Self-Management Portfolio System with Adaptive Association Mining: A Practical Application on Taiwan Stock Market

A well-established financial trading system should well perform in resource allocation, risk management, and sustainability. In this paper, we propose a self-management portfolio system with adaptive association mining for practical applications. The system allocates funds into independent units for risk management, and utilizes association mining and adaptive closing mechanism for resource allocation and sustainability, and adopts a self-management module for monitoring positions. The proposed system boosts the annual return and Sharpe ratio to 9.1% and 0.578 (increased to 2.28 and 2.48 times), and reduces the drawdown risk to 34.6% (decreased to almost half). Furthermore, the system rapidly closes the stock positions to avoid drawdown risk in the bear markets, and gradually increases the stock positions when the market turns into bull. Compared with benchmarks, proposed system outperforms all benchmarks in all measurements and on randomly sampled dataset.

Automated summary

The proposed self-management portfolio system, utilizing adaptive association mining, significantly improves annual return rate and Sharpe ratio, while reducing drawdown risk. It also features rapid closing and gradual increasing of positions, outperforming benchmarks in all measurements and on randomly sampled datasets.