Financial Analytics & Modeling
Predictive Models • Risk Quantification • Portfolio Optimization
Financial data analysis is the art of finding order within the chaos of markets. Just as advanced meteorological models track pressure changes in the atmosphere to predict storms in advance; we analyze historical data, market sentiment, and visual signals to model the financial climate. The solution I offer processes complex mathematical algorithms and multi-layered datasets (text, image, numerical data) to "separate signal from noise". My goal is not to overwhelm you with data; it is to provide the compass that will transform these complex calculations into clear, strategic, and profitable investment decisions.
News & Sentiment Analysis (NLP for Market Pulse)
Scenario
Markets move not just with numbers, but with news.
Solution
My NLP models scan central bank minutes, CEO statements, or tens of thousands of news headlines in seconds, measuring the market's "sentiment".
Benefit
Provides early warning by detecting negative news waves about a stock before it begins to decline.
Internal Financial Data Analysis & Business Intelligence
Scenario
Your company generates vast amounts of financial data daily—sales, expenses, cash flow, inventory—but extracting actionable insights from this data requires specialized data science expertise.
Solution
I apply advanced analytics and machine learning to your internal financial data, building predictive models for revenue forecasting, cost optimization, and cash flow management. From identifying spending patterns to detecting anomalies, I transform your financial data into strategic intelligence.
Benefit
Enables data-driven decision making by revealing hidden patterns in your financial operations, optimizing costs, and improving profitability through predictive insights.
Computer Vision for "Alternative Data" Analysis
Scenario
How do you measure a retail chain's or factory's performance before financial statements are released?
Solution
Algorithms processing satellite images count parking lot occupancy rates of retail stores or analyze container movement activity in ports.
Benefit
You gain leading indicators about a company's operational performance months before official financial reports are published.
Algorithmic Risk Management & Portfolio Optimization
Scenario
Knowing which assets are correlated with each other and where hidden risks lie.
Solution
Deep learning models that simulate historical price movements and volatility test how your portfolio would be affected in "Black Swan" (unexpected crisis) scenarios.
Benefit
Protects your capital by establishing the return-risk balance most suitable to your risk appetite.
Geopolitical Portfolio Optimizer
Control & Macro Inputs
Bloc formation, sanctions and kinetic risk. Gold and energy spike, US assets attract safe-haven flows, EM and Europe de-rate.
Structural trends carry most of the weight.
- Gold returns 10% (safe-haven bid)8.4% · c 51%
- US beats Europe/Japan by 300bp2.3% · c 44%
- EM returns 1.5% (sanctions, capital flight)3.4% · c 44%
- Energy-led commodities return 8.5%7.4% · c 41%
- Treasuries return 4.5% (flight to quality)4.3% · c 37%
- Developed ex-US returns 3%4.1% · c 37%
Asset Allocation · w*
| Asset | Weight |
|---|---|
BILCash / T-Bills | 30.0% |
GLDGold | 25.5% |
SPYUS Large-Cap Equities | 24.5% |
IEFUS 7-10Y Treasuries | 20.0% |
EFADeveloped Markets ex-US | 0.0% |
EEMEmerging Markets | 0.0% |
TIPUS TIPS | 0.0% |
DBCCommodities / Energy | 0.0% |
Monte Carlo Wealth Fan
Strategy Comparison
| Metric | Adaptive Geopolitical | Static 60/40 | 100% T-Bills |
|---|---|---|---|
| Expected returnμ_BL · annual | 5.07% | 5.43% | 3.50% |
| Volatilityσ · annual | 6.00% | 9.73% | 0.51% |
| 95% CVaR1-yr expected tail loss | 8.35% | 16.34% | -2.36% |
| Simulated Sharpefrom the paths | 0.28 | 0.25 | -0.32 |
| Median wealthyear 15 | $1.05M | $1.13M | $877k |
| Stress wealth5th percentile | $842k | $726k | $846k |
| Bull wealth95th percentile | $1.39M | $1.81M | $897k |
| P(shortfall)ends below money paid in | 0.0% | 0.0% | 0.0% |
Risk & Performance HUD
| Adaptive | 60/40 | |
|---|---|---|
| μ | 5.49% | 5.10% |
| σ | 5.97% | 9.69% |
| CVaR₉₅ | 7.87% | 16.58% |
- BL views
- 6 · τ 0.05
- Solver
- 2894 iters · λ 7.482
- Target μ
- 5.06%
- Tail model
- t(ν=5) · κ 2.239
- Allocation
- —
- Simulation
- —
Stylised capital-market assumptions; not investment advice. Posterior μ_BL and Σ_BL follow the He–Litterman closed form with τ = 0.05 and Ω from view confidence. Weights minimise the 95% CVaR of the one-year loss under an elliptical t(ν=5) model, subject to 0 ≤ wᵢ ≤ 40%, Σw = 1 and a return target set by the risk profile; the hedged profile minimises the worse of the current and full-conflict regimes. The fan simulates 1,000 paths with a shared fat-tail mixing variable so the three strategies are compared on identical shocks.
I build robust financial models that transform raw market data into actionable intelligence. From stress-tested LSTM networks for price prediction to Monte Carlo simulations for risk assessment, each model is designed for real-world deployment.
My approach combines traditional quantitative finance with modern machine learning, ensuring models are both statistically rigorous and practically applicable to trading strategies, portfolio management, and investment decisions.
Key Capabilities
- Time-Series Forecasting (ARIMA, LSTM, Prophet)
- Value-at-Risk & Stress Testing
- Portfolio Optimization & Factor Models
- Quantitative Trading Signals
Technology Stack
Sample Output
Data is the new oil; but it doesn't turn into fuel until it's processed. I help you chart your course in uncertain markets by transforming raw data into strategic intelligence.