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.

Market Structure, Informational Efficiency and Liquidity: An Experimental Comparison of Auction and Dealer Markets

Abstract: This paper reports the results of 18 market experiments that were conducted in order to compare the call market, the continuous auction and the dealer market. The design incorporates asymmetric information but guarantees that the ex-ante quality of the private signals of all traders is identical. Therefore, the aggregation of diverse information can be analyzed in the absence of insider trading. Single transaction prices in the call and continuous auction market are found to be much more efficient than prices in the dealer market. The latter is, however, very efficient when average prices are analyzed. Averaging the prices of a trading period largely eliminates the bid-ask spread. The conclusion is therefore that prices in a dealer market convey high quality information, but at the expense of high transaction costs. The call market, although exhibiting small pricing errors, shows a systematic tendency towards underadjustment to new information. An analysis of market liquidity using various measures proposed in the literature shows that execution costs are lowest in the call market and highest in the dealer market. The analysis also reveals that both the trading volume and Roll's (1984) serial covariance estimator are inappropriate measures of execution costs in the present context. The quality of the private signals traders receive influences portfolio structure but does not influence end-of-period wealth. This result is consistent with efficient price discovery in the experimental markets.

Information, Trading Volume, and International Stock Return Comovements: Evidence from Cross-Listed Stocks

Abstract: We investigate the joint dynamics of returns and trading volume of 556 foreign stocks cross-listed on U.S. markets. Heterogeneous-agent trading models rationalize how trading volume reflects the quality of traders’ information signals and how it helps to disentangle whether returns are associated with portfolio-rebalancing trades or information-motivated trades. Based on these models, we hypothesize that returns in the home (U.S.) market on high-volume days are more likely to continue to spill over into the U.S. (home) market for those cross-listed stocks subject to the risk of greater informed trading. Our empirical evidence provides support for these predictions, which confirms the link between information, trading volume, and international stock return comovements that has eluded previous empirical investigations.

Buy Low, Sell High: A High Frequency Trading Perspective

Abstract: We develop a high frequency (HF) trading strategy where the HF trader uses her superior speed to process information and to post limit sell and buy orders. By introducing a multifactor mutually exc...

Social signals and algorithmic trading of Bitcoin.

Abstract: The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behaviour offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin, reaching very high profits in less than a year. We verify this high profitability with robust statistical methods that take into account risk and trading costs, confirming the long-standing hypothesis that trading-based social media sentiment has the potential to yield positive returns on investment.

Block Ownership, Trading Activity, and Market Liquidity

Abstract: We examine the impact of block ownership on the firm's trading activity and secondary-market liquidity. Our empirical results show that block ownership takes potential trading activity off the table relative to a diffuse ownership structure and impairs the firm's market liquidity. These adverse liquidity effects disappear, however, once we control for trading activity. Our findings suggest that block ownership is detrimental to the firm's market liquidity because of its adverse impact on trading activity - a real friction effect. After controlling for this real friction effect, we find little evidence that block ownership has a negative impact on informational friction. Our results suggest that the relative lack of trading, and not the threat of informed trading, explains the inverse relation between block ownership and market liquidity.