Systematic value factor timing research — updated for 2026
Who we are and what drives our research
I came here because I was tired of hearing that value investing was dead. What I found
instead was a team that treats valuation cycles the way a geologist reads rock strata —
layer by layer, signal by signal, with disciplined patience.
Quantitative rigor at every step
Every framework we use traces back to a measurable input — price-to-book spreads, earnings yield differentials, macro regime indicators. There is no gut feeling here, only structured reasoning. As factor researchers have long argued, the value premium is real, but it is cyclical. We map those cycles with systematic precision, adjusting exposure when the data says so — not when sentiment does.
Macro and valuation signals together
I realized the missing piece was never the factor itself — it was timing. Our approach combines macro regime signals with cross-sectional valuation spreads to identify when the conditions historically associated with value factor outperformance are present. It is not prediction; it is probabilistic positioning grounded in decades of academic and practitioner research on factor cyclicality.
A team built around methodology
The people here carry titles like Lead Quantitative Analyst and Research Director, but what unites them is a shared insistence on reproducibility. Every signal is back-tested, every assumption documented. The methodology is reviewed on a rolling basis — because a framework that cannot be stress-tested is not a framework at all. Rigorous, transparent, and always open to revision when the data demands it.
Our approach to systematic value factor timing research
Understanding why exposure timing matters more than factor selection alone — a perspective
built on quantitative discipline and macro awareness
The value factor does not perform uniformly across all market environments. Recognizing that
is where honest research begins.
The difference between holding a factor statically and adjusting exposure dynamically based on cycle signals is not a small refinement — it is a different philosophy
Why systematic timing research changes how practitioners think about factor exposure
I used to think holding value exposure through every cycle was the disciplined thing to do. Then I looked at the data more carefully.
Static factor exposure assumes the premium is always on offer. The historical record
suggests otherwise. Value's return profile is deeply path-dependent — concentrated in
specific macro regimes, specific spread environments, specific points in the earnings cycle.
Ignoring that structure is not patience; it is noise tolerance mistaken for conviction.
Dynamic adjustment based on systematic signals is not market timing in the traditional
sense. It is regime-aware positioning — a distinction that matters both intellectually and
practically. The signals we track are not price momentum or sentiment surveys. They are
structural inputs: valuation dispersions, macro factor loadings, and cross-sectional return
predictors with documented academic pedigree.
The framework is designed to be transparent enough that a practitioner can interrogate every
decision. Why did the model reduce exposure in a given period? Which signal crossed which
threshold? These questions have documented answers. That level of accountability is,
frankly, rare — and it is the standard we hold ourselves to without exception.
Past performance does not guarantee future results, and no systematic framework eliminates the uncertainty inherent in financial markets. What it does is replace guesswork with structure — and that distinction, compounded over full market cycles, is where the real analytical value of this work becomes visible.
Rooted in data, shaped by cycles
I came to this work with a simple frustration: why does a factor with decades of
academic support still surprise practitioners with its volatility? The answer, it
turns out, is cycle blindness. Our research exists to correct that — systematically,
quantitatively, without apology.
We document the conditions under which value factor dynamics have historically
shifted. The work is methodical, the standards are high, and the intellectual
honesty is non-negotiable.
Four principles that anchor every analytical decision we make
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Rules-based frameworks over discretionary judgment at every decision point
Every signal threshold, every regime classification, every exposure adjustment
recommendation follows a documented rule. There is no room for ad hoc overrides. When
the framework says the conditions have changed, that assessment is based on pre-defined
criteria — not on the analyst's mood that morning or the most recent headline in the
financial press.
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Valuation spread analysis reviewed across four distinct macro regime categories
We do not treat the macro environment as a single backdrop. Our framework classifies
conditions across four distinct regime types — each with its own historical value factor
return profile. This granularity allows for more nuanced exposure assessments than a
simple bull-bear binary, and it reflects the actual complexity of how markets behave
across full economic cycles.
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Research methodology stress-tested against historical analogue periods with rigor
Before any signal enters the framework, it is evaluated against a broad set of
historical periods — including those where value underperformed significantly. We
actively try to break our own models. A signal that only works in favorable environments
is not a signal; it is a coincidence. Our standards for inclusion are deliberately
demanding, and our review process is ongoing.
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Macro and valuation inputs tracked continuously across the full research cycle
The framework draws on more than a dozen distinct inputs spanning valuation spreads,
earnings dynamics, credit conditions, and macro factor loadings. Each input is monitored
on a defined schedule, and the composite regime assessment is updated systematically. No
single input dominates — the architecture is designed to be robust to individual signal
noise through diversification of evidence.
The intellectual foundation behind our value factor timing research methodology
Academic rigor, practitioner discipline, and a healthy skepticism toward any framework
that cannot survive its own stress tests
Factor research is only as good as the questions it asks of itself. We ask hard ones,
regularly.
The academic case for the value factor is well-established — from Fama and French's
foundational work to more recent research on valuation spread dynamics and factor
cyclicality. We build on that literature deliberately, treating it as a starting point
rather than a conclusion. Our own research extends the conversation by focusing on the
timing dimension that academic factor models often treat as secondary.
Macro regime identification is central to our methodology. We classify the current environment using a structured set of inputs — credit spreads, earnings revision trends, inflation dynamics, and yield curve shape among them. Each classification maps to a historical distribution of value factor return outcomes, giving practitioners a probabilistic rather than binary view of the opportunity set.
Valuation spread analysis complements the macro layer. The width of the spread between value and growth characteristics has historically been one of the stronger predictors of forward value factor performance. We track this spread across multiple definitions — price-to-book, price-to-earnings, enterprise value ratios — to ensure the signal is robust and not sensitive to a single measurement convention.
The combination of macro regime and valuation spread signals forms a two-dimensional assessment of the value factor environment. Neither dimension alone is sufficient. Together, they provide a more complete picture of when the structural conditions associated with value outperformance are present — and when caution is the more analytically defensible posture.
What sets our research framework apart from conventional factor analysis
Most factor research tells you what happened. Ours focuses on the conditions that shaped those outcomes — and whether those conditions are present now. The difference is not cosmetic; it is the entire point. Value timing without macro context is like reading a map without knowing which direction you are facing. We provide the compass.
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Cycle-aware signal construction
Our signals are not built in a vacuum. Each one is constructed with explicit reference to the macro regime in which it is being evaluated. A valuation spread that signals opportunity in one regime may carry a different implication in another. That context-sensitivity is built into the architecture of the framework from the ground up, not added as an afterthought.
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Transparent and auditable process
Every model decision has a documented rationale. Every threshold has a tested basis. Practitioners who engage with our research can trace any recommendation back through the signal chain to the underlying data. That level of transparency is not common in quantitative research — and it is something we consider a baseline requirement, not a differentiating feature.
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Rolling methodology review cadence
The framework is not static. We review signal definitions, threshold calibrations, and regime classifications on a defined schedule. When academic research introduces new evidence on factor dynamics, we evaluate its implications systematically. The goal is a living methodology that remains disciplined without becoming brittle in the face of new information.
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Cross-sectional and time-series integration
We combine cross-sectional valuation spread signals — which measure relative cheapness across the opportunity set — with time-series macro signals that capture the broader environment. Neither approach alone captures the full picture. The integration of both is where the analytical depth of the framework becomes most apparent and most useful for practitioners thinking about dynamic exposure.
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Documented historical analogues for every regime
Each regime classification comes with a mapped set of historical analogues — periods where similar macro and valuation conditions were present. These analogues inform the return distribution associated with the current environment without asserting that history will repeat exactly. They provide context, not certainty, which is the intellectually honest way to use historical data in forward-looking analysis.
Our core research values
The principles that govern how we build, test, and communicate our systematic value factor timing frameworks — from signal construction to regime classification to the way we document every analytical decision we make
Stress-tested signals
No signal enters the framework without first surviving a battery of historical
tests — including periods where value factors underperformed significantly. We
are more interested in understanding failure modes than confirming what already
works. A signal that breaks under pressure is a signal that should not be
trusted under any conditions.
Documented assumptions
Every assumption embedded in the methodology is written down, reviewed
periodically, and available for scrutiny. Undocumented assumptions are the
hidden risks in any analytical framework. We treat explicit documentation not as
administrative overhead but as a core quality control mechanism that protects
the integrity of the research.
Auditable decisions
Every exposure adjustment signal can be traced back through the framework to its
source inputs. There are no black boxes in our methodology. Practitioners who
engage with our research should be able to understand why the model reached a
given conclusion — not just what conclusion it reached. That traceability is a
design principle, not an afterthought.
Open review process
Our methodology review cadence is structured and pre-announced. When we update a signal definition or revise a threshold, we document the rationale and the evidence that prompted the change. The framework evolves, but every evolution is deliberate and recorded — because a methodology that changes without explanation offers no reliable analytical foundation.
Regime classification depth
We classify macro environments across four distinct regime categories, each with
its own associated value factor return profile derived from historical analysis.
This granularity matters because the value premium does not behave uniformly
across market environments. Coarse regime classifications miss the nuances that
drive meaningful differences in factor dynamics over time.
Multi-input signal design
The macro layer of the framework draws on a diverse set of inputs — credit
conditions, earnings revision trends, yield curve dynamics, and inflation
measures among them. No single input dominates the composite assessment. This
diversification of evidence is intentional: it makes the regime classification
more robust to the noise inherent in any individual economic indicator.
Uncertainty acknowledged
Past performance does not guarantee future results. Our framework is designed to
improve the quality of analytical reasoning about value factor exposure, not to
eliminate the uncertainty that is inherent in financial markets. Every regime
assessment comes with a distribution of historical outcomes — not a single
expected return — because honest analysis requires acknowledging the full range
of possibilities.
Limitations documented
We publish the conditions under which our signals have historically performed
less well, alongside those where they have shown stronger analytical relevance.
Selective presentation of backtested results is a form of intellectual
dishonesty that we actively work against. Understanding where a framework has
limits is as important as understanding where it adds value.
Reproducible findings
Our analytical conclusions are built to be reproducible by any practitioner who has access to the same underlying data and applies the same framework rules. Reproducibility is the minimum standard for credible quantitative research. We do not publish findings that depend on data mining, look-ahead bias, or parameter choices that cannot be defended on a priori grounds.
Academic grounding
The framework draws explicitly on peer-reviewed research into factor dynamics, valuation spreads, and macro regime effects on cross-sectional returns. We cite our sources, engage with contradictory evidence, and update our thinking when the literature warrants it. Research that ignores inconvenient findings is not research — it is confirmation bias dressed in quantitative language.
Actionable framing
Academic factor research is valuable but often stops short of the practical question: what does this mean for current exposure decisions? Our framework bridges that gap by translating regime assessments and spread signals into structured, actionable analytical conclusions. The goal is to make rigorous quantitative research usable by practitioners who need more than theoretical validation.
Clear communication standard
Every research output we produce is written to be understood without a PhD in quantitative finance. Technical precision matters, but so does clarity. We hold ourselves to a communication standard that respects the intelligence of our audience without hiding analytical complexity behind inaccessible jargon. Good research that cannot be understood is research that cannot be used.