Journal ArticleDOI
A linear programming model for selection of sparse high-dimensional multiperiod portfolios
Chi Seng Pun,Hoi Ying Wong +1 more
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TLDR
It is proved that the classicalplug-in estimation seriously distorts the optimal MV portfolio in the sense that the probability of the plug-in portfolio outperforming the bank deposit tends to 50% for p ≫ n and a large n.About:
This article is published in European Journal of Operational Research.The article was published on 2019-03-01. It has received 34 citations till now. The article focuses on the topics: Portfolio.read more
Citations
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Multi-objective imprecise programming for financial portfolio selection with fuzzy returns
TL;DR: A multi-objective FPS approach involving fuzzy parameters, where possibility distributions are given by fuzzy numbers from the information supplied by the decision-making environment (investor, analyst, financial market environment, etc.) is proposed.
Posted Content
Persistent-Homology-based Machine Learning and its Applications -- A Survey
TL;DR: This paper provides a systematical review of PH and PH-based supervised and unsupervised models from a computational perspective, and focuses on the recent development of mathematical models and tools, including PH softwares andPH-based functions, feature representations, kernels, and similarity models.
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Persistent-homology-based machine learning: a survey and a comparative study
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Deep-Learning Solution to Portfolio Selection with Serially Dependent Returns
Ka Ho Tsang,Hoi Ying Wong +1 more
TL;DR: Under the condition that the global minimum of the DNN training problem is attained, it is proved that the algorithm converges with the quadratic utility function when the risky asset returns jointly follow multivariate AR(1) models and/or multivariate GARCH(1, 1) models.
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Resolution of Degeneracy in Merton's Portfolio Problem
Chi Seng Pun,Hoi Ying Wong +1 more
TL;DR: This study proposes a constrained $\ell_1$-minimization approach to resolve the degeneracy in the high-dimensionalSetting and stabilize the performance in the low-dimensional setting and proves the consistency of the framework that the estimate of the optimal control tends to be the optimal value.
References
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Journal ArticleDOI
Common risk factors in the returns on stocks and bonds
Eugene F. Fama,Kenneth R. French +1 more
TL;DR: In this article, the authors identify five common risk factors in the returns on stocks and bonds, including three stock-market factors: an overall market factor and factors related to firm size and book-to-market equity.
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Sparse inverse covariance estimation with the graphical lasso
TL;DR: Using a coordinate descent procedure for the lasso, a simple algorithm is developed that solves a 1000-node problem in at most a minute and is 30-4000 times faster than competing methods.
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The Dantzig selector: Statistical estimation when p is much larger than n
Emmanuel J. Candès,Terence Tao +1 more
TL;DR: In many important statistical applications, the number of variables or parameters p is much larger than the total number of observations n as discussed by the authors, and it is possible to estimate β reliably based on the noisy data y.
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Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?
TL;DR: In this article, the authors evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1-N portfolio.
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Simultaneous analysis of lasso and dantzig selector
TL;DR: In this article, the Lasso estimator and the Dantzig selector exhibit similar behavior under a sparsity scenario, and they derive, in parallel, oracle inequalities for the prediction risk in the general nonparametric regression model, as well as bounds on the l p estimation loss for 1 ≤ p ≤ 2.