Informed trading and the price impact of block trades: A high frequency trading analysis

Abstract

Using high frequency data from the London Stock Exchange (LSE), we investigate the relationship between informed trading and the price impact of block trades on intraday and inter-day basis. Price impact of block trades is stronger during the first hour of trading; this is consistent with the hypothesis that information accumulates overnight during non-trading hours. Furthermore, private information is gradually incorporated into prices despite heightened trading frequency. Evidence suggests that informed traders exploit superior information across trading days, and stocks with lower transparency exhibit stronger information diffusion effects when traded in blocks, thus informed block trading facilitates price discovery.

The Effect of Online Investor Sentiment on Stock Movements: An LSTM Approach

“…Early studies suggest that informed traders prefer using large trades to minimize transaction costs and to maximize the profit gained from their informed trading activities. is is because they face competition from other informed traders Complexity and their private information could be short-lived [45]. On the other hand, to hide information, informed traders like to trade on the medium size transactions and prefer to big trade more than block trade.…”

Research on Information-Driven Trades in China

“…For example, Chan and Fong (2000) find a significant return impact of order imbalance, and that the order imbalance in the large trade size category affects the return more than that in smaller trade size categories for stocks traded on the New York Stock Exchange (NYSE). As regards the London Stock Exchange (LSE), Sun and Ibikunle (2017) also find a positive (negative) relationship between informed trading and the permanent price impact of block purchases (sales), thereby suggesting that private information is impounded via block trading on this exchange.…”

“…Such results may indicate the existence of an influential price impact of large trades if the directional relationship varies on the inclusion of large trades. Following Sun and Ibikunle (2017), we introduce trade imbalance in (5) as follows:…”

The Accuracy of the Tick Rule in the Bitcoin Market

“…Order imbalance here is defined as ( B − S )/( B + S ), where B ( S ) denotes buyer-initiated (seller-initiated) variables. This study examines imbalances (Barber & Odean, 2008; Bernile et al, 2016; Feng et al, 2018; Ibikunle et al, 2020; Ning & Tse, 2009; Sun & Ibikunle, 2017; Wang et al, 2020) estimated using the tick rule, namely order imbalance based on number of trades (OIN), trade size (OIS), and volume in USD (OID), using market data from Bitstamp. In addition, large-size orders are usually considered to be related to informed trading, because informed traders are prone to using block trades to cut down transaction costs.…”