About

Duane Seppi is the Richard C. Green Professor of Financial Economics and serves as Senior Associate Dean, Faculty at the Tepper School of Business. His role involves leadership within the faculty and contributing to the school's strategic vision. The Tepper School emphasizes a focus on the intersection of business, technology, and analytics, guided by its strategic plan Building The Intelligent Future, which aims to shape the future of business education through pillars such as AI for Business, Economic Prosperity, and Entrepreneurial Pursuit. While specific details about his research focus or academic background are not provided in the page text, his position indicates a significant involvement in financial economics and faculty leadership at Carnegie Mellon University.

Research topics

• Economics
• Finance
• Monetary economics
• Financial economics
• Computer Science
• Mathematics
• Microeconomics
• Statistics
• Mathematical analysis
• Econometrics
• Business

Selected publications

• Price impact in Nash equilibria

  Finance and Stochastics · 2023-03-21 · 4 citations

  articleSenior author
  PublisherDOI

• Race-Related Events and Stock Prices

  SSRN Electronic Journal · 2022-01-01 · 1 citations

  articleOpen accessSenior author
  PublisherDOI

• Learning about latent dynamic trading demand $$^*$$

  Mathematics and Financial Economics · 2022 · 5 citations

  Senior authorCorresponding
  • Economics
  • Financial economics
  • Microeconomics

  Abstract We present an equilibrium model of dynamic trading, learning, and pricing by strategic investors with trading targets and price impact. Since trading targets are private, investors filter the child order flow dynamically over time to estimate the latent underlying parent trading demand imbalance and to forecast its impact on subsequent price-pressure dynamics. We prove existence of an equilibrium and solve for equilibrium trading strategies and prices as the solution to a system of coupled ODEs. Trading strategies are combinations of trading towards investor targets, liquidity provision for other investors’ demands, and speculation based on learning about latent underlying trading-demand imbalances.

  PublisherOA PDFDOI

• Optimal Market Asset Pricing

  RePEc: Research Papers in Economics · 2021-01-01 · 1 citations

  preprintOpen accessSenior author

  We determine optimal market access pricing for an exchange or Social Planner. Exchanges optimally use rebate-based pricing (vs. strictly positive fees) when ex ante gains-from-trade and trading activity are low (high). Exchange rebate-based pricing increases (decreases) welfare when investor valuation dispersion and trading activity are low (high). A Social Planner increases welfare using rebate-based pricing. High-frequency traders strengthen exchange incentives for rebate-based pricing; a new explanation for widespread Maker-Taker and Taker-Maker pricing. With HFTs, rebate-based pricing improves total welfare, but Pareto transfers are needed to improve investor welfare. Sequential bargaining games between competing exchanges setting fees have pure-strategy equilibria. JEL classification: G10, G20, G24, D40 Keywords: Market access fees, make-take, limit order markets, liquidity, market microstructure

  Publisher

• Optimal Market Access Pricing

  SSRN Electronic Journal · 2021-01-01 · 3 citations

  articleOpen accessSenior author
  PublisherDOI

• Learning about latent dynamic trading demand

  SSRN Electronic Journal · 2021-01-01

  articleOpen accessSenior author
  PublisherDOI

• Learning about latent dynamic trading demand

  arXiv (Cornell University) · 2021-05-27

  preprintOpen accessSenior author

  This paper presents an equilibrium model of dynamic trading, learning, and pricing by strategic investors with trading targets and price impact. Since trading targets are private, rebalancers and liquidity providers filter the child order flow over time to estimate the latent underlying parent trading demand imbalance and its expected impact on subsequent price pressure dynamics. We prove existence of the equilibrium and solve for equilibrium trading strategies and prices in terms of the solution to a system of coupled ODEs. We show that trading strategies are combinations of trading towards investor targets, liquidity provision for other investors' demands, and front-running based on learning about latent underlying trading demand imbalances and future price pressure.

  PublisherOA PDFDOI

• Information, Liquidity, and Dynamic Limit Order Markets

  RePEc: Research Papers in Economics · 2020 · 10 citations

  Senior authorCorresponding
  • Economics
  • Financial economics
  • Business

  This paper describes price discovery and liquidity provision in a dynamic limit order market with asymmetric information and non-Markovian learning. Investors condition on information in both the current limit order book and also, unlike in previous research, on the prior order history when deciding whether to provide or take liquidity. Our analysis shows that the information content of the prior order history can be substantial. Surprisingly, the information content of equilibrium orders can differ from order direction and aggressiveness. JEL classiffication: G10, G20, G24, D40. Keywords: Limit order markets, asymmetric information, liquidity, market microstructure.

  OA PDF

• Equilibrium effects of intraday order-splitting benchmarks

  Mathematics and Financial Economics · 2020 · 18 citations

  Senior authorCorresponding
  • Computer Science
  • Econometrics
  • Economics
  PublisherDOI

• Resolving Asset Pricing Puzzles with Price Impact

  SSRN Electronic Journal · 2019-01-01

  preprintOpen accessSenior author
  PublisherDOI

Frequent coauthors

• Chester S. Spatt

  150 shared

• Mark A. Chen

  Georgia State University

  148 shared

• Cindy Alexander

  144 shared

• Kasper Larsen

  43 shared

• Aziz A. Lookman

  Moody's Corporation (United States)

  41 shared

• Jin Hyuk Choi

  Ulsan National Institute of Science and Technology

  40 shared

• Norman Schürhoff

  Swiss Finance Institute

  38 shared

• Zhihua Chen

  Tianjin University

  37 shared

Education

• Ph.D., Operations Research

  Carnegie Mellon University

  1991

• M.S., Operations Research

  Carnegie Mellon University

  1988

• B.S., Operations Research and Industrial Engineering

  University of California, Berkeley

  1985