Blockchain asset portfolio optimization with proportional and fixed transaction fees - Liyuan Zhang; Limian Ci; Yonghong Wu; Benchawan Wiwatanapataphee (AIMS Mathematics)
The rapid expansion of blockchain technology has created both opportunities and challenges in financial markets, particularly in the investment of blockchain-based real estate tokens. Unlike traditional financial assets, these investments exhibit high volatility, decentralized trading mechanisms, and complex transaction fee structures, all of which significantly influence portfolio management strategies. This study tackled the core issue of portfolio optimization in blockchain asset markets by incorporating both proportional and fixed transaction costs, factors often overlooked in conventional models. To address this, we proposed a multi-period investment optimization framework that leveraged Lagrange multipliers and dynamic programming to determine optimal asset allocation. A key feature of our model was its ability to define an optimal no-trade region, balancing transaction costs with investment returns under varying fee structures. Through numerical experiments, we analyzed how different levels of transaction costs impacted trading frequency, risk exposure, and portfolio efficiency. Our findings indicated that higher transaction costs expanded the no-trade region, reducing trading frequency, while lower costs encouraged more frequent rebalancing. Additionally, we highlighted the practical benefits of blockchain real estate tokenization, including lower investment barriers, enhanced market liquidity, and seamless cross-border transactions. By providing a robust theoretical and empirical framework, this research offered valuable insights for investors navigating blockchain-based financial markets and contributed to the broader discourse on decentralized finance (DeFi) and tokenized real estate investments.