Alternative portfolio methods

Portfolio optimization in an uncertain environment has great practical value in investment decision process. But this area is highly fragmented due to fast evolution of market structure and changing investor behavior. In this dissertation, four methods are investigated/designed to explore their efficiency under different circumstances. Parametric portfolio decomposes weights by set of factors whose coefficients are uniquely determined via maximizing utility function. A robust bootstrap method is proposed to assist factor selection. If investors exhibit asymmetric aversion of tail risk, pessimistic models on Choquet utility maximization and coherent risk measures acquire superiority. A new hybrid method that inherits advantage of parameterization and tail risk minimization is designed. Mean-variance, which is optimal with elliptical return distribution, should be employed in the case of capital allocation to trading strategies. Nonparametric classifiers may enhance homogeneity of inputs before feeding the optimizer. Traditional factor portfolio can be extended to functional settings by applying FPCA to return curves sorted by factors. Diversification is always achieved by mixing with detected nonlinear components. This research contributes to existing literature on portfolio choice in three-folds: strength and weakness of each method is clarified; new models that outperform traditional approaches are developed; empirical studies are used to facilitate comparison

A study of data-driven momentum and disposition effects in the Chinese stock market by functional data analysis

We apply a functional data analysis approach to decompose the cross-sectional Fama–French three-factor model residuals in the Chinese stock market. Our results indicate that other than Fama–French three factors, there are two orthonormal asset pricing factors describing the behavioral biases in their historical performances: between winner and loser stocks, and extreme and mediocre-performing stocks, respectively. We explain these two factors through investors’ overreaction, overconfidence and the lead-lag effect. These findings empirically show the existence of momentum and disposition effects in the Chinese stock market. A buy-and-hold mean-variance optimized portfolio incorporating these two market anomalies boosts the Sharpe ratio to 1.27

Multivariate Volatility Regulated Kelly Strategy: A Superior Choice in Low Correlated Portfolios

International audienc