Computational finance, portfolio management, pricing of securities, risk management, and trading. ← all categories
Government AI investment appraisals typically ignore two categories of risk: standard public sector procurement risks and AI-specific technical risks. We contribute an open-source Monte Carlo tool addressing both, with two modeling improvements.
Can LLMs accelerate the hypothesis-generation phase of government AI investment appraisal? We present GovAI-Scout, a decision-support tool — explicitly not an autonomous oracle — that uses Claude to generate structured investment hypotheses for human expert review.
We present GovAI-Scout, an LLM-augmented autonomous agent for government AI opportunity assessment that addresses the critical methodological gap between qualitative sector analysis and quantitative financial modeling. The system introduces a transparent 4-step parameter derivation chain grounded in UK HM Treasury Green Book (2022) optimism bias methodology, applying benefit discounts of 50-97% beyond standard guidelines.
We present GovAI-Scout, an LLM-augmented autonomous agent for government AI opportunity assessment that addresses the critical methodological gap between qualitative sector analysis and quantitative financial modeling. The system introduces a transparent 4-step parameter derivation chain grounded in UK HM Treasury Green Book (2022) optimism bias methodology, applying benefit discounts of 50-97% beyond standard guidelines.
We present GovAI-Scout, an LLM-augmented autonomous agent for government AI opportunity assessment. The system addresses a critical methodological gap: how to transparently connect qualitative AI sector analysis to quantitative financial modeling.
Public discourse increasingly frames artificial intelligence investment as a speculative bubble comparable to the dot-com crash of 2000 or the 2008 housing crisis. We test this claim systematically by identifying six structural features that characterize historical asset bubbles — widespread denial, mass retail participation, leverage amplification, exit liquidity, speculative disconnect from fundamentals, and rapid unwind mechanisms — and scoring each feature as present, partial, or absent across four confirmed historical bubbles and current AI investment.
The cryptocurrency market faces an existential crisis as it grapples with prolonged crypto winters, investor fatigue from extreme volatility, and a fundamental shift in its identity. This paper examines whether cryptocurrency is doomed to irrelevance or undergoing a necessary transformation.
Penelitian ini menyajikan kerangka kerja quant engineering yang mengintegrasikan data pasar keuangan Indonesia dengan sentimen berita untuk membangun model prediktif yang lebih akurat. Kami mendemonstrasikan bahwa kombinasi harga historis, volume perdagangan, dan skor sentimen dari berita ekonomi Indonesia dapat meningkatkan akurasi prediksi return harian hingga 23% dibandingkan model yang hanya menggunakan data teknikal.
This paper examines the emerging agentic economy—a future where autonomous AI agents execute financial transactions on behalf of businesses and consumers—and the critical role of stablecoins as the foundational payment layer. While the convergence of AI agents and stablecoins promises to revolutionize global commerce with projected volumes of $3-5 trillion by 2030, it also introduces significant risks.
This paper presents a comprehensive framework for AI risk management in financial services, drawing from the MindForge Consortium industry collaboration. It examines the implementation experiences of four financial institutions at different maturity levels and provides operational guidance for governing AI across the enterprise.