Análise da sobre-reação e sub-reação nos ativos de primeira e segunda lina do mercado acionário brasileiro

The Behavioral Finance develop as it is perceived anomalies in these markets efficient. This fields of study can be grouped into three major groups: heuristic bias, tying the shape and inefficient markets. The present study focuses on issues concerning the heuristics of representativeness and anchoring. This study aimed to identify the then under-reaction and over-reaction, as well as the existence of symmetry in the active first and second line of the Brazilian stock market. For this, it will be use the Fuzzy Logic and the indicators that classify groups studied from the Discriminant Analysis. The highest present, indicator in the period studied, was the Liabilities / Equity, demonstrating the importance of the moment to discriminate the assets to be considered "winners" and "losers." Note that in the MLCX biases over-reaction is concentrated in the period of financial crisis, and in the remaining periods of statistically significant biases, are obtained by sub-reactions. The latter would be in times of moderate levels of uncertainty. In the Small Caps the behavioral responses in 2005 and 2007 occur in reverse to those observed in the Mid-Large Cap. Now in times of crisis would have a marked conservatism while near the end of trading on the Bovespa speaker, accompanied by an increase of negotiations, there is an overreaction by investors. The other heuristics in SMLL occurred at the end of the period studied, this being a under-reaction and the other a over-reaction and the second occurring in a period of financial-economic more positive than the first. As regards the under / over-reactivity in both types, there is detected a predominance of either, which probably be different in the context in MLCX without crisis. For the period in which such phenomena occur in a statistically significant to note that, in most cases, such phenomena occur during the periods for MLCX while in SMLL not only biases are less present as there is no concentration of these at any time . Given the above, it is believed that while detecting the presence of bias behavior at certain times, these do not tend to appear to a specific type or heuristics and while there were some indications of a seasonal pattern in Mid- Large Caps, the same behavior does not seem to be repeated in Small Caps. The tests would then suggest that momentary failures in the Efficient Market Hypothesis when tested in semistrong form as stated by Behavioral Finance. This result confirms the theory by stating that not only rationality, but also human irrationality, is limited because it would act rationally in many circumstances

Avaliação da usabilidade de Websites de hotéis segundo as heurísticas de Nielsen: pesquisa exploratória em hotéis da Região Rordeste do Brasil

This Thesis deals with the usability in some sites of hotels placed in a specific region of Brazil. Usability is the property of a site that shows how it allows the user get all information available without a large effort and in a clear and objective way. A questionnaire was used as a tool to value, by the common user perspective, navigation, on line reservation simulation, appreciation of the available content and, by this way, an easiness in the interaction site-users. As a result, it is possible to say that, considering all material analyzed, those sites that present usability criterion are more interactive and dynamic, demonstrating an easy navigation in a users view. By other side, it was possible to note that, about heuristics used in the search, the most of the websites demonstrated a deficiency in questions as firmness, user control and how to avoid mistakes. There was still valued as deficiency by the most of users in websites the general characters and the view of user. It was presented, seeing general characters, that a big part of the websites needs tools that can make user rapidly find what looks for. It was yet possible to see that users don't believe in secret when websites ask them some information. This is a point that needs to be appreciated because it causes some insecurity during navigation

Heurísticas usando construção de vocabuilário aplicadas ao problema da atribuição de localidades a anéis em redes SONET/SDH

The SONET/SDH Ring Assignment Problem (PALAS) treats to group localities in form of some rings, being respected the traffic's limitations of the equipment. Each ring uses a DXC (Digital Cross Connect) to make the communication with the others, being the DXC the equipment most expensive of the net, minimizing the number total of rings, will minimize the total net cost, problem's objective . This topology in rings provides a bigger capacity of regeneration. The PALAS is a problem in Combinatorial Optimization of NP-hard Class. It can be solved through Heuristics and Metaheuristics. In this text, we use Taboo Search while we keep a set of elite solutions to be used in the formation of a part of the collection of vocabulary's parts that in turn will be used in the Vocabulary Building. The Vocabulary Building will be started case Taboo Search does not reach the best solution for the instance. Three approaches had been implemented: one that only uses vocabulary's parts deriving of Taboo Search, one that it only uses vocabulary's parts randomly generated and a last one that it uses half come of the elite and half randomly generated

Um algoritmo híbrido para o problema de roteamento de veículos com frotas heterogêneas

This paper aims to propose a hybrid meta-heuristics for the Heterogeneous Fleet Vehicle Routing Problem (HVRP), which is a combinatorial optimization problem NP-hard, and is characterized by the use of a limited fleet consists of different vehicles with different capacities. The hybrid method developed makes use of a memetic algorithm associated with the component optimizer Vocabulary Building. The resulting hybrid meta-heuristic was implemented in the programming language C + + and computational experiments generated good results in relation to meta-heuristic applied in isolation, proving the efficiency of the proposed method.

Contribuições da pesquisa operacional para gestão da produção e operações : uma análise exploratória da literatura

This research aims to investigate the evolution presented during three decades (1980, 1990 and 2000) of using the tools of Operations Research (OR) as a suport to decision making in Production Operation Management (POM). Hypothesis tests were made to verify the proportional growth of a given area over the decades to the detriment of the areas of facility layout, capacity planning, production scheduling and inventory management. Six journals were selected and from them more than 800 articles were used for classification and analysis in the grounds of review. It also discussed possible ways for future research and comparisons are made with other papers of literature review. As a result, it was found that areas of heuristics and simulation showed a greater quantity of contributions in all POM areas of this study

Desenvolvimento de um modelo computacional para a ampliação do atendimento do Programa de Acessibilidade Especial Porta a Porta - PRAE

Worldwide, the demand for transportation services for persons with disabilities, the elderly, and persons with reduced mobility have increased in recent years. The population is aging, governments need to adapt to this reality, and this fact could mean business opportunities for companies. Within this context is inserted the Programa de Acessibilidade Especial porta a porta PRAE, a door to door public transportation service from the city of Natal-RN in Brazil. The research presented in this dissertation seeks to develop a programming model which can assist the process of decision making of managers of the shuttle. To that end, it was created an algorithm based on methods of generating approximate solutions known as heuristics. The purpose of the model is to increase the number of people served by the PRAE, given the available fleet, generating optimized schedules routes. The PRAE is a problem of vehicle routing and scheduling of dial-a-ride - DARP, the most complex type among the routing problems. The validation of the method of resolution was made by comparing the results derived by the model and the currently programming method. It is expected that the model is able to increase the current capacity of the service requests of transport

Metodologia estatística na solução do problema do caixeiro viajante e na avaliação de algoritmos : um estudo aplicado à transgenética computacional

The problems of combinatory optimization have involved a large number of researchers in search of approximative solutions for them, since it is generally accepted that they are unsolvable in polynomial time. Initially, these solutions were focused on heuristics. Currently, metaheuristics are used more for this task, especially those based on evolutionary algorithms. The two main contributions of this work are: the creation of what is called an -Operon- heuristic, for the construction of the information chains necessary for the implementation of transgenetic (evolutionary) algorithms, mainly using statistical methodology - the Cluster Analysis and the Principal Component Analysis; and the utilization of statistical analyses that are adequate for the evaluation of the performance of the algorithms that are developed to solve these problems. The aim of the Operon is to construct good quality dynamic information chains to promote an -intelligent- search in the space of solutions. The Traveling Salesman Problem (TSP) is intended for applications based on a transgenetic algorithmic known as ProtoG. A strategy is also proposed for the renovation of part of the chromosome population indicated by adopting a minimum limit in the coefficient of variation of the adequation function of the individuals, with calculations based on the population. Statistical methodology is used for the evaluation of the performance of four algorithms, as follows: the proposed ProtoG, two memetic algorithms and a Simulated Annealing algorithm. Three performance analyses of these algorithms are proposed. The first is accomplished through the Logistic Regression, based on the probability of finding an optimal solution for a TSP instance by the algorithm being tested. The second is accomplished through Survival Analysis, based on a probability of the time observed for its execution until an optimal solution is achieved. The third is accomplished by means of a non-parametric Analysis of Variance, considering the Percent Error of the Solution (PES) obtained by the percentage in which the solution found exceeds the best solution available in the literature. Six experiments have been conducted applied to sixty-one instances of Euclidean TSP with sizes of up to 1,655 cities. The first two experiments deal with the adjustments of four parameters used in the ProtoG algorithm in an attempt to improve its performance. The last four have been undertaken to evaluate the performance of the ProtoG in comparison to the three algorithms adopted. For these sixty-one instances, it has been concluded on the grounds of statistical tests that there is evidence that the ProtoG performs better than these three algorithms in fifty instances. In addition, for the thirty-six instances considered in the last three trials in which the performance of the algorithms was evaluated through PES, it was observed that the PES average obtained with the ProtoG was less than 1% in almost half of these instances, having reached the greatest average for one instance of 1,173 cities, with an PES average equal to 3.52%. Therefore, the ProtoG can be considered a competitive algorithm for solving the TSP, since it is not rare in the literature find PESs averages greater than 10% to be reported for instances of this size.

Análise e síntese de antenas e superfícies seletivas de frequência utilizando computação evolucionária e inteligência de enxames

The frequency selective surfaces, or FSS (Frequency Selective Surfaces), are structures consisting of periodic arrays of conductive elements, called patches, which are usually very thin and they are printed on dielectric layers, or by openings perforated on very thin metallic surfaces, for applications in bands of microwave and millimeter waves. These structures are often used in aircraft, missiles, satellites, radomes, antennae reflector, high gain antennas and microwave ovens, for example. The use of these structures has as main objective filter frequency bands that can be broadcast or rejection, depending on the specificity of the required application. In turn, the modern communication systems such as GSM (Global System for Mobile Communications), RFID (Radio Frequency Identification), Bluetooth, Wi-Fi and WiMAX, whose services are highly demanded by society, have required the development of antennas having, as its main features, and low cost profile, and reduced dimensions and weight. In this context, the microstrip antenna is presented as an excellent choice for communications systems today, because (in addition to meeting the requirements mentioned intrinsically) planar structures are easy to manufacture and integration with other components in microwave circuits. Consequently, the analysis and synthesis of these devices mainly, due to the high possibility of shapes, size and frequency of its elements has been carried out by full-wave models, such as the finite element method, the method of moments and finite difference time domain. However, these methods require an accurate despite great computational effort. In this context, computational intelligence (CI) has been used successfully in the design and optimization of microwave planar structures, as an auxiliary tool and very appropriate, given the complexity of the geometry of the antennas and the FSS considered. The computational intelligence is inspired by natural phenomena such as learning, perception and decision, using techniques such as artificial neural networks, fuzzy logic, fractal geometry and evolutionary computation. This work makes a study of application of computational intelligence using meta-heuristics such as genetic algorithms and swarm intelligence optimization of antennas and frequency selective surfaces. Genetic algorithms are computational search methods based on the theory of natural selection proposed by Darwin and genetics used to solve complex problems, eg, problems where the search space grows with the size of the problem. The particle swarm optimization characteristics including the use of intelligence collectively being applied to optimization problems in many areas of research. The main objective of this work is the use of computational intelligence, the analysis and synthesis of antennas and FSS. We considered the structures of a microstrip planar monopole, ring type, and a cross-dipole FSS. We developed algorithms and optimization results obtained for optimized geometries of antennas and FSS considered. To validate results were designed, constructed and measured several prototypes. The measured results showed excellent agreement with the simulated. Moreover, the results obtained in this study were compared to those simulated using a commercial software has been also observed an excellent agreement. Specifically, the efficiency of techniques used were CI evidenced by simulated and measured, aiming at optimizing the bandwidth of an antenna for wideband operation or UWB (Ultra Wideband), using a genetic algorithm and optimizing the bandwidth, by specifying the length of the air gap between two frequency selective surfaces, using an optimization algorithm particle swarm

Contribuições aos Processos de Clustering com Base em Métricas não-Euclidianas

In this work we present a new clustering method that groups up points of a data set in classes. The method is based in a algorithm to link auxiliary clusters that are obtained using traditional vector quantization techniques. It is described some approaches during the development of the work that are based in measures of distances or dissimilarities (divergence) between the auxiliary clusters. This new method uses only two a priori information, the number of auxiliary clusters Na and a threshold distance dt that will be used to decide about the linkage or not of the auxiliary clusters. The number os classes could be automatically found by the method, that do it based in the chosen threshold distance dt, or it is given as additional information to help in the choice of the correct threshold. Some analysis are made and the results are compared with traditional clustering methods. In this work different dissimilarities metrics are analyzed and a new one is proposed based on the concept of negentropy. Besides grouping points of a set in classes, it is proposed a method to statistical modeling the classes aiming to obtain a expression to the probability of a point to belong to one of the classes. Experiments with several values of Na e dt are made in tests sets and the results are analyzed aiming to study the robustness of the method and to consider heuristics to the choice of the correct threshold. During this work it is explored the aspects of information theory applied to the calculation of the divergences. It will be explored specifically the different measures of information and divergence using the Rényi entropy. The results using the different metrics are compared and commented. The work also has appendix where are exposed real applications using the proposed method

Localização de um robô móvel usando odometria e marcos naturais

Several methods of mobile robot navigation request the mensuration of robot position and orientation in its workspace. In the wheeled mobile robot case, techniques based on odometry allow to determine the robot localization by the integration of incremental displacements of its wheels. However, this technique is subject to errors that accumulate with the distance traveled by the robot, making unfeasible its exclusive use. Other methods are based on the detection of natural or artificial landmarks present in the environment and whose location is known. This technique doesnt generate cumulative errors, but it can request a larger processing time than the methods based on odometry. Thus, many methods make use of both techniques, in such a way that the odometry errors are periodically corrected through mensurations obtained from landmarks. Accordding to this approach, this work proposes a hybrid localization system for wheeled mobile robots in indoor environments based on odometry and natural landmarks. The landmarks are straight lines de.ned by the junctions in environments floor, forming a bi-dimensional grid. The landmark detection from digital images is perfomed through the Hough transform. Heuristics are associated with that transform to allow its application in real time. To reduce the search time of landmarks, we propose to map odometry errors in an area of the captured image that possesses high probability of containing the sought mark

Escalabilidade Paralela de um Algoritmo de Migração Reversa no Tempo (RTM) Pré-empilhamento

The seismic method is of extreme importance in geophysics. Mainly associated with oil exploration, this line of research focuses most of all investment in this area. The acquisition, processing and interpretation of seismic data are the parts that instantiate a seismic study. Seismic processing in particular is focused on the imaging that represents the geological structures in subsurface. Seismic processing has evolved significantly in recent decades due to the demands of the oil industry, and also due to the technological advances of hardware that achieved higher storage and digital information processing capabilities, which enabled the development of more sophisticated processing algorithms such as the ones that use of parallel architectures. One of the most important steps in seismic processing is imaging. Migration of seismic data is one of the techniques used for imaging, with the goal of obtaining a seismic section image that represents the geological structures the most accurately and faithfully as possible. The result of migration is a 2D or 3D image which it is possible to identify faults and salt domes among other structures of interest, such as potential hydrocarbon reservoirs. However, a migration fulfilled with quality and accuracy may be a long time consuming process, due to the mathematical algorithm heuristics and the extensive amount of data inputs and outputs involved in this process, which may take days, weeks and even months of uninterrupted execution on the supercomputers, representing large computational and financial costs, that could derail the implementation of these methods. Aiming at performance improvement, this work conducted the core parallelization of a Reverse Time Migration (RTM) algorithm, using the parallel programming model Open Multi-Processing (OpenMP), due to the large computational effort required by this migration technique. Furthermore, analyzes such as speedup, efficiency were performed, and ultimately, the identification of the algorithmic scalability degree with respect to the technological advancement expected by future processors

Análise de escalabilidade de uma implementação paralela do simulated annealing acoplado

This paper analyzes the performance of a parallel implementation of Coupled Simulated Annealing (CSA) for the unconstrained optimization of continuous variables problems. Parallel processing is an efficient form of information processing with emphasis on exploration of simultaneous events in the execution of software. It arises primarily due to high computational performance demands, and the difficulty in increasing the speed of a single processing core. Despite multicore processors being easily found nowadays, several algorithms are not yet suitable for running on parallel architectures. The algorithm is characterized by a group of Simulated Annealing (SA) optimizers working together on refining the solution. Each SA optimizer runs on a single thread executed by different processors. In the analysis of parallel performance and scalability, these metrics were investigated: the execution time; the speedup of the algorithm with respect to increasing the number of processors; and the efficient use of processing elements with respect to the increasing size of the treated problem. Furthermore, the quality of the final solution was verified. For the study, this paper proposes a parallel version of CSA and its equivalent serial version. Both algorithms were analysed on 14 benchmark functions. For each of these functions, the CSA is evaluated using 2-24 optimizers. The results obtained are shown and discussed observing the analysis of the metrics. The conclusions of the paper characterize the CSA as a good parallel algorithm, both in the quality of the solutions and the parallel scalability and parallel efficiency

Estudo Comparativo de Métricas de Pontuação para Aprendizagem Estrutural de Redes Bayesianas

Bayesian networks are powerful tools as they represent probability distributions as graphs. They work with uncertainties of real systems. Since last decade there is a special interest in learning network structures from data. However learning the best network structure is a NP-Hard problem, so many heuristics algorithms to generate network structures from data were created. Many of these algorithms use score metrics to generate the network model. This thesis compare three of most used score metrics. The K-2 algorithm and two pattern benchmarks, ASIA and ALARM, were used to carry out the comparison. Results show that score metrics with hyperparameters that strength the tendency to select simpler network structures are better than score metrics with weaker tendency to select simpler network structures for both metrics (Heckerman-Geiger and modified MDL). Heckerman-Geiger Bayesian score metric works better than MDL with large datasets and MDL works better than Heckerman-Geiger with small datasets. The modified MDL gives similar results to Heckerman-Geiger for large datasets and close results to MDL for small datasets with stronger tendency to select simpler network structures

O problema biobjetivo da árvore geradora quadrática em adjacência de arestas

The Quadratic Minimum Spanning Tree Problem (QMST) is a version of the Minimum Spanning Tree Problem in which, besides the traditional linear costs, there is a quadratic structure of costs. This quadratic structure models interaction effects between pairs of edges. Linear and quadratic costs are added up to constitute the total cost of the spanning tree, which must be minimized. When these interactions are restricted to adjacent edges, the problem is named Adjacent Only Quadratic Minimum Spanning Tree (AQMST). AQMST and QMST are NP-hard problems that model several problems of transport and distribution networks design. In general, AQMST arises as a more suitable model for real problems. Although, in literature, linear and quadratic costs are added, in real applications, they may be conflicting. In this case, it may be interesting to consider these costs separately. In this sense, Multiobjective Optimization provides a more realistic model for QMST and AQMST. A review of the state-of-the-art, so far, was not able to find papers regarding these problems under a biobjective point of view. Thus, the objective of this Thesis is the development of exact and heuristic algorithms for the Biobjective Adjacent Only Quadratic Spanning Tree Problem (bi-AQST). In order to do so, as theoretical foundation, other NP-hard problems directly related to bi-AQST are discussed: the QMST and AQMST problems. Bracktracking and branch-and-bound exact algorithms are proposed to the target problem of this investigation. The heuristic algorithms developed are: Pareto Local Search, Tabu Search with ejection chain, Transgenetic Algorithm, NSGA-II and a hybridization of the two last-mentioned proposals called NSTA. The proposed algorithms are compared to each other through performance analysis regarding computational experiments with instances adapted from the QMST literature. With regard to exact algorithms, the analysis considers, in particular, the execution time. In case of the heuristic algorithms, besides execution time, the quality of the generated approximation sets is evaluated. Quality indicators are used to assess such information. Appropriate statistical tools are used to measure the performance of exact and heuristic algorithms. Considering the set of instances adopted as well as the criteria of execution time and quality of the generated approximation set, the experiments showed that the Tabu Search with ejection chain approach obtained the best results and the transgenetic algorithm ranked second. The PLS algorithm obtained good quality solutions, but at a very high computational time compared to the other (meta)heuristics, getting the third place. NSTA and NSGA-II algorithms got the last positions

Uma análise experimental de abordagens heurísticas aplicadas ao problema do caixeiro viajante

Due to great difficulty of accurate solution of Combinatorial Optimization Problems, some heuristic methods have been developed and during many years, the analysis of performance of these approaches was not carried through in a systematic way. The proposal of this work is to make a statistical analysis of heuristic approaches to the Traveling Salesman Problem (TSP). The focus of the analysis is to evaluate the performance of each approach in relation to the necessary computational time until the attainment of the optimal solution for one determined instance of the TSP. Survival Analysis, assisted by methods for the hypothesis test of the equality between survival functions was used. The evaluated approaches were divided in three classes: Lin-Kernighan Algorithms, Evolutionary Algorithms and Particle Swarm Optimization. Beyond those approaches, it was enclosed in the analysis, a memetic algorithm (for symmetric and asymmetric TSP instances) that utilizes the Lin-Kernighan heuristics as its local search procedure