Information processing filters out the noise in data but it takes time. Hence, low precision signals are available before high precision signals. To capture this feature, we develop a model of securities trading in which investors can acquire signals (about future cash flows) of increasing...

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 use a more efficient trading technology than peripheral dealers, who are heterogeneously connected to...

We compare the seller's expected revenue in a second price sealed bid auction for a single object in which bidders receive multidimensional signals. Bidders' valuations for the object depend on their signals and a signal observed privately by the seller. We show in various examples that the...

We study how the quality of investors' information across horizons influences investment. In our theory, managers care about how investment is impounded in current stock prices. Because prices imperfectly reflect investment’s value, they under-invest. However, they under-invest less when...

We let "Algorithmic Market-Makers" (AMMs), using Q-learning algorithms, choose prices for a risky asset when their clients are privately informed about the asset payoff. We find that AMMs learn to cope with adverse selection and to update their prices after observing trades, as predicted by...

We develop a model of limit order trading in which some traders have better information on future price volatility. As limit orders have option-like features, this information is valuable for limit order traders. We solve for informed and uninformed limit order traders' bidding strategies in...