Science Applications International (SAIC) Price to Earnings (2013 - 2026)
Science Applications International's (SAIC) Price to Earnings came in at 12.95 for fiscal Q2 2027 (quarter ended Jul 31, 2026), up 1.4% from 12.77 a year earlier and up 27.1% from the prior quarter.
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Analysis
Science Applications International (SAIC) Price to Earnings (2013 - 2026) Analysis & Trends
For FY2026 (ended Jan 30, 2026), Science Applications International's Price to Earnings stood at 12.51, down 12.9% from FY2025.
- In prior fiscal years, Science Applications International's Price to Earnings was 14.36 in FY2025 (+2.1%), 14.06 in FY2024 (-24.9%), 18.73 in FY2023 (+14.0%) and 16.44 in FY2022 (-38.3%).
- The fiscal Q2 2027 figure stands as the highest quarterly Price to Earnings since fiscal Q1 2026.
- On a year-over-year basis, Price to Earnings increased in four of the last eight quarters, with an average decline of 2.7%.
- The strongest year-over-year quarter for Price to Earnings in the past five years was fiscal Q3 2025, with growth of 107.6%; the weakest was fiscal Q3 2026, with a decline of 51.4%.
- According to Business Quant data, Price to Earnings for the three prior fiscal quarters was 10.18 (Q1 2027), 12.51 (Q4 2026) and 11.37 (Q3 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Infosys | 44.69 Bn | 44.75 Bn | 1.60 Bn |
| 2 | Cognizant Technology Solutions | 26.45 Bn | 19.61 Bn | 1.83 Bn |
| 3 | Td Synnex | 22.21 Bn | 16.36 Bn | 1.43 Bn |
| 4 | Cdw | 16.70 Bn | 14.69 Bn | 1.32 Bn |
| 5 | Cgi | 15.63 Bn | 13.41 Bn | - |
| 6 | Arrow Electronics | 12.33 Bn | 11.35 Bn | 1.13 Bn |
| 7 | Avnet | 8.78 Bn | 7.96 Bn | 865.03 Mn |
| 8 | Ingram Micro Holding | 6.70 Bn | 2.31 Bn | 958.68 Mn |
| 9 | EPAM Systems | 5.58 Bn | 1.22 Bn | 429.57 Mn |
| 10 | Science Applications International | 5.26 Bn | 4.80 Bn | 239.00 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jul 31, 2026 | 12.95 |
| May 1, 2026 | 10.18 |
| Jan 30, 2026 | 12.51 |
| Oct 31, 2025 | 11.37 |
| Aug 1, 2025 | 12.77 |
| May 2, 2025 | 15.71 |
| Jan 31, 2025 | 14.36 |
| Nov 1, 2024 | 23.40 |
| Aug 2, 2024 | 21.21 |
| May 3, 2024 | 14.71 |
| Feb 2, 2024 | 14.06 |
| Nov 3, 2023 | 11.27 |
| Aug 4, 2023 | 12.93 |
| May 5, 2023 | 16.79 |
| Feb 3, 2023 | 18.73 |
| Oct 28, 2022 | 21.91 |
| Jul 29, 2022 | 20.49 |
| Apr 29, 2022 | 17.33 |
| Jan 28, 2022 | 16.44 |
| Oct 29, 2021 | 17.29 |
API Access
Science Applications International Price to Earnings API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=price-to-earnings&ticker=SAIC&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "price-to-earnings", "ticker": "SAIC", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=price-to-earnings&ticker=SAIC&period=max&api_key=YOUR_API_KEY");
const data = await res.json();