A dynamic limit order market with fast and slow traders

188

We analyze the contribution to price discovery of market and limit orders by high‐frequency traders (HFTs) and non‐HFTs. While market orders have a larger individual price impact, limit orders are far more numerous. This results in price discovery occurring predominantly through limit orders. HFTs submit the bulk of limit orders and these limit orders provide most of the price discovery. Submissions of limit orders and their contribution to price discovery fall with volatility due to changes in HFTs’ behavior. Consistent with adverse selection arising from faster reactions to public information, HFTs’ informational advantage is partially explained by public information.

supporting

confidence: 92%

“…Theoretical models of limit order books provide insights into the roles that different orders by different traders play in price discovery (e.g., Goettler, Parlour, and Rajan, [GPR]; Hoffmann, ) . These models focus on traders’ choice between market orders and limit orders based on traders’ information and the state of the limit order book.…”

mentioning

confidence: 99%

Price Discovery without Trading: Evidence from Limit Orders

Brogaard¹,

Hendershott²,

Riordan

2019

188

“…And, Manela and Moreira () use news to form an uncertainty index and show that it is related to expected returns. Theoretical models of algorithmic trading (e.g., Hoffman (), Foucault, Hombert, and Rosu (), Du and Zhu ()) focus on this ability to react to news announcements faster for individual firms. By contrast, our results suggest that the LASSO's success comes from quickly identifying the unexpected consequences of news announcements for other firms.…”

mentioning

confidence: 99%

Sparse Signals in the Cross‐Section of Returns

Chinco¹,

Clark-Joseph²,

2018

221

This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) to make rolling one‐minute‐ahead return forecasts using the entire cross‐section of lagged returns as candidate predictors. The LASSO increases both out‐of‐sample fit and forecast‐implied Sharpe ratios. This out‐of‐sample success comes from identifying predictors that are unexpected, short‐lived, and sparse. Although the LASSO uses a statistical rule rather than economic intuition to identify predictors, the predictors it identifies are nevertheless associated with economically meaningful events: the LASSO tends to identify as predictors stocks with news about fundamentals.

“…See Cartea and Penalva (), Jovanovic and Menkveld (), Pagnotta and Philippon (), Aït‐Sahalia and Saglam (), Budish, Cramton, and Shim (), Biais, Foucault, and Moinas (), Du and Zhu (), Hoffmann (), and Weller (), among others.…”

mentioning

confidence: 99%

News Trading and Speed

Foucault¹,

Hombert²,

Roşu³

2016

293

We compare the optimal trading strategy of an informed speculator when he can trade ahead of incoming news (is “fast”), versus when he cannot (is “slow”). We find that speed matters: the fast speculator's trades account for a larger fraction of trading volume, and are more correlated with short‐run price changes. Nevertheless, he realizes a large fraction of his profits from trading on long‐term price changes. The fast speculator's behavior matches evidence about high‐frequency traders. We predict that stocks with more informative news are more liquid even though they attract more activity from informed high‐frequency traders.