交易策略
结对贸易
文件夹
计算机科学
算法交易
加权
补语(音乐)
交易成本
期限(时间)
计量经济学
分类
库存(枪支)
投资策略
鉴定(生物学)
启发式
市场时机
股票市场
金融经济学
经济
另类交易系统
微观经济学
市场流动性
财务
算法
表型
工程类
放射科
古生物学
物理
操作系统
基因
生物
机械工程
医学
化学
互补
量子力学
植物
马
生物化学
作者
Andrea Flori,Daniele Regoli
标识
DOI:10.1016/j.ejor.2021.03.009
摘要
This work examines a deep learning approach to complement investors' practices for the identification of pairs-trading opportunities among cointegrated stocks. We refer to the reversal effect, consisting in the fact that temporarily market deviations are likely to correct and finally converge again, to generate valuable pairs-trading signals based on the application of Long Short-Term Memory networks (LSTM). Specifically, we propose to use the LSTM to estimate the probability of a stock to exhibit increasing market returns in the near future compared to its peers, and we compare and combine these predictions with trading practices based on sorting stocks according to either price or returns gaps. In so doing, we investigate the ability of our proposed approach to provide valuable signals under different perspectives including variations in the investment horizons, transaction costs and weighting schemes. Our analysis shows that strategies including such predictions can contribute to improve portfolio performances providing predictive signals whose information content goes above and beyond the one embedded in both price and returns gaps.
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