In this episode of Excess Returns, we sit down with Joe Gubler, Director of Quantitative Research at Causeway Capital. Joe shares a deep dive into Causeway’s distinctive approach to factor investing, blending traditional quant signals with fundamental insights, and building models that adapt to market context. From constructing proprietary sustainability alpha signals to using machine learning to refine quality definitions, Joe reveals a cutting-edge playbook for the future of quantitative investing.
đź“Ś In This Episode, You'll Learn:
Why Causeway doesn't treat its quant model as sacrosanct
How to blend fundamental overlays with systematic strategies
The logic behind composite factors and contextual weighting
Unconventional factor signals like corporate events and peer-based momentum
How machine learning enhances quality assessment
How Causeway adapts factor models across regimes and markets
What most quants miss when it comes to factor construction and interpretation
The evolving role of AI and NLP in alpha generation
Joe's views on passive flows and fundamental mispricings
⏱️ Timestamps
00:00 – Introduction and Joe’s path from NASA to investing
04:13 – Blending quant and fundamental: the Causeway framework
07:42 – How Causeway tests and evolves factor models
11:26 – Lessons from value winter and why judgment still matters
12:17 – Deep dive: Causeway’s unique value definition
15:04 – Sentiment as a predictive signal
17:19 – Momentum and peer-based network momentum
21:00 – Growth as a standalone alpha factor
23:25 – Quality: blending competitive strength and financial health
25:10 – Corporate events: the overlooked alpha signal
30:59 – ESG and sustainability as material performance drivers
35:12 – Why composite scoring beats factor sleeves
39:06 – Contextual weighting: tailoring factor importance by stock type
45:06 – Causeway’s custom-built risk model
50:00 – Portfolio size, concentration, and the alpha curve
52:13 – Rebalancing dynamically with turnover-based optimization
56:01 – Using machine learning to improve factor interpretation
59:47 – Closing thoughts on factor resilience, AI, and being willing to intervene
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