Insight: Accenture

Accenture

Accenture is a global professional services firm focused on helping enterprises reinvent through digital transformation and AI, leveraging a workforce of about 779,000 employees and a broad mix of consulting and managed services. In fiscal 2025, the company generated $69.7 billion in revenue (+7% YoY) and $80.6 billion in bookings (-1%), with balanced contributions across the Americas (50%), EMEA (35%), and Asia Pacific (14%). Operating margin remained stable at 14.7% (15.6% adjusted), while EPS reached $12.15 (+6%), reflecting solid profitability despite macroeconomic and geopolitical uncertainty. Growth was driven by demand for AI, cloud, and large-scale transformation projects, although discretionary spending remained constrained and federal business faced headwinds. Accenture maintains strong liquidity, with $11.5 billion in cash and $11.5 billion operating cash flow, enabling $8.3 billion returned to shareholders via dividends and buybacks. Overall, the company demonstrates resilient growth, strong cash generation, and a strategy centered on AI-enabled reinvention, though margins face pressure from rising labor costs and ongoing business optimization efforts.

Example DCF valuation

The discounted cash flow (DCF) analysis is based on a 5-year explicit forecast period with a weighted average cost of capital (WACC) of 9% and a terminal growth assumption of 0–3% depending on the scenario. Under these assumptions, Accenture yields an estimated enterprise value of roughly $225–235 billion and an equity value of about $228–238 billion, translating to an intrinsic share price in the $365–385 range. This indicates the stock is fairly valued around the mid-to-high $300s based on moderate expectations of 5% free cash flow growth and a 9% discount rate. The valuation is heavily driven by terminal value, highlighting how sensitive the outcome is to long-term growth assumptions. If the current market price sits meaningfully above this range, it suggests investors are pricing in stronger growth or lower risk; if it is below, the shares could be considered modestly undervalued under these inputs.

import numpy as np
import pandas as pd

# =========================================================
# 1. INPUTS
# =========================================================

# -----------------------------
# Historical FCF (USD)
# -----------------------------
historical_fcf = {
    "year": [2022, 2023, 2024, 2025],
    "fcf": [
        8_823_131_000,
        8_996_096_000,
        8_614_518_000,
        10_874_360_000
    ]
}

# -----------------------------
# Base Year FCF (TTM)
# -----------------------------
last_year_fcf = 12_496_959_000

# -----------------------------
# Growth Assumptions
# -----------------------------
fcf_growth_rate = 0.05   # 5% conservative growth
forecast_years = 5

# -----------------------------
# Discounting
# -----------------------------
wacc = 0.09
terminal_growth = 0.025

# -----------------------------
# Capital Structure
# -----------------------------
cash = 11_484_674_000
debt = 8_182_866_000
shares_outstanding = 621_855_920  # shares

# =========================================================
# 2. HISTORICAL ANALYSIS
# =========================================================

df_hist = pd.DataFrame(historical_fcf)
df_hist["YoY_growth_%"] = df_hist["fcf"].pct_change() * 100
print(df_hist)

fcf_start = df_hist.loc[df_hist["year"] == 2023, "fcf"].values[0]
fcf_end = df_hist.loc[df_hist["year"] == 2024, "fcf"].values[0]
cagr = (fcf_end / fcf_start) - 1

print(f"\nFCF CAGR (2023–2024): {cagr:.2%}")

# =========================================================
# 3. FORECAST
# =========================================================

years = np.arange(1, forecast_years + 1)

fcf_forecast = np.array([
    last_year_fcf * (1 + fcf_growth_rate) ** t
    for t in years
])

# =========================================================
# 4. DISCOUNTING
# =========================================================

discount_factors = 1 / (1 + wacc) ** years
discounted_fcf = fcf_forecast * discount_factors

# =========================================================
# 5. TERMINAL VALUE
# =========================================================

terminal_value = (
    fcf_forecast[-1] * (1 + terminal_growth)
    / (wacc - terminal_growth)
)

discounted_terminal_value = terminal_value / (1 + wacc) ** forecast_years

# =========================================================
# 6. VALUATION
# =========================================================

enterprise_value = discounted_fcf.sum() + discounted_terminal_value
equity_value = enterprise_value + cash - debt
intrinsic_share_price = equity_value / shares_outstanding

# =========================================================
# 7. OUTPUT
# =========================================================

results = pd.DataFrame({
    "Year": years,
    "Projected FCF": fcf_forecast,
    "Discount Factor": discount_factors,
    "Discounted FCF": discounted_fcf
})

print(results)

print("\n--- DCF VALUATION SUMMARY ---")
print(f"Enterprise Value: {enterprise_value/1e9:.2f} B USD")
print(f"Equity Value: {equity_value/1e9:.2f} B USD")
print(f"Intrinsic Share Price: {intrinsic_share_price:.2f} USD")

Conclusion and quality of earnings.

The company is valuable, which is great news. However, true skill is in assessing the expected growth and this is the billion dollar question. One approach can be used to assess the likelihood of growth is to evaluate the quality of earnings (see checklist bellow).