Cross-Asset Speculation in Stock Markets∗ - Econometrics at Illinois ...

of the less volatile asset’s value. In sum, the division of information between assets matters incomplicated ways for trading strategies, but not aggregate outcomes.[Figure 8 about here]IV ConclusionThis paper develops a method to solve for strategic speculative outcomes in a multi-asset stockmarket when speculators see prices and internalize how their trades affect the informational contentof prices. We thereby build on Admati’s (1985) noisy rational expectations model to allow forstrategic behavior by speculators. In particular, speculators in our model internalize the fact thatthey are informationally large and that their trades have impacts on prices that convey informationto other speculators. Because asset values are correlated, a signal about one asset contains informationabout other assets, and because there are multiple speculators, each speculator uses theinformation in prices to try to unravel the signals of others. We prove that there is a unique linearequilibrium by developing an iterative best-response mapping and showing that it is a contraction.We then offer several testable predictions. We generate predictions about the covariance structuresof prices changes (returns) and order flows across stocks that are driven solely by strategicbehavior. In particular, we find that the covariance structure of prices is driven by the covariancestructure of asset fundamentals, while the covariance structure of order flows is driven by the covariancestructure of liquidity trade. We also show that in integrated markets, the use of cross-asset informationby market makers causes prices to be more correlated than the underlying fundamentals,and that this, in turn, raises the volatility of prices. Finally, we provide theoretical underpinningsfor the Hasbrouck and Seppi (2001) finding that commonality in order flows explains two-thirds ofthe commonality in returns, generating this in our model with a moderate cross-asset correlationin asset value fundamentals plus a slight positive correlation in liquidity trade across assets.While one must be careful translating predictions from a stylized model to the real world, thequalitative implications are clear. The transparency of pricing and order flows across assets hasgreatly increased in recent years. This suggests that the market has become “closer” to our observableprice setting. Accordingly, (a) the volatility of returns and net order flows should have27