Alexander Campbell
· Campbell Ramble
· March 26, 2026 at 02:51
· ⏱ 18 min read
| Read on Substack ↗
Summary
Alexander Campbell argues that alpha comes from deviating from the market portfolio with a falsifiable, causal thesis, not from being right about obvious facts. He outlines a spectrum from pure discretionary trading to systematic quant approaches, emphasizing that AI can enhance the analyst role (Level 2) — organizing fundamentals into rankable screeners — but should not replace human judgment on universe, metrics, and weights. The article is educational with no actionable trade recommendations or personal positions disclosed.
•Alpha is the difference between your return and the market portfolio’s return; any deviation implies you think you’re smarter than the market.
•Quants are overconfident in data; discretionary traders are overconfident in themselves — each school covers the other’s biases.
•Bridgewater’s 'fundamental and systematic' approach writes down human logic then builds it into a machine, a fusion AI might eventually deliver.
•The author uses a gold miner screener (30 names from GDX/GDXJ) as a Level-2 example: raw fundamentals converted to z-scores with adjustable weight sliders (Balanced, Value, Quality, etc.).
•Wesdome Gold Mines tops the balanced composite; Franco-Nevada ranks near bottom due to 26x forward earnings and negative revenue growth.
•A copper miner screener (17 names) includes Grupo Mexico top, Ero Copper and Solaris Resources second/third; diversified majors (BHP, Rio, Glencore) rank lower due to diluted copper exposure.
•The author warns data is lagged (screenshots from March 15, 2026) and that backtesting requires continuous rebalancing — not just one-off snapshots.