explore | Grilled Cheese

@ssrn.bsky.social Oct 15, 2026, 7:00 PM

This paper shows that when hedging trades move market prices, the payoff being hedged can change as well. The results imply that option pricing depends not only on market conditions but also on the trader's own market impact. spkl.io/633287n40G #Options #QuantFinance

Figure 2: The left panel shows the amount paid (positive N) or received (negative N) when executing N shares of the underlying asset. The execution price is given by 100 + φsign(N) |N| and φ = 0.5λ. The right panel shows the prices, using Proposition 2.4, for a call option with S0 =100, ST(u) = 105, ST(d) = 95, r = 0.01, K = 100. Here I(x) = λx and ϕ(x) is given by (2.9). In the plots, n represents a “typical” size in the top of book of the underlying and m represents the same for the options market; thus, the dotted lines represent the region where the spread usually sits. The parameter n = 0.2 is such that the region plotted satisfies |x| ≤ 4φ2 λ−2

@signumhq.bsky.social Oct 6, 2026, 10:30 AM

Meta (META) closed Monday at $741.90 (+1.9%), after a 27% gain in September. Oct 9 options map:
1) Max pain $720
2) Call wall $750
3) Implied move about ±3%

Levels, not a forecast.
#options #quantfinance #derivatives $META
Search "SIGNUM HQ" on the App Store

SIGNUM HQ card for META, Oct 9 options expiry: max pain $720, call wall $750, Monday close $741.90, next to the app's Live Options Flow screen showing MAX PAIN $720.

@signumhq.bsky.social Oct 5, 2026, 5:36 PM

MSFT closed Friday at $517.53 (+0.9%). Oct 9 options map:
1) Max pain $505
2) Call wall $530 · Put floor $480
3) Implied move about ±2%

Levels, not a forecast.
Search "SIGNUM HQ" on the App Store
https://www.signumhq.com/app?from=bluesky_post
#options #quantfinance #derivatives $MSFT

SIGNUM HQ card for MSFT, Oct 9 options expiry: max pain $505, call wall $530, put floor $480, Friday close $517.53, next to the app's Live Options Flow screen showing MAX PAIN $505.

@gradientbrief.bsky.social Sep 29, 2026, 4:01 PM

A new arXiv paper studies "search scaling" in autonomous LLM agents for quantitative finance, running 50 factor-mining tasks across nine models to see how larger search budgets affect research performance. The work extends…

#AIResearch #LLMAgents #QuantFinance
https://arxiv.org/abs/2609.35559

@ssrn.bsky.social Sep 25, 2026, 6:00 PM

This paper separates the effects of clustering and trading models in statistical arbitrage, showing that clustering both adds alpha and reduces portfolio volatility by 28% to 42%. spkl.io/633287Tgys

#QuantFinance #MachineLearning

Figure 2: Alpha against volatility at the trained component. This figure plots each arm’s annualized alpha against the annualized volatility of its book, both at the trained component over the 6,913-day sample (July 1997 to December
2024). Alphas are against FF5+MOM with Newey–West(21) standard errors on the seed-averaged payoff, and volatilities are computed per head seed and averaged over the five seeds, the conventions of Table 2. The shaded band spans the 12 clusterizers’ alphas. Four clusterizers are named: k-means on returns at the lowest volatility, the graph Sharpe clusterizer at the highest alpha, gics at the second-highest volatility with a mid-band alpha, and spectral clustering at the highest volatility and the lowest alpha; the eight unnamed circles are the remaining clusterizers, whose coordinates the replication package lists. The two controls are red

@ssrn.bsky.social Sep 17, 2026, 2:00 PM

This paper shows that separating strategic and tactical #asset allocation can be dynamically suboptimal when returns are predictable and trading costs matter. Optimal #portfolios should account for future opportunities, not just current signals. spkl.io/633237rDKd
#QuantFinance