Aymane Oubella

Bank of England - Measuring the stability of the banking system This study develops a stochastic balance sheet based microstructural banking model to quantify the dynamic interplay between solvency and liquidity risks – two traditionally distinct dimensions in stress testing. By incorporating endogenous bank reactions, feedback loops, and amplification mechanisms, the model captures how management responses to shocks can escalate financial distress, potentially leading to insolvency and illiquidity. The authors apply the model to granular loan and security exposure data from UK banks over 2015–24, estimating capital and liquidity at risk and deriving a systemic default probability indicator. Results indicate an average one-year bank default probability of 0.7%, consistent with market-implied estimates but diverging during stress episodes. Amplification effects, driven by balance sheet constraints and behavioural responses, account for approximately one third of default risk on average. Counterfactual analyses further evaluate the effectiveness of capital requirements and identify optimal capital levels under hypothetical stress scenarios.

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We’ve expanded analytical capabilities with Fundamental Graphs. The tool lets you plot revenue, EPS, cash flow, and more — nine metrics in total — across multiple companies in a single, adjustable chart. Traders and investors can now compare fundamentals side by side, switch date ranges or scales in seconds, and share each configuration with a link — turning raw financial reports into clear, decision-ready visuals without leaving the chart. Get all the details: https://lnkd.in/eygVRGra

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AlphaAgents: Multi-Agent LLMs for Equity Portfolio Construction — and what happens when you plug in institutional sentiment + data feeds Just read AlphaAgents, a fresh paper from BlackRock researchers exploring role-based, debate-driven LLM agents for systematic stock selection and portfolio construction. The system mirrors a real investment team with three specialists that collaborate and argue their way to a decision: Fundamental Analyst, Sentiment Analyst, and Valuation Analyst. - What’s novel 1. Role specialization + debate: Agents run in a group-chat workflow (AutoGen) and must reach consensus, which helps reduce hallucinations and surfaces assumptions. 2. Risk-aware behavior: Recommendations are explicitly conditioned on investor risk tolerance (risk-seeking / neutral / averse), not just point estimates. 3. Tool-augmented analysis: - Fundamental agent uses a tailored 10-K/10-Q RAG tool; - Sentiment agent summarizes and critiques news before opining; - Valuation agent computes returns/vol and reads price/volume structure. 4. Evaluation signals: They treat back-testing as a downstream metric, in addition to task-level checks. - Why it matters This architecture is auditable, modular, and closer to how human teams actually work—diverse priors, debate, and a clear hand-off into portfolio construction. It also offers a path to mitigate cognitive biases (loss aversion, overconfidence) that creep into discretionary workflows. - Now imagine the next step Pair AlphaAgents’ workflow with institutional-grade sentiment and deep data coverage: real-time news/analyst-notes + FactSet fundamentals + PitchBook private-market context + filings + broker research. Then fine-tune or retrain reasoning models on domain-specific corpora (sentiment events, accounting footnotes, sector playbooks). The result is a futuristic portfolio management copilot that feels AGI-like in practice: - Unified data fabric (EDGAR, news, FactSet, PitchBook) → cleaned, labeled, time-aligned - Hybrid retrieval (RAG + knowledge graph) to ground every claim - Multi-agent committee (Fundamental / Sentiment / Valuation + Macro / Risk / ESG extensions) with debate + confidence scores - Risk & compliance layer (limits, exposure, scenario analysis) gating trades - Optimizer (MV, Black-Litterman, risk parity, or RL) consuming agent views - Monitoring (drift, rationale tests, counterfactuals) for production safety This is how we move from “LLM that chats” to institutional-grade, explainable, end-to-end portfolio decisioning. Paper: Attached! #AI #MultiAgent #LLM #QuantFinance #PortfolioConstruction #RAG #AutoGen #Research #FactSet #PitchBook #SentimentAnalysis #RiskManagement #AGI #AIFinance #ArtificialIntelligence #ResearchAI

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HAR MODEL FOR VOLATILITY FORECASTING 📊 AR models assume volatility follows a simple autoregressive pattern, fundamentally missing how different market participants operate across heterogeneous time horizons. The HAR (Heterogeneous Autoregressive) framework revolutionizes volatility forecasting by explicitly modeling three distinct trader categories: short-term day traders reacting to daily moves, medium-term investors focused on quarterly patterns, and long-term institutions considering semi-annual trends. The fundamental paradigm shift: AR Models: "Yesterday's volatility predicts tomorrow's volatility" HAR Model: "Volatility persistence emerges from the cascade of heterogeneous trading horizons - monthly, quarterly, and semi-annual components simultaneously drive future volatility" Our empirical application to Microsoft demonstrates transformative results: - 6.9% RMSE reduction versus AR(1) benchmarks - Monthly component coefficient of 0.39, quarterly 0.25, semi-annual -0.09 - Multi-horizon forecast accuracy from 1 to 6 months ahead - Ljung-Box p-value of 0.954 confirming no residual autocorrelation This framework delivers three game-changing advantages: 📈 Heterogeneous Horizon Modeling: Captures how different trader types create volatility persistence ⚡ Superior Long Memory Approximation: Simple structure mimics complex fractional integration 🎯 Multi-Horizon Forecast Accuracy: Consistent performance from daily to semi-annual predictions Real-world applications transforming volatility forecasting: - VaR calculations reflecting multi-horizon volatility components - Option pricing with improved term structure modeling - Portfolio rebalancing based on heterogeneous volatility dynamics - Trading cost models incorporating different participant behaviors - Risk budgeting across multiple time horizons How does your risk management framework account for heterogeneous market participants? Are you still assuming all traders react to the same volatility signals? 🤔 #VolatilityForecasting #HARModel #RiskManagement #QuantitativeFinance #MarketMicrostructure

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