# Modified Ohlson Model: Firm Valuation with Python
> Learn how to use the Modified Ohlson Model and Python to calculate the intrinsic value of firms using accounting data from the ASX.

Tags: ohlson-model, firm-valuation, python-finance, asx, accounting-analysis, intrinsic-value, pandas
## Modified Ohlson Model: Firm Value Analysis with Python
- **Project Scope**: Evaluating firm worth using accounting metrics (Net Profit, Book Value) for Australian ASX-listed firms.
- **Objective**: Identify overvalued or undervalued shares by comparing intrinsic value to market price.

## Data Strategy & Preparation
- **Sources**: Company Annual Reports, ASX Filings, and Yahoo Finance.
- **Process**: Data ingestion, missing value treatment, error detection, and standardization using Python's Pandas library.

## The Adjusted Ohlson Framework
- **Formula**: V = BVE + Σ(Residual Income_t / (1+r)^t)
- **Key Terms**:
  - **BVE**: Book Value of Equity (Total Assets - Total Liabilities).
  - **Residual Income**: Net Income - (r × BVE).
  - **r**: Cost of Equity (required rate of return).

## Python Implementation
- Automation of residual income calculations across multiple firms.
- Use of standard libraries (Pandas) to reduce manual calculation errors and increase processing speed.

## Preliminary Findings for ASX Firms
- Observed gaps between modeled values and actual stock prices for companies like CBA, BHP, CSL, and WBC.
- Preliminary results indicate that firms with higher residual income correlate with larger estimated firm values.
- Evidence suggests that accounting metrics provide insights that raw market sentiment may miss.
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