1,721,043 research outputs found
Semi on-line scheduling on two parallel processors with known sum and lower bound on the size of the tasks
No full text
On the application of Markov processes to the large scale portfolio selection problem
No full textTechnical Report n. 347, Department of Quantitative Methods, University of Bresci
American and European Portfolio Selection Strategies: the Markovian Approach
No full textIn this chapter we propose portfolio selection strategies using the assumption that the portfolio returns evolve as Markov processes. In particular, we distinguish the analysis for parametric and non parametric Markov processes and discuss the construction of the transition matrix in the two different cases. Under the assumption that returns are Markov processes we propose several possible strategies where the investors recalibrate their portfolios at a fixed temporal horizon or within a fixed temporal horizon. Thus, we analyze the computational complexity of the proposed strategies and propose an heuristic algorithm for the global optimum in order to overcome the intrinsic computational complexity of the proposed models. Furthermore, we show how the Markov assumption can be used to forecast the portfolio returns and we examine some simple empirical comparisons between Markovian strategies and classic reward-risk ones
Set-Portfolio Selection with the Use of Market Stochastic Bounds
Full text linkThis paper proposes an ex-post comparison of portfolio selection strategies. These are
applied to certain preselected assets among about ten thousand stocks on the global market. In
particular, we preselected a few assets for each portfolio selection problem, taking into account
different return characteristics. The preselecting criteria take into account the joint Markovian
behavior of the returns; furthermore, they consider the assets who optimize the association with
market stochastic bounds, having the highest ex-ante reward-risk performance. The results
obtained with different pre-selection criteria are merged in order to identify assets with common
characteristics which are appealing for investors. The impact of assets pre-selection on the
portfolio choices is also studied. In particular, we compare the performance of different strategies
that use or do not use the preselecting criteria. We finally propose the comparison of the ex-post
final wealth obtained with the optimization of several reward-risk functionals that use the
stochastic bounds of the preselected assets. For every comparison, we assume that the returns
follow a non-parametric Markov chain, where the investors recalibrate their portfolios on a weekly
basis
Exploring greedy criteria for the dynamic traveling purchaser problem
No full textGiven a set of products and a set of markets, the traveling purchaser problem looks for a tour visiting a subset of the markets to satisfy products demand at the minimum purchasing and traveling costs. In this paper, we analyze the dynamic variant of the problem (D-TPP) where the quantity made available in each market for each product may decrease over time. We introduce and compare several greedy
strategies and test their impact on the solution in terms of feasibility and costs. In particular, we study an incremental approach where an initial naive strategy is improved
and refined by a number of variants. Some of the proposed heuristics take into account either one of the two objective costs, while others are based on both traveling and purchasing costs. Extensive computational results are also provided on randomly generated instances
Kernel Search: A heuristic framework for MILP problems with binary variables
No full textTechnical Report R.T. 2007-04-56, Department of Electronics for Automation, University of Bresci
GARCH type portfolio selection models with the Markovian approach
No full textThis paper describes different GARCH type portfolio models using a bivariate Markov process. In particular we approximate the GARCH process with a Markov chain in order to value the price/return distribution at the investor’s temporal horizon. Then we discuss the computational complexity of the optimization problem and we implement an heuristic algorithm for the global optimum. Finally we propose an ex-post comparison among portfolio
selection strategies based on reward/risk performance ratios
The Dynamic Travelling Purchaser Problem
No full textAbstracts book. Decision and optimization models for evaluation and management, XL Annual Conference Italian Operational Research Society, Siena September, 8-11 2009
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