Cooperation in Human-AI Groups Depends on Behavior
AI agents in group settings can influence cooperation just like humans do, showing that social norms can adjust for both AI and human participants.
0 shares2 citations todaySource ↗
Quant LetterNo. 126
46 items across 8 sections, as sent to readers on 2 February 2026. Paper titles open their ML-Quant page; ↗ goes to the source.
Quantitative-finance and ML-for-finance preprints from arXiv.
2 items
AI agents in group settings can influence cooperation just like humans do, showing that social norms can adjust for both AI and human participants.
0 shares2 citations todaySource ↗
The article introduces a new Bayesian model that analyzes compositional time series data, effectively handling structural breaks and enhancing forecasting accuracy during these changes.
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Economics working papers from RePEc's NEP field reports.
10 items
Daily VIX can be predicted using machine learning, with jobless claims being a vital factor for better risk management.
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Machine learning assesses equities' impact on the Pakistan Stock Exchange, emphasizing effective portfolio optimization in volatile markets.
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Algorithmic trading and passive investing make asset management tough during downturns, but an Automated Adaptive Trading System may help stabilize portfolios.
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Research shows that non-Gaussian methods for risk parity portfolio optimization enhance risk-adjusted returns in turbulent market conditions.
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In predicting Eurozone bank stock prices, traditional machine learning models like XGBoost perform better than deep learning models due to dataset limitations.
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Global Sharpe Ratio Analysis: Sharpe Ratio trading strategies outperform buy-and-hold in global stocks, supporting the Adaptive Market Hypothesis through market efficiency analysis.
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AI Insights: Accurately measured news sentiment, particularly using GPT-4, significantly impacts stock return volatility, showing better classification than RavenPack.
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A new machine learning method for dynamic time series modeling proves more flexible and accurate than traditional techniques, especially for financial data during COVID-19.
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Two new deep learning frameworks are developed to enhance Value at Risk (VaR) and Expected Shortfall (ES) estimates, improving financial risk management.
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The study finds that differences in macroeconomic expectations affect financial risk premia, influencing stock market returns tied to future consumption and productivity.
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Papers that shipped their code, from the Papers with Code feed (2023-25).
7 items
AI agents require better safety protocols when using tools and interacting with their surroundings.
176 shares
Adaptive agents that learn from feedback can adapt more effectively to changing environments and enhance knowledge sharing.
145 shares
MathForge enhances AI's mathematical reasoning by adjusting question difficulty and reformulating problems.
82 shares
Automated Decision Framework: ASTRA trains language models with synthetic data to improve their ability to make complex decisions.
80 shares
Visual Reasoning Tool: AdaReasoner enables multimodal models to learn how to use tools effectively for enhanced visual reasoning through scalable data and adaptive methods.
42 shares
Token filtering during pretraining minimizes undesirable traits in language models and enhances performance while managing noisy labels.
31 shares
SelfDistillation Policy Optimization (SDPO) enhances reinforcement learning by incorporating textual feedback to make training language models more efficient and accurate.
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Repositories the letter featured.
10 items
Alpha Library is a Rust library designed for quick financial calculations that can be used with Python.
36 shares
A new framework helps explore automated trading strategies for cryptocurrencies using advanced language models.
54 shares
Binance Downloader: CryptoFetch allows users to easily download historical price data for cryptocurrencies from Binance via command-line.
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This project tests how ChatGPT can manage a real-money investment portfolio in microcap stocks.
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Protocol Buffers is an efficient data interchange format developed by Google.
70,605 shares
AI Assistant: An AI assistant designed to function seamlessly across various operating systems and platforms.
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AI Assistant: A user-friendly AI assistant compatible with all operating systems and platforms.
67,458 shares
ChatGPT Prompts: A platform dedicated to finding, sharing, and organizing ChatGPT prompts for personal use.
144,080 shares
A collection of different mathematical optimization challenges for problem-solving practice.
177 shares
A detailed resource list for building and developing AI web agents effectively.
1,039 shares
Episodes on markets, quant methods and economics.
10 items
Maximizing Income: Elisa Piscopiello explains the role of dividends in equity returns and shares tips to avoid falling into dividend traps.
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Market Signals 2026: Katy Kaminski analyzes market signals for 2026, stressing the impact of volatility and commodity trends due to global disruptions.
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Russell Napier points out essential questions for investors amid changing monetary regimes and warns against chasing yields in inflationary times.
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Investor Missteps at a Turning Point: Alan Dunne and Dario Perkins highlight misinterpretations of global macro trends and caution against overheating risks from recent policies.
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Policy Risks and Growth: Russell Napier underscores the risks of yield chasing and emphasizes gold's importance in adapting to significant financial shifts.
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The article discusses how China is evolving from a follower to a leader in Bretton Woods institutions, shaping global economic policies.
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VortexNet harnesses fluid dynamics to solve major challenges in deep learning, potentially advancing AI technology significantly.
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Investment experts David Lebovitz and Jared Gross analyze asset allocation and market trends, providing insights for opportunities in 2026.
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JPMorgan analysts examine recent volatility in commodity markets, noting significant price swings in metals and ongoing issues in oil and gas.
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Sharmin Mossavar-Rahmani from Goldman Sachs offers investment strategies for 2026, highlighting the importance of informed decision-making amid dynamic markets.
3 shares
Posts from quant and economics blogs and newsletters.
4 items
Green bond issuance has significantly increased over the last five years, leading to comparisons with conventional bonds.
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Russell Korgaonkar discusses how investors can use market signals to enhance risk prediction and optimize portfolio management.
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The article details methods for wisely distributing investments in various markets while following trends.
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It examines possible difficulties and concerns that may occur when using the trend-following investment strategy.
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Posts from quant researchers on X.
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The Commodity Insights Digest Winter 2025 provides important research and insights on commodities, covering areas such as machine learning, trading signals, and energy spreads.
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Threads from r/quant, r/algotrading and friends.
2 items
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