Why this algorithm-driven money manager likes RBC, Walmart and sold Thomson Reuters

The algorithmic, momentum-based strategy (AUM ~$250M) has returned 7.2% YTD, 14.6% over one year, 21.3% over three years and 17.6% over five years (total returns, net of fees, as of Mar 31). The fund is monthly-rebalanced and currently allocates 54% to the Americas, 36% Europe and 10% Asia, with top sectors: IT 22%, financials 17%, energy 17%, health care 15% and consumer staples 11%. Current top holdings include Royal Bank of Canada (core bank exposure), Eni (bought in early April for energy/geo risk exposure) and Walmart (defensive consumer retail), and the manager fully exited Thomson Reuters amid AI-driven software pricing uncertainty.

Analysis

Sartorial’s process is a classic momentum engine: monthly rebalances create predictable, concentrated flow windows that amplify trends for names that have recently cleared behavioural filters. That mechanical cadence favors liquid large-caps (WMT, RY) and commodity-sensitive stocks (E) because they can absorb herded inflows quickly — expect 3–10 trading days of accentuated moves around month-end as the model tilts or trims positions.

Second-order competitive effects matter. Eni’s optionality derives less from spot oil than from Europe’s fast rerouting of gas contracts and incremental LNG capacity commitments; that flow benefits European midstream and LNG contractors, and can widen Eni’s EBITDA sensitivity to Brent/HH by multiples versus a pure renewables peer. Conversely, Walmart’s advertising and fulfillment profit pools create an asymmetric moat versus regional grocers — if discretionary weakness deepens, ad RPMs and e-fulfilment leverage should compound margins faster than consensus models assume.

Key risks and time horizons are idiosyncratic: banking exposure (RY) is sensitive to a 2–4 quarter macro shock to Canadian housing/liquidity that would compress NIM and raise provisions; Eni’s upside is front-loaded to geopolitical shocks in the Strait of Hormuz over weeks–months but can reverse if global growth falters; TRI’s recent removal reflects sector multiple compression from AI — that’s a 6–12 month structural question about pricing power, not just a momentum trade. The micro hedges and pairings that follow convert these directional views into defined-risk opportunities aligned with the algorithmic flow calendar.

Key Decisions for Investors

• Buy RY (2–4% position) ahead of the next month-end rebalance window; pair with a 6–9 month 7–10% OTM put (cost ~2–4% premium) to cap tail risk. Rationale: capture momentum-driven month-end inflows + dividend/capital markets upside; downside protected to limit drawdown to mid-single digits vs upside potential 15–30% over 6–12 months if rates and markets normalize.
• Long E (ENI) 1–2% position via 9–12 month call spread (buy ATM, sell 25–30% OTM) to keep premium low. Entry: after any immediate post-news spike or on pullback >5%; timeframe 0–6 months for geopolitically-driven upside. Risk/reward: asymmetric commodity exposure — limited upfront cost for >25% upside on a sustained tightening of oil/gas markets, with capped loss = premium paid.
• Pair trade: long WMT (2–3%) vs short XRT (or a basket of specialty/department stores) equal notional. Timeframe 3–12 months to harvest defensive share gain + ad/fulfilment margin expansion while short cyclical retail facing inventory and traffic risk. Expect 20–30% relative outperformance in a mild recession; set stop-losses at 8–10% absolute move against either leg.
• Contrarian small-size play on TRI: initiate a 6–12 month contrarian call spread (buy 12–18 month LEAP calls, offset with nearer OTM calls) or a 1% equity position with a 20–25% stop. Thesis: if AI increases demand for authoritative, curated data, TRI can mean-revert independent of the broader SaaS multiple reset; downside limited to premium/stop, upside 30–50% if market rewards differentiated content over generic SaaS.