Algorithmic trading

About: Algorithmic trading is a research topic. Over the lifetime, 6718 publications have been published within this topic receiving 162209 citations. The topic is also known as: algotrading & Algorithmic trading.

IQ, Trading Behavior, and Performance

Abstract: We analyze whether IQ influences trading behavior, performance, and transaction costs. The analysis combines equity return, trade, and limit order book data with two decades of scores from an intelligence test administered to nearly every Finnish male of draft age. Controlling for a variety of factors, we find that high-IQ investors are less subject to the disposition effect, more aggressive about tax-loss trading, and more likely to supply liquidity when stocks experience a one-month high. High-IQ investors also exhibit superior market timing, stock-picking skill, and trade execution.

Insider and Liquidity Trading in Stock and Options Markets

Abstract: We analyze the introduction of a nonredundant option, which completes the markets, and the effects of this on information revelation and risk sharing. The option alters the interaction between liquidity and insider trading. We find that the option mitigates the market breakdown problem created by the combination of market incompleteness and asymmetric information. The introduction of the option has ambiguous consequences on the informational efficiency of the market. One the one hand, by avoiding market breakdown, it enables trades to occur and convey information. On the other hand, the introduction of the option enlarges the set of trading strategies the insider can follow. This can make it more difficult for the market makers to interpret the information content of trades and consequently can reduce the informational efficiency of the market. The introduction of the option also has an ambiguous effect on the profitability of insider trades, which can either increase or decrease depending on parameter values. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

Algorithmic Trading and the Market for Liquidity

Abstract: We examine the role of algorithmic traders (ATs) in liquidity supply and demand in the 30 Deutscher Aktien Index stocks on the Deutsche Boerse in Jan. 2008. ATs represent 52% of market order volume and 64% of nonmarketable limit order volume. ATs more actively monitor market liquidity than human traders. ATs consume liquidity when it is cheap (i.e., when the bid-ask quotes are narrow) and supply liquidity when it is expensive. When spreads are narrow ATs are less likely to submit new orders, less likely to cancel their orders, and more likely to initiate trades. ATs react more quickly to events and even more so when spreads are wide.

Noise Trading, Costly Arbitrage, and Asset Prices: Evidence from Closed‐end Funds

Abstract: If arbitrage is costly and noise traders are active, asset prices may deviate from fundamental values for long periods of time. We use a sample of 158 closed-end funds to show that noise-trader sentiment, as proxied by retail-investor flows, leads to fluctuations in the discount. Nevertheless, we reject the hypothesis that noise-trader risk is the cause of the long-run discount. Instead we find that funds which are more difficult to arbitrage have larger discounts, due to: (1) the censoring of the discount by the arbitrage bounds, and (2) the freedom of managers to increase charges when arbitrage is costly. Copyright The American Finance Association 2002.