Systematic Equity/ Quantitative Research/ Applied ML
Associate Portfolio Manager at a leading asset management firm. I work on systematic equity SMAs — portfolio construction, rebalancing, and Python-based investment analytics.
About
An optimizer result only becomes a portfolio once it clears tax lots, client restrictions, corporate actions and a transaction cost budget. Closing that gap is most of my job, and it is why I spend about as much time writing code as running analysis.
Building and rebalancing custom core SMAs against client-specific benchmarks, balancing tracking error, tax impact and transaction costs against the constraints in each mandate. Scenario analysis on flows, rebalances and benchmark changes, written up so advisors can act on it.
Network analysis and machine learning applied to market structure. My undergraduate honors thesis forecast bull/bear regimes from the topology of industry correlation networks. The habit stuck: look for the structure first, then test carefully whether it predicts anything.
Python applications used in daily PM workflows, plus the data-integrity checks behind the benchmark feeds. Selected as an AI Ambassador at work, where I help colleagues adopt AI tools and build their own.
Experience
I started where risk numbers are produced, learned what breaks them, and moved to the desk that has to act on them. Each step has taken me closer to the investment decision itself — from measuring exposure, to building the portfolios that carry it, toward the quantitative research and systematic trading.
Asset Management
Construct and rebalance systematic equity custom core SMAs against client-specific benchmarks, trading tracking error against tax and transaction costs. Build the PM-facing Python analytics and the benchmark data-integrity layer underneath them.
Risk Management
Daily Rates and FX sensitivity analysis: risk-engine output aggregated under a delta–gamma approximation to estimate scenario PnL across market environments. Built the Python and SQL pipelines that monitored those sensitivities against limits.
Risk Management
Portfolio analytics and reporting across the corporate lending book in SQL, Python, R and VBA.
Research
Work on market structure, machine learning and portfolio construction. The honors thesis below asked whether the topology of an industry correlation network carries a forecastable signal about the market regime that follows it — at short horizons it appears to, over longer ones the signal largely disappears.
Honors Thesis · UNC Chapel Hill Economics · May 2020
Can industry correlation networks help forecast bull and bear markets? My honors thesis tested network features across 16 forecasting configurations, with the strongest reported result at a one-week horizon and generally weaker results at longer horizons.
Small-sample result. The 48 evaluated regimes leave wide uncertainty around the headline accuracy, so the pattern matters more than the point estimate. These historical academic classification results do not measure investment returns or assure future predictive accuracy.
STEP 01
Partition the regimes
Hanna's top-down turning-point algorithm over daily S&P 500 prices, 1978–2019: 30 bull, 26 bear, 5 rallies, 7 corrections.
STEP 02
Correlate the industries
Within each regime window, the Pearson correlation matrix over daily log returns of the 49 Fama–French industry portfolios.
STEP 03
Build the tree
Correlations become Euclidean distances, dij = √(2(1−Cij)), reduced to a minimum spanning tree scored by Kruskal stress-1.
STEP 04
Extract topology
Degree distribution, in-component degree, stress and density per network — plus the standard deviation, kurtosis and range of market returns.
STEP 05
Classify & validate
Trees, KNN, SVM, ensembles and Naive Bayes across 16 window×lag combinations, each under 10-fold cross-validation.
Forecast accuracy by estimation window and lag
Sixteen model configurations, best classifier per cell. The signal is concentrated: only the shortest horizon breaks meaningfully away from chance.
| Window | Lag | Accuracy | Best model |
|---|---|---|---|
| 1 month | 1 week | 91.7% | Fine Tree |
| 1 month | 2 weeks | 77.1% | Cosine KNN |
| 1 month | 3 weeks | 62.5% | Subspace Discriminant |
| 1 month | 4 weeks | 62.5% | Naive Bayes |
| 3 months | 1 month | 62.5% | Fine Tree |
| 3 months | 3 months | 52.1% | Boosted Tree |
| 3 months | 6 months | 58.3% | Linear Discriminant |
| 3 months | 12 months | 66.7% | Kernel Naive Bayes |
| 6 months | 1 month | 64.2% | Linear Discriminant |
| 6 months | 3 months | 54.7% | Cubic SVM |
| 6 months | 6 months | 58.3% | Fine Tree |
| 6 months | 12 months | 52.1% | Coarse KNN |
| 12 months | 1 month | 54.7% | Coarse Gaussian SVM |
| 12 months | 3 months | 52.8% | Coarse KNN |
| 12 months | 6 months | 54.2% | RUSBoosted Tree |
| 12 months | 12 months | 52.1% | Coarse KNN |
Accuracy under 10-fold cross-validation. Rallies and corrections were excluded from training — too few observations for a valid sample — so the task is a bull/bear binary.
Where the winning model gets it right
Fine Tree, 1-month window, 1-week lag. Four misclassifications out of 48 regimes, split evenly between the two error types.
| Actual | Predicted bear | Predicted bull |
|---|---|---|
| Bear | 21 | 2 |
| Bull | 2 | 23 |
For context, Kole & Dijk (2010) forecast the same one-week-ahead bull/bear call with Markovian logit models at 89.3%. This model reaches 91.7% with an AUC of 0.92 — on 48 regime observations, so the difference sits well inside the noise.
The MST minimisation criterion did not fully converge, so the trees may not be the most efficient representation of each network; and several algorithm-partitioned regimes are only days long — artefacts of black-swan events sitting too close to the following rally to be separately forecastable. Both are named in the paper as the first things to fix. With 48 regimes in the sample, the accuracy figures carry wide error bars either way.
Recurring trade and portfolio-analysis scenarios summarized with an LLM copilot and clustered into a categorised template library.
Co-authored at an internal, firmwide GenAI hackathon at work: generate text embeddings from 10-K filings, surface the fundamentals that matter to a credit view, and automate the first draft of the credit memo. Drafting time fell by about 70% in the hackathon evaluation.
Teaching Assistant for data science courses at Duke's Fuqua School of Business and the Department of Statistical Science. Research Assistant at Duke's Center for Advanced Hindsight, a behavioural economics lab.
Building
I have built analytical tools and systems used daily across departments, connecting systematic analysis, reliable data, and coordinated workflows to support decisions and execution.
01 / Systems
Reusable frameworks translate complex questions into structured inputs, consistent calculations, and interpretable results.
Integrated analytical views support comparison, prioritisation, and informed decisions within a common framework for daily work.
02 / Systems
Validation and reconciliation frameworks check consistency across data sources and make exceptions visible for review.
Structured pipelines organise and prepare data for analysis, monitoring, and reporting, connecting source information with downstream workflows.
03 / Systems
Reusable automation connects recurring processes and supports coordinated work across teams, from information preparation through communication and follow-up.
Shared reporting systems surface process status and exceptions, helping teams stay aligned and identify where attention is needed throughout daily operations.
Toolkit
Education
Duke University
/ MAY 2022
Teaching Assistant for data science courses at the Fuqua School of Business and the Department of Statistical Science. Research Assistant at the Center for Advanced Hindsight.
University of North Carolina at Chapel Hill
/ MAY 2020
Second major in Psychology, minor in Mathematics. Graduated with Highest Distinction and the Herbert Brown Mayo Financial Economics Research Fellowship.
Off the desk