EventsThe 2nd International Online Conference on Risk and Financial Management
Published
This submission belongs to the session S2. Machine Learning for Next-Generation Risk Modeling of the event The 2nd International Online Conference on Risk and Financial Management
Published date
28 Oct, 2026
Academic Editor
author-avatarThanasis Stengos
Contribution&Funding
Conceptualization,
[Bose Kaikini]
Methodology,
[Dao Bui] ; [Bose Kaikini]
Software,
[Dao Bui] ; [Bose Kaikini]
Validation,
[Bose Kaikini]
Formal analysis,
[Bose Kaikini]
Investigation,
[Bose Kaikini]
Resources,
[Bose Kaikini]
Data curation,
[Bose Kaikini]
Writing - original draft,
[Bose Kaikini]
Writing - review,
[editing] ; [Dao Bui] ; [Bose Kaikini]
Visualization,
[Dao Bui]
Supervision,
[Bose Kaikini]
Project administration,
[Bose Kaikini]
Funding acquisition,
[Dao Bui] ; [Bose Kaikini]
Funding: --
Citation
Dao Bui, Bose Kaikini, Regime Effects Are Turnover Effects: Matched Controls for Conditional Portfolio Optimization, in Proceedings of The 2nd International Online Conference on Risk and Financial Management, 2 November–4 November 2026, MDPI: Basel, Switzerland
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Regime Effects Are Turnover Effects: Matched Controls for Conditional Portfolio Optimization

1. Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, 27599, USA
2. Mathematics, University of South Carolina, Columbia, 29208, USA
Abstract

A substantial literature reports that conditioning portfolio allocation on an
inferred market regime improves risk-adjusted performance, particularly net of
transaction costs. We show that in a realistic cardinality-constrained setting the
measured effect, at all three points where such a signal can enter, is attributable
to a variable the mechanism was never designed to control: how much the resulting
portfolio trades.

We evaluate 32 arms on CRSP daily data from 2011 to 2024, with 30 matched seeds and
four cost levels, injecting a three-state hidden Markov signal at each point where
it can act on a mean-variance decision: the optimizer's preferences, its covariance
estimate, and its expected returns. Measured conventionally all three work, gaining
+0.113, +0.388 and +0.294 Sharpe at 150 bps. Across 27 comparable arms, net-of-cost
performance is ordered almost perfectly by realized turnover alone (Spearman
rho = -0.95, p = 2.1e-14), while the same correlation gross of costs is
insignificant.

We introduce a control that resolves the confound: an unconditional strategy,
identical in optimizer, constraints and seeds, whose turnover penalty is solved by
bisection to match the treatment's trading. Against it the regime effect is -0.069,
-0.034 and -0.047 Sharpe, negative at every injection point and cost level, the
sign holding in 20-27 of 30 seeds. Reseeding one arm moves Sharpe by 0.233,
exceeding four of five effects measured. The finding survives solving the weighting
sub-problem exactly. We do not establish that regime conditioning cannot work, but
that a turnover-matched control was necessary to find out.

Keywords
portfolio optimization
regime switching
transaction costs
evaluation methodology
matched controls
reproducibility
hidden Markov models
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