Worsley Investors Limited NPV
BalancedWINV·XLON·🇬🇧 GB
50%buys·50%sells
0 transactions$0Total buys·0 buys
$0Total sells·0 sells
00 insiders
—Last: —
| Date | Insider | Type | Value | |
|---|---|---|---|---|
| Feb 10, 26 | SCOTT WILLIAM | Buy | $36K | |
| Feb 3, 25 | Blake Nixon | Buy | $4K | |
| Jan 22, 25 | Blake Nixon | Buy | $8K | |
| Jan 22, 25 | SCOTT WILLIAM | Buy | $22K | |
| Mar 27, 24 | SCOTT WILLIAM | Buy | $36K | |
| Sep 25, 23 | SCOTT WILLIAM | Buy | $14K | |
| Jan 19, 23 | SCOTT WILLIAM | Buy | $12K | |
| Sep 12, 22 | SCOTT WILLIAM | Buy | $43K | |
| Aug 10, 22 | SCOTT WILLIAM | Buy | $8K | |
| Aug 8, 22 | SCOTT WILLIAM | Buy | $5K |
companyInsight.narrative_dormancy
Worsley Investors Limited NPV (WINV) has recorded 0 insider operations, including 0 open-market purchases ($0), 0 sales ($0). The insider sentiment is balanced.
Recent activity is concentrated over 1 months in the last year.
Data sourced from regulatory filings (SEC Form 4, AMF, BaFin, FI, and other European authorities).
Worsley Investors Limited NPV has recorded 0 insider operations in total, including 0 open-market purchases ($0), 0 sales ($0).
Insiders at Worsley Investors Limited NPV show balanced activity with 50% purchases and 50% sales. Total buy volume is $0 versus $0 in sales.
InsiderLens tracks 0 insiders at Worsley Investors Limited NPV (WINV). Data is sourced from mandatory regulatory filings for persons with access to privileged information.
The insider signal for WINV is neutral, based on a 50% buy ratio across 0 buy and sell transactions reported to financial regulators. Transactions from C-suite executives (CEO, CFO) and board members are generally considered the most informative signals.
Insider trading data for Worsley Investors Limited NPV comes from official regulatory filings: SEC Form 4 (United States), AMF (France), BaFin (Germany), FI (Sweden), and other European financial authorities. These filings are mandatory for directors, officers, and holders of more than 10% of a company's shares. InsiderLens aggregates and normalizes this data for analysis.