April 2026 Recap

The disclosed holdings of managers in OWL’s India group performed well in April, a stark turnaround after coming in as the worst-performing group in January and March and the third worst in February.

None of our OWL Groups had negative estimated performance in April, in contrast to March when only our Energy and Cyclical’s group was up.

As a reminder, the table below is based on our “OWL Groups” – curated lists of over 500 managers frequently found in leading allocators’ portfolios. These lists are categorized by geography, sector, and style, enabling our users to easily monitor groups of managers and their underlying disclosed holdings. All returns shown are estimates based on publicly disclosed holdings.

For our users, last Friday’s newsletter contained a list of some of the best and worst performing managers in April. One of April’s best performers (according to OWL estimates) was Tensor Edge, a Boston-based long/short equity investor focused on semiconductors and software.

Founded last year by Romit Shah and Gal Munda, both former senior managing directors at Matrix Capital, Tensor Edge has already seen meaningful growth in its disclosed regulatory AUM, starting at $110 million in 2025 and reaching $585 million as of March 2026. We also featured Tensor Edge in our New 13F Filers newsletter in February.

Shah, who serves as managing general partner at Tensor Edge, spent over six years at Matrix after an eight-and-a-half-year career as a managing director at Nomura. He has spent nearly his entire career focused on semiconductors, listing them as his primary research topic earlier in his career across Barclays, Lehman Brothers and JPMorgan Chase.

Munda is Tensor Edge’s head of software investing, a topic he’s studied as an equities researcher for more than a decade across various roles at Berenberg Capital Markets and Matrix. He began his career in Europe, first analyzing fund performance at Raiffesien Bank in Slovenia and then as a financial consultant at PwC’s London office. Other Matrix alumni at Tensor Edge include the CCO, Head of Quantitative Research, and Head of Business Development.

Tensor Edge discloses 17 long positions as of 3/31, the top five of which make up nearly 60 percent of its portfolio.

To see how Tensor Edge has achieved such strong estimated performance in the short time it’s been around, users can look to OWL’s Batting Average chart to understand which positions are driving the manager’s estimated performance.

Using OWL’s newly-updated employee search function, users can see more detail on who has moved from Matrix to Tensor Edge (or other managers). OWL people data is a great way to spot spinouts from well-known funds and monitor the movement of both individuals and teams, including the one from Matrix.

OWL is an intelligence platform built for allocators, by allocators. Leading endowments, foundations, and family offices use the system to find, monitor, and connect with thousands of fund managers globally. OWL's analytics engine has collected over one billion data points from 65 countries. We make it easy for allocators to find and track information about the managers they care about – not just positions but also performance analytics, people data, business information, and details about the manager investments of other allocators.

Returns represent the return on invested capital of publicly disclosed long positions, as calculated by OWL. Actual returns may vary based on a number of factors, including (but not limited to) undisclosed positions, short exposure, non-equity holdings, cash holdings, and lagged disclosure of positions.

This newsletter and the material on the Old Well Labs platform are for informational purposes only and should not be considered investment advice or a recommendation of any particular security, manager, or strategy. Certain investment managers, funds, or limited partners (“LPs”) referenced herein may be current or prospective clients of Old Well Labs, and Old Well Labs may have business relationships with such parties. Accordingly, references to any manager, fund, or LP should not be construed as an endorsement, recommendation, or solicitation. Old Well Labs shall not be liable for any investment gain or loss that may occur from the use of this material. No part of this material may be reproduced in any form or used in any publication without express written permission from Old Well Labs.