Shareholders

Japanese disclosure splits shareholder information across several filings, and jfinance exposes each one separately rather than merging them.

Method	Source
`major_holders`	Annual report, ownership breakdown
`major_shareholders`	Annual report, major shareholders
`institutional_holders`	Large shareholding reports, special filing rule
`insider_roster_holders`	Annual report, officers
`large_holders`, `large_holder_transactions`, `large_holder_activity`, `large_holding_reports`	Large shareholding reports

From the annual report

get_major_holders

Ticker.major_holders -> pandas.DataFrame
Ticker.get_major_holders(as_dict=False) -> pandas.DataFrame | dict

Ownership percentages by holder type, from the ownership breakdown in the annual report. Available from fiscal 2019 annual reports.

Parameters

Name	Type	Default	Description
`as_dict`	`bool`	`False`	Return a `dict` instead of a `DataFrame`

Returns — pandas.DataFrame with one column, Value, indexed by the keys below.

Index	Type	Description
`institutionsPercentHeld`	`float`	Financial institutions + securities firms + foreign corporations
`institutionsCount`	`float`	Number of such holders
`insidersPercentHeld`	`float`	Officers’ holdings
`financialInstitutionsPercentHeld`	`float`	Financial institutions
`financialServiceProvidersPercentHeld`	`float`	Securities firms
`otherCorporationsPercentHeld`	`float`	Other corporations
`foreignCorporationsPercentHeld`	`float`	Foreign corporations
`foreignIndividualsPercentHeld`	`float`	Foreign individuals
`individualsAndOthersPercentHeld`	`float`	Individuals and others
`asOfDate`	`int`	Record date, Unix seconds
`sourceDocumentId`	`str`	EDINET document ID

Percentages are decimals, so 0.8669 is 86.69%. Where the unit of an officer’s shareholding cannot be read from the filing, insidersPercentHeld is omitted — jfinance does not fill a value by guessing.

get_major_shareholders

Ticker.major_shareholders -> pandas.DataFrame
Ticker.get_major_shareholders(as_dict=False, year=None) -> pandas.DataFrame | dict

The top ten shareholders named in the annual report. Registered holders, so trust bank accounts appear at the top; this is not a beneficial ownership list.

Parameters

Name	Type	Default	Description
`as_dict`	`bool`	`False`	Return a `dict` instead of a `DataFrame`
`year`	`int`	`None`	Fiscal year of the annual report. Latest when omitted. Available years are in `.attrs["years"]`

Returns — pandas.DataFrame, one row per shareholder.

Column	Type	Description
`Date Reported`	`datetime64`	Record date
`Holder`	`str`	Holder name as filed
`Address`	`str`	Address as filed
`pctHeld`	`float`	Holding ratio, as a decimal
`Shares`	`int`	Shares held
`Rank`	`int`	Rank, 1 to 10
`Document ID`	`str`	EDINET document ID

get_insider_roster_holders

Ticker.insider_roster_holders -> pandas.DataFrame
Ticker.get_insider_roster_holders(as_dict=False) -> pandas.DataFrame | dict

Officers and the shares they hold, from the annual report, under yfinance’s column names.

Returns — pandas.DataFrame

Column	Type	Description
`Name`	`str`	Name as filed
`Position`	`str`	Title
`Shares Owned Directly`	`int`	Shares held
`Position Direct Date`	`datetime64`	Record date
`URL`, `Most Recent Transaction`, `Latest Transaction Date`	—	Present for yfinance compatibility and always empty; EDINET discloses no insider transactions

From large shareholding reports

Large shareholding reports are filed under the 5% rule. They are filed by the holder, not by the company, and cover companies and individuals as well as institutions.

get_institutional_holders

Ticker.institutional_holders -> pandas.DataFrame
Ticker.get_institutional_holders(as_dict=False) -> pandas.DataFrame | dict

Institutions holding over 5%, from large shareholding reports filed under the special rule available to institutional investors. Column names follow yfinance.

Returns — pandas.DataFrame

Column	Type	Description
`Date Reported`	`datetime64`	Record date
`Holder`	`str`	Holder name
`pctHeld`	`float`	Holding ratio, as a decimal
`Shares`	`int`	Shares held
`pctChange`	`float`	Change from the previous report
`holderEdinetCode`	`str`	EDINET code of the holder
`sourceDocumentId`	`str`	EDINET document ID
`Value`	—	Present for yfinance compatibility and always empty; valuing the holding needs a share price

get_large_holders

Ticker.large_holders -> pandas.DataFrame
Ticker.get_large_holders(as_dict=False) -> pandas.DataFrame | dict

Every filer of a large shareholding report, including companies and individuals, one row per holder showing their most recent report.

Returns — pandas.DataFrame

Column	Type	Description
`Date Reported`	`datetime64`	Record date
`Holder`	`str`	Holder name
`pctHeld`	`float`	Holding ratio, as a decimal
`Shares`	`float`	Shares held
`pctChange`	`float`	Change from the previous report
`Date Filed`	`datetime64`	Filing date
`Holder EDINET Code`	`str`	EDINET code of the holder
`Document ID`	`str`	EDINET document ID

get_large_holder_transactions

Ticker.large_holder_transactions -> pandas.DataFrame
Ticker.get_large_holder_transactions(as_dict=False) -> pandas.DataFrame | dict

Acquisitions and disposals in the preceding 60 days, as disclosed in large shareholding reports. The 100 most recent, with identical trades merged into one row.

Returns — pandas.DataFrame

Column	Type	Description
`Date`	`datetime64`	Trade date
`Holder`	`str`	Holder name
`Action`	`str`	`acquire` or `dispose`
`Action Text`	`str`	Wording used in the filing
`Shares`	`float`	Shares traded
`Unit Price`	`float`	Price per share
`Value`	`float`	Consideration
`Security Type`	`str`	Type of security
`Market`	`str`	Venue
`Holder EDINET Code`	`str`	EDINET code of the holder
`Document ID`	`str`	EDINET document ID

get_large_holder_activity

Ticker.large_holder_activity -> pandas.DataFrame
Ticker.get_large_holder_activity(as_dict=False) -> pandas.DataFrame | dict

The above summarised over six months, counted back from the most recent filing date in the data. Ordinary shares and investment units only. The shape matches yfinance’s insider_purchases: seven rows, with Shares and Trans columns.

get_large_holding_reports

Ticker.large_holding_reports -> pandas.DataFrame
Ticker.get_large_holding_reports(limit=100, offset=0) -> pandas.DataFrame

Every large shareholding report filed for the security, newest first, one row per joint holder. The total number of reports is in .attrs["total"].

Parameters

Name	Type	Default	Description
`limit`	`int`	`100`	Rows to return
`offset`	`int`	`0`	Rows to skip

Returns — pandas.DataFrame

Column	Type	Description
`Date Filed`, `Base Date`	`datetime64`	Filing date and record date
`Title`	`str`	Title of the filing
`Holder`, `Holder Type`, `Is Joint`	`str`, `str`, `bool`	Holder, its classification, and whether it is a joint holder
`pctHeld`, `Previous pctHeld`	`float`	Holding ratio now and in the previous report
`Shares`, `Total Outstanding`	`float`	Shares held and shares outstanding
`Purpose`, `Important Proposal`, `Reason`	`str`	Purpose of holding, whether an important proposal is intended, and the reason for filing
`Holder EDINET Code`, `Document ID`	`str`	EDINET codes