Hello and welcome back to another paid post!
There is a comfortable story in quantitative finance that says the right optimization, aware of trading costs and of how signals decay, should convert a static asset mix into a smarter, cost-conscious one. A recent paper makes that case with unusual rigor and a clean headline number: its dynamic rule recovers slightly more than half of the performance gap between a naive tactical portfolio and a hypothetical oracle that knows the future.
We reproduced that result exactly. Then we did the thing the paper does not do, which is to hand the same machinery real ETF prices, real estimated forecasts, and real trading frictions, and let it compete against a portfolio that simply rebalances to fixed weights and otherwise does nothing. Across thirteen years, a second asset universe, and a battery of pre-specified controls, the do-nothing portfolio won.
This piece explains why.
The Question
Tactical asset allocation promises to add value on top of a strategic policy by leaning into assets when their expected returns look high and away when they look low. The academic version of this promise is more careful than the marketing version. It acknowledges that every tilt costs money to put on, that forecasts decay at different speeds, and that a portfolio which chases each new signal will churn itself into the ground. The state of the art therefore frames allocation as a dynamic control problem: choose today’s trades while accounting for tomorrow’s costs and the persistence of today’s information.
Kolm and Ritter (2026) solve exactly that problem in closed form and show, in a controlled numerical setting, that their value-optimal rule captures 51.1 percent of the objective loss that a conventional tactical rule leaves on the table relative to a dynamic oracle. It is an elegant result. The natural question for a practitioner, and the one this study asks, is narrower and more stubborn: when you replace the paper’s known model parameters with quantities you have to estimate from data, and when you charge real trading costs, does any of that theoretical advantage survive contact with the market?
Our answer is that it does not. The optimizer works. The forecasts do not. And once the forecasts fail, every layer of sophistication built on top of them, dynamic planning, entropic risk adjustment, restricted policies, inherits the failure. The rest of this piece walks the evidence.
Reproducing the Model
Before testing a framework it is worth confirming the framework is sound. We rebuilt the paper’s Section 8 numerical illustrations from the stated equations, using no author code, no market data, and no paid datasets. We estimated the restricted policy parameters independently rather than copying the published values. Every one of the twelve entries in the paper’s central table reproduced to its displayed precision.
Reproduced values match the paper to five decimal places on all twelve entries. The value-optimal rule recovers 51.113574 percent of conventional TAA’s loss relative to the oracle, against the paper’s reported 51.1 percent.
Our independently optimized restricted policy landed at retention and tracking parameters of (1.16929, 1.00000, 0.88727), matching the paper’s (1.1693, 1.0000, 0.8873). Fixed-point residuals for the oracle and reported policies sat below 1e-12, and a separate 350-point parameter search failed to beat the optimized loss, a numerical cross-check rather than a proof of global optimality. In short, the mathematics is not in question. The 51.1 percent figure is a reduction in a model-implied objective, not an investment return, and reproducing it tells us the engine runs. It says nothing yet about whether the fuel, estimated forecasts, has any energy in it.