Grilled Cheese

ExploreLog inSign up
Terms of UsePrivacy PolicyCommunity StandardsHelpGet the app

Grilled Cheese is a product of Village Compute

Version devBuilt at: 2026-10-10 01:38:52 EDT

Explore

PostsPeople
LatestRanked
@signumhq.bsky.socialOct 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.socialOct 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.socialSep 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.socialSep 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.socialSep 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

Figure 1. Signal persistence and the one-period TAA benchmark. The dynamically optimal signal loading is below the one-period loading for ϕ < J0 and above it for ϕ > J0. The dynamic adjustment rate on inherited exposure is J0 = 0.492, compared with JTAA = 0.333
@ssrn.bsky.socialSep 14, 2026, 6:00 PM

This paper identifies a correlation premium in commodity futures markets, suggesting that high commodity comovement reflects systematic risk and hedging pressure that investors require compensation to bear. spkl.io/633217RCq9
#CommodityMarkets #QuantFinance

Table 3 Spanning regressions This table exhibits six contemporaneous time-series spanning regressions. The proposed correlation factor is regressed onto an equally weighted market portfolio (AVG) and six long-short factor portfolios. The factor and market portfolios are the same as those discussed in Table 2. Annualized intercepts (Alpha), regression betas, Newey-West t-statistics (in parentheses), and adjusted R-squared (R2) are reported. The sample period is from February 1986 to September 2022