Financial management module is currently not started, with references to timeseries data included.
Understanding stock values involves face value, book value, market value, and intrinsic value, each serving different purposes in assessing a stock's worth. Face value is static and used for bookkeeping, while book value reflects a company's net worth. Market value is the current trading price, and intrinsic value estimates the true worth based on fundamentals. Investors use these values to make informed decisions about buying, holding, or selling stocks, with strategies based on comparing market and intrinsic values.
The content covers a comprehensive analysis of stock market data, focusing on setting a 70-year historical date range for economic analysis, retrieving U.S. Real Potential GDP data from FRED, and calculating Year-over-Year and Quarter-over-Quarter growth rates. It includes detailed Python code examples for data retrieval, growth rate calculations, and visualizations using dual y-axis plots to display GDP values and growth rates effectively.
Financial statement analysis involves examining a company's financial reports to assess its health, performance, and profitability through liquidity, profitability, solvency, and efficiency metrics. Business analysis expands on this by evaluating strategic, operational, and economic factors impacting the company. Key components include credit and equity analysis, with tools like ratio analysis, cash flow analysis, and prospective analysis to forecast future performance. Understanding the interconnections between financial statements, such as the balance sheet, income statement, and cash flow statement, is crucial for comprehensive analysis. Additionally, external factors like accounting standards, management motivations, and regulatory environments significantly influence financial reporting and analysis.
Yahoo Finance is a popular source for stock market data, and the yfinance library provides reliable access to its unofficial API. The library allows users to fetch historical prices, fundamental data, and options chain data for stocks. Key functionalities include retrieving OHLCV data, transforming data structures for convenience, and utilizing various parameters for data requests. Additionally, users can plot candlestick charts and customize bar sizes using pandas. The article emphasizes the ease of use and versatility of yfinance for backtesting and research.