Pedro Godinho

The AGAP system: A GDSS for project analysis and evaluation

Abstract This paper presents the ‘Aid to Groups of Analysis and evaluation of Projects’ (AGAP) system, a distributed group decision support system (GDSS) allowing multiple decision makers to cooperate in the evaluation and selection of investment projects. The system has a state of the art set of economic measures that can be set as criteria for use in several multicriteria decision aid methods. It supports both synchronous and asynchronous usage, and tries to enhance the communications and data sharing during asynchronous group meetings, providing support for decision at individual, inter-personal and collective levels. The system is described at a conceptual level, and compared to some other tools available to achieve the same aims.

A Forex trading system based on a genetic algorithm

In this paper, a genetic algorithm will be described that aims at optimizing a set of rules that constitute a trading system for the Forex market. Each individual in the population represents a set of ten technical trading rules (five to enter a position and five others to exit). These rules have 31 parameters in total, which correspond to the individuals’ genes. The population will evolve in a given environment, defined by a time series of a specific currency pair. The fitness of a given individual represents how well it has been able to adapt to the environment, and it is calculated by applying the corresponding rules to the time series, and then calculating the ratio between the profit and the maximum drawdown (the Stirling ratio). Two currency pairs have been used: EUR/USD and GBP/USD. Different data was used for the evolution of the population and for testing the best individuals. The results achieved by the system are discussed. The best individuals are able to achieve very good results in the training series. In the test series, the developed strategies show some difficulty in achieving positive results, if you take transaction costs into account. If you ignore transaction costs, the results are mostly positive, showing that the best individuals have some forecasting ability.

A two-player competitive discrete location model with simultaneous decisions

In this paper we will describe and study a competitive discrete location problem in which two decision-makers (players) will have to decide where to locate their own facilities, and customers will be assigned to the closest open facilities. We will consider the situation in which the players must decide simultaneously, unsure about the decisions of one another, and we will present the problem in a franchising environment. Most problems described in the literature consider sequential rather than simultaneous decisions. In a competitive environment, most problems consider that there is a set of known and already located facilities, and new facilities will have to be located, competing with the existing ones. In the presence of more than one decision-maker, most problems found in the literature belong to the class of Stackelberg location problems, where one decision-maker, the leader, locates first and then the other decision-maker, the follower, locates second, knowing the decisions made by the first. These types of problems are sequential and differ significantly from the problem tackled in this paper, where we explicitly consider simultaneous, non-cooperative discrete location decisions. We describe the problem and its context, propose some mathematical formulations and present an algorithmic approach that was developed to find Nash equilibria. Some computational tests were performed that allowed us to better understand some of the features of the problem and the associated Nash equilibria. Among other results, we conclude that worsening the situation of a player tends to benefit the other player, and that the inefficiency of Nash equilibria tends to increase with the level of competition.

Two-player simultaneous location game: preferential rights and overbidding

Competitive location problems can be characterized by the fact that the decisions made by others will affect our own payoffs. In this paper, we address a discrete competitive location game in which two decision-makers have to decide simultaneously where to locate their services without knowing the decisions of one another. This problem arises in a franchising environment in which the decision-makers are the franchisees and the franchiser defines the potential sites for locating services and the rules of the game. At most one service can be located at each site, and one of the franchisees has preferential rights over the other. This means that if both franchisees are interested in opening the service in the same site, only the one that has preferential rights will open it. We consider that both franchisees have budget constraints, but the franchisee without preferential rights is allowed to show interest in more sites than the ones she can afford. We are interested in studying the influence of the existence of preferential rights and overbidding on the outcomes for both franchisees and franchiser. A model is presented and an algorithmic approach is developed for the calculation of Nash equilibria. Several computational experiments are defined and their results are analysed, showing that preferential rights give its holder a relative advantage over the other competitor. The possibility of overbidding seems to be advantageous for the franchiser, as well as the inclusion of some level of asymmetry between the two decision-makers.

Adaptive policies for multi-mode project scheduling under uncertainty

In this paper we propose an adaptive model for multi-mode project scheduling under uncertainty. We assume that there is a due date for concluding the project and a tardiness penalty for failing to meet this due date, and that several distinct modes may be used to undertake each activity. We define scheduling policies based on a set of thresholds. The starting time of the activity is compared with those thresholds in order to define the execution mode.

Cost-Benefit Analysis and the Optimal Timing of Road Infrastructures

Standard cost-benefit analysis is based on a static setting, allowing the decision to be made of whether or not a new infrastructure should be built, but not allowing the conclusion to be made of if it would be preferable to build it right now or in the future. In this paper, the optimal timing for building a road within a cost-benefit framework is addressed. A general approach for choosing the optimal timing, taking into account the characteristics of a road infrastructure, is proposed. A model of the expected net present value with two sources of uncertainty (gross domestic product growth and fuel prices) is proposed. Both these variables are assumed to be stochastic, so Monte Carlo simulation is used for the implementation of the model. A methodology is also proposed to estimate the thresholds that define the optimal starting time for the infrastructure. The model is applied to a real infrastructure currently under development and the rules that define the optimal timing for starting its construction are analyzed.

Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules

NSGA II outperforms SPEA 2 with wider fronts and better relations return-risk.Technical Analysis shows added value to trading when compared to Buy & Hold.Bollinger Bands is the strategy of election with strong positive net results. Recent work has been devoted to study the use of multiobjective evolutionary algorithms (MOEAs) in stock portfolio optimization, within a common mean-variance framework. This article proposes the use of a more appropriate framework, mean-semivariance framework, which takes into account only adverse return variations instead of overall variations. It also proposes the use and comparison of established technical analysis (TA) indicators in pursuing better outcomes within the risk-return relation. Results show there is some difference in the performance of the two selected MOEAs non-dominated sorting genetic algorithm II (NSGA II) and strength pareto evolutionary algorithm 2 (SPEA 2) within portfolio optimization. In addition, when used with four TA based strategies relative strength index (RSI), moving average convergence/divergence (MACD), contrarian bollinger bands (CBB) and bollinger bands (BB), the two selected MOEAs achieve solutions with interesting in-sample and out-of-sample outcomes for the BB strategy.

Some issues about the application of the analytic hierarchy process to R&D project selection

The analytic hierarchy process (AHP) has been used in the process of selecting research and development (R&D) projects. Such a selection process usually possesses some particular features that require adjustments in the application of the AHP method, such as the existence of a large number of very different alternatives or the integration of qualitative and quantitative criteria. In this paper, we discuss the application of AHP to the selection of a portfolio of R&D projects, and we propose some methods for handling the issues that arise from such an application.

A note on the use of bicriteria decision trees in capital budgeting

A new approach for the use of decision trees in capital budgeting is presented. It is argued that the use of criteria other than the financial value, particularly time, may often make sense in the assessment of investment projects, and an approach for the use of time and financial value in bicriteria decision trees is defined. The approach focuses on the identification of non-dominated strategies and uses a discrete-time option valuation model to aggregate the financial value. A methodology based on the elicitation of a few certainty equivalent times is used to aggregate time. An illustration example of application of this approach is also presented.

The Impact of Expectations, Match Importance, and Results in the Stock Prices of European Football Teams

We analyze the relation between stock returns and results in national league matches for 13 clubs of six European countries. We assume that the stock prices should only respond to the unexpected component of match results, and we use betting odds to separate the expected component of results from the unexpected one. We consider both the unweighted results and the results weighted by a new measure of match importance that we propose. When this measure is used, a significant relation between the results and stock performance is found for most teams.