Solving the mean–variance customer portfolio in Markov chains using iterated quadratic/Lagrange programming: A credit-card customer limits approach

Sánchez, Emma M.; Clempner, Julio B.; Poznyak, Alexander S.

doi:10.1016/j.eswa.2015.02.018

Cited by 13 publications

(8 citation statements)

References 57 publications

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“…In addition, it assumes that all actions are uniformly distributed. On the other hand, the actor-critic architecture is based on an iterated quadratic/Lagrange programming maximization method for computing the mean-variance customer portfolio optimization (Sánchez et al, 2015). This process can be viewed as a specific form of asynchronous value iteration with optimized computational properties.…”

Section: Resultsmentioning

confidence: 99%

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A priori-knowledge/actor-critic reinforcement learning architecture for computing the mean–variance customer portfolio: The case of bank marketing campaigns

Sánchez

Clempner

Poznyak

2015

Engineering Applications of Artificial Intelligence

Self Cite

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Section: Resultsmentioning

confidence: 99%

“…The iterated quadratic/Lagrange programming method (Sánchez et al, 2015) consists in a two step procedure in order to solve the mean-variance costumer portfolio optimization problem. The first step solves the following quadratic programming problem:…”

Section: Appendix a Iterated Quadratic/lagrange Programming Methodsmentioning

confidence: 99%

A priori-knowledge/actor-critic reinforcement learning architecture for computing the mean–variance customer portfolio: The case of bank marketing campaigns

Sánchez

Clempner

Poznyak

2015

Engineering Applications of Artificial Intelligence

Self Cite

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“…We consider the modeling and solution of the multi-period mean-variance customer constrained Markowitz's portfolio optimization problem in Markov chains. The proposed multi-period mean-variance model pioneered by [19,18,1] provides a solution method able to find an optimal investment strategy in a finite time-horizon which to maximize the final wealth while minimize the risk and determine the exit time. For solving the problem, we present a two-step iterated procedure for the extraproximal method: a) the first step (the extra-proximal step) consists of a "prediction" which calculate the preliminary position approximation to the equilibrium point, and b) the second step is designed to find a "basic adjustment" of the previous prediction.…”

Section: 2main Resultsmentioning

confidence: 99%

“…One may formally state Markowitz's decision model for mean-variance customer portfolio as follows [19,18,1]. We define The resulting customer portfolio optimization problem includes a model-user's tolerance for risk, and it is represented by the following expression:…”

Section: 1single Period Optimizationmentioning

confidence: 99%

Multiperiod Mean-Variance Customer Constrained Portfolio Optimization for Finite Discrete-Time Markov Chains

Domiguez¹,

Clempner²

2019

ECECSR

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The multi-period formulation aims at selecting an optimal investment strategy in a time-horizon able to maximize the final wealth while minimize the risk and determine the exit time. This paper is dedicated to solve the multi-period mean-variance customer constrained Markowitz's portfolio optimization problem employing the extraproximal method restricted to a finite discrete time, ergodic and controllable Markov chains for finite time horizon. The extraproximal method can be considered as a natural generalization of the convex programming approximation methods that largely simplifies the mathematical analysis and the economic interpretation of such model settings. We show that the multi-period mean-variance optimal portfolio can be decomposed in terms of coupled nonlinear programming problems implementing the Lagrange principle, each having a clear economic interpretation. This decomposition is a multi-period representation of single-period mean variance customer portfolio which naturally extends the basic economic intuition of the static Markowitz's model (where the investment horizon is practically never known at the beginning of initial investment decisions). This implies that the corresponding multi-period mean-variance customer portfolio is determined for a system of equations in proximal format. Each equation in this system is an optimization mean-variance problem which is solved using an iterating projection gradient method. Iterating these steps, we obtain a new quick procedure which leads to a simple and logically justified computational realization: at each iteration of the extraproximal method the functional of the mean-variance portfolio converges to an equilibrium point. We provide conditions for the existence of a unique solution to the portfolio problem by employing a regularized Lagrange function. We present the convergence proof of the method and all the details needed to implement the extraproximal method in an efficient and numerically stable way. Empirical results are finally provided to illustrate the suitability and practical performance of the model and the derived explicit portfolio strategy.

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“…The Transitive-constraint: Given and as the connected components, let oi and the value of oj be objects in and , respectively. CCb, then(Sánchez et al, 2015):…”

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confidence: 99%

Using the Deep Constrained Clustering Approach to Create a Business Profile

Latif

Sutarman

Damius

2022

SinkrOn

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Identification of customers in the business sector that really needs to be done as an evaluation of a business that is run so that it can continue to grow and be able to follow business developments in the same sector. The deep constraint clustering approach is used to cluster customers towards a business. In this study, a clustering of customers using rail mass transportation will be carried out. The results achieved are the formation of 6 clusters using trains be built. The result of research expected to be a consideration in improving services to the company

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Solving the mean–variance customer portfolio in Markov chains using iterated quadratic/Lagrange programming: A credit-card customer limits approach

Cited by 13 publications

References 57 publications

A priori-knowledge/actor-critic reinforcement learning architecture for computing the mean–variance customer portfolio: The case of bank marketing campaigns

A priori-knowledge/actor-critic reinforcement learning architecture for computing the mean–variance customer portfolio: The case of bank marketing campaigns

Multiperiod Mean-Variance Customer Constrained Portfolio Optimization for Finite Discrete-Time Markov Chains

Using the Deep Constrained Clustering Approach to Create a Business Profile

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