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WORKING PAPERS

MAN VS. MACHINE: QUANTITATIVE AND DISCRETIONARY EQUITY MANAGEMENT (the paper can be downloaded here)

I use a machine learning technique to classify the universe of active equity mutual funds into “quantitative”, who mostly rely of computer-driven models and fixed rules, or "discretionary", who mostly rely on human judgement. I propose an equilibrium model in which quantitative funds have greater information processing capacity but less adaptive strategies. The model predicts that quantitative funds hold more stocks and display pro-cyclical performance, but their trades are vulnerable to “overcrowding”. Discretionary funds alternate between stock picking in expansions and market timing in recessions, display counter-cyclical performance and focus on stocks for which less overall information is available. My empirical evidence supports these predictions.

MARKET TIMING IN BAYESIAN PORTFOLIO OPTIMIZATION (the paper can be downloaded here)

I propose a portfolio allocation model that combines a data-based approach with macroeconomic considerations of the business cycle. It accounts for the two key features of business cycles, namely co-movement among macroeconomic variables and asymmetric development of the cycles. The joint treatment of these characteristics improves the ability of the model to time market turns, consequently enhancing portfolio gains. The estimation technique developed allows to simultaneously address the issues of parameter uncertainty, mispricing uncertainty and the uncertainty relative to structural instability within a Bayesian portfolio optimization problem. 

WORK IN PROGRESS

MACHINE LEARNING AND THE CHANGING ECONOMICS OF KNOWLEDGE PRODUCTION (with Laura Veldkamp)

ASK EDGAR: THE INFORMATION CONTENT OF MUTUAL FUND PROSPECTUSES (with Anton Lines)

LEARNING FROM SOFT INFORMATION: CAPITAL ALLOCATION IN THE MUTUAL FUND INDUSTRY (with Anton Lines)

THE IMPACT OF MUTUAL FUND REGULATION ON ALLOCATIVE EFFICIENCY (with Anton Lines)

RESEARCH