About

Murray Z. Frank is a Professor of Finance at the University of Minnesota, working in the Finance Department. His research interests include the impact of machine learning on finance, empirical corporate capital structure, and the effect of taxation on corporate financing and investment. He has recently been exploring the implications of artificial intelligence and its growth in the age of self-improving AI.

Research topics

• Finance
• Economics
• Business
• Financial system
• Monetary economics
• Mathematics

Selected publications

• Corporate Governance and the Coordination Channel of Debt Capacity

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• <div> What Do Treatment Effects Measure? </div><div> Marginal Responses and Financial Constraints in Corporate Finance</div>

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Empirical corporate capital structure

  Edward Elgar Publishing eBooks · 2024-02-06 · 9 citations

  book-chapter1st authorCorresponding

  Corporate capital structure has been a key, challenging puzzle for finance for more than 50 years. Why do firms use the observed financing methods? The literature has developed useful ideas and a much-improved sense of the relevant facts to solve this puzzle. Taxes, the need to fund investments, and informational imperfections all play a role in the capital structure decisions of firms. Many other factors may also be significant, at least under some circumstances. Currently, no generally accepted unified theory adequately accounts for all of the stylized facts. This survey provides a review that focuses on the empirical aspects of the issue.

  PublisherDOI

• Monetary Policy and Corporate Investment: The Equity Financing Channel

  SSRN Electronic Journal · 2024-01-01 · 3 citations

  articleOpen access
  PublisherDOI

• The Changing Structure of Corporate Profits

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Behavioral Machine Learning? Regularization and Forecast Bias

  arXiv (Cornell University) · 2023-03-25

  preprintOpen access1st authorCorresponding

  Standard forecast efficiency tests interpret violations as evidence of behavioral bias. We show theoretically and empirically that rational forecasters using optimal regularization systematically violate these tests. Machine learning forecasts show near zero bias at one year horizon, but strong overreaction at two years, consistent with predictions from a model of regularization and measurement noise. We provide three complementary tests: experimental variation in regularization parameters, cross-sectional heterogeneity in firm signal quality, and quasi-experimental evidence from ML adoption around 2013. Technically trained analysts shift sharply toward overreaction post-2013. Our findings suggest reported violations may reflect statistical sophistication rather than cognitive failure.

  PublisherOA PDFDOI

• Behavioral Machine Learning? Computer Predictions of Corporate Earnings also Overreact

  SSRN Electronic Journal · 2023-01-01 · 2 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Equilibrium Defaultable Corporate Debt and Investment

  SSRN Electronic Journal · 2022-01-01 · 2 citations

  articleOpen accessSenior author
  PublisherDOI

• Empirical Corporate Capital Structure

  SSRN Electronic Journal · 2022-01-01 · 4 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Equilibrium Defaultable Corporate Debt and Investment

  arXiv (Cornell University) · 2022-02-11

  preprintOpen accessSenior author

  In dynamic capital structure models with an investor break-even condition, the firm's Bellman equation may not generate a contraction mapping, so the standard existence and uniqueness conditions do not apply. First, we provide an example showing the problem in a classical trade-off model. The firm can issue one-period defaultable debt, invest in capital and pay a dividend. If the firm cannot meet the required debt payment, it is liquidated. Second, we show how to use a dual to the original problem and a change of measure, such that existence and uniqueness can be proved. In the unique Markov-perfect equilibrium, firm decisions reflect state-dependent capital and debt targets. Our approach may be useful for other dynamic firm models that have an investor break-even condition.

  PublisherOA PDFDOI

Frequent coauthors

• Vidhan K. Goyal

  42 shared

• Thanasis Stengos

  13 shared

• Tao Shen

  Hohai University

  10 shared

• Werner Antweiler

  University of British Columbia

  8 shared

• Ali Sanati

  7 shared

• Hong Chen

  Shihezi University

  7 shared

• Tracy Yue Wang

  University of Minnesota

  5 shared

• Charles Bram Cadsby

  University of Guelph

  5 shared

Awards & honors

• President of the Midwest Finance Association (2017-2018)
• co-founder and organizer of Virtual Corporate Finance Wednes…