Julio A. Crego
I am an Associate Professor of Finance at Tilburg University. I received my Ph.D. from CEMFI, Madrid in 2017.
jacrego (at) uvt (dot) nl
Finance Department - Office I 502
5037 AB Tilburg - The Netherlands
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.
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.
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.
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.
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.
We estimate the causal effects of 334 different types of health shocks on medical expenses, mortality, disability, labor market participation, labor earnings, and the need for nursing home care using detailed data on 6.9 million people diagnosed by medical specialists between 2013 and 2017. We quantify the benefits of eliminating diseases with distinct consequences for people of different social strata by incorporating the estimates into a standard life-cycle model. Our results reveal substantial heterogeneity in welfare gains by types of disease for different people. We discuss the potential implications of our results for the financing of medical research.
Financial data is characterized by a low signal-to-noise ratio making it difficult to identify robust functional forms that map the characteristics of financial securities to expected returns (Lettau and Pelger, 2020). In this paper, we modify the standard prediction problem in empirical asset pricing by replacing realized returns with an estimator for expected return developed by Martin and Wagner (2019). We use a neural network to map expected returns to 164 stock characteristics and their interactions with eight macroeconomic time-series, resulting in 1476 predictors. Portfolios based on the predictions from the neural network generate risk-adjusted returns with respect to the Fama-French 6-factor model in the range of 1.4% (t-statistic of 3.04) to 1.2% (t-statistic of 2.65) before and after transaction costs; out-of-sample. The corresponding Sharpe ratios are 1.15 and 1.06. A similar analysis based on realized returns results in Sharpe ratios below the market portfolio.