Prof. Semyon Malamud

Semyon Malamud is Associate Professor of Finance at the École Polytechnique Fédérale de Lausanne. Professor Malamud is a regular speaker at leading academic conferences worldwide, and his papers have been published in the top journals in finance and economics.

Professor Malamud focuses on predicting asset prices and market returns. While the industry is experiencing a boom in the adoption of machine learning techniques to improve portfolio construction, little is known about the underlying theoretical processes. His theoretical work shows that simple models severely understate return predictability compared to "complex" models. In other words, the performance of machine learning portfolios can be improved by further pushing the level of model complexity. Empirical results show that US equity market data is remarkably well aligned with complex frameworks. These findings are not a license to add every arbitrary predictor one encounters to a machine learning–based model, but do suggest that the finance profession should focus on rich nonlinear models that include plausibly relevant predictors.

• Financial Markets
  • Central Banks and Monetary Policy
  • Financial Forecasting
  • International Financial Markets and Emerging Markets
• Portfolio Management and Asset Classes
  • Asset Pricing
  • Options and Other Derivatives
  • Portfolio Management
• Financial Institutions
  • Institutional Investors and Funds
• Corporate Finance and Governance
  • Financial Risk and Risk Management
  • Financing Policy and Capital Structure
• Frontier Topics
  • Big Data and Fintech
  • Operations Research and Decision Theory