Working Papers

Expected Return, Realized Return, and Machine Learning with Jens Kvaerner and Marc Stam​

Machine learning models struggle to estimate expected returns due to stock returns’ low signal-to-noise ratio (SNR). Direct estimates of expected ​returns from the implied cost of capital or indirect estimates via option prices have higher SNR but are only available for a short period and a ​particular sample. We develop a methodology that circumvents the low SNR in realized stock returns and is applicable to all stocks. We uncover a ​stable relationship between characteristics and stock returns across all US stocks that hold out-of-sample and across geographical regions.

The Economic Value of Eliminating Diseases with Daniel Kárpati, Jens Kvaerner and luc Renneboog

We develop a framework to quantify the welfare gains of reducing health risks. The framework integrates causal effect estimates of health shocks ​on medical expenses, mortality, disability, labor market participation, earnings, and the need for nursing home care into a life cycle model. ​Economic benefits reflect both individuals' willingness-to-pay to reduce a particular health risk and net effects on government finances. We apply ​our framework to Dutch administrative data on medical diagnoses of 6.9 million people and 334 distinct medical diagnoses. Our estimates show ​that curing cancer or cardio-vascular diseases would result in economic benefits equivalent to 9.5% and 9.1% of the GDP. The corresponding ​estimates for preventive measures such as eradicating smoking or preventing overweight and obesity are 7.7% and 5.6%.

Evolutionary Arbitrage with Jens Kvaerner, Åvald Sommervoll, Dag Einar Sommervoll, Niek Stevens

The prices of exchange-traded funds (ETFs) can deviate significantly from their net asset values (NAVs). Exploiting such inefficiencies is often too ​costly because it involves taking positions in hundreds of underlying illiquid securities. We develop a method that identifies a liquid mimicking ​portfolio that tracks the NAV using only ETFs. Our method combines a genetic algorithm with non-negative least squares. We apply it to the fixed ​income ETF market. Our long-short strategy generates a Sharpe ratio of 4-5, incurs little transaction cost, and does well under all market conditions.