Transaction Costs and the Stochastic Discount Factor

Abstract

Transaction costs determine which characteristic exposures are worth maintaining in equilibrium, yet standard stochastic discount factor (SDF) estimates often ignore them. We embed stock-specific trading costs into the no-arbitrage condition to identify a nonlinear SDF, which we estimate via adversarial neural networks across a large cross-section of U.S. equities. The transaction-cost-aware pricing kernel endogenously reallocates away from high-turnover fundamentals, improving cross-sectional pricing, mean-variance efficiency, and anomaly absorption. This reveals a taxonomy of implementation-dependent versus cost-invariant risk premia, with absorbed anomalies clustering among canonical limits-to-arbitrage signals. These findings hold across different cost specifications, market regimes, and a linear SDF specification.

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Citation

Bianchi, Daniele, Teng Jiao, and Hao Ma. Transaction Costs and the Stochastic Discount Factor. Working paper.

@article{bianchi2025transaction,
  title={Transaction Costs and the Stochastic Discount Factor},
  author={Bianchi, Daniele and Jiao, Teng and Ma, Hao},
  journal={Available at SSRN 5365375},
  year={2025}
}