: delisted)

#### Corporate Actions

This document provides information on accessing corporate actions data for the Korean stock market, including adjustment and dividend factors.

**Example code**

```python
from finter.data import ContentFactory

cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("adjust_factor")
```

**Metadata**

| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
|            |                   |           |                |

**Item List**

**Corporate Actions**

* `adjust_factor`: A multiplier applied to stock prices and volumes to account for historical changes due to stock splits, dividends, and other corporate actions, ensuring comparability over time.
* `dividend_factor`: A factor reflecting the adjustment made to a stock's price to incorporate the distribution of dividends, aiming to present a price that accurately represents the stock's value post-dividend payout.

### Economic

#### Indicators

This document provides an overview of the economic indicators data related to the Korean stock market, available for use in financial analysis.

**Example code**

```python
from finter.data import ContentFactory

cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("currency")
```

**Metadata**

| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
|            |                   |           |                |

**Item List**

**Currency**

* `currency`: Information about the currency rates.

**Interest Rates**

* `interest_rate`: Data on the prevailing interest rates.

**Market Indices**

* `index_close`: Closing values of the market indices.
* `index_mkt_cap`: Market capitalization based on the indices.

### Unstructured

#### Theme

**VAIV Theme**

This dataset provides various metrics and scores related to companies, businesses, themes, and items, including keyword analysis and sales ratios.

**Example code**

```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20240101, 20240201)
df = cf.get_df("business-ma")
```

**Metadata**

| Valid From | Delivery Schedule  | Time Zone  | Data Frequency |
| ---------- | ------------------ | ---------- | -------------- |
| 2018-01-01 | UTC 10 22 \* \* \* | Asia/Seoul | 1d             |

**Structure**

<table data-header-hidden><thead><tr><th width="157"></th><th></th></tr></thead><tbody><tr><td>ma</td><td>7-day moving average of buzz volume in the news sector and blog sector (normalized per 100,000 keyword occurrences)</td></tr><tr><td>score</td><td>Theme score</td></tr><tr><td>z_score</td><td>180-day z_score of buzz volume in the news sector and blog sector (normalized per 100,000 keyword occurrences)</td></tr></tbody></table>

**Item List**

**Business Metrics**

* `business-ma`
* `business-score`
* `business-z_score`

**Theme Metrics**

* `theme-ma`
* `theme-score`
* `theme-z_score`

**Item Metrics**

* `item-ma`
* `item-score`
* `item-z_score`

**VAIV Company Keyword**

This dataset provides various metrics related to company keywords, including their relevance scores, sales ratios, and total sales.

**Example code**

```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20240101, 20240201)
df = cf.get_df("company-keyword_score")
```

**Metadata**

| Valid From | Delivery Schedule  | Time Zone  | Data Frequency |
| ---------- | ------------------ | ---------- | -------------- |
| 2018-01-01 | UTC 10 22 \* \* \* | Asia/Seoul | 1d             |

**Structure**

<table data-header-hidden><thead><tr><th width="152"></th><th></th></tr></thead><tbody><tr><td>index</td><td>date</td></tr><tr><td>column</td><td><p>multi-index</p><p>level 0 - ccid</p><p>level 1 - theme keyword</p></td></tr><tr><td>value</td><td>float</td></tr></tbody></table>

**Item List**

* `company-keyword_pearson`: Pearson correlation coefficient for company keywords.
* `company-keyword_score`: Relevance score for company keywords.
* `company-keyword_sales_ratio`: Sales ratio associated with company keywords.
* `company-keyword_total_sales`: Total sales associated with company keywords.
* `company-keyword_last_upt_dt`: Last update date for company keyword data.

#### EwsEarlyWarningSignal

**Korea Sentiment Index**

This dataset provides various sentiment and tone metrics related to the Korean market and the S\&P 500.

**Example code**

```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20240101, 20240201)
df = cf.get_df("korea_tone")
```

**Metadata**

| Valid From | Delivery Schedule  | Time Zone  | Data Frequency |
| ---------- | ------------------ | ---------- | -------------- |
| 2005-01-02 | UTC 30 21 \* \* \* | Asia/Seoul | 1d             |

**Item List**

**Sentiment and Tone Metrics**

* `korea_tone`: Korean Sentiment Tone Index
* `korea_tone_neutral`: Korean Sentiment Neutral Index (Valid From: 2020-10-21)
* `korea_tone_SP500`: English Sentiment Tone Index on Korea (Valid To: 2021-07-31)

**Korea EWS Index**

This dataset provides early warning signals (EWS) for the Korean market and a specific subset for the S\&P 500, including Principal Component Analysis (PCA) related data, proxy, and sentiment metrics.

**Example code**

```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20240101, 20240201)
df = cf.get_df("pca_proxy")
df = cf.get_df("korea_EWS", freq="1W") # weekly
df = cf.get_df("korea_EWS", freq="1M") # monthly
```

**Metadata**

| Valid From | Delivery Schedule   | Time Zone  | Data Frequency |
| ---------- | ------------------- | ---------- | -------------- |
| 2008-01-02 | UTC 30 21 \* \* 0-4 | Asia/Seoul | 1d, 1W, 1M     |

**Item List**

* `pca_proxy`: PCA Integrated Macroeconomic and Financial Indicators Input Feature
* `pca_sentiment`: PCA Integrated Macroeconomic and Financial Indicators
* weekly
  * `korea_EWS`: Weekly KOSPI EWS Based on Korean Sentiment Index
* monthly
  * `korea_EWS`: Monthly KOSPI EWS Based on Korean Sentiment Index
  * `korea_EWS_SP500`: Monthly KOSPI EWS Based on English Sentiment Index

**VAIV Keyterm**

This dataset provides metrics on news articles, segmented by sentiment (positive, negative, neutral) and category, including rates and counts of materials.

**Example code**

```python
from finter.data import ContentFactory
cf = ContentFactory("kr_stock", 20240101, 20240201)

#### sentiment_us category
df = cf.get_df("all-N_of_mat_key[all_cat]", cagegory='sentiment')

#### sentiment_exp_us category
df = cf.get_df("all-N_of_mat_key[all_cat]", cagegory='sentiment_exp')
```

**Metadata**

| Valid From | Delivery Schedule  | Time Zone  | Data Frequency |
| ---------- | ------------------ | ---------- | -------------- |
| 2005-01-01 | UTC 00 21 \* \* \* | Asia/Seoul | 1d             |

**Item List**

* Use `sentiment` category for Korea
* Use `sentiment_exp` category for Korea Expanded

**All Sentiment**

* `all-N_of_mat_key[all_cat]`: Number of total key terms for overall sentiment on the given day in Korea.
* `all-N_of_mat_key[in_cat]`: Number of matching key terms for overall sentiment in the specified category on the given day in Korea.
* `all-N_of_news`: Number of total news articles for overall sentiment on the given day in Korea.
* `all-mat_key_rate[in_cat%all_cat]`: Category key term rate for overall sentiment on the given day in Korea.

**Negative Sentiment**

* `neg-N_of_mat_key[all_cat]`: Number of total key terms for negative sentiment on the given day in Korea.
* `neg-N_of_mat_key[in_cat]`: Number of matching key terms for negative sentiment in the specified category on the given day in Korea.
* `neg-N_of_news`: Number of total news articles for negative sentiment on the given day in Korea.
* `neg-mat_key_rate[in_cat%all_cat]`: Category key term rate for negative sentiment on the given day in Korea.

**Neutral Sentiment**

* `neu-N_of_mat_key[all_cat]`: Number of total key terms for neutral sentiment on the given day in Korea.
* `neu-N_of_mat_key[in_cat]`: Number of matching key terms for neutral sentiment in the specified category on the given day in Korea.
* `neu-N_of_news`: Number of total news articles for neutral sentiment on the given day in Korea.
* `neu-mat_key_rate[in_cat%all_cat]`: Category key term rate for neutral sentiment on the given day in Korea.

**Positive Sentiment**

* `pos-N_of_mat_key[all_cat]`: Number of total key terms for positive sentiment on the given day in Korea.
* `pos-N_of_mat_key[in_cat]`: Number of matching key terms for positive sentiment in the specified category on the given day in Korea.
* `pos-N_of_news`: Number of total news articles for positive sentiment on the given day in Korea.
* `pos-mat_key_rate[in_cat%all_cat]`: Category key term rate for positive sentiment on the given day in Korea.

### \[Free] Open dataset

The data is released for free in the public interest by Dart, a Korean open data portal and publication site, and the data column is returned with the 6-digit stock short\_code.

**Open data will gradually be added in the future.**

#### Example code

```python
from finter.data import ContentFactory

cf = ContentFactory("kr_stock", 20200101, 20200201)
df = cf.get_df("price_close")
```

#### Metadata

| Valid From | Delivery Schedule | Time Zone      | Data Frequency |
| ---------- | ----------------- | -------------- | -------------- |
|            |                   | **Asia/Seoul** | **1d**         |

#### Item List

**Price Data**

* `price_close`: Closing price
* `price_high`: Highest price
* `price_low`: Lowest price
* `volume_sum`: Total volume
* `listed_shares`: Number of listed shares
* `mkt_cap`: Market capitalization

**Where does the data come from?**

* Korea public data portal : [https://www.data.go.kr/](https://www.data.go.kr/)

## US ETF

### Market

#### Price Volume

A concise reference for accessing US etf market price and volume data.

**Example code**

```python
from finter.data import ContentFactory

cf = ContentFactory("us_etf", 20200101, 20200201)
df = cf.get_df("price_close")
```

**Metadata**

| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 1983-12-30 |                   |           | 1d             |

**Item List**

**Price Data**

* `price_close`: Closing price
* `price_high`: Base price
* `price_open`: Opening price
* `price_low`: Highest price

**Volume Data**

* `trading_volume`: Total volume

**Shares Data**

* `shares_outstanding`: Number of listed shares

**Dividend Data**

* `indicated_annual_dividend`: Indicated annual dividend

**Earnings Data**

* `eps`: Earnings per share (EPS)

**Additional Data**

* `amount`: Amount
* `ctoc`: Daily CtoC (close-to-close) return
* `ctoc_total`: Total CtoC (close-to-close) return (dividend applied)

## US STOCK

### Market

#### Price Volume

A concise reference for accessing US stock market price and volume data.

**Example code**

```python
from finter.data import ContentFactory

cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("price_close")
```

**Metadata**

| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 1998-04-01 |                   |           | 1d             |

**Item List**

**Price Data**

* `price_close`: Closing price
* `price_high`: Base price
* `price_open`: Opening price
* `price_low`: Highest price

**Volume Data**

* `trading_volume`: Total volume

**Shares Data**

* `shares_outstanding`: Number of listed shares

**Dividend Data**

* `indicated_annual_dividend`: Indicated annual dividend

**Earnings Data**

* `eps`: Earnings per share (EPS)

**Additional Data**

* `amount`: Amount
* `ctoc`: Daily CtoC (close-to-close) return
* `ctoc_total`: Total CtoC (close-to-close) return (dividend applied)

#### Classification

This dataset provides classification information for US stocks, specifically using the GICS (Global Industry Classification Standard).

**Example code**

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

**Metadata**

| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 1998-04-01 |                   |           | 1d             |

**Item List**

* gics

### Financial

#### Financial Statement

**Summary**

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**

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

**Metadata**

| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 1982-01-31 |                   |           | 1d             |

**Item List**

**Balance Sheet Items**

* Assets
  * **atq**: Total Assets
  * **actq**: Current Assets
  * **rectq**: Accounts Receivable
  * **cheq**: Cash and Short-Term Investment
  * **invtq**: Inventories
  * **ancq**: Non-Current Assets
  * **intanq**: Intangible Assets
  * **ivaoq**: Investment and Advances
  * **ivstq**: Short-Term Investments
  * **ppegtq**: Property, Plant and Equipment Gross
  * **ppentq**: Property, Plant and Equipment Net
* Liabilities
  * **ltq**: Total Liabilities
  * **lctq**: Current Liabilities
  * **apq**: Accounts Payable
  * **dlcq**: Short-Term Debt
  * **txpq**: Income Taxes Payable
  * **dlttq**: Long-Term Debt
  * **txditcq**: Deferred Taxes and Investment Credit
* Equity
  * **pstkq**: Preferred Stock
  * **seqq**: Shareholders Equity

**Income Statement Items**

* **revtq**: Sales
* **cogsq**: Cost of Goods Sold
* **gpq**: Gross Profit
* **xsgaq**: Selling, General and Administrative Expenses
* **xrdq**: Research and Development Expenses
* **xstq**: Staff Expense - Wages/Salaries
* **xstoq**: Staff Expense - Other
* **spiq**: Special Items
* **xoprq**: Operating Expenses
* **oibdpq**: Operating Income Before Depreciation
* **dpq**: Depreciation and Amortization
* **oiadpq**: Operating Income After Depreciation
* **xintq**: Interest Expenses
* **piq**: Earnings before Tax and Extraordinary Items
* **txtq**: Income Tax
* **xidoq**: Extraordinary Items and Discontinued Operations
* **ibq**: Net Income
* **niq**: Net Income Including Extraordinary Items
* **dvtq**: Total Dividends
* **saleq**: Net Sales
* **req**: Retained Earnings

**Earning Per Shares Items**

* **epsf12**: Earnings Per Share (Diluted) - Excluding Extraordinary Items - 12 Months Moving
* **epsfi12**: Earnings Per Share (Diluted) - Including Extraordinary Items - 12 Months Moving
* **epsfiq**: Earnings Per Share (Diluted) - Including Extraordinary Items
* **epsfxq**: Earnings Per Share (Diluted) - Excluding Extraordinary items
* **epspi12**: Earnings Per Share (Basic) - Including Extraordinary Items - 12 Months Moving
* **epspiq**: Earnings Per Share (Basic) - Including Extraordinary Items
* **epspxq**: Earnings Per Share (Basic) - Excluding Extraordinary Items
* **epsx12**: Earnings Per Share (Basic) - Excluding Extraordinary Items - 12 Months Moving
* **oepsxq**: Earnings Per Share (Diluted) - from Operations
* **opepsq**: Earnings Per Share from Operations
* **oepf12**: Earnings Per Share (Diluted) - from Operations - 12 Months Moving
* **oeps12**: Earnings Per Share from Operations - 12 Months Moving

#### PIT Financial Statement

**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.**

**Example code**

```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20200101, 20200201)
df_modeling = cf.get_df("pit-atq")
df_original = cf.get_df("pit-atq", mode = "original")
df_unpivot = cf.get_df("pit-atq", mode = "unpivot")
```

**Metadata**

| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
| 1982-01-31 |                   |           | 1d             |

**Item List**

**Balance Sheet Items**

* Assets
  * **pit-atq**: Total Assets
  * **pit-actq**: Current Assets
  * **pit-rectq**: Accounts Receivable
  * **pit-cheq**: Cash and Short-Term Investment
  * **pit-invtq**: Inventories
  * **pit-ancq**: Non-Current Assets
  * **pit-intanq**: Intangible Assets
  * **pit-ivaoq**: Investment and Advances
  * **pit-ivstq**: Short-Term Investments
  * **pit-ppegtq**: Property, Plant and Equipment Gross
  * **pit-ppentq**: Property, Plant and Equipment Net
* Liabilities
  * **pit-ltq**: Total Liabilities
  * **pit-lctq**: Current Liabilities
  * **pit-apq**: Accounts Payable
  * **pit-dlcq**: Short-Term Debt
  * **pit-txpq**: Income Taxes Payable
  * **pit-dlttq**: Long-Term Debt
  * **pit-txditcq**: Deferred Taxes and Investment Credit
* Equity
  * **pit-pstkq**: Preferred Stock
  * **pit-seqq**: Shareholders Equity

**Income Statement Items**

* **pit-revtq**: Sales
* **pit-cogsq**: Cost of Goods Sold
* **pit-gpq**: Gross Profit
* **pit-xsgaq**: Selling, General and Administrative Expenses
* **pit-xrdq**: Research and Development Expenses
* **pit-xstq**: Staff Expense - Wages/Salaries
* **pit-xstoq**: Staff Expense - Other
* **pit-spiq**: Special Items
* **pit-xoprq**: Operating Expenses
* **pit-oibdpq**: Operating Income Before Depreciation
* **pit-dpq**: Depreciation and Amortization
* **pit-oiadpq**: Operating Income After Depreciation
* **pit-xintq**: Interest Expenses
* **pit-piq**: Earnings before Tax and Extraordinary Items
* **pit-txtq**: Income Tax
* **pit-xidoq**: Extraordinary Items and Discontinued Operations
* **pit-ibq**: Net Income
* **pit-niq**: Net Income Including Extraordinary Items
* **pit-dvtq**: Total Dividends
* **pit-saleq**: Net Sales
* **pit-req**: Retained Earnings

**Earning Per Shares Items**

* **pit-epsf12**: Earnings Per Share (Diluted) - Excluding Extraordinary Items - 12 Months Moving
* **pit-epsfi12**: Earnings Per Share (Diluted) - Including Extraordinary Items - 12 Months Moving
* **pit-epsfiq**: Earnings Per Share (Diluted) - Including Extraordinary Items
* **pit-epsfxq**: Earnings Per Share (Diluted) - Excluding Extraordinary items
* **pit-epspi12**: Earnings Per Share (Basic) - Including Extraordinary Items - 12 Months Moving
* **pit-epspiq**: Earnings Per Share (Basic) - Including Extraordinary Items
* **pit-epspxq**: Earnings Per Share (Basic) - Excluding Extraordinary Items
* **pit-epsx12**: Earnings Per Share (Basic) - Excluding Extraordinary Items - 12 Months Moving
* **pit-oepsxq**: Earnings Per Share (Diluted) - from Operations
* **pit-opepsq**: Earnings Per Share from Operations
* **pit-oepf12**: Earnings Per Share (Diluted) - from Operations - 12 Months Moving
* **pit-oeps12**: Earnings Per Share from Operations - 12 Months Moving

### Quantitative

#### Factor

Access a comprehensive collection of quantitative factor data for US stocks, suitable for financial analysis and modeling.

**Example code**

```python
from finter.data import ContentFactory

cf = ContentFactory("us_stock", 20200101, 20200201)
df = cf.get_df("ebitda_at")
```

**Item List**

**Growth - Percentage**

• at\_gr1 : Asset Growth 1yr

• sale\_gr1 : Sales Growth 1yr

• ca\_gr1 : Current Asset Growth 1yr

• nca\_gr1 : Non-Current Asset Growth 1yr

• lt\_gr1 : Total Liabilities Growth 1yr

• cl\_gr1 : Current Liabilities Growth 1yr

• ncl\_gr1 : Non-Current Liabilities Growth 1yr

• be\_gr1 : Book Equity Growth 1yr

• pstk\_gr1 : Preferred Stock Growth 1yr

• debt\_gr1 : Total Debt Growth 1yr

• cogs\_gr1 : Cost of Goods Sold Growth 1yr

• sga\_gr1 : Selling, General, and Administrative Expenses Growth 1yr

• opex\_gr1 : Operating Expenses Growth 1yr

• 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

• 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

• debt\_me : Total Debt scaled by ME

• netdebt\_me : Net Debt scaled by ME

• 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

• aliq\_mat : Liquidity scaled by lagged Market Assets

• tangibility : Tangibility

• eq\_dur : Equity Duration

• o\_score : Ohlson O-Score

• z\_score : Altman Z-Score

• kz\_index : Kaplan-Zingales Index

• earnings\_variability : Earnings Variability

• ni\_ar1 : 1 yr lagged Net Income to Assets

• ni\_ivol : Net Income Idiosyncratic Volatility

**Reference and Formula**

[Jensen, Theis Ingerslev, Bryan Kelly, and Lasse Heje Pedersen. "Is there a replication crisis in finance?." _The Journal of Finance_ 78.5 (2023): 2465-2518.](https://onlinelibrary.wiley.com/doi/full/10.1111/jofi.13249)

[Jensen, Theis Ingerslev, Bryan Kelly, and Lasse Heje Pedersen. "Global Factor Data Documentation." (2021).](https://jkpfactors.s3.amazonaws.com/documents/Documentation.pdf)

{% hint style="warning" %}
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_, 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_.
{% endhint %}

### Unstructured

#### EwsEarlyWarningSignal

**US sentiment index**

This dataset includes raw and post-processed SARIMA sentiment analysis metrics from business news articles, along with various US market news tone metrics.

**Example code**

```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20240101, 20240201)
df = cf.get_df("raw_sentiment_biz_news-1_1-sum_sum")
```

**Metadata**

| Valid From | Delivery Schedule | Time Zone  | Data Frequency |
| ---------- | ----------------- | ---------- | -------------- |
| 2004-01-01 | UTC 0 18 \* \* \* | US/Eastern | 1d             |

**Item List**

* `raw_sentiment_biz_news-1_1-sum_sum`: Past processing method, global English sentiment index extracted using sum + sum method
* `raw_sentiment_biz_news-1_1-sum_per`: Past processing method, global English sentiment index extracted using sum + neutralization method
* `raw_sentiment_biz_news-1_1-mean_sum`: Past processing method, global English sentiment index extracted using mean + sum method
* `raw_sentiment_biz_news-1_1-mean_per`: Past processing method, global English sentiment index extracted using mean + neutralization method
* `raw_sentiment_biz_news-1_2-sum_sum`: Latest processing method, global English sentiment index extracted using sum + sum method
* `raw_sentiment_biz_news-1_2-sum_per`: Latest processing method, global English sentiment index extracted using sum + neutralization method
* `raw_sentiment_biz_news-1_2-mean_sum`: Latest processing method, global English sentiment index extracted using mean + sum method
* `raw_sentiment_biz_news-1_2-mean_per`: Latest processing method, global English sentiment index extracted using mean + neutralization method
* `post_sarima_sent_biz_news-1_1-sum_sum`: Past processing method, time series processed index of global English sentiment index extracted using sum + sum method
* `post_sarima_sent_biz_news-1_1-sum_per`: Past processing method, time series processed index of global English sentiment index extracted using sum + neutralization method
* `post_sarima_sent_biz_news-1_2-sum_sum`: Latest processing method, time series processed index of global English sentiment index extracted using sum + sum method
* `post_sarima_sent_biz_news-1_2-sum_per`: Latest processing method, time series processed index of global English sentiment index extracted using sum + neutralization method
* `us_tone_m3`: Sentiment index calculated for positive, negative, and neutral sentiments related to the key term sentiment\_us
* `us_tone_m3_exp`: Sentiment index calculated for positive, negative, and neutral sentiments related to the key term sentiment\_exp\_us

**US EWS Index**

This dataset includes various S\&P 500 EWS versions from original, and business news sources. It also contains metrics on the tone of US market news.

**Example code**

```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20240101, 20240201)

#### weekly
df = cf.get_df("SP500_EWS_origin_biz_news", freq="1W")

#### monthly
df = cf.get_df("SP500_EWS_origin_biz_news", freq="1M")
```

**Metadata**

| Valid From | Delivery Schedule | Time Zone  | Data Frequency |
| ---------- | ----------------- | ---------- | -------------- |
| 2008-02-01 | UTC 0 9 \* \* \*  | US/Eastern | 1W, 1M         |

**Item List**

* weekly
  * `SP500_EWS_origin_biz_news`: Previous version of the weekly S\&P 500 EWS based on global sentiment time series index (post\_sarima\_sent\_biz\_news-1\_1-sum\_per)
  * `SP500_EWS_biz_news`: Latest version of the weekly S\&P 500 EWS based on global sentiment time series index (post\_sarima\_sent\_biz\_news-1\_2-sum\_sum)
* monthly
  * `SP500_EWS_origin_biz_news`: Previous version of the monthly S\&P 500 EWS based on global sentiment time series index (post\_sarima\_sent\_biz\_news-1\_1-sum\_per)
  * `SP500_EWS_biz_news`: Latest version of the monthly S\&P 500 EWS based on global sentiment time series index (post\_sarima\_sent\_biz\_news-1\_2-sum\_sum)
  * `us_tone_m3_EWS_compustat`: Monthly S\&P 500 EWS based on key term sentiment US sentiment index
  * `us_tone_m3_exp_EWS_compustat`: Monthly S\&P 500 EWS based on key term sentiment export US sentiment index

**VAIV Keyterm**

This dataset provides metrics on news articles, segmented by sentiment (positive, negative, neutral) and category, including rates and counts of materials.

**Example code**

```python
from finter.data import ContentFactory
cf = ContentFactory("us_stock", 20240101, 20240201)

#### sentiment_us category
df = cf.get_df("all-mat_cat_rate", cagegory='sentiment_us')

#### sentiment_exp_us category
df = cf.get_df("all-mat_cat_rate", cagegory='sentiment_exp_us')
```

**Metadata**

| Valid From | Delivery Schedule  | Time Zone  | Data Frequency |
| ---------- | ------------------ | ---------- | -------------- |
| 2005-01-01 | UTC 00 21 \* \* \* | Asia/Seoul | 1d             |

**Item List**

* Use `sentiment_us` category for US
* Use `sentiment_exp_us` category for US Expanded

**All Sentiment**

* `all-mat_cat_rate`: Category rate for overall sentiment on the given day in the U.S.
* `all-N_of_mat_cat_each`: Number of news articles for overall sentiment in each category on the given day in the U.S.
* `all-N_of_news`: Number of total news articles for overall sentiment on the given day in the U.S.

**Negative Sentiment**

* `neg-mat_cat_rate`: Category rate for negative sentiment on the given day in the U.S.
* `neg-N_of_mat_cat_each`: Number of news articles for negative sentiment in each category on the given day in the U.S.
* `neg-N_of_news`: Number of total news articles for negative sentiment on the given day in the U.S.

**Neutral Sentiment**

* `neu-mat_cat_rate`: Category rate for neutral sentiment on the given day in the U.S.
* `neu-N_of_mat_cat_each`: Number of news articles for neutral sentiment in each category on the given day in the U.S.
* `neu-N_of_news`: Number of total news articles for neutral sentiment on the given day in the U.S.

**Positive Sentiment**

* `pos-mat_cat_rate`: Category rate for positive sentiment on the given day in the U.S.
* `pos-N_of_mat_cat_each`: Number of news articles for positive sentiment in each category on the given day in the U.S.
* `pos-N_of_news`: Number of total news articles for positive sentiment on the given day in the U.S.

## VN STOCK

### Market

#### Price Volume

This dataset provides daily price and volume information for the Vietnamese market, including stock prices, trading volumes, and other financial metrics.

**Example code**

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

**Metadata**

<table><thead><tr><th>Valid From</th><th>Delivery Schedule</th><th width="209">Time Zone</th><th>Data Frequency</th></tr></thead><tbody><tr><td>20060711</td><td>00 22 * * 1-5</td><td>Asia/Ho_Chi_Minh</td><td>1d</td></tr></tbody></table>

**Item List**

**Price Information**

* price\_close: Closing price of the stock.
* price\_high: Highest price of the stock during the trading day.
* price\_open: Opening price of the stock.
* price\_low: Lowest price of the stock during the trading day.

**Volume Information**

* trading\_volume: Number of shares traded during the day.
* shares\_outstanding: Total number of shares outstanding.
* amount: The total value of transactions during the trading day.

#### Classification

This dataset provides classification information for Vietnamese stocks, specifically using the GICS (Global Industry Classification Standard).

**Example code**

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

**Metadata**

| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
|            |                   |           | 1d             |

**Item List**

* gics

### Event

#### Corporate Actions

This dataset provides adjustment factors and total return factors for Vietnamese stocks.

**Example code**

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

**Metadata**

| Valid From | Delivery Schedule | Time Zone | Data Frequency |
| ---------- | ----------------- | --------- | -------------- |
|            |                   |           |                |

**Item List**

* adjust\_factor
* total\_return\_factor
