Building Robust, Data-Driven Investment Strategies
From alpha research to real-world portfolio construction.
ABOUT ME
I am a systematic portfolio manager and quantitative strategist with 15+ years of experience developing predictive signals, cross-asset models, and robust portfolio construction frameworks for real-world investment strategies.
My work sits at the intersection of research, portfolio engineering, and risk-aware implementation. Over my career, I have developed quantitative models across equities, rates, FX, options, and statistical arbitrage, and I served as the internal portfolio manager of an AI-driven global equity strategy, where I owned the full research and model portfolio process. My focus is on transforming raw predictive information into stable, scalable, and profitable systematic strategies.
I approach markets through a combination of economic intuition, empirical validation, and disciplined optimization. I have a strong track record of stabilizing live strategies when predictive signal quality deteriorates – not through forecasting luck, but through thoughtful allocation, risk controls, factor-aware construction, and continuous diagnostics.
My research spans:
- cross-sectional factor modeling
- signal decomposition and transfer-coefficient optimization
- ML-driven feature engineering
- alternative data integration
- yield-curve and macro-based signals
- futures and options strategies
- short-horizon and statistical arbitrage techniques
I also build tools and systems that enhance productivity and transparency. One example is a Python/LLM-based information system I designed using a modular object-oriented architecture. It improves research throughput, strengthens communication, and delivers context-specific insights with extremely low hallucination risk.
What motivates me is simple:
Understanding how markets work, identifying where mispricings persist, and translating that insight into robust, systematic investment processes.
I thrive in environments where ownership, scientific rigor, and clear incentives drive performance, and where ideas are measured not by how elegant they look on paper, but by how they behave under pressure in live portfolios.
Expertise
Systematic Portfolio Construction
Designing robust portfolio frameworks that convert alpha signals into stable return streams. Experience with factor-aware optimization, turnover control, dynamic risk allocation, diversification engineering, and implementation under operational constraints.
Signal Research & Predictive Modeling
Development and evaluation of cross-sectional and time-series signals across equities, rates, FX, and derivatives. Expertise in blending signals, controlling factor drift, signal decomposition, and maximizing the transfer coefficient.
Machine Learning in Finance
Applied ML for feature engineering, predictive modeling, and validation of high-dimensional datasets. Practical experience integrating ML methods into empirical asset pricing and real-world strategy development.
Alternative Data & Information Extraction
Using non-traditional data sources to enhance factor insights and predictive power. Experience with structured and unstructured data, including NLP-driven signal extraction.
Risk Engineering & Factor Control
Building and validating risk models, and embedding risk constraints directly into portfolio design to improve stability and interpretability.
Research Engineering & Automation
Designing research pipelines, backtesting infrastructure, diagnostic tools, and automation systems. Creator of a Python/LLM-based information engine with object-oriented architecture for modularity and scalability.
Research Portfolio
The following examples illustrate how I approach quantitative problems, including modeling assumptions, validation steps, and interpretation of results.
They are not representative of my proprietary research, as the majority of my production-level work is confidential, uses significantly more complex modeling, and incorporates alternative data, advanced signal engineering, and portfolio-level optimization.
These examples demonstrate my analytical style and clarity of thought when breaking down quantitative finance problems.
Portfolio Construction
Portfolio Construction
Project:
Portfolio Construction Using the Black-Litterman Model and Factors
Option Pricing
Option Pricing
Project:
Navigating the Complexities of Monte Carlo Simulations in Option Pricing
Machine Learning
Machine Learning
Certifications & Education
CQF – First in Class (2022)
Awarded for outstanding performance in a practitioner-focused program covering quantitative finance, derivatives, portfolio construction, and machine learning. The CQF strengthened my technical foundation and reinforced my applied research approach.
CIIA & CEFA
Internationally recognized certifications emphasizing portfolio management, valuation, financial statement analysis, and investment decision-making across global markets.
Deep Learning Specialization – deeplearning.ai
Advanced training in deep learning methods, complementing my practical ML experience in financial markets.
Academic Background
MA Sociology, University of Vienna (with coursework in Business & Economics)
Additional studies in machine learning, statistics, and computational methods through Stanford, Columbia, and Harvard programs.