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We present a novel approach to the portfolio selection problem for a multiperiod investor facing multiple risky assets, trading constraints, and return predictability. Our objective is to maximize mean-variance utility while addressing the computational challenges arising from the curse of dimensionality associated with dynamic programming in the presence of trading constraints. To overcome this, we employ model predictive control, a computationally efficient method for solving the problem. Additionally, we propose the use of a non-parametric Bayesian model, specifically the hierarchical Dirichlet process based Hidden Markov Model (HDP-HMM), to predict the multiperiod mean and covariance of returns. Then, we consider a time-varying maximum drawdown to adjust the risk aversion, which can effectively cope with the limit loss problems. Through extensive simulation studies and empirical analysis, we demonstrate that trading strategies based on our proposed method outperform existing approaches in out-of-sample performance. [ABSTRACT FROM AUTHOR]

We propose a novel method for regression adjustment in approximate Bayesian computation to help improve the accuracy and computational efficiency of the posterior inference. The proposed method uses random forest regression to model the connection between summary statistics and the parameters of interest. Compared with existing approaches, the proposed method bypasses the need of preselection of summary statistics in the model, and is capable of capturing the potential nonlinear relationship between the parameters of interest and summary statistics. We also introduce a measure to quantify the importance of each summary statistic used in the model. We study the asymptotic properties of the proposed estimator and show that it has an excellent finite-sample numerical performance via two simulation examples and an application to a population genetic study. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel Bayesian data augmentation approach for estimating the parameters of the generalized logistic regression model. We propose a Pólya–Gamma sampler algorithm that allows us to sample from the exact posterior distribution, rather than relying on approximations. A simulation study illustrates the flexibility and accuracy of the proposed approach to capture heavy and light tails in binary response data of different dimensions. The algorithm performance is tested on simulated data. Furthermore, the methodology is applied to two different real datasets, where we demonstrate that the Pólya–Gamma sampler provides more precise estimates than the empirical likelihood method, outperforming approximate approaches. [ABSTRACT FROM AUTHOR]