This study enhances the classical z-score-based pairs trading strategy by introducing dynamic signal delay mechanisms to develop a time-adaptive approach to statistical arbitrage. Using Apple Inc. (AAPL) as a benchmark, 29 stock pairs from the Dow Jones Industrial Average (DJIA) index were analyzed to assess the impact of execution delays ranging from t+1 to t+5 on trading performance. Positions were opened and closed based on z-score signals derived from daily closing prices, where delayed execution aimed to reduce short-term market noise and optimize trade timing.Empirical results demonstrate that the proposed strategy achieved favorable performance, with 93.8% of the pairs generating positive returns and 89.7% attaining a Sharpe Ratio greater than 1.0. On average, the t+3 delay window yielded the most effective balance between risk and return, achieving a Sharpe Ratio of 2.371 and a cumulative return of 192.98%. Only 24.1% of the pairs performed best under immediate execution (t+0), highlighting the advantages of adaptive timing in arbitrage.Overall, the findings confirm that optimizing trade timing significantly enhances the profitability and stability of arbitrage models. The results provide empirical evidence supporting the potential of time-adaptive execution as a valuable improvement to traditional pairs trading frameworks.
KANBER et al. (Fri,) studied this question.