Valerio Lacagnina

A stochastic soft constraints fuzzy model for a portfolio selection problem

The financial market behavior is affected by several non-probabilistic factors such as vagueness and ambiguity. In this paper we develop a multistage stochastic soft constraints fuzzy program with recourse in order to capture both uncertainty and imprecision as well as to solve a portfolio management problem. The results we obtained confirm the studies carried out in literature addressed to integrate stochastic and possibilistic programming.

A Class of Label-Correcting Methods for the K Shortest Paths Problem

In this paper we deal with the problem of finding the firstK shortest paths from a single origin node to all other nodes of a directed graph. In particular, we define the necessary and sufficient conditions for a set of distance label vectors, on the basis of which we propose a class of methods which can be viewed as an extension of the generic label-correcting method for solving the classical single-origin all-destinations shortest path problem. The data structure used is characterized by a set ofK lists of candidate nodes, and the proposed methods differ in the strategy used to select the node to be extracted at each iteration. The computational results show that: 1. some label-correcting methods are generally much faster than the double sweep method of Shier (1979); 2. the most efficient node selection strategies, used for solving the classical single-origin all-destinations shortest path problem, have proved to be effective also in the case of theK shortest paths.

A simulation analysis of the microstructure of an order driven financial market with multiple securities and portfolio choices

In this paper we propose an artificial market where multiple risky assets are exchanged. Agents are constrained by the availability of resources and trade to adjust their portfolio according to an exogenously given target portfolio. We model the trading mechanism as a continuous auction order-driven market. Agents are heterogeneous in terms of desired target portfolio allocations, but they are homogeneous in terms of trading strategies. We investigate the role played by the trading mechanism in affecting the dynamics of prices, trading volume and volatility. We show that the institutional setting of a double auction market is sufficient to generate a non-normal distribution of price changes and temporal patterns that resemble those observed in real markets. Moreover, we highlight the role played by the interaction between individual wealth constraints and the market frictions associated with a double auction system to determine the negative asymmetry of the stock returns distribution.

Comparison between statistical and fuzzy approaches for improving diagnostic decision making in patients with chronic nasal symptoms

This paper compares a fuzzy model, expressed in rule-form, with a well known statistical approach (i.e. logistic regression model) for diagnostic decision making in patients with chronic nasal symptoms. The analyses were carried out using a database obtained from a questionnaire administered to 1359 patients with nasal symptoms containing personal data, clinical data and skin prick test (SPT) results. Both the fuzzy model and the logistic regression model developed were validated using a data set obtained from another medical institution. The accuracy of the two models in identifying patients with positive or negative SPT was similar. This study is a preliminary step to the creation of a software that primary care doctors can use to make a diagnostic decision, when deciding whether patients with nasal symptoms need allergy testing or not.

Threshold Rule and Scaling Behavior in a Multi-Agent Supply Chain

In this paper an agent-based model of self organized criticality is developed in a network economy characterized by lead time and a threshold behavior of firms. Instead of considering the aggregate production of the economy as a whole, we focus on both the propagation and amplification effects of a demand shock in the sectorial productions of a multi-agent supply chain. We study a static network structure representing a relation of firms in a lower-upper stream in an industrial organization. In our model, the individual (R, nQ) policies play an important role in generating a propagation effect across the different layers of the economy, and the propagation turns into the large fluctuations and amplifications of sectorial productions.

An Optimized System Dynamics Approach for a Hotel Chain Management

The proposed model consists of an integrated system dynamics-data envelopment analysis approach to value, in a dynamic framework, the effects over time of the policies implemented according to the relative efficiency analysis. Rooms’ price and competing facilities (the hedonics) are the decision variables to move in order to push the hotels towards a higher relative efficiency at the end of the observation periods. In fact, in competitive markets as tourism, hotels compete for money offering differentiated quality. Moreover, according to the microeconomic theory, a producer of differentiated goods is not a price taker but a price maker. Therefore, we assume that the decision maker of the hotel chain can freely set the rooms’ price and the hedonics that will increase the relative economic efficiency of all the hotels of the chain. The proposed model treats the rooms’ pricing and the hedonic setting problem in an environment characterized by uncertainty of the customers’ preferences. The relative efficiency analysis is carried out by making use of data envelopment analysis that identifies the peer group and targets for the inefficient units. The dynamic analysis of the effects over time of the policies implemented is carried out using system dynamics methodology. This combined approach will help the decision maker in answering the following questions: which hotels of the chain will be attractive, and which ones will be efficient? What adjustments on prices and hedonics will attract more tourism demand? What are the dynamic effects of the DEA policies? The remaining sections of this paper are organized as follows. Section 3.2 is devoted to a brief survey of the theoretical background with particular attention to data envelopment analysis and system dynamics. Section 3.3 describes our model both from the customer side and the hotel management side. Section 3.4 shows the

An integrated fuzzy-stochastic model for revenue management: The hospitality industry case.

Revenue management aims at improving the performance of an organization by selling the right product/service to the right customer at the right time. This task is very dependent on uncontrollable external factors. In the hospitality industry, rooms of the hotel represent perishable assets and fixed capacities at the same time. Therefore, in the case of a stochastic process for customers calling in reservations prior to a particular booking date, a common problem for hotels is to devise a policy for maximizing the total expected profit conditional on the set of bookings. We propose a fuzzy model for the hotel revenue management under an uncertain and vague environment. Fuzziness of objective and constraint functions have been incorporated into a stochastic booking model considering multiple-day stays to show the effect of uncertainty on the optimal demand. By changing the relaxation parameters of the objective function, we have found a set of optimal solutions with, in most of the cases, a value of the objective function equal to the optimal solution of the stochastic model, providing several alternative optimal room allocations.

Hotel Chain Performance: A Gravity-DEA Approach

Generally speaking, competitiveness is a comparative concept of the ability and performance of a firm, sub-sector or country to sell and supply goods and/or services in a given market. At an operational level, instead, competitiveness is viewed in terms of the size of the market share secured by the firm, sub-sector or country considered. Moreover, in an operational context, while identifying that efficiency is a vital factor in competitive markets, it should also be acknowledged that it is, by itself, an insufficient determinant of competitiveness. Indeed, while competitiveness has more to do with “pursuing the correct strategy” towards the conservation and/or increase of the market share, operational efficiency is mainly a measure of how well the firm, sub-sector or country under study processes inputs to achieve its outputs, as compared to its maximum potential for doing so as represented by its production possibility frontier.

Asset Return Dynamics under Alternative Learning Schemes

In this paper we design an artificial financial market where endogenous volatility is created assigning to the agents diverse prior beliefs about the joint distribution of returns, and, over time, making agents rationally update their beliefs using common public information. We analyze the asset price dynamics generated under two learning environments: one where agents assume that the joint distribution of returns is IID, and another where agents believe in the existence of regimes in the joint distribution of asset returns. We show that the regime switching learning structure can generate all the most common stylized facts of financial markets: fat tails and long-range dependence in volatility coexisting with relatively efficient markets.

The Dynamics of Quote Prices in an Artificial Financial Market with Learning Effects

In this paper we study the evolution of bid and ask prices in an electronic financial market populated by portfolio traders who optimally choose their allocation strategy on the basis of their views about market conditions. Recently, a growing literature has investigated the consequences of learning about the returns process1. There has been an increasing interest in analyzing what are the implications of relaxing the assumption that agents hold correct expectations. In particular, it has been asked the fundamental question of understanding if typical asset-pricing anomalies (like returns predictability, and excess volatility) can be generated by a learning process about the underlying economy. In this paper we focus on the process by which information is incorporated into prices, examining the relationships among the dynamics of price changes, and the time variation of liquidity and of trading activity. We design an order book market system. Agents enter the market sequentially, and they trade to adjust their portfolio according to their optimal target