OVVO Labs

Beyond the Models Everyone Else Still Uses The models are not wrong because nobody noticed. They are wrong because replacing foundations is harder than adding parameters. ❌ Mean-variance assumes quadratic utility. ❌ Pearson correlation reduces all dependence to a single linear number. ❌ Parametric distributions impose structure markets do not naturally satisfy. At OVVO Labs, we asked a different question: What if quantitative finance were rebuilt without forcing markets into assumptions they do not naturally satisfy? The result is a unified framework built on partial moments and nonlinear nonparametric statistics. Mean, variance, skewness, kurtosis, covariance, and even the empirical CDF are all special cases of one broader mathematical structure. Not a collection of tools. A coherent alternative to the parametric foundations the entire industry inherited by default. That single foundation powers three production-grade applications. ✳️ Options Pricing Call = upper partial moment. Put = lower partial moment. No lognormality assumption. No dependence on a calibrated volatility surface. Honest confidence intervals from the actual empirical distribution tested live against real market prices, including on the days when assumptions matter most. ✳️ Portfolio Optimizer Embeds your utility preferences directly into the covariance structure using four directional quadrants of dependence. Captures crash dependence and asymmetric tail behavior that Pearson correlation was never designed to see. Two assets can show zero correlation and still crash together every time. Standard optimization misses it. This one does not. ✳️ MacroNow Nonparametric vector autoregression across 30+ Federal Reserve variables. No arbitrary lag selection. No stationarity assumptions imposed on data that does not satisfy them. Built for live nowcasting, not retrospective fit. Harry Markowitz reviewed this work over multiple years of correspondence and wrote: "I agree that your approach is more general than old-fashioned mean variance... I wish you the best in getting your ideas out." That is not a marketing slogan. That is the founder of modern portfolio theory acknowledging that mean-variance (the framework every institution on Earth still uses) is a special case of something more general. The full suite is priced at a fraction of a single Bloomberg terminal. A fundamentally different way to approach pricing, portfolio construction, and macro forecasting, built on decades of original research, unified under one mathematical framework, and available today. Try the live tools here → ovvolabs.com Markets don’t care about your assumptions. Neither do we.

Position size is the most important decision. OVVO Alpha’s scalar went to 0.0 after Oct 1 and stayed there. Treating it as a size dial (not a binary on/off) would have reduced exposure while $SPY gave back the October high and finished the month red. You don’t have to trade the signal to use the information. See here for more information: ovvolabs.com/media • Past performance is not indicative of future results • Not investment advice • Not an offer to sell or a solicitation to buy securities

$SPY $QQQ $IWM

This is Biden’s $INTC position! 😂

$INTC did not make our list at all since June!

$NOW only made our list twice over the last few months, both bearish 🐻

✳️ Who we are — OVVO Labs Founder - Fred Viole This video explains why the work exists and the persistence required to turn a statistical objection into production tools. youtu.be/DYDTD3_76VA ✳️ What makes us different — Partial Moments in Quantitative Finance This video supplies the technical “why this is different” argument. youtu.be/7_2on2WF9Gs Most managers claim they're different and think outside the box. None of them has these receipts.

Always interesting to update this $SPY time-series classification analysis... One invariant is the different definitions of similarity will yield different periods of association. “The most similar historical period” is not a well-defined object independent of the loss or distance you choose! Yet this uncomfortable truth does not mitigate the number of these comparisons saturating daily feeds. ovvo-financial.github.io/NNS… *can easily modify for $QQQ or $IWM

Client: "You actually beat the $SPY with less downside trading just the $SPY, I want to put more money to work!" Us: "Great, we have to move to the discussed fee schedule going forward." Client: "So even after the fees, I am still beating the $SPY?" Us: "Yes." Former client: "But I don't want to pay the fees." Us: "Best of luck!"

If you cannot elucidate your objective function, then you shouldn’t be managing portfolios

Very pertinent to this $EWZ and @Polymarket shitshow!

Certainty = Risk linkedin.com/pulse/expected-…

$EWZ ranked near the bottom (82 out of 100) in the rankings of the top 100 ETFs by AUM at the close on 10/23/26 and you would have avoided this portfolio landmine completely! We have previously mentioned our stock ranking tool embedded in our portfolio app (lnkd.in/g8TFxv5j), as well as standalone version where you can import your own data for a ranking. Useful insight if you're running a L/S book... 🧐 ovvolabs.com ranks every security in the universe you select, for the holding period you care about, calibrated to your actual risk profile using expected partial moments, not volatility.

SKILLS.md is the new IP

There is one universal objective function: all of the upside and none of the downside. We only want and need diversification on the downside. This is shared for all risk preferences. See how we deliver on this: ovvolabs.com/media

Keep asking “why?” like a child and eventually you’ll uncover assumptions everyone else has stopped questioning. You may not like what you find. That’s why we rebuilt statistics from first principles using partial moments. Variance is an aggregation of upside and downside deviations. Covariance is an aggregation of four distinct directional interactions. Why discard that information before analyzing it? Classical statistics isn't necessarily wrong. It's a special case of a more general framework. We didn't set out to build another statistical model. We questioned the foundations upon which the existing models were built.

Fixed it for them 😂