OPTIMAL PORTFOLIO ALLOCATION UNDER FRACTAL THEORY

The efficient market hypothesis (EMH) has been dominating the literature of finance for a long time. Meanwhile, the problematic assumptions and inappropriateness of EMH in explaining real-life financial markets have dictated the significance of developing new theories and approaches. On the other hand, the Fractal Market Hypothesis postulates that financial markets are structured as fractals, they exhibit statistical self-similarity, and long-term memory in their time series. The portfolio applications to this hypothesis are quite limited in the literature. In this study, a portfolio optimization approach based on Fractal Market Hypothesis is developed. This paper suggests a portfolio optimization method, the Mean-MFTWXDFA which is based on multifractal temporally weighted cross-correlation analysis. The suggested method is also compared with those of classical portfolio applications such as the Mean-Variance, Mean-Value at Risk, and Mean-Conditional Value at Risk methods. Applications of the fractal-based portfolio perform reasonably well into out-of-sample analyses of a portfolio including the cryptocurrency market and three diversifying assets: oil, clean energy, and equity, outperforming conventional ones.