“Equilibrium Data Mining and Data Abundance”, with Jérôme Dugast. Work in Progress. Last revised April 14, 2020.
- Latest draft: here
- Slides: here
- Abstract: “We analyze how information processing power and data abundance affect speculators’ search for predictors. Speculators optimally search for a predictor whose signal-to-noise ratio exceeds an endogenous threshold. Greater computing power raises this threshold, and therefore price informativeness, because it reduces the cost of search. In contrast, data abundance can lower this threshold because (i) it intensifies competition among speculators, which reduces the benefit of finding a good predictor and (ii) it increases the total expected cost of finding a predictor. In the former (latter) case, price informativeness increases (decreases) with data abundance. We present additional testable implications of these effects.”
“Demand for information, uncertainty and the response of U.S. treasury securities to news”, with Hedi Benamar and Clara Vega. Last Revised: March 2020. Conditionally accepted at the Review of Financial Studies. Presented at the NBER conference on big data and the Future of Financial Information Conference.
- Shows that information demand is high when macro-economic uncertainty is high
- Latest draft available (SSRN),
- Abstract: “We use clickstream data to show that investors’ demand for information about macroeconomic factors affecting the path of future interest rates is a measure of their uncertainty about this path. In particular, an increase in information demand ahead of influential economic announcements affecting investors’ beliefs about future interest rates predicts a stronger reaction of U.S. Treasury note yields to these announcements, as it should if information demand covaries positively with uncertainty. This relationship does not vanish after using standard measures of uncertainty as predictors, suggesting that clickstream data contain unique information about investors’ uncertainty.”
“Inventory Management, Dealers’ Connections and Prices in OTC Markets” with Jean-Edouard Colliard and Peter Hoffman. Last revised: December 2019.
- Predicts that the distribution of aggregate inventories between core and peripheral dealers affect the distribution of transaction prices and bid-ask spreads in OTC markets.
- Latest draft (SSRN) available here.
- Online appendix available here.
- Abstract: “We propose a new model of trading in OTC markets. Dealers accumulate inventories by trading with end-investors and trade among each other to reduce their inventory holding costs. Core dealers have access to a more efficient trading technology than peripheral dealers, who are heterogeneously connected to core dealers and trade with each other bilaterally. Connectedness affects prices and allocations if and only if the peripheral dealers’ aggregate inventory position differs from zero. The resulting price dispersion increases in the size of this position. The model generates new predictions about the joint effects of peripheral dealers’ connectedness and dealers’ aggregate inventories on transaction prices, both among dealers and between dealers and their clients.”
A new survey on HFT: “Is trading fast dangerous“, forthcoming in Global Algorithmic Capital Markets: High Frequency Trading, Dark Pools, and Regulatory Challenges, 2019.
“Noisy Stock Prices and Corporate Investment” with Olivier Dessaint, Laurent Frésard, and Adrien Matray. Accepted for publication in the Review of Financial Studies.
- Shows that non fundamental shocks to firms’ stock prices affects corporate investment because managers use stock prices a signals and have limited ability to filter out the noise in these signals
- Advance Access at the Review of Financial Studies, Latest draft available (SSRN). On-line Appendix, Slides
- Abstract: Firms significantly reduce their investment in response to non-fundamental drops in the stock price of their product-market peers. We argue that this result arises because of managers’ limited ability to filter out the noise in stock prices when using them as signals about their investment opportunities. The resulting losses of capital investment and shareholders’ wealth are economically large, and affect even firms that are not facing severe financing constraints or agency problems. Our findings offer a novel perspective on how stock market inefficiencies can affect the real economy, even in the absence of financing or agency frictions.