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L1-regularization for multi-period portfolio selection [article]

Stefania Corsaro, Valentina De Simone, Zelda Marino, Francesca Perla
2018 arXiv   pre-print
In this work we present a model for the solution of the multi-period portfolio selection problem. The model is based on a time consistent dynamic risk measure.  ...  We apply l1-regularization to stabilize the solution process and to obtain sparse solutions, which allow one to reduce holding costs.  ...  Regularized portfolio selection model In this section we introduce an l 1 -regularized model for multi-period portfolio selection. Let m be the number of investment periods.  ... 
arXiv:1809.01460v1 fatcat:lryfbu4srncpnn47q6dnazd3rq

l1-Regularization in Portfolio Selection with Machine Learning

Stefania Corsaro, Valentina De Simone, Zelda Marino, Salvatore Scognamiglio
2022 Mathematics  
We refer to a l1 regularized multi-period model; the choice of the l1 norm aims at producing sparse solutions.  ...  We propose an algorithm based on neural networks for the automatic selection of the regularization parameter.  ...  Multi-Period l 1 -Regularized Mean-Variance Markowitz Model In this section, we recall the l 1 -regularized model for multi-period portfolio selection introduced in [5] .  ... 
doi:10.3390/math10040540 fatcat:dpcdou7nx5gntkyfx3txb4lf3q

A subspace-accelerated split Bregman method for sparse data recovery with joint l1-type regularizers [article]

Valentina De Simone, Daniela di Serafino, Marco Viola
2020 arXiv   pre-print
Numerical experiments on multi-period portfolio selection problems using real datasets show the effectiveness of the proposed method.  ...  operator, e.g. a finite difference operator, as in anisotropic Total Variation and fused-lasso regularizations.  ...  Marco Viola also thanks Daniel Robinson for useful discussions about orthant-based methods for ℓ 1 -regularized optimization problems.  ... 
arXiv:1912.06805v2 fatcat:ocszklbwfjab5ehowj632mnnte

Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model [article]

Hyeong Kyu Choi
2018 arXiv   pre-print
Predicting the price correlation of two assets for future time periods is important in portfolio optimization.  ...  Our work implies that it is worth considering the ARIMA LSTM model to forecast correlation coefficient for portfolio optimization.  ...  For the constant correlation model and the multi-group model, we regarded the 150 assets we selected randomly to be our portfolio constituents.  ... 
arXiv:1808.01560v5 fatcat:j3wj52hkmbhvremkd7u2hrv4ne

Distributed Majorization-Minimization for Laplacian Regularized Problems [article]

Jonathan Tuck, David Hallac, Stephen Boyd
2018 arXiv   pre-print
model fitting, regularizing stratified models, and multi-period portfolio optimization.  ...  We develop a distributed majorization-minimization method for this general problem, and derive a complete, self-contained, general, and simple proof of convergence.  ...  Acknowledgements The authors would like to thank Peter Stoica for his insights and comments on early drafts of this paper.  ... 
arXiv:1803.10317v2 fatcat:i34ap4ddqrgmdapkj7nxraxbha

Variable Selection and Regularization via Arbitrary Rectangle-range Generalized Elastic Net [article]

Yujia Ding, Qidi Peng, Zhengming Song, Hansen Chen
2021 arXiv   pre-print
We introduce the arbitrary rectangle-range generalized elastic net penalty method, abbreviated to ARGEN, for performing constrained variable selection and regularization in high-dimensional sparse linear  ...  As a natural extension of the nonnegative elastic net penalty method, ARGEN is proved to have variable selection consistency and estimation consistency under some conditions.  ...  the same period, except for 30-stock ARGEN portfolios.  ... 
arXiv:2112.07785v1 fatcat:y7ocltjidbebxheqghft75hcvy

Sparse minimax portfolio and Sharpe ratio models

Chenchen Zu, Xiaoqi Yang, Carisa Kwok Wai Yu
2021 Journal of Industrial and Management Optimization  
In this paper, we investigate sparse portfolio selection models with a regularized lp-norm term (0 < p ≤ 1) and negatively bounded shorting constraints.  ...  We obtain some basic properties of several linear lp-sparse minimax portfolio models in terms of the regularization parameter.  ...  The authors would like to thank two reviewers for their constructive and detailed suggestions and comments which have improved the presentation of the paper.  ... 
doi:10.3934/jimo.2021111 fatcat:k7egpundlfb45l7zm277frcqpq

Continuous-Time Portfolio Selection: A Cursory Survey

Se Yung Bae, Junkee Jeon, Hyeng Keun Koo
2020 Frontiers in Applied Mathematics and Statistics  
Then, we discuss Bismut's application of the Pontryagin maximum principle to portfolio selection and the dual martingale approach.  ...  In this article we provide a short survey on continuous-time portfolio selection. We explain the pioneering contribution of Merton and the use of dynamic programming.  ...  Merton [4, 5] made a pioneering contribution by casting portfolio selection in a multi-period continuous-time framework.  ... 
doi:10.3389/fams.2020.00004 fatcat:qgij67gydfbvhgionsvacbp54m

TMAC: A Toolbox of Modern Async-Parallel, Coordinate, Splitting, and Stochastic Methods [article]

Brent Edmunds, Zhimin Peng, Wotao Yin
2016 arXiv   pre-print
These algorithms can run in a multi-threaded fashion, either synchronously or asynchronously, to take advantages of all the cores available.  ...  TMAC is a toolbox written in C++11 that implements algorithms based on a set of modern methods for large-scale optimization.  ...  TMAC is designed for fast prototyping of scalable algorithms, which can be single-threaded or multi-threaded, and the multi-threaded code can run either synchronously or asynchronously.  ... 
arXiv:1606.04551v1 fatcat:m7k3np47fja2vek5d3j2im5oli

Revisiting Investability of Heritage Properties through Indexation and Portfolio Frontier Analysis

Chin Tiong Cheng, Gabriel Hoh Teck Ling, Yee-Siang Gan, Wai Fang Wong, Kong Seng Lai
2021 Risks  
Thus, this study incorporates a self-developed heritage properties Index (PIHPI_HR) into the conventional investment portfolio for assessing diversification effects.  ...  This study has collected 853 units of transacted properties for constructing a 10-year price index (PIHPI_HR).  ...  Acknowledgments: This research was supported by the Centre for Construction Research, Faculty of Built Environment, Tunku Abdul Rahman University College, Malaysia.  ... 
doi:10.3390/risks9050091 fatcat:dozsblhlyzcn7hxaol7qpsttji

A Constrained Consensus Based Optimization algorithm and its Application to Finance [article]

Hyeong-Ohk Bae, Seung-Yeal Ha, Myeongju Kang, Hyuncheul Lim, Chanho Min, Jane Yoo
2021 arXiv   pre-print
As a practical application of the proposed algorithm, we study the portfolio optimization problem in finance.  ...  Our proposed algorithm generalizes the CBO algorithm in [11] to tackle a constrained optimization problem for the global minima of the non-convex function defined on a convex domain.  ...  w with multi-asset returns' covariance Σ, respectively.  ... 
arXiv:2110.04499v2 fatcat:atr5gkjtp5gg5dazrjhvsmcnjq

Forecasting in Big Data Environments: an Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet) [article]

Ali Habibnia Emory University)
2019 arXiv   pre-print
This approach selects significant predictors as well as the topology of the neural network.  ...  It offers an appreciable improvement over current univariate and multivariate models by RMSE and actual portfolio performance.  ...  IV.1 illustrates portfolio excess returns for the out-of-sample period for the proposed model (ASShNet) against competing approaches.  ... 
arXiv:1904.11145v1 fatcat:6xdamh6bj5egxdbo7xemxap654

Optimal Allocation of Retirement Portfolios

Kevin Maritato, Morton Lane, Matthew Murphy, Stan Uryasev
2022 Journal of Risk and Financial Management  
For the pessimistic case, the retiree can withdraw up to $25,000 with zero ETS. Summary We developed a multi-period investment model for retirement portfolios.  ...  Introduction The problem of selecting optimal portfolios for retirement has unique features that are not addressed by more commonly used portfolio selection models used in trading.  ... 
doi:10.3390/jrfm15020065 fatcat:kioush7j6nehjdztds6dvjhhm4

Using multi-state markov models to identify credit card risk

Daniel Evangelista Régis, Rinaldo Artes
2015 Production  
relationships over time, thereby generating score models for various purposes.  ...  The main interest of this work is to analyze the application of multi-state Markov models to evaluate credit card risk by investigating the characteristics of different state transitions in client-institution  ...  provided by rating agencies, or in transitions between internal company ratings, would be a good area for future research.  ... 
doi:10.1590/0103-6513.160814 fatcat:zpivekwaszgerbfd6uk6x5ippe

Optimal ETF Selection for Passive Investing [article]

David Puelz, Carlos M. Carvalho, P. Richard Hahn
2015 arXiv   pre-print
Optimal portfolios are constructed from selected ETFs by maximizing the Sharpe ratio posterior mean, and they are compared to the (unknown) optimal portfolio based on the full Bayesian model.  ...  We compare our selection results to popular ETF advisor Additionally, we consider selecting ETFs by modeling a large set of mutual funds.  ...  This is emphasized in Hahn and Carvalho's DSS paper where l 1 -regularization is accompanied with integration over uncertainty for model selection.  ... 
arXiv:1510.03385v2 fatcat:orqig4zxsvdulkqhabydw5vroa
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