Reverse DCF: Valuing Stocks with Precision using Python

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Discounted Cash Flow (DCF) analysis is a cornerstone of stock valuation, enabling investors to estimate the intrinsic value of a company based on its future cash flows. However, a Reverse DCF works in reverse — determining the growth rate and assumptions the market is pricing into the current stock price. This technique is invaluable for assessing whether a stock’s price aligns with reasonable growth expectations or if it is overvalued or undervalued.

In this article, we’ll explain the concept of Reverse DCF, its importance in valuation, and provide a Python implementation using the yfinance library, with RELIANCE.NS as a practical example.

What is Reverse DCF?

Unlike traditional DCF, which estimates intrinsic value based on forecasted cash flows and a discount rate, Reverse DCF starts with the current market price and works backward to deduce the implied assumptions. These include:

• Revenue growth rate

• Operating margin

• Free Cash Flow (FCF) growth rate

• Discount rate (Cost of Capital)

This approach helps investors:

1. Understand market expectations: Identify the growth assumptions embedded in the current stock price.

2. Validate assumptions: Compare implied growth rates with the company’s historical performance and industry benchmarks.

3. Spot mis-priced stocks: Recognise when the market might be overly optimistic or pessimistic.

Steps to Perform a Reverse DCF

1. Gather Financial Data: Obtain historical financials, including revenue, operating income, and free cash flows.

2. Define Key Inputs: Use the Weighted Average Cost of Capital (WACC) as the discount rate and determine the terminal growth rate.

3. Iterate Growth Rates: Solve for the growth rate that matches the present value of cash flows to the current market price.

Python Implementation of Reverse DCF

Here’s how you can calculate the implied growth rate for RELIANCE.NS (Reliance Industries Limited) using Python.

import yfinance as yf
import numpy as np
from scipy.optimize import minimize

# Constants
TICKER = "RELIANCE.NS"
DISCOUNT_RATE = 0.1  # Assumed WACC (10%)
TERMINAL_GROWTH_RATE = 0.03  # Assumed terminal growth (3%)
YEARS = 5  # Forecast horizon

# Fetch stock price and financials
stock = yf.Ticker(TICKER)
#market_price = stock.history(period="1d")['Close'][-1]
market_price = stock.info['marketCap']/10e6
revenue = stock.financials['2024-03-31']['Total Revenue']/10e6
fcf = stock.financials['2024-03-31']['Net Income']/10e6 #Assuming no capex, net income = fcf

# Example financial data (replace with actual financials)
latest_revenue = revenue  # Example Revenue in Crores
latest_fcf_margin = fcf/revenue  # Assumed FCF Margin i.e. FCF/Revenue
current_fcf = latest_revenue * latest_fcf_margin

# Reverse DCF Function
def reverse_dcf(growth_rate):
    growth_rate = growth_rate[0]
    cash_flows = [current_fcf * ((1 + growth_rate) ** i) for i in range(1, YEARS + 1)]
    terminal_value = cash_flows[-1] * (1 + TERMINAL_GROWTH_RATE) / (DISCOUNT_RATE - TERMINAL_GROWTH_RATE)
    discounted_cash_flows = [cf / ((1 + DISCOUNT_RATE) ** i) for i, cf in enumerate(cash_flows, start=1)]
    discounted_cash_flows.append(terminal_value / ((1 + DISCOUNT_RATE) ** YEARS))
    total_value = sum(discounted_cash_flows)
    return total_value

# Objective Function: Minimize the difference between market cap and calculated value
def objective_function(growth_rate):
    intrinsic_value = reverse_dcf(growth_rate)
    difference = intrinsic_value - market_price
    #print(f"Testing Growth Rate: {growth_rate[0]:.4f}, Intrinsic Value: {intrinsic_value:.2f}, Difference: {difference:.2f}")
    return abs(difference)

# Improved Optimization Parameters
initial_guess = [0.15]  # Initial guess for growth rate
bounds = [(0.0, 10.50)]  # Reasonable bounds for growth rate (1% to 30%)

# Solve for implied growth rate
result = minimize(objective_function, x0=initial_guess, bounds=bounds, method="L-BFGS-B")

# Extract the result
implied_growth_rate = result.x[0]

# Output the results
print("\n=== Final Results ===")
print(f"Market cap of {TICKER}: {market_price}")
print(f"Implied Growth Rate: {implied_growth_rate:.2%}")

Executing above code gave me below results:

=== Final Results ===
Market Price of RELIANCE.NS: 1644795.4747392
Implied Growth Rate: 14.50%

Explanation of the Code

1.Inputs and Assumptions:

• DISCOUNT_RATE: The Weighted Average Cost of Capital (WACC), set at 10% here.

• TERMINAL_GROWTH_RATE: Growth rate after the forecast horizon, set at 3%.

• YEARS: The forecast period (10 years in this case).

2.Reverse DCF Logic:

• Project Free Cash Flows (FCF) based on an assumed growth rate.

• Calculate the terminal value at the end of the forecast horizon.

• Discount all future cash flows to their present value.

3. Optimization:

• Use scipy.optimize.minimize to find the growth rate that aligns the DCF value with the current market price.

Interpretation of Results

The output of the code provides the implied growth rate. Compare this rate to:

• The company’s historical growth rate.

• Industry averages.

• Analysts’ growth projections.

If the implied growth rate seems overly optimistic or pessimistic, the stock may be mispriced.

Conclusion

Reverse DCF is a powerful tool for validating market expectations and making informed investment decisions. By leveraging Python, you can automate this process, allowing for quick analysis of multiple stocks. For RELIANCE.NS, this approach uncovers the growth rate assumptions driving its current valuation, empowering investors to make data-driven choices.

Feel free to adapt the Python script for other companies and fine-tune assumptions like WACC and terminal growth rates for more accurate results. Happy investing!