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# Company Information
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The Company class provides comprehensive access to corporate data including company overview, shareholder structure, management information, subsidiaries, affiliates, news, and corporate events. It supports Vietnamese data sources with detailed corporate governance and business intelligence.
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## Capabilities
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### Company Class
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Adapter for company-related data with dynamic method dispatch supporting VCI and TCBS data sources. Provides detailed corporate information for Vietnamese listed companies.
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```python { .api }
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class Company:
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    """
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    Company data adapter for corporate information.
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    Supported sources: VCI, TCBS
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    """
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    def __init__(self, source: str = "vci", symbol: str = None, random_agent: bool = False, show_log: bool = False):
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        """
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        Initialize Company data adapter.
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        Args:
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            source (str): Data source ("vci", "tcbs"), defaults to "vci"
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            symbol (str): Default company symbol, defaults to None
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            random_agent (bool): Use random user agent, defaults to False
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            show_log (bool): Enable logging, defaults to False
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        """
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```
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### Company Overview
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Comprehensive company profile including business description, key metrics, financial highlights, and basic corporate information.
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```python { .api }
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def overview(self, *args, **kwargs) -> pd.DataFrame:
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    """
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    Get comprehensive company overview and profile data.
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    Common parameters (vary by source):
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        symbol (str): Company symbol
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        lang (str): Language ("vi", "en"), defaults to "vi"
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    Returns:
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        pd.DataFrame: Company overview with information including:
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            - company_name: Official company name
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            - symbol: Trading symbol
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            - exchange: Stock exchange (HOSE, HNX, UPCOM)
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            - industry: Industry classification
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            - sector: Business sector
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            - market_cap: Market capitalization
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            - shares_outstanding: Number of shares outstanding
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            - book_value: Book value per share
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            - eps: Earnings per share
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            - pe_ratio: Price-to-earnings ratio
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            - dividend_yield: Dividend yield percentage
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            - business_summary: Company business description
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            - address: Company headquarters address
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            - phone: Contact phone number
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            - website: Company website
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            - established_date: Company establishment date
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            - listing_date: Stock listing date
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    """
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```
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#### Usage Examples
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```python
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from vnstock import Company
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# Initialize Company adapter
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company = Company(source="vci", symbol="TCB")
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# Get company overview
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tcb_overview = company.overview(symbol="TCB", lang="vi")
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# Get overview for multiple companies
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vcb_overview = company.overview(symbol="VCB")
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hpg_overview = company.overview(symbol="HPG")
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# Using TCBS source
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tcbs_company = Company(source="tcbs")
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company_profile = tcbs_company.overview(symbol="FPT", lang="en")
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```
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### Shareholder Information
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Detailed shareholder structure including major shareholders, ownership percentages, and shareholding changes over time.
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```python { .api }
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def shareholders(self, *args, **kwargs) -> pd.DataFrame:
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    """
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    Get company shareholder structure and ownership information.
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    Common parameters (vary by source):
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        symbol (str): Company symbol
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        page_size (int): Number of records per page
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        page (int): Page number for pagination
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    Returns:
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        pd.DataFrame: Shareholder data including:
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            - shareholder_name: Name of shareholder
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            - shareholding_percentage: Ownership percentage
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            - shares_held: Number of shares held
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            - shareholder_type: Type ("individual", "institutional", "foreign")
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            - change_percentage: Recent change in ownership
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            - report_date: Reporting date
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    """
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```
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#### Usage Examples
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```python
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# Get shareholder structure
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shareholders_data = company.shareholders(symbol="TCB")
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# Get detailed shareholder information with pagination
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detailed_shareholders = company.shareholders(
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    symbol="VCB", 
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    page_size=50,
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    page=1
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)
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# Track shareholder changes
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historical_shareholders = company.shareholders(symbol="HPG")
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```
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### Management Information
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Company officers and management team including board of directors, executives, and key personnel with their roles and background.
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```python { .api }
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def officers(self, *args, **kwargs) -> pd.DataFrame:
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    """
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    Get company management and officer information.
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    Common parameters (vary by source):
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        symbol (str): Company symbol
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        filter_by (str): Filter criteria ("working", "resigned", "all")
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    Returns:
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        pd.DataFrame: Management data including:
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            - full_name: Officer full name
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            - position: Job title/position
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            - status: Employment status ("working", "resigned")
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            - start_date: Start date in position
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            - end_date: End date (if resigned)
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            - shares_held: Number of shares held by officer
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            - ownership_percentage: Ownership percentage
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            - background: Professional background
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            - education: Educational background
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    """
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```
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#### Usage Examples
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```python
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# Get current management team
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current_officers = company.officers(symbol="TCB", filter_by="working")
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# Get all officers including resigned ones
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all_officers = company.officers(symbol="VCB", filter_by="all")
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# Get only resigned officers
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former_officers = company.officers(symbol="HPG", filter_by="resigned")
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```
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### Subsidiary Companies
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Information about subsidiary and affiliated companies including ownership structure and business relationships.
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```python { .api }
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def subsidiaries(self, *args, **kwargs) -> pd.DataFrame:
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    """
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    Get company subsidiary and affiliate information.
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    Common parameters (vary by source):
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        symbol (str): Company symbol
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        filter_by (str): Filter criteria ("all", "subsidiary")
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    Returns:
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        pd.DataFrame: Subsidiary data including:
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            - subsidiary_name: Name of subsidiary company
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            - ownership_percentage: Ownership percentage by parent
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            - business_type: Type of business
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            - established_date: Establishment date
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            - capital: Registered capital
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            - address: Business address
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            - main_business: Primary business activities
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            - relationship_type: Relationship type ("subsidiary", "affiliate")
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    """
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```
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#### Usage Examples
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```python
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# Get all subsidiaries
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subsidiaries_data = company.subsidiaries(symbol="TCB", filter_by="all")
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# Get only direct subsidiaries
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direct_subs = company.subsidiaries(symbol="VCB", filter_by="subsidiary")
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# Comprehensive subsidiary analysis
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hpg_subsidiaries = company.subsidiaries(symbol="HPG")
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```
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### Affiliate Information
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Details about affiliated companies and business partnerships including joint ventures and strategic alliances.
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```python { .api }
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def affiliate(self, *args, **kwargs) -> pd.DataFrame:
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    """
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    Get company affiliate and partnership information.
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    Common parameters (vary by source):
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        symbol (str): Company symbol
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    Returns:
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        pd.DataFrame: Affiliate data including:
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            - affiliate_name: Name of affiliated company
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            - relationship_type: Type of relationship
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            - partnership_percentage: Partnership or ownership percentage
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            - business_cooperation: Areas of business cooperation
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            - agreement_date: Partnership agreement date
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            - main_activities: Primary cooperative activities
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    """
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```
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#### Usage Examples
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```python
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# Get affiliate information
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affiliates_data = company.affiliate(symbol="TCB")
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# Comprehensive affiliate analysis
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vcb_affiliates = company.affiliate(symbol="VCB")
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```
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### Company News
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Recent news, press releases, and announcements related to the company including corporate events and market updates.
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```python { .api }
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def news(self, *args, **kwargs) -> pd.DataFrame:
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    """
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    Get company news and press releases.
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    Common parameters (vary by source):
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        symbol (str): Company symbol
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        page_size (int): Number of news items per page
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        page (int): Page number for pagination
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        start_date (str): Start date for news filter
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        end_date (str): End date for news filter
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    Returns:
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        pd.DataFrame: News data including:
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            - title: News headline
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            - summary: News summary
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            - publish_date: Publication date
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            - source: News source
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            - category: News category
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            - url: Link to full article
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            - importance: News importance level
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    """
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```
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#### Usage Examples
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```python
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# Get recent company news
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recent_news = company.news(symbol="TCB", page_size=20)
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# Get news for specific date range
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filtered_news = company.news(
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    symbol="VCB",
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    start_date="2023-01-01",
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    end_date="2023-12-31",
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    page_size=50
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)
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# Get latest news updates
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latest_news = company.news(symbol="HPG", page=1)
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```
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### Corporate Events
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Corporate events including dividends, stock splits, shareholder meetings, and other significant corporate actions.
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```python { .api }
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def events(self, *args, **kwargs) -> pd.DataFrame:
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    """
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    Get corporate events and actions.
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    Common parameters (vary by source):
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        symbol (str): Company symbol
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        event_type (str): Type of event filter
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        start_date (str): Start date for events
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        end_date (str): End date for events
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    Returns:
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        pd.DataFrame: Corporate events including:
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            - event_type: Type of corporate event
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            - event_date: Event date
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            - ex_date: Ex-dividend/ex-rights date
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            - record_date: Record date
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            - event_description: Event description
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            - dividend_rate: Dividend rate (if applicable)
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            - split_ratio: Stock split ratio (if applicable)
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            - meeting_type: Meeting type (AGM, EGM)
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            - announcement_date: Event announcement date
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    """
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```
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#### Usage Examples
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```python
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# Get all corporate events
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all_events = company.events(symbol="TCB")
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# Get dividend events only
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dividend_events = company.events(
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    symbol="VCB", 
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    event_type="dividend",
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    start_date="2023-01-01",
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    end_date="2023-12-31"
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)
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# Get upcoming shareholder meetings
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upcoming_meetings = company.events(
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    symbol="HPG",
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    event_type="meeting"
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)
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```
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## Data Source Specifics
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### VCI (Vietnam Capital Securities)
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- **Coverage**: All Vietnamese listed companies (HOSE, HNX, UPCOM)
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- **Data Depth**: Comprehensive corporate governance data
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- **Update Frequency**: Daily updates for corporate actions
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- **Languages**: Vietnamese and English support
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- **Historical Data**: Multi-year historical corporate data
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#### VCI-Specific Features
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- Detailed shareholder tracking with ownership changes
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- Comprehensive management background information
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- Complete subsidiary mapping with ownership percentages
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- Real-time corporate event notifications
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### TCBS (Techcombank Securities)
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- **Coverage**: Vietnamese listed companies with focus on major stocks
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- **Data Depth**: Detailed financial and corporate information
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- **Update Frequency**: Real-time corporate event updates
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- **Languages**: Vietnamese and English
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- **Specialization**: Enhanced analytical data and ratios
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#### TCBS-Specific Features
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- Advanced company screening capabilities
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- Enhanced financial ratio analysis
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- Comprehensive industry comparisons
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- Real-time news aggregation from multiple sources
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## Data Integration Examples
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### Comprehensive Company Analysis
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```python
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from vnstock import Company
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# Initialize company adapter
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company = Company(source="vci")
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# Get complete company profile
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symbol = "TCB"
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# Basic information
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overview = company.overview(symbol=symbol)
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shareholders = company.shareholders(symbol=symbol)
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management = company.officers(symbol=symbol, filter_by="working")
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# Corporate structure
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subsidiaries = company.subsidiaries(symbol=symbol, filter_by="all")
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affiliates = company.affiliate(symbol=symbol)
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# Recent developments
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news = company.news(symbol=symbol, page_size=10)
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events = company.events(symbol=symbol)
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# Combine for comprehensive analysis
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print(f"Company: {overview['company_name'].iloc[0]}")
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print(f"Market Cap: {overview['market_cap'].iloc[0]}")
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print(f"Number of Shareholders: {len(shareholders)}")
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print(f"Management Team Size: {len(management)}")
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print(f"Subsidiaries Count: {len(subsidiaries)}")
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print(f"Recent News Items: {len(news)}")
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```
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### Multi-Company Comparison
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```python
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# Compare multiple companies
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symbols = ["TCB", "VCB", "BID"]
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comparison_data = {}
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for symbol in symbols:
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    comparison_data[symbol] = {
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        'overview': company.overview(symbol=symbol),
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        'shareholders': company.shareholders(symbol=symbol),
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        'management': company.officers(symbol=symbol, filter_by="working")
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    }
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# Analyze comparative metrics
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for symbol, data in comparison_data.items():
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    overview = data['overview']
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    print(f"{symbol}: Market Cap = {overview['market_cap'].iloc[0]}, P/E = {overview['pe_ratio'].iloc[0]}")
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```