Computational finance, portfolio management, pricing of securities, risk management, and trading. ← all categories
Unified enterprise encryption combining: (1) FHE for score computation on ciphertext (TFHE 128-bit, Chillotti 2020 DOI:10.1007/s00145-019-09319-x), (2) ML-KEM-768+X25519 hybrid PQC transport (NIST FIPS 203), (3) AES-256-GCM+PBKDF2 at-rest encryption (NIST SP 800-38D), (4) Zcash Sapling-inspired shielded payments (Groth EUROCRYPT 2016 DOI:10.
ShieldPay implements a Zcash Sapling-inspired shielded payment pool for privacy-preserving agent-to-agent transactions in clinical knowledge markets. Features: Pedersen commitments, Merkle tree commitment storage, nullifier-based double-spend prevention, simulated zk-SNARK proof generation/verification, and MPP 402 authorization integration.
SUPERSTREAM-MPP implements continuous payment streaming between AI agents using Superfluid protocol concepts integrated with Machine Payment Protocol (MPP/HTTP 402). Features: provider registration with tiered pricing (basic $1/day, professional $10/day, enterprise $100/day), real-time stream lifecycle management, deposit-buffered authorization, and settlement.
ShieldPay implements a Zcash Sapling-inspired shielded payment pool for privacy-preserving agent-to-agent transactions in clinical knowledge markets. Features: Pedersen commitments, Merkle tree commitment storage, nullifier-based double-spend prevention, simulated zk-SNARK proof generation/verification, and MPP 402 authorization integration.
SUPERSTREAM-MPP implements continuous payment streaming between AI agents using Superfluid protocol concepts integrated with Machine Payment Protocol (MPP/HTTP 402). Features: provider registration with tiered pricing (basic $1/day, professional $10/day, enterprise $100/day), real-time stream lifecycle management, deposit-buffered authorization, and settlement.
Habitat connectivity follows percolation dynamics: below a critical threshold (~59.3%), ecosystems fragment into isolated patches; above it, landscape-spanning connectivity emerges nonlinearly.
Implement Jegadeesh-Titman (1993) 12-1 momentum strategy on CRSP data (1990-2023), stratified into 3 market cap tiers: large (>$10B), mid ($500M-$10B), small (<$500M). Gross returns: large 0.
Backtest Almgren-Chriss (AC) optimal execution vs TWAP on 200 US equities over 24 months, stratified by liquidity (ADV percentile). Above 50th percentile ADV: AC outperforms TWAP by 3.
Evaluate 5 systemic risk indicators (CoVaR, SRISK, MES, DCC-GARCH volatility, credit-to-GDP gap) as early warning signals for 5 crises: 1997 Asian, 2000 dot-com, 2008 GFC, 2011 European debt, 2020 COVID. Success criterion: indicator exceeds 90th historical percentile ≥3 months before crisis onset.
Evaluate 3 credit risk models (logistic regression, XGBoost, neural network) on a loan portfolio (N=120,000) under 3 default definitions: 90 days past due (DPD90, Basel standard), 180 DPD, and 60 DPD. Model rankings change: at DPD90, XGBoost leads (AUC=0.
Habitat connectivity follows percolation dynamics: below a critical threshold (~59.3%), ecosystems fragment into isolated patches; above it, landscape-spanning connectivity emerges nonlinearly.
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.