Curriculum Vitae
Teaching
Contact
jacrego (at) uvt (dot) nl
Tilburg University
Finance Department - Office I 502
Warandelaan 2
5037 AB Tilburg - The Netherlands
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Publications
Why does public news augment information asymmetries? [Abstract][Appendix] Journal of Financial Economics
The arrival of a public signal worsens the adverse selection problem if informed investors are risk averse. Precisely, the public signal reduces uncertainty which boosts informed investors' participation leading to a more toxic order flow. I confirm the model's empirical predictions by estimating the effect of the publication of the weekly change in oil inventories on liquidity via a difference-in-differences strategy. I show the mean bid-ask spread doubles immediately after the release and volume increases by 32 % regardless of the report's surprise. Further, in line with the model, implied volatility drops and insider's trading increases after the report's publication.
Endogenous health groups and heterogeneous dynamics of the elderly [Abstract][Appendix][Classification: HRS SHARE and ELSA] with Dante Amengual and Jesús Bueren Journal of Applied Econometrics
We propose a methodology to classify individuals into few but meaningful health groups by estimating a panel Markov switching model that exploits rich information from panel household surveys. Using the HRS, we identify four persistent health groups, depending on individual's physical and mental disabilities. Our classification outperforms existing health measures at explaining entry in nursing homes, home health care, out-of-pocket medical expenses, and mortality for individuals in the HRS, ELSA, and SHARE. Through a workhorse model of savings, we recover an asset cost of bad health that is twice as big as when using self-reported health.
The Dynamic Informativeness of Scheduled News [Abstract][Appendix] with Jasmin Gider Management Science (Accepted)
We propose a method to identify the informativeness of a future scheduled announcement at the daily level, exploiting the discontinuity it creates in the term structure of option volatility. We implement the strategy in a panel data model to estimate the relation between prior signals and the future announcement. This method allows to separate substitutes from complements, can isolate multiple signals within the same quarter and can condition on the timing and signal characteristics. We find that analyst forecasts substitute earnings announcement information and recommendation provide extra information on top of forecasts. Moreover, our evidence suggests that insiders sell to avoid uncertainty when the announcement is far away but pull forward earnings information when they trade one month before.
Working papers
The Economic Value of Eliminating Diseases [Abstract] with Dániel Kárpáti, Jens Kvaerner , 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 [Abstract] 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.
Cyclical dependence and timing in market-neutral hedge funds [Abstract][Appendix] with Julio Gálvez
We explore a new dimension of dependence of hedge fund returns with the market portfolio by examining linear correlation and tail dependence conditional on the financial cycle. Using a large sample of hedge funds that are considered "market neutral", we document that the low correlation of market neutral hedge funds with the market is composed of a negative correlation during bear periods and a positive one during bull periods. In contrast, the remaining styles present a positive correlation throughout the cycle. We also find that while they present tail dependence during bull periods, we cannot reject tail neutrality in times of financial turmoil. Consistent with these results, we show that market neutral hedge funds present state timing ability that cannot be explained by other forms of timing ability. Using individual hedge fund data, we find that funds that implement share restrictions are more likely to time the state.
Early birds and second mice in the stock market [Abstract]with Jin Huang
This paper studies learning in the stock market. Our contribution is to propose a model to illustrate the endogenous timing decision on trading, taking into account the incentive of learning from others about the fundamental value. The model is similar to Easley and O'Hara (1992), except that we introduce less-informed traders whose private information is inferior to fully-informed traders, but superior to that of random noise traders, and a zero-profit market maker. We also allow both types of informed traders to optimize timing of trading. We show that fully-informed traders act as early birds because it is optimal for them to buy or sell at the earliest possible time; meanwhile, less-informed traders could be better off as second mice by delaying transactions to learn from previous trades. The greater information asymmetry between the less-informed traders and the market maker, the larger profits the former could make even though the latter is learning from all trades.
Machine Learning and Expected Returns [Abstract] with Jens Kvaerner , Marc Stam
Machine learning models struggle to identify robust functional forms of expected return due to the low signal-to-noise ratio of stock returns (Israel et al., 2020). We substitute realized stock returns with the option-based estimator for expected returns derived by Martin and Wagner (2019). These return expectations reflect the optimal choices of log-utility investors; they are forward-looking, measured in real-time, and have a high signal-to-noise ratio. Our algorithm predicts option-implied expected returns from stock characteristics. Then, it adjusts those predictions with a transfer learning model that allows expected returns to depend on the security’s expected future skewness. Using non-parametric portfolio sorts, we show that predicted and observed expected returns lead to similar spreads in realized returns for stocks with liquid options and that the predictions spread returns for all stocks. A long-short portfolio formed on our predictions gives a high and significant abnormal return relative to standard factor models after transaction costs and out-of-sample.
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