Trading team composition for the intraday multistock market

作者:

Highlights:

摘要

Automated traders operate market shares without human intervention. We propose a Trading Team based on atomic traders with opportunity detectors and simple effectors. The detectors signalize trading opportunities. For each trading signal, the effectors follow deterministic rules on when and what to trade in the market. The detectors are based on Partial Least Squares. We perform some trading experiments with twelve BM&FBovespa stocks. The empirical findings indicate that the proposed trading strategy reaches a 77.26 % annualized profit, outperforming by 380.07 % the chosen baseline strategy with a 16.07 % profit. We also investigate Multistock Resolution Strategy (MSR) performance subject to brokerage commissions and income tax. Whenever the initial investment is at least US$ 50, 000, the MSR strategy provides a profit of at least 38.63 %.

论文关键词:Trading team,Partial Least Squares,Weighted Interval Scheduling,Computational finance,Machine learning

论文评审过程:Received 19 April 2011, Revised 29 June 2012, Accepted 18 September 2012, Available online 25 September 2012.

论文官网地址:https://doi.org/10.1016/j.dss.2012.09.009