Datasea Intelligent Technology (DTSS) Change in Accured Expenses (2015 - 2025)
Datasea Intelligent Technology's Change in Accured Expenses was $37,769 in fiscal Q2 2026 (quarter ended Dec 31, 2025), compared with -$170,237 a year earlier and down 49.6% from the prior quarter.
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Analysis
Datasea Intelligent Technology (DTSS) Change in Accured Expenses (2015 - 2025) Analysis & Trends
On a trailing twelve-month basis, Datasea Intelligent Technology's Change in Accured Expenses was $157,977 through Dec 31, 2025; for FY2025 (ended Jun 30, 2025), it came in at -$45,306.
- In earlier fiscal years, Change in Accured Expenses was -$108,736 in FY2024, $523,534 in FY2023 (-29.4%), $741,328 in FY2022 (+232.7%) and $222,808 in FY2021 (+799.7%).
- Quarterly Change in Accured Expenses has moved between -$170,237 (fiscal Q2 2025) and $561,330 (fiscal Q4 2022) over five years.
- The best year-over-year quarter for Change in Accured Expenses over five years was fiscal Q4 2022 (growth of 327.9%); the worst was fiscal Q3 2021 (a decline of 95.9%).
- Per Business Quant data, DTSS's Change in Accured Expenses in the three fiscal quarters before Q2 2026 was $74,927 (Q1 2026), $69,263 (Q4 2025) and -$23,982 (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Change Accured Exp. (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 | 105.67 Mn |
| 7 | Avnet | 8.78 Bn | 7.96 Bn | 865.03 Mn | 100.83 Mn |
| 8 | Ingram Micro Holding | 6.70 Bn | 2.31 Bn | 958.68 Mn | -86.93 Mn |
| 9 | EPAM Systems | 5.58 Bn | 1.22 Bn | 429.57 Mn | -67.67 Mn |
| 10 | Datasea Intelligent Technology | 13.66 Mn | -441.93 Bn | 1.19 Mn | 37,769.00 |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Dec 31, 2025 | 37,769.00 |
| Sep 30, 2025 | 74,927.00 |
| Jun 30, 2025 | 69,263.00 |
| Mar 31, 2025 | -23,982.00 |
| Dec 31, 2024 | -170,237.00 |
| Sep 30, 2024 | 79,650.00 |
| Jun 30, 2024 | 20,428.00 |
| Mar 31, 2024 | -89,922.00 |
| Dec 31, 2023 | 17,273.00 |
| Sep 30, 2023 | -56,515.00 |
| Jun 30, 2023 | -142,948.00 |
| Mar 31, 2023 | 274,532.00 |
| Dec 31, 2022 | 199,415.00 |
| Sep 30, 2022 | 192,535.00 |
| Jun 30, 2022 | 561,330.00 |
| Mar 31, 2022 | -11,291.00 |
| Dec 31, 2021 | -32,627.00 |
| Sep 30, 2021 | 223,916.00 |
| Jun 30, 2021 | 131,193.00 |
| Mar 31, 2021 | 1,853.00 |
API Access
Datasea Intelligent Technology Change in Accured Expenses 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=change-in-accured-expenses&ticker=DTSS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "change-in-accured-expenses", "ticker": "DTSS", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=change-in-accured-expenses&ticker=DTSS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();