r/quantfinance

Hello! After receiving some very good feedback last week I’ve edited my cv to be a one page cv, any other suggestions would be much appreciated!
In the meantime I got rejections from SIG and JS after clearing their OAs but life goes on

I’ve been working on an identification problem in cross-asset market impact, and I’d be interested in having people here try to break the argument.
The motivating observation is pretty uncomfortable.
In one published one-minute cross-asset return-on-flow specification that motivated this project, the mean estimated cross-asset coefficient is positive. After adding a single cross-sectional principal-component control, the mean changes sign and the fraction of negative coefficients changes dramatically.
That raises a basic question:
When we regress asset returns on contemporaneous order flow across many assets, under what assumptions can the off-diagonal coefficients actually be interpreted as structural cross-impact?
I considered the simultaneous system
r_t = Lambda q_t + Gamma f_t + u_t
q_t = B r_t + Delta_f f_t + v_t
where:
Lambda = structural price-impact matrix
f_t = K latent common factors
B = same-bin return/flow feedback
Gamma and Delta_f = factor loadings
If A denotes the population coefficient matrix obtained by regressing returns on flows, then
A = Lambda + G
where G is the confounding gap.
The first result is that
rank(G) <= K + rank(B).
So latent commonality does not produce arbitrary estimation error. It produces a structured, low-rank distortion.
But low rank does not mean small.
If feedback is absent and the true structural impact matrix Lambda is diagonal, then the entire estimated matrix must lie in
D_K = { D + R : D diagonal, rank(R) <= K }.
In other words, a purely diagonal structural model can generate a dense-looking cross-impact matrix whose off-diagonal entries are comparable in magnitude to genuine own-impact.
That led me to what I think is the more important result:
Lambda is generally set-identified rather than point-identified from the relevant second moments.
There is a family of different structural impact matrices that reproduce exactly the same observable second moments after changing the latent-factor channel appropriately.
So controlling for a factor does not necessarily “remove the confounding and reveal the structural matrix.” It can move the estimate along the confounding directions without selecting the true structural matrix.
In a permutation-invariant one-factor geometry, I can solve the identified set analytically. At the calibration I’m using, the sharp interval for the structural off-diagonal coefficient contains zero, and its half-width is roughly 7.4 to 8.9 times the observed cross-impact coefficient.
So in that case the data do not identify even the sign of structural cross-impact.
What surprised me more is what happens to execution costs.
Suppose a desk evaluates a trade x using quadratic execution cost
C(x, M) = x' M x.
Then using the regression matrix instead of the structural matrix produces error
x' G x.
Since G is low rank, the error itself has low-dimensional structure.
There is therefore a large set of directions that are immune to the confounding. But this does not mean a randomly chosen trade is approximately safe.
In the registered N=30, K=3 known-truth experiment, an equal-weight index basket is mispriced by about 54% while a particular dollar-neutral basket has exactly zero error.
The dollar-neutral result is geometry-specific, though. In the general model, dollar neutrality by itself does not imply immunity. The relevant object is the null structure of the confounding gap.
This suggests an odd distinction:
The impact matrix can be unidentified while the execution cost of a particular trade is point-identified.
I also wanted the theory to be falsifiable rather than just saying “maybe factors explain everything.”
So I defined a normalized distance from an estimated impact matrix to the diagonal-plus-rank-K variety:
psi_K(A) = distance(A, D + rank-K matrices) / norm(offdiag(A)).
Under the pure-confounding null, the population value is zero.
A materially nonzero value therefore rejects the maintained model consisting of diagonal structural impact, no feedback, K factors, and the accompanying covariance assumptions.
Importantly, psi_K = 0 does not prove that structural cross-impact is absent. The test is one-sided in interpretation: it can falsify the pure-confounding model but cannot confirm it.
The finite-sample behavior is also not magically nice. In simulations the plug-in bootstrap over-rejects badly at small T and only starts controlling a nominal 5% size around roughly
T >= 5 N^2.
I tried a simple degrees-of-freedom variance correction and it completely failed because the bootstrap problem is primarily mis-centering rather than insufficient dispersion.
The theoretical/known-truth stage is preregistered. For the main verification I used
N = 30
K = 3
T = 10,000,000
and checked 1,800 coefficient targets. The maximum relative discrepancies against the population formulas were below the preregistered 0.001 gate.
One important caveat: I have not yet used the external market dataset for the registered empirical test. The current version is deliberately a pre-results manuscript for that stage. So I’m not claiming that real cross-impact is spurious. The claim at this point is an identification result plus a falsifiable empirical design.
Repo / preprint / derivations / preregistration / code:
https://github.com/ITheClixs/spurious-or-structural
The questions I’d particularly like criticism on are:
Is there a structural restriction used in actual market-impact work that defeats the set-identification argument without simply assuming the answer?
Does the low-rank characterization miss an economically important confounding channel that would change the rank bound?
Is distance to the diagonal-plus-low-rank set the right object to test, or is there a better way to formulate the falsification problem?
For people who work with institutional flow or market-impact estimation: what empirical result would actually convince you that an observed off-diagonal coefficient is structural rather than common-flow contamination?
I’m especially interested in counterexamples. If the identification argument breaks under a realistic microstructure assumption, that’s more useful to me than agreement.

Does this mean rejection(fuck off )or will I get OA

Any experience anyone

Most of the de-risking talk here comes down to price rules: hold while SPY is above its 200-day, step aside when it drops below. It works, but it whipsaws, and it's always reacting to price after the move has already started.

I spent the last year on a different version of the same question. Can you estimate the probability of a large S&P drawdown before it shows up in price, from macro and credit data instead of a moving average? That turned into a paper, and then into a model I now re-run every month.

What it actually does: each month it scores the odds of a 10%+ S&P 500 drawdown over the next 1, 3, 6 and 12 months from a set of macro and credit-market indicators. It's estimated walk-forward, so every month's forecast only uses data that existed at the time. The track record is out-of-sample, not a fit done in hindsight. There's a threshold around 30% where the model would say cut equity exposure.

Right now it reads calm. Its latest run (macro inputs go through June) puts the one-month odds of a 10%+ drop near 5% and the six-month near 18%. Nothing close to the 30% line, so on this signal you'd still be fully in.

The limits, because this sub will poke at them anyway and should:

• It forecasts S&P drawdowns, not the volatility decay that actually grinds down a leveraged position. Related, but not the same thing.
• It's a slow signal. It's built to catch credit and macro deterioration building over months, not a flash crash or a one-week geopolitical shock. If the next drawdown is a sudden stop, this won't warn you.
• The live, in-public history is short. The out-of-sample tests in the paper run back decades, but actually running it monthly where I can't quietly re-fit is only a few months old.
• I have not tested it as a TQQQ/UPRO overlay against the 200-SMA rule. That's the comparison I most want to see and haven't done properly yet.

That last point is really why I'm posting. Plenty of you have backtest setups for exactly this. If you swapped "SPY above/below its 200-day" for "de-risk when this model crosses 30%", how would it have gone through 2018, 2020 and 2022? My hunch is it gets out slower but whipsaws less. That's only a hunch.

It's free and there's nothing to buy. It's a research framework, not a signal service. The model, the current read and the papers behind it are at agreeableinvestments.com, and I'm happy to get into the indicator set or the walk-forward setup in the comments if anyone wants it.