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graysonsking/README.md

Grayson King

Quantitative Research | Portfolio Construction | Applied Machine Learning in Finance

M.S. candidate in Financial Technology and Analytics at Wake Forest University (Aug 2026). B.S. in Business Administration from NC State. I build and test systematic investment strategies in Python, with a focus on index construction, factor signals, and NLP applied to financial text.

Currently seeking investment research, asset class analytics, and quantitative analyst roles in the Research Triangle area.

Focus Areas

  • Portfolio construction — mean variance optimization, risk parity, minimum variance with Ledoit-Wolf shrinkage, alternative index weighting
  • Systematic strategy research — walk-forward backtesting, cointegration and copula methods, cross-sectional momentum
  • NLP for finance — FinBERT sentiment applied to SEC filings and news for signal generation
  • Fixed income and macro — Treasury curve construction, Taylor Rule modeling, macro Z-score signals

Featured Work

Project What it does Stack
multi-strategy-portfolio Research framework combining systematic trading, portfolio optimization, and risk management Python, pandas, NumPy, SciPy
sp500-concentration-research M.S. capstone testing whether alternative S&P 500 weighting schemes restore diversification (2000 to 2025) Python, WRDS, statsmodels
nlp-sector-momentum Sector rotation signal built from FinBERT sentiment on financial text Python, transformers, PyTorch
copula-pairs-trading Statistical arbitrage on S&P 100 pairs using Johansen cointegration and copula-based entry rules Python, statsmodels, SciPy
macro-zscore-commodities Cross-sectional commodity signals from point-in-time macro Z-scores built on FRED series Python, FRED API
equity-valuation-engine Automated DCF and comparables valuation across the S&P 500, with a ranked fair value table Python, numpy, pandas

Toolkit

Python SQL pandas NumPy SciPy scikit-learn statsmodels PyTorch transformers Matplotlib Excel AWS Capital IQ WRDS

Currently

Finishing my capstone on S&P 500 concentration risk and alternative weighting strategies. Defense is August 2026.

Contact: LinkedIn · GraysonSKing@gmail.com

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  1. copula-pairs-trading copula-pairs-trading Public

    Statistical arbitrage on S&P 100 pairs using copula tail dependence rather than linear spread Z-scores

    Python

  2. equity-valuation-engine equity-valuation-engine Public

    Automated DCF and comparables valuation across the S&P 500, with a ranked fair value table

    Python

  3. macro-zscore-commodities macro-zscore-commodities Public

    Cross-sectional commodity signals from point-in-time macro Z-scores built on FRED series

    Python

  4. multi-strategy-portfolio multi-strategy-portfolio Public

    Walk-forward backtesting framework for systematic strategies with a shared risk and construction layer

    Python

  5. nlp-sector-momentum nlp-sector-momentum Public

    Sector rotation from FinBERT sentiment on financial text, benchmarked against price momentum

    Python

  6. sp500-concentration-research sp500-concentration-research Public

    Do alternative index weightings restore diversification to a concentrated S&P 500? M.S. capstone.

    Python