Portfolio selection for individual passive investing

Puelz, David; Hahn, P. Richard; Carvalho, Carlos M.

doi:10.1002/asmb.2483

Cited by 16 publications

(8 citation statements)

References 60 publications

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“…Thus, investors aim to beat the market through their experience, in-depth research, financial forecasting, and stock analysis [19,34,35]. In contrast, investors who adopt a passive strategy believe in the efficient market hypothesis (EMH), which states that market values accurately include and reflect all information at all times [40,41,51]. Under the EMH assumption, investors believe that it is difficult to outthink market performance, and they assume that the market posts positive returns over time.…”

Section: Introductionmentioning

confidence: 99%

A metaheuristic-based framework for index tracking with practical constraints

Yuen¹,

Leung³

et al. 2021

Complex Intell. Syst.

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Recently, numerous investors have shifted from active strategies to passive strategies because the passive strategy approach affords stable returns over the long term. Index tracking is a popular passive strategy. Over the preceding year, most researchers handled this problem via a two-step procedure. However, such a method is a suboptimal global-local optimization technique that frequently results in uncertainty and poor performance. This paper introduces a framework to address the comprehensive index tracking problem (IPT) with a joint approach based on metaheuristics. The purpose of this approach is to globally optimize this problem, where optimization is measured by the tracking error and excess return. Sparsity, weights, assets under management, transaction fees, the full share restriction, and investment risk diversification are considered in this problem. However, these restrictions increase the complexity of the problem and make it a nondeterministic polynomial-time-hard problem. Metaheuristics compose the principal process of the proposed framework, as they balance a desirable tradeoff between the computational resource utilization and the quality of the obtained solution. This framework enables the constructed model to fit future data and facilitates the application of various metaheuristics. Competitive results are achieved by the proposed metaheuristic-based framework in the presented simulation.

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Section: Introductionmentioning

confidence: 99%

A metaheuristic-based framework for index tracking with practical constraints

Yuen¹,

Leung³

et al. 2021

Complex Intell. Syst.

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“…Polson and Tew (1999) consider the S&P 500 index and develop a Bayesian approach for large-scale stock selection and portfolio optimization from the index's constituents. Other insightful Bayesian approaches to optimal portfolio choice include Johannes, Korteweg and Polson (2014), Irie and West (2016), Zhao, Xie and West (2016), Gron, Jørgensen and Polson (2012), Jacquier and Polson (2010), Puelz, Carvalho and Hahn (2015) and Pettenuzzo and Ravazzolo (2015). Methodological papers exploring high dimensional dynamic models relevant to this work are Carvalho et al (2007) and Wang et al (2011).…”

Section: Previous Researchmentioning

confidence: 99%

Regret-Based Selection for Sparse Dynamic Portfolios

2017

Self Cite

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“…In essence, the paper introduces a two step Bayesian framework for sparse model summary. Since then, this approach has been shown to be effective in a variety of other contexts such as sparse graph estimation (Bashir et al, 2019), portfolio optimization (Puelz et al, 2015) and Bayesian function-on-scalar regression (Kowal and Bourgeois, 2020). In this paper, we extend the decoupled approach to Bayesian dynamic linear models in order to select the optimal changepoints.…”

Section: Introductionmentioning

confidence: 99%

Drift vs Shift: Decoupling Trends and Changepoint Analysis

Wu¹,

Ryan²,

Matteson³

2022

Preprint

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This paper introduces a new Bayesian changepoint approach called the decoupled approach that separates the process of modeling and changepoint analysis. The approach utilizes a Bayesian dynamic linear model (DLM) for the modeling step and a weighted penalized likelihood estimator on the posterior of the Bayesian DLM to identify changepoints. A Bayesian DLM, with shrinkage priors, can provide smooth estimates of the underlying trend in presence of complex noise components; however, the inability to shrink exactly to zero make changepoint analysis difficult. Penalized likelihood estimators can be effective in estimating location of changepoints; however, they require a relatively smooth estimate of the data. The decoupled approach combines the flexibility of the Bayesian DLM along with the hard thresholding property of penalized likelihood estimator to extend application of changepoint analysis. The approach provides a robust framework that allows for identification of changepoints in highly complex Bayesian models. The approach can identify changes in mean, higher order trends and regression coefficients. We illustrate the approach's flexibility and robustness by comparing against several alternative methods in a wide range of simulations and two real world examples.

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Portfolio selection for individual passive investing

Cited by 16 publications

References 60 publications

A metaheuristic-based framework for index tracking with practical constraints

A metaheuristic-based framework for index tracking with practical constraints

Regret-Based Selection for Sparse Dynamic Portfolios

Drift vs Shift: Decoupling Trends and Changepoint Analysis

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