The efficient market hypothesis through the eyes of an artificial technical analyst

The academic literature has been reluctant to accept technical analysis as a rational strategy of traders in financial markets. In practice traders and analysts heavily use technical analysis to make investment decisions. To resolve this incongruence the aim of this study is to translate technical analysis into a rigorous formal framework and to investigate its potential failure or success. To avoid subjectivism we design an Artificial Technical Analyst. The empirical study presents the evidence of past market inefficiencies observed on the Tokyo Stock Exchange. The market can be perceived as inefficient if the technical analyst's transaction costs are below the break-even level derived from technical analysis. (English

Explaining changes of property rights among Afar pastoralists, Ethiopia

"This study aims at explaining property right changes in selected areas of Afar region in Ethiopia. Based on primary and secondary data, explanations are given on the existing types of land use arrangements and how the traditional communal rights of pastoralists have been changing. Both communal rights and individualized rights exist the latter being introduced with the establishment of commercial farms. The state is identified as one driving force behind property right changes especially in one study site (Ambash), which is suitable for irrigated agriculture whereas its direct intervention is minimal in other sites. The coercive interventions started in 1960s have had detrimental impacts on the livelihoods of pastoral households. In addition to the state as a change agent, natural as well as socioeconomic challenges are important in explaining the current changes in land use arrangements." (author's abstract

Farm Management. Proceedings of NJF Seminar No. 345, 2-4 October 2002

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Solving Multi-objective Integer Programs using Convex Preference Cones

Esta encuesta tiene dos objetivos: en primer lugar, identificar a los individuos que fueron víctimas de algún tipo de delito y la manera en que ocurrió el mismo. En segundo lugar, medir la eficacia de las distintas autoridades competentes una vez que los individuos denunciaron el delito que sufrieron. Adicionalmente la ENVEI busca indagar las percepciones que los ciudadanos tienen sobre las instituciones de justicia y el estado de derecho en Méxic

Sensitivity Analysis for Hierarchical Decision Models

In this dissertation, a comprehensive algorithm is developed to analyze the sensitivity of hierarchical decision models (HDM), which include the well-known analytic hierarchy process (AHP) and its variants, to single and multiple changes in the local contribution matrices at any level of the decision hierarchy. The algorithm is applicable to all HDM that use an additive function to derive the overall contribution vector. It is independent of pairwise comparison scales, judgment quantification techniques and group opinion combining methods. The direct impact of changes to a local contribution value on decision alternatives\u27 overall contributions, allowable range/region of perturbations, contribution tolerance, operating point sensitivity coefficient, total sensitivity coefficient and the most critical decision element at a certain level are defined by five groups of theorems and corollaries and two groups of propositions in the HDM SA algorithm. Two examples are presented to demonstrate the applications of the HDM SA algorithm on technology evaluation and energy portfolio forecast. Significant insights gained by the two applications demonstrate the contributions of the algorithm. Theorems and corollaries in the HDM SA algorithm were verified and validated by data from the two application models

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Building Information Filtering Networks with Topological Constraints: Algorithms and Applications

We propose a new methodology for learning the structure of sparse networks from data; in doing so we adopt a dual perspective where we consider networks both as weighted graphs and as simplicial complexes. The proposed learning methodology belongs to the family of preferential attachment algorithms, where a network is extended by iteratively adding new vertices. In the conventional preferential attachment algorithm a new vertex is added to the network by adding a single edge to another existing vertex; in our approach a new vertex is added to a set of vertices by adding one or more new simplices to the simplicial complex. We propose the use of a score function to quantify the strength of the association between the new vertex and the attachment points. The methodology performs a greedy optimisation of the total score by selecting, at each step, the new vertex and the attachment points that maximise the gain in the score. Sparsity is enforced by restricting the space of the feasible configurations through the imposition of topological constraints on the candidate networks; the constraint is fulfilled by allowing only topological operations that are invariant with respect to the required property. For instance, if the topological constraint requires the constructed network to be be planar, then only planarity-invariant operations are allowed; if the constraint is that the network must be a clique forest, then only simplicial vertices can be added. At each step of the algorithm, the vertex to be added and the attachment points are those that provide the maximum increase in score while maintaining the topological constraints. As a concrete but general realisation we propose the clique forest as a possible topological structure for the representation of sparse networks, and we allow to specify further constraints such as the allowed range of clique sizes and the saturation of the attachment points. In this thesis we originally introduce the Maximally Filtered Clique Forest (MFCF) algorithm: the MFCF builds a clique forest by repeated application of a suitably invariant operation that we call Clique Expansion operator and adds vertices according to a strategy that greedily maximises the gain in a local score function. The gains produced by the Clique Expansion operator can be validated in a number of ways, including statistical testing, cross-validation or value thresholding. The algorithm does not prescribe a specific form for the gain function, but allows the use of any number of gain functions as long as they are consistent with the Clique Expansion operator. We describe several examples of gain functions suited to different problems. As a specific practical realisation we study the extraction of planar networks with the Triangulated Maximally Filtered Graph (TMFG). The TMFG, in its simplest form, is a specialised version of the MFCF, but it can be made more powerful by allowing the use of specialised planarity invariant operators that are not based on the Clique Expansion operator. We provide applications to two well known applied problems: the Maximum Weight Planar Subgraph Problem (MWPSP) and the Covariance Selection problem. With regards to the Covariance Selection problem we compare our results to the state of the art solution (the Graphical Lasso) and we highlight the benefits of our methodology. Finally, we study the geometry of clique trees as simplicial complexes and note how the statistics based on cliques and separators provides information equivalent to the one that can be achieved by means of homological methods, such as the analysis of Betti numbers, however with our approach being computationally more efficient and intuitively simpler. Finally, we use the geometric tools developed to provide a possible methodology for inferring the size of a dataset generated by a factor model. As an example we show that our tools provide a solution for inferring the size of a dataset generated by a factor model