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Norman Schürhoff is Professor of Finance at the University of Lausanne. Professor Schürhoff's work has been published in the top academic journals in finance and has won several prestigious publication awards. At the University he has trained a number of teams that have won the CFA Institute Research Challenge in Switzerland as well as one team that became world champion.

Expertise

Professor Schürhoff focuses on social media's role in sharing and acquiring financial information. Data from StockTwits reveals a high dispersion in the quality of advice provided by "finfluencers", 28% being skilled and generating positive abnormal returns, 16% being unskilled, and 56% being "antiskilled" and generating negative abnormal returns. Ironically, antiskilled finfluencers have more followers and influence on retail trading than do skilled finfluencers. Antiskilled finfluencers are also found to ride return and social sentiment momentums. Unsurprisingly, investing contrary to the tweets of antiskilled finfluencers yields abnormal returns. Overall, these findings shed light on the quality of finfluencers' unsolicited financial advice, which has become a concern for regulators such as the Securities and Exchange Commission (SEC). Professor Schürhoff actively participates in SFI Knowledge Exchange activities on artificial intelligence and sentiment analysis in investment management.

Expertise Fields

  • Financial Markets
    • Information and Market Efficiency
  • Portfolio Management and Asset Classes
    • Asset Pricing
    • Fixed Income
  • Financial Institutions
    • Banks
    • Rating Agencies
  • Corporate Finance and Governance
    • Capital Budgeting and Investment Policy
    • Financial Valuation
    • Financing Policy and Capital Structure
  • Frontier Topics
    • Big Data and Fintech

Current Publications:

N°24-45: Beyond Peers: Cross-Industry Competition and Strategic Financing

When failure is an option: Fragile liquidity in over-the-counter markets

N°23-85: Life After Default: How Dealer Intermediation Improves Default Recovery

N°23-30: Finfluencers

N°22-79: Identifiability and Generalizability from Multiple Experts in Inverse Reinforcement Learning

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