De-humanizing the customer experience: a conceptual framework

Technologies like smart services, artificial intelligence and cloud-based systems are reengineering current best practices, de-humanizing the customer experience and leading to new forms of interactions between human and machines. The topic is quite new in marketing and management. In particular, the effects of sales and marketing automation on customer experience are quite under investigated. This theoretical paper will develop a conceptual framework to find out variables moderating the relationship between automation (SMA) processes and customer experience (CX), as well as factors impacting positively and negatively on it. Important managerial implications derive from this study given the increasing number of companies who will approach the topic fast, by prompting to re-design daily operations and inherently change customer-firm interactions

Split Bregman iteration for multi-period mean variance portfolio optimization

This paper investigates the problem of defining an optimal long-term investment strategy, where the investor can exit the investment before maturity without severe loss. Our setting is a multi-period one, where the aim is to make a plan for allocating all of wealth among the n assets within a time horizon of m periods. In addition, the investor can rebalance the portfolio at the beginning of each period. We develop a model in Markowitz context, based on a fused lasso approach. According to it, both wealth and its variation across periods are penalized using the l1 norm, so to produce sparse portfolios, with limited number of transactions. The model leads to a non-smooth constrained optimization problem, where the inequality constraints are aimed to guarantee at least a minimum level of expected wealth at each date. We solve it by using split Bregman method, that has proved to be efficient in the solution of this type of problems. Due to the additive structure of the objective function, the alternating split Bregman at each iteration yields to easier subproblems to be solved, which either admit closed form solutions or can be solved very quickly. Numerical results on data sets generated using real-world price values show the effectiveness of the proposed model

L1-regularization for multi-period portfolio selection

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. The core problem is a nonsmooth optimization one, with equality constraints. We present an iterative procedure based on a modified Bregman iteration, that adaptively sets the value of the regularization parameter in order to produce solutions with desired financial properties. We validate the approach showing results of tests performed on real data

l1-Regularization in Portfolio Selection with Machine Learning

In this work, we investigate the application of Deep Learning in Portfolio selection in a Markowitz mean-variance framework. We refer to a l1 regularized multi-period model; the choice of the l1 norm aims at producing sparse solutions. A crucial issue is the choice of the regularization parameter, which must realize a trade-off between fidelity to data and regularization. We propose an algorithm based on neural networks for the automatic selection of the regularization parameter. Once the neural network training is completed, an estimate of the regularization parameter can be computed via forward propagation. Numerical experiments and comparisons performed on real data validate the approach

Interval linear systems: the state of the art

Linear systems represent the computational kernel of many models that describe problems arising in the field of social, economic as well as technical and scientific disciplines. Therefore, much effort has been devoted to the development of methods, algorithms and software for the solution of linear systems. Finite precision computer arithmetics makes rounding error analysis and perturbation theory a fundamental issue in this framework (Higham 1996). Indeed, Interval Arithmetics was firstly introduced to deal with the solution of problems with computers (Moore 1979, Rump 1983), since a floating point number actually corresponds to an interval of real numbers. On the other hand, in many applications data are affected by uncertainty (Jerrell 1995, Marino & Palumbo 2002), that is, they are only known to lie within certain intervals. Thus, bounding the solution set of interval linear systems plays a crucial role in many problems. In this work, we focus on the state of the art of theory and methods for bounding the solution set of interval linear systems. We start from basic properties and main results obtained in the last years, then we give an overview on existing methods

Adaptive l1-regularization for short-selling control in portfolio selection

We consider the l1 -regularized Markowitz model, where a l1 -penalty term is added to the objective function of the classical mean-variance one to stabilize the solution process, promoting sparsity in the solution. The l1 -penalty term can also be interpreted in terms of short sales, on which several financial markets have posed restrictions. The choice of the regularization parameter plays a key role to obtain optimal portfolios that meet the financial requirements. We propose an updating rule for the regularization parameter in Bregman iteration to control both the sparsity and the number of short positions. We show that the modified scheme preserves the properties of the original one. Numerical tests are reported, which show the effectiveness of the approach

A parallel wavelet-based pricing procedure for Asian options

In this paper, we present a parallel pricing algorithm for Asian options based on the Discrete Wavelet Transform. The computational kernel of the pricing model is the solution of integral equations. We obtain a sparse and accurate representation of the kernel of such equations in wavelet function bases. It is worth pointing out that the execution time of our procedure is almost constant with respect to the number of monitoring dates. Thus, our pricing procedure is particularly competitive when the number of monitoring dates is large. We moreover discuss the parallelization of the algorithm. Numerical results that show the accuracy and efficiency of the procedure are reported in the paper

Algorithms and Programming Tools for Next-Generation High-Performance Scientific Software HPSS 2011

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