Search results for " optimization."
showing 10 items of 2333 documents
Learning competitive pricing strategies by multi-agent reinforcement learning
2003
Abstract In electronic marketplaces automated and dynamic pricing is becoming increasingly popular. Agents that perform this task can improve themselves by learning from past observations, possibly using reinforcement learning techniques. Co-learning of several adaptive agents against each other may lead to unforeseen results and increasingly dynamic behavior of the market. In this article we shed some light on price developments arising from a simple price adaptation strategy. Furthermore, we examine several adaptive pricing strategies and their learning behavior in a co-learning scenario with different levels of competition. Q-learning manages to learn best-reply strategies well, but is e…
Why is equity order flow so persistent?
2015
Abstract Order flow in equity markets is remarkably persistent in the sense that order signs (to buy or sell) are positively autocorrelated out to time lags of tens of thousands of orders, corresponding to many days. Two possible explanations are herding, corresponding to positive correlation in the behavior of different investors, or order splitting, corresponding to positive autocorrelation in the behavior of single investors. We investigate this using order flow data from the London Stock Exchange for which we have membership identifiers. By formulating models for herding and order splitting, as well as models for brokerage choice, we are able to overcome the distortion introduced by bro…
How does learning affect market liquidity? A simulation analysis of a double-auction financial market with portfolio traders
2007
We study the relationship between liquidity and prices in an artificial financial market where portfolio traders with limited resources interact through a continuous, electronic open book. We depart from the standard asset pricing framework in two ways. First, we assume that investors have incomplete information about the distribution of returns. Second, we model the portfolio choice problem using prospect-type preferences. We model the utility function in terms of deviations of the portfolio growth rate from a specified target growth rate, and we assume that investors are more sensitive to downside movements. We show that the parameters defining the learning process affect the price dynami…
Discrete frequency models for inventory management – an introduction
2001
Abstract The paper deals with the problem of devising a periodic replenishment policy when orders must be periodic, but only a given, discrete set of order frequencies can be used. The multi-item, instantaneous replenishment case with known demand is studied. In particular, staggering policies somehow arranging replenishments not to come at the same time instants are considered. The paper is composed of three parts: first, a taxonomy of several versions of the discrete frequency problem is proposed, according to different elements; in the second part, a general mixed integer programming model is proposed which is able to capture the peculiarities of the whole spectrum of this kind of proble…
Solving multiobjective optimization problems with decision uncertainty: an interactive approach
2018
We propose an interactive approach to support a decision maker to find a most preferred robust solution to multiobjective optimization problems with decision uncertainty. A new robustness measure that is understandable for the decision maker is incorporated as an additional objective in the problem formulation. The proposed interactive approach utilizes elements of the synchronous NIMBUS method and is aimed at supporting the decision maker to consider the objective function values and the robustness of a solution simultaneously. In the interactive approach, we offer different alternatives for the decision maker to express her/his preferences related to the robustness of a solution. To conso…
A branch & bound algorithm for cutting and packing irregularly shaped pieces
2013
Abstract Cutting and packing problems involving irregular shapes, usually known as Nesting Problems, are common in industries ranging from clothing and footwear to furniture and shipbuilding. Research publications on these problems are relatively scarce compared with other cutting and packing problems with rectangular shapes, and are focused mostly on heuristic approaches. In this paper we make a systematic study of the problem and develop an exact Branch & Bound Algorithm. The initial existing mixed integer formulations are reviewed, tested and used as a starting point to develop a new and more efficient formulation. We also study several branching strategies, lower bounds and procedures f…
A model for designing callable bonds and its solution using tabu search
1997
Abstract We formulate the problem of designing callable bonds as a non-linear, global, optimization problem. The data of the model are obtained from simulations of holding-period returns of a given bond design, which are used to compute a certainty equivalent return, viz., some target assets. The design specifications of the callable bond are then adjusted so that the certainty equivalent return is maximized. The resulting problem is multi-modal, and a tabu search procedure, implemented on a distributed network of workstations, is used to optimize the bond design. The model is compared with the classical portfolio immunization model, and the tabu search solution technique is compared with s…
The Random-Time Binomial Model
1999
In this paper we study Binomial Models with random time steps. We explain, how calculating values for European and American Call and Put options is straightforward for the Random-Time Binomial Model. We present the conditions to ensure weak-convergence to the Black-Scholes setup and convergence of the values for European and American put options. Differently to the CRR-model the convergence behaviour is extremely smooth in our model. By using extrapolation we therefore achieve order of convergence two. This way it is an efficient tool for pricing purposes in the Black-Scholes setup, since the CRR model and its extrapolations typically achieve order one. Moreover our model allows in a straig…
A problem-adjusted genetic algorithm for flexibility design
2013
Many present markets for goods and services have highly volatile demand due to short life cycles and strong competition in saturated environments. Determination of capacity levels is difficult because capacities often need to be set long before demand realizes. In order to avoid capacity-demand mismatches, operations managers employ mix-flexible resources which allow them to shift excess demands to unused capacities. The Flexibility Design Problem (FDP) models the decision on the optimal configuration of a flexible (manufacturing) network. FDP is a difficult stochastic optimization problem, for which traditional exact approaches are not able to solve but the smallest instances in reasonable…
A naïve approach to speed up portfolio optimization problem using a multiobjective genetic algorithm
2012
a b s t r a c t Genetic algorithms (GAs) are appropriate when investors have the objective of obtaining mean-variance (VaR) efficient frontier as minimising VaR leads to non-convex and non-differential risk-return optimisation problems. However GAs are a time-consuming optimisation technique. In this paper, we propose to use a naive approach consisting of using samples split by quartile of risk to obtain complete efficient frontiers in a reasonable computation time. Our results show that using reduced problems which only consider a quartile of the assets allow us to explore the efficient frontier for a large range of risk values. In particular, the third quartile allows us to obtain efficie…