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Internship teaser: Macro‑regime timing for market‑neutral long/short equity strategies

Objective

Build a coherent, practical framework to condition equity market‑neutral L/S factor and fundamentals‑based strategies on macro regimes. The project consolidates a scattered literature, replicates core findings, and tests robust regime‑detection methods. It will also explore linear and non‑linear ML approaches to combine macro indicators into stable allocation tilts rather than brittle on/off switches.

Why now

Evidence suggests macro states (volatility, funding/liquidity, growth, inflation/real rates, policy uncertainty, market drawdowns/rebounds) materially shape the risk and crash profiles of L/S factors such as value, momentum, quality/profitability, low risk/BAB, size, investment/asset growth, and stat‑arb. Yet results are dispersed across asset classes, samples, and definitions, making replication and robust implementation non‑trivial.

Workplan

• Literature mapping and replication
• Consolidate key strands and replicate results on volatility scaling, funding/liquidity sensitivity, momentum crash states, business‑cycle, inflation/real‑rate, and uncertainty regimes.
• Deliver a reproducible compendium of factor constructions and macro‑conditioning tests using release‑timestamped macro data.
• Robust regime detection and testing
• Define regime sets (e.g., growth, inflation/real rates, financial conditions/liquidity, volatility, market drawdown/rebound, etc).
• Employ simple thresholds, clustering and other statistical techniques with strict out‑of‑sample evaluation, multiple‑testing controls, and real‑time data vintages.
• ML combination of indicators
• Explore linear and non‑linear models (regularized regression, tree/boosting, shallow nets, reservoir computing) to combine macro signals, with stability/interpretability constraints and cross‑validated hyperparameters.
• Compare against simple baselines; emphasize robustness, turnover control, and economic plausibility.

Illustrative references (non‑exhaustive; examples highlight the field’s fragmentation)

• Volatility and momentum crash management: Moreira & Muir (2017); Barroso & Santa‑Clara (2015); Daniel & Moskowitz (2016).
• Funding/liquidity and intermediaries: Brunnermeier & Pedersen (2009); Pástor & Stambaugh (2003); Adrian, Etula & Muir (2014); Khandani & Lo (2007/2011).
• Business cycle: Petkova & Zhang (2005); Stivers & Sun (various).
• Inflation/real rates: Lettau & Wachter (2007); AQR notes on value vs. rates/inflation.
• Policy/macro uncertainty: Baker, Bloom & Davis (2016).
• Cautions on timing and testing: Asness/Ilmanen/Israel (AQR); Goyal & Welch (2008); Harvey, Liu & Zhu (2016).

Data and engineering CRSP/Compustat or global equivalents for L/S staregies; FRED/ALFRED, ISM, BLS/BEA, ICE BofA OAS, VIX/MOVE, NFCI for macro; codebase in Python.

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Expected deliverables

• Replication library of macro‑timed L/S factor tests with real‑time data treatment.
• Regime‑detection toolkit and allocation rules that improve drawdowns and risk‑adjusted returns net of costs.