This paper proposes PALM (Point-in-time Adaptation for financial Language Models) as an alternative to annual pre-training of financial language models. It achieves adaptation by fitting low-rank adapters on permitted financial text without modifying any pre-trained weights.
Read originalBased on 4.57 million news articles covering approximately 3,000 US stocks from 2023 to 2026, the paper uses a compact classifier distilled from a large model teacher to label news with 17 event categories and 5 attributes, clustering them into 1.68 million stock-day events, of which 364,405 are neutral sentiment events serving as a placebo.
Read originalA new arXiv paper uses Gaussian surrogate models to precisely calculate the finite-sample risk of candidate scoring decision models, analyzing the boundaries of the role of three training elements: teacher soft targets, frozen pre-trained backbones, and temperature calibration. The paper points out that after a student model inherits teacher bias, more student data cannot eliminate that bias; fixed representations only reduce risk below an explicit sample size threshold; under the probit model, exact recalibration maps to temperature scaling, and can only eliminate all excess risk when the direction of the teacher's coefficients is correct.
Read originalAuDQ, an auditable data quality layer for high-risk tabular data pipelines, proposes an ontology-driven, warehouse-native design vision that treats data quality rules as first-class artifacts, supporting version control, governance text traceability, and immutable execution trajectory replay. Its agentic rule discovery core includes both fully autonomous and guided rule acquisition modes, along with human-machine collaborative review interfaces, and has completed preliminary validation experiments on real-world datasets from the finance and retail industries.
Read originalA new arXiv paper studies the predictive performance of foundation models for return prediction when historical observations are inconsistent with recent return distributions. Authored by Levent Bulut, the 10-page paper includes 5 figures and belongs to the statistical finance (q-fin.ST) field.
Read originalThis 9-page paper accepted by ICAIF '26 proposes a diagnostic protocol to examine the true source of incremental information from LLM embeddings of r/WallStreetBets posts for volatility prediction. The study covers panel data of 202,145 mentioned stock-days from 2015 to 2022, using a retrospective rolling out-of-sample test.
Read originalResearchers have distilled five principles from established practices in software engineering and security research to form the TRACE framework for governing AI-assisted research, covering five dimensions: record tracking, source verification, independent acceptance, process control, and correction evaluation. The framework is accompanied by the open-source operating system TRACE-OS, which can adapt to researchers' existing workflows, helping teams maintain control over their research work when adopting AI and promoting the establishment of trust standards for AI-assisted research.
Read originalOpenAI is improving ChatGPT’s code generation performance for low-resource programming languages, adding three evaluation dimensions: software engineering, SQLite, and COBOL migration. These capability improvements are centered on code generation scenarios for low-resource programming languages.
Read originalThe SEC announced it will host a roundtable on machine-readable data in the age of superintelligence on November 9, 2026, at its Washington, D.C. headquarters. The event will discuss the applications of machine-readable data and superintelligence, as well as related risks and costs. The roundtable is open to the public; in-person attendance is limited, and it will be streamed live on the SEC’s website, with a recording to be made available afterward.
Read originalA former equity strategy partner and independent investment firm operator used AI in retirement to backtest his long-standing personal strategy, which combined Peter Lynch's growth stock selection, Hilary Kramer's undervalued obscure stock selection, and earnings revision discipline. He corrected for survivorship bias, used point-in-time data, calculated pre-fee returns, and compared it against random stock selection from the same investment pool. The results showed that the strategy did not outperform random selection, with ordinary index funds performing better than the strategy.
Read originalResearch shows that large language model pricing agents can deviate from the repeated Bertrand competition benchmark where prices converge to marginal costs in homogeneous goods markets, with their performance depending on deployment decisions. Whether LLM pricing agents can maintain prices above marginal costs in self-play is directly related to deployment choices: whether they set prices period-by-period based directly on raw market history including competitor prices, or periodically output probability distributions based on summaries of their own profit history.
Read originalThe research team built behavioral models for three types of trading agents—rational arbitrageurs, manipulators, and retail momentum followers—based on reinforcement learning, and conducted a pilot study on hourly cryptocurrency data (AAVE, ETC, SUI). The results show that the behavioral characteristics of different agents have differentiated impacts on market pressure, liquidity, volatility, and risk-adjusted returns. The research conclusions support formulating differentiated regulatory rules for different behavioral types, rather than uniformly restricting volatility or turnover rates.
Read originalAn experiment on a simulated collateralized lending market shows that shared action ordering significantly affects the scale of executed credit for LLM borrowers. The experiment set up 12 ordinary order markets and 12 balanced order markets, using an exact randomization test to obtain a two-sided p-value of 2/8100, with a credit difference of 1.12 units of initial unused reference borrowing capacity between leverage-prioritized and deleveraging-prioritized ordinary order markets. No liquidations or actual bad debts occurred in all 24 markets; under the fixed-price 30% shock benchmark, most write-offs came from collateral consumed by liquidation bonuses.
Read originalThis arXiv paper proposes a NoLBERT encoder pre-trained only on texts from 1976 to 1995, comparing it with LDA, MiniLM, and FinBERT on 1.26 million news articles from 2012 to 2024 to predict returns of the 1000 largest U.S. stocks.
Read originalThe Bank for International Settlements (BIS) released Working Paper No. 1386, which proposes an LLM-based reasoning pipeline to identify firms' actions in response to geoeconomic pressure from earnings call transcripts and summarize the underlying logic. Compared with traditional NLP methods, it can extract implicit conditional relationships in the text rather than merely counting keywords.
Read originalMoontower has released this issue, mentioning the Metagame event held at Lighthaven in Berkeley from November 6 to 8. Moontower readers can enjoy a $50 discount on regular tickets. This issue also shares AI-related thoughts, TIPS investment analysis, crude oil market learning resources, and pre-sale information for "How I Invest Volume 2", which will be shipped on October 20.
Read originalItaú is implementing its AI First strategy in customer and employee services through the conversational interface ia.i and the IARA governance platform. This application scenario operates in one of Brazil's most heavily regulated industries.
Read originalThe AI boom is becoming an ESG test. The water and electricity consumption of AI data centers will affect corporate profit margins, capital expenditures, and return on investment. Cross-industry investment portfolios may collectively face ESG exposure from data center expansion and power bottlenecks.
Read originalBenefit Street Partners speeds up question answering with OpenAI's Codex tool. The firm's Anant Kumar uses Codex to conduct code reviews, improve follow-up efficiency, and build tools to compare loan valuations and analyze credit agreements.
Read originalSullivan & Cromwell has partnered with OpenAI to develop Agreement Analyzer, an AI tool designed to identify issues in draft agreements. The tool integrates legal expertise into the agreement review process.
Read originalThe HKMA launched the "ART" framework on October 15, centered on the core principles of Accountability, Resilience, and Trust, to support the banking sector in responsibly adopting artificial intelligence. The framework includes the release of the latest regulatory guidelines on responsible AI adoption, the joint establishment of an AI training data sharing platform, and the advancement of agentic AI application testing. Concurrently, the HKMA and Cyberport co-hosted a two-day "Finance + AI Forum," marking the successful completion of the second phase of the Generative AI Sandbox technical testing.
Read originalAmp leverages OpenAI's GPT-6 Astra, Ultrafast, and GPT-Live-1 models, along with Sign in with ChatGPT, to expand AI agent use cases beyond coding into other domains. This practice aims to explore the capability boundaries of AI agents.
Read originalMoontower host Kris Abdelmessih breaks down an interview with LTCM co-founder Victor Haghani, arguing that investment decisions consist of two parts: instrument selection and position sizing, and that LTCM's failure stemmed from flawed position sizing rather than poor instrument selection.
Read originalAnthropic opened an office in Singapore, its fifth in the Asia-Pacific region, and signed a memorandum of understanding with Singapore's Ministry of Digital Development and Information. The two sides will cooperate in areas such as AI safety, model evaluation, and responsible AI implementation. Singapore is one of Anthropic's most active global markets, and Claude has been deployed in multiple local sectors including government, finance, and technology.
Read originalGoogle DeepMind is advancing the widespread deployment of Gemini 4 Argon while expanding the coverage of trusted cyber defenders in the Fairwind Program. The model solved all 12 scenarios in end-to-end simulated cyberattack tests, a significant improvement from 5 in the previous generation. Its refusal rate for violating requests nearly doubled compared to the previous generation, and violations accounted for only 2% of attempts in coding benchmark tests.
Read originalGoogle DeepMind officially released the frontier large model Gemini 4 Argon on October 13, 2026, available globally to developers, enterprises, researchers, and consumers. The model ranks first on the Vals Index benchmark, with input token pricing at $2 per million and output tokens at $10 per million, with a 95% discount for cached input tokens. It has already been deployed in multiple practical applications across engineering optimization, quantum chemistry, cybersecurity, and other fields.
Read originalThe Hong Kong Monetary Authority (HKMA) announced on October 14 the official launch of the Chongqing-Hong Kong Cross-Border Data Verification Platform (CQ-HK DVP), jointly promoted by the HKMA and relevant Chongqing authorities. Leveraging blockchain technology and hash-based data encoding, the platform enables verification of customer-submitted data without cross-border transmission or storage of original documents. Initially focused on cross-border financial scenarios, it supports verification of northbound and southbound data flows for banks in Chongqing and Hong Kong, serving a range of banking services such as account opening and loan applications for customers in both regions.
Read originalAlpha Architect interprets Dmitriy Muravyev's 2026 working paper, which uses an in-house LLM replication pipeline to reconstruct the core results of 1,328 papers published in the Journal of Finance, Journal of Financial Economics, and Review of Financial Studies between 2000 and 2020, of which 1,005 were eligible for subsequent out-of-sample testing.
Read originalMost portfolio managers and analysts in the investment industry have not yet adopted large language models in their daily work, with only a few well-resourced large institutions having implemented them. The article notes that LLMs can improve investment decision hit rates and long-term productivity, and that current barriers to adoption stem primarily from non-technical factors such as organizational processes, incentive structures, and psychological safety.
Read originalNew York University Professor Aswath Damodaran reviewed the performance of US stocks in September 2026: the 10-year US Treasury yield rose from 4.75% to 5.29% during the month, an increase of 54 basis points, placing it in the top 10% of 770 monthly changes since 1962. Despite this, the total market capitalization of US stocks still increased by $2.5 trillion during the month, with nearly $1.5 trillion of that increase coming from the technology sector.
Read originalAnthropic has released an update to its usage policy for 2026, which will take effect on November 12. This update mostly clarifies existing rules, adds integrated specialized provisions for deceptive activities, refines restrictions related to elections, weapons, and surveillance, specifies requirements for high-risk use cases, and adds prohibitions against malicious behavior targeting models. The full policy is available on the corresponding page.
Read originalGoogle DeepMind released EmbeddingGemma 2 on October 6, 2026, based on the Gemma 4 architecture, open-sourced under the Apache 2.0 license, with a total parameter count of 740 million, capable of mapping text, code, images, audio, and video into a unified embedding space.
Key Takeaways:Learn about EmbeddingGemma 2's modular parameter configuration, vector truncation rules, and on-device memory usage for building local multimodal retrieval and RAG pipelines.
Federal Reserve Governor Christopher Waller reviewed the history of FRED in a speech, calling it the most valuable public good built by the Federal Reserve. FRED now hosts over 850,000 data series and reached 18 million unique users in 2025. He also noted that AI introduces risks such as incorrect data citations and interpretive bias. FRED traffic is currently growing at an annual rate of 150%, with roughly half of visits coming from AI agents. FRED has launched the FRED MCP Connector, allowing users to retrieve data through their preferred AI agents.
Read originalGoogle DeepMind announced the launch of the frontier large model Gemini 4 Argon on September 30, 2026. The model is designed for complex long-cycle workflows such as software engineering, legal and financial enterprise knowledge work, and cybersecurity defense, and is currently being phased in to trusted cyber defenders through the Fairwind Program.
Read originalFed Governor Waller delivered a speech at the Sibos conference, focusing on the current state and potential impact of AI technology applications in the payments sector. He noted that AI can improve the efficiency of compliance screening for cross-border payments, optimize payment routing and foreign exchange conversion decisions, and also analyzed the two mainstream models and development prospects of agentic commerce driven by AI agents.
Read originalMoontower author Kris Abdelmessih released this newsletter, first sharing Adam Mastroianni's article on 'thickness', distinguishing between deep works that require attention to extract value and content-free AI-generated 'slop', then step-by-step deriving the optimal Kelly bet size formula from first principles.
Read originalAQR co-founder and dean of Northwestern University’s Kellogg School of Management co-authored an article pointing out that AI can automate most analytical and technical capabilities in quantitative investment, making creativity and emotional intelligence enduring competitive advantages that are difficult to replace. AQR is leveraging the Quanta Academy, launched in 2015, to expand relevant capability development, while Kellogg School of Management also promotes students’ simultaneous development of analytical skills and soft skills through programs such as MBAi. Research shows that the impact of AI on empathy is not fixed, and depends on tool design choices and organizational culture building.
Read originalNYU valuation professor Aswath Damodaran published a lengthy article proposing a four-stage framework for revolutionary change, arguing that AI is currently entering its coming-of-age phase, shifting from investment and infrastructure building to commercial implementation. The article notes that since the release of ChatGPT in November 2022, the Mag Seven contributed 45% of the total increase in U.S. stock market capitalization from 2022 to 2025. Six leading AI companies have cumulatively spent $1.7 trillion in capital expenditures, while the current upper bound of total revenue from AI products and services is only about $250 billion.
Read originalBridgewater has published an AI policy commentary arguing that AI is the most impactful technological change of our time, and that policy must be enacted immediately to balance its productivity benefits against risks of social disruption and safety incidents. Bridgewater proposes that US AI policy must prioritize societal resilience to technological shocks and establish a robust AI safety regulatory framework; otherwise, AI development will be forced to stall due to political unsustainability or major incidents.
Read originalModel Context Protocol has released the 2026-07-28 Release Candidate (RC) version. The current specification is provided as a draft, and detailed changes can be found in the corresponding draft changelog. This specification is not yet finalized and may still be adjusted between the RC and official release. Each SDK will adapt to this version at its own pace, and older versions of the specification may continue to be used for an unspecified period.
Read original
