# s&p 500 Published articles for s&p 500. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Thinking Fast, Trading Late: System One Decision Models vs the Market DevFeed: [Thinking Fast, Trading Late: System One Decision Models vs the Market]() Original publisher: [Read original article]() Author: Amir Dehkordi (Independent Researcher), Alireza Kargarzadeh (TailState Intelligence Ltd), Nariman Khaledian (Independent Researcher), Navid Parvini (Zanista AI Ltd), Sid Ghatak (Increase Alpha, LLC), Published: 2026-11-04T05:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence](), [arXiv: Machine Learning]() Topics: [Artificial Intelligence](), [Data analysis](), [Finance]() Tags: [ai-decision-models](), [company-news](), [costs](), [cs-ai](), [cs-cl](), [cs-lg](), [deflated-sharpe](), [large-language-models-llms](), [masking](), [open-weights-model](), [protocol](), [q-fin-st](), [s-p-500](), [validation]() ### AI overview OLIVIA tests whether AI decision models can predict stock returns without look-ahead, selection bias or company-name memorization, using point-in-time masked news, validation-only selection, a hash-frozen analysis plan and a single logged unblinding of the test period. A fine-tuned open-weights model replicated its signal on two validation quarters, but its daily information coefficient fell to 0.020 (t = 1.7) in the blinded January-July 2026 test. Three commercial models tracked overnight moves to the open (IC 0.074-0.077), but not subsequent moves, and no preregistered strategy survived costs and a deflated Sharpe ratio. The models could not distinguish overreaction from underreaction; commercial models also identified masked companies in 62%-74% of cases versus a 28% baseline, showing that masking names did not conceal identities. ### Source excerpt arXiv:2611.03985v1 Announce Type: cross Abstract: Large language models (LLMs) read company news at scale, and backtests suggest that their readings predict stock returns. We ask how much of that predictability survives a test that rules out look-ahead, memorization and selection. We propose OLIVIA, a protocol for such tests: point-in-time inputs with company identities masked, selection on validation data only, an analysis plan frozen by cryptographic hash, and a test period unblinded once through a logged lock. We apply it to AI decision models, non-generative language models that return probabilities for typed questions, reading news about S&P 500 stocks: an open-weights model we fine-tuned on 2024-2025 data, three commercial models and standard baselines. The fine-tuned model's signal replicated on two validation quarters (daily information coefficients 0.055 and 0.065) but fell to 0.020 (t = 1.7) on the blinded January-July 2026 test. The commercial models' readings line up with the overnight move to the open (information coefficient 0.074 to 0.077) but not with the move after it, so the news is priced by the time a trader can act on it at the open. No model can tell over- from under-reaction, and no pre-registered strategy survives costs and a deflated Sharpe ratio. Commercial models identify masked companies in 62% to 74% of cases against a 28% baseline: masking hides names, not companies. The market reads the news first. ## FIRME: Registry-Anchored Memory Graphs for Evolving, Heterogeneous Investor Knowledge DevFeed: [FIRME: Registry-Anchored Memory Graphs for Evolving, Heterogeneous Investor Knowledge]() Original publisher: [Read original article]() Author: Yunbai Zhang, Yunyi Gao, Han-Tai Shiao Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [knowledge-engineering](), [база знаний]() Tags: [ai-workflows](), [aliases](), [audit](), [cash-flow](), [cs-ai](), [cs-cl](), [earnings](), [fusion](), [graphs](), [identity](), [knowledge-graph](), [ontology](), [persistent-memory](), [provenance](), [registry](), [s-p-500]() ### AI overview FIRME uses a registry-anchored memory graph to keep recurring investor observations linked to canonical relationships while retaining source, time, and context. Within each period, rule-based fusion connects investor-call discussions with portfolio cash-flow signals, directly or through a fixed product-topic ontology. Across periods, persistent aliases help reattach differently worded observations to the same history; deterministic updates summarize reinforcement, inactivity, and reactivation. Against a historical registry without alias memory, FIRME improved final-period Correct Match Rate by 7.8-12.1 percentage points across four dataset-backend settings. A blinded audit found gains of 9.0-16.5 points in Correct Match Rate and 22.5-29.0 points in reattachment recall, with higher observed merge precision in every sample. A separate investor-call audit found valid evidence paths for 293 of 300 full-fusion outputs. Results are from the described evaluation and audit protocols. ### Source excerpt arXiv:2610.40482v1 Announce Type: new Abstract: Financial AI workflows that reuse investor knowledge need persistent memory that integrates observations across sources and periods without losing identity or provenance. In a knowledge graph, recurring observations may use different surface forms, fragmenting relationship histories and preventing evidence from accumulating. We introduce Fusion and Identity Registry for Memory Evolution (FIRME), a registry-anchored memory graph that reconciles incoming observations to persistent canonical relationships and retains their source, time, and context. Within periods, rule-based fusion links investor-call discussions to portfolio cash-flow signals, directly or through a fixed product-topic ontology. Across periods, canonical identities and persistent aliases direct recurring observations to the same relationship history; deterministic updates summarize reinforcement, inactivity, and reactivation. We evaluate FIRME on proprietary investor-call and cash-flow records and on S&P 500 earnings-call transcripts. Compared with a historical registry that retains canonical identities and semantic fallback but omits alias memory, FIRME increases final-period Correct Match Rate by 7.8-12.1 percentage points across four dataset-backend settings under a fixed-judge and fallback-review protocol. A blinded human audit of 200 incoming observations per setting finds Correct Match Rate gains of 9.0-16.5 points and reattachment-recall gains of 22.5-29.0 points, with higher observed merge precision in all four samples. A separate audit finds valid evidence paths for 293 of 300 outputs from full within-period fusion in the investor-call setting. Extraction-faithfulness and round-trip diagnostics assess source grounding and preservation of extractor-derived graph content. ## Apollo analysis shows no one besides the biggest tech giants is seeing benefits from AI DevFeed: [Apollo analysis shows no one besides the biggest tech giants is seeing benefits from AI]() Original publisher: [Read original article]() Author: Mike Wheatley Published: 2026-10-26T01:31:12Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [Artificial Intelligence](), [Finance](), [Data analysis](), [decision-making]() Tags: [accountability](), [agentic](), [agentic-ai](), [ai](), [ai-investments](), [ai-payoff](), [ai-productivity-growth](), [ai-spending](), [analysis](), [analyst](), [apollo](), [apollo-global-management](), [artificial-intelligence](), [business](), [challenges](), [corporate](), [employee](), [enhancing](), [financial](), [financial-payoff](), [flat](), [investment](), [magnificent-seven](), [news](), [profit-margins](), [return-on-investment](), [revenue-per-worker](), [s-p-493](), [s-p-500](), [tech-firms](), [technology](), [technology-firms](), [the-latest](), [top-story-5](), [top-story-6](), [torsten-slok]() ### AI overview Apollo analyst Torsten Slok's comparison of revenue per worker, profit margins and productivity growth finds financial gains from AI concentrated in the seven large technology firms excluded from the S&P 493. At those firms, average revenue per employee rose from about $550,000 in 2022 to nearly $800,000 this year; the S&P 493 has stayed near $550,000 since 2019, while its margins and productivity show no clear improvement. Slok says established companies are treating token spending as a cost and questions whether cheaper AI can translate into higher revenue and margins. He points to business judgment as a barrier: AI lacks context around culture, accountability, ethics, fairness and trust, factors tied to reputational risk. The article says some venture-backed startups are increasing token spending, unlike more established firms. The supplied text presents Apollo's analysis and Slok's interpretation; it does not establish that AI caused the gains at the large tech firms. ### Source excerpt Despite all of the hype and the multiple millions of dollars invested in artificial intelligence, most of corporate America outside of its biggest technology firms is yet to see any financial payoff, according to an analysis by one of the world's largest investment firms. In the analysis published on Saturday, Apollo Global Management Inc. said [...] The post Apollo analysis shows no one besides the biggest tech giants is seeing benefits from AI appeared first on SiliconANGLE.