Lishu Zhang 张郦姝
Financial Economist · PhD candidate, Tilburg University · Postdoctoral Researcher, CREST (Paris)
I am a sixth-year PhD candidate in Economics at Tilburg University, supervised by Harry Huizinga, Harald Benink, and Louis Raes (defense expected in Spring 2027). I am also a postdoctoral researcher in the Finance group at CREST in Paris, working with Peter Tankov and Olivier David Zerbib.
I am on the job market 2026-2027.
My research interests lie within Trade, Environmental Economics, and Finance. I draw on trade and macroeconomic theory to motivate and structure my empirical analysis. I study how global financial markets respond to climate change, and how climate-related risks are redistributed across countries, firms, and investors. My dissertation examines this through cross-border equity investment, M&A, and bank lending. At CREST, I extend the agenda to critical raw material (CRM) risk, combining production networks, trade, climate transition, and AI-assisted measurement.
Job Market Paper
Measuring Critical Raw Material Dependence
From a material to a firm
Technical evidence
→ production links
Input cost shares
→ cost elasticity
Product revenues
→ firm dependence
Technical evidence identifies the production paths specific to each material.
Schematic network and firms · 55 materials · 12,481 firms
Adjustment, financial exposure, and risk
A / MICRO ADJUSTMENT
Operating cash flow
Net external financing
% of initial assets per predicted cost increase of 1% of identified revenue · 95% intervals · “Before”: pre-window
B / EVENT REPLAY
Six selected events · implied amounts, not realized losses
Cash flow at risk $20.0bn across all 28 replays
External financing $35.4bn across all 28 replays
C / EQUITY RISK
Adjusted associations within narrow industries and years
Annualized return volatility
At the same dependence, which materials matter?
Downside risk Expected shortfall
At the same dependence, which materials matter?
Material comparisons: +1 SD in each exposure-weighted basket, holding dependence and other controls fixed. Separate estimates, not an additive decomposition.
Sources, units, and interpretation
A · How firms adjust. We trace cash flow and financing after material-price changes, comparing the component associated with identified supply-tightening events with the remaining price variation. For a predicted cost increase of 1% of identified revenue, the event-driven estimates at quarter 4 imply cumulative cash flow lower by 0.391% of initial assets and net external financing higher by 0.692%. This pattern is consistent with firms meeting liquidity needs through external finance. Shading and error bars show 95% confidence intervals; “Before” shows pre-event responses. The comparison series labeled “Predictable price changes” also includes pre-announcement price movements.
B · What past events imply for firms today. We apply historical event-driven price changes to firms’ 2024–2025 material exposures, using the estimated quarter-4 cash-flow and financing responses. Across all 28 replays, the implied amounts are $20.0 billion of cash flow at risk and $35.4 billion of external financing. These are scenarios, not recorded losses; totals include offsetting price declines. The brown segments show relationships where specialist reports miss uses accounting for more than half of a firm’s material dependence. Their size shows how relying on those reports alone can miss financially important exposure.
C · How dependence relates to equity risk. We compare firms within narrow industries and years, accounting for country conditions and standard firm characteristics. Moving from median to 90th-percentile dependence is associated with 1.79 percentage points higher annualized return volatility and 0.15 points higher expected shortfall—a measure of losses on the worst trading days. The rows below ask whether the materials themselves matter among firms with the same dependence. A one-standard-deviation increase in the exposure-weighted price-risk basket is associated with 0.74 points higher volatility and 0.063 points higher expected shortfall. The same increase in the China-restriction basket is associated with 0.61 points higher volatility; its 0.014-point expected-shortfall estimate is not statistically significant. Price instability relates to both ordinary return fluctuations and downside losses, while the restriction evidence is clearest for volatility. The headline and basket results come from separate regressions and samples; they are not additive shares or causal effects.
Physical dependence connects material supply shocks to firms’ liquidity needs and equity risk. Event replays reveal the scale of financial exposure—including relationships absent from specialist reports.
A production-based view of corporate financial vulnerability.
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