The purpose of this thesis is to investigate if modifications to the Small-Minus-Big and High-Minus-Low factors in the Fama-French three-factor model (FF3) will improve the models explanatory power in the North Americ...
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The purpose of this thesis is to investigate if modifications to the Small-Minus-Big and High-Minus-Low factors in the Fama-French three-factor model (FF3) will improve the models explanatory power in the North American stock market for the period 2000-2015, utilizing data from Wharton Research Data Centre (WRDS). The modifications of the factors are carried out by a reconstruction of the six portfolios of stocks in the SMB (Small-Minus-Big) and HML (High-Minus-Low) factors, utilizing a combination of stock correlation networks and label propagation algorithms. The theory part of the thesis is split into two major topics, the rationale and methods behind the applied investment theory and the problems that lead to further development of the Fama French three factor model, and a semi-supervised learning approach to the reconstruction of the underlying six portfolios in the Fama French three factor model. The empirical tests show that the standard Fama French three factor model does not hold a high level of explanatory power, and the graph-based semi-supervised learning approach was only able to outperform the standard FF3 model, when basic statistical tests pruned out stocks not being coherent with the theoretical description of the FF3 model.
We propose a game-theoretic approach to generalizing the classical Schelling model. At the core of our model are two features that did not receive much attention before. First, we allow multiple individuals to occupy ...
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ISBN:
(纸本)9781450375184
We propose a game-theoretic approach to generalizing the classical Schelling model. At the core of our model are two features that did not receive much attention before. First, we allow multiple individuals to occupy the same location. Second, each individual's choice of location is influenced by their social network neighbors that also choose the same location. In addition, an individual's choice is influenced by others in the adjacent locations in a network-structured way, which captures the main spirit of the classical Schelling model and its numerous extensions. Our solution concept is a stable configuration represented as a pure-strategy Nash equilibrium (PSNE). We show that even for various special cases of the problem, computing or counting PSNE is provably hard. We give algorithms for computing PSNE, including efficient algorithms for several special cases. We highlight some of the attractive features of our model, such as predicting very few PSNE, through experiments.
This paper proposes a model for estimating probabilities in the presence of abrupt concept drift. This proposal is based on a dynamic Bayesian network. As the exact estimation of the parameters is unfeasible we propos...
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This paper proposes a model for estimating probabilities in the presence of abrupt concept drift. This proposal is based on a dynamic Bayesian network. As the exact estimation of the parameters is unfeasible we propose an approximate procedure based on discretizing both the possible probability values and the parameter representing the probability of change. The result is a method which is quite efficient in time and space (with a complexity directly related to the number of points used in the discretization) and providing very accurate predictions as well. These benefits are checked with a detailed comparison with other standard procedures based on variable size windows or forgetting rates. (C) 2019 Elsevier B.V. All rights reserved.
Constraint Programming (CP) is a proven set of techniques for solving complex combinatorial problems from a range of disciplines. The problem is specified as a set of decision variables (with finite domains) and const...
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Constraint Programming (CP) is a proven set of techniques for solving complex combinatorial problems from a range of disciplines. The problem is specified as a set of decision variables (with finite domains) and constraints linking the variables. Local reasoning (propagation) on the constraints is central to CP. Many constraints have efficient constraint-specific propagation algorithms. In this work, we generate custom propagators for constraints. These custom propagators can be very efficient, even approaching (and in some cases exceeding) the efficiency of hand-optimised propagators. Given an arbitrary constraint, we show how to generate a custom propagator that establishes GAC in small polynomial time. This is done by precomputing the propagation that would be performed on every relevant subdomain. The number of relevant subdomains, and therefore the size of the generated propagator, is potentially exponential in the number and domain size of the constrained variables. The limiting factor of our approach is the size of the generated propagators. We investigate symmetry as a means of reducing that size. We exploit the symmetries of the constraint to merge symmetric parts of the generated propagator. This extends the reach of our approach to somewhat larger constraints, with a small run-time penalty. Our experimental results show that, compared with optimised implementations of the table constraint, our techniques can lead to an order of magnitude speedup. propagation is so fast that the generated propagators compare well with hand-written carefully optimised propagators for the same constraints, and the time taken to generate a propagator is more than repaid. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license
Bow tie diagram has become a popular method to implement safety barriers. It defines several preventive and protective barriers to reduce respectively the frequency and severity of a given risk. These barriers are oft...
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Bow tie diagram has become a popular method to implement safety barriers. It defines several preventive and protective barriers to reduce respectively the frequency and severity of a given risk. These barriers are often defined by experts that ignore the real aspect of the system. However, the definition of barriers based on experts experiences limits this method because it seems unrealistic to use static recommendations in real dynamic systems. This paper proposes a new multi-objectives approach to implement preventive and protective barriers. The proposed approach is mainly based on three phases namely;a parameters learning phase, a simulation phase and a selection phase. (C) 2014 Elsevier Ltd. All rights reserved.
The Extended Global Cardinality Constraint (EGCC) is a vital component of constraint solving systems, since it is very widely used to model diverse problems. The literature contains many different versions of this con...
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The Extended Global Cardinality Constraint (EGCC) is a vital component of constraint solving systems, since it is very widely used to model diverse problems. The literature contains many different versions of this constraint, which trade strength of inference against computational cost. In this paper, I focus on the highest strength of inference usually considered, enforcing generalized arc consistency (GAC) on the target variables. This work is an extensive empirical survey of algorithms and optimizations, considering both GAC on the target variables, and tightening the bounds of the cardinality variables. I evaluate a number of key techniques from the literature, and report important implementation details of those techniques, which have often not been described in published papers. Two new optimizations are proposed for EGCC. One of the novel optimizations (dynamic partitioning, generalized from AllDifferent) was found to speed up search by 5.6 times in the best case and 1.56 times on average, while exploring the same search tree. The empirical work represents by far the most extensive set of experiments on variants of algorithms for EGCC. Overall, the best combination of optimizations gives a mean speedup of 4.11 times compared to the same implementation without the optimizations. (C) 2010 Elsevier B.V. All rights reserved.
The phase-induced amplitude apodization (PIAA) coronagraph utilizes highly aspheric optics to produce a strongly apodized beam without the large loss of light that would result from using a graded transmission mask. T...
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ISBN:
(纸本)9780819482211
The phase-induced amplitude apodization (PIAA) coronagraph utilizes highly aspheric optics to produce a strongly apodized beam without the large loss of light that would result from using a graded transmission mask. The rapid variations in surface curvature at the edge of the PIAA apodizing optic creates large wavefront phase changes that cannot be adequately represented in conventional Fourier-based diffraction propagation algorithms. A rapid technique is required for propagating arbitrarily-aberrated wavefronts through the system. An alternative numerical method has been proposed that combines a high-accuracy algorithm to compute edge diffraction effects with a quick modified angular spectrum propagator that handles wavefront errors. We present the results of applying this method to realistically aberrated wavefronts as compared to more complex and time consuming techniques.
Combining constraints using logical connectives such as disjunction is ubiquitous in constraint programming, because it adds considerable expressive power to a constraint language. We explore the solver architecture n...
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Combining constraints using logical connectives such as disjunction is ubiquitous in constraint programming, because it adds considerable expressive power to a constraint language. We explore the solver architecture needed to propagate such combinations of constraints efficiently. In particular we describe two new features named satisfying sets and constraint trees. We also make use of movable triggers (Gent et al., 2006) [1], and with these three complementary features we are able to make considerable efficiency gains. A key reason for the success of Boolean Satisfiability (SAT) solvers is their ability to propagate OR constraints efficiently, making use of movable triggers. We successfully generalise this approach to an OR of an arbitrary set of constraints, maintaining the crucial property that at most two constraints are active at any time, and no computation at all is done on the others. We also give an AND propagator within our framework, which may be embedded within the OR. Using this approach, we demonstrate speedups of over 10,000 times in some cases, compared to traditional constraint programming approaches. We also prove that the OR algorithm enforces generalised arc consistency (GAC) when all its child constraints have a GAC propagator, and no variables are shared between children. By extending the OR propagator, we present a propagator for ATLEASTK, which expresses that at least k of its child constraints are satisfied in any solution. Some logical expressions (e.g. exclusive-or) cannot be compactly expressed using AND, OR and ATLEASTK. Therefore we investigate reification of constraints. We present a fast generic algorithm for reification using satisfying sets and movable triggers. (C) 2010 Published by Elsevier B.V.
Constraint programming (CP) has been used with great success to tackle a wide variety of constraint satisfaction problems which are computationally intractable in general. Global constraints are one of the important f...
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Constraint programming (CP) has been used with great success to tackle a wide variety of constraint satisfaction problems which are computationally intractable in general. Global constraints are one of the important factors behind the success of CP. In this paper, we study a new global constraint, the multiset ordering constraint, which is shown to be useful in symmetry breaking and searching for leximin optimal solutions in CP. We propose efficient and effective filtering algorithms for propagating this global constraint. We show that the algorithms maintain generalised arc-consistency and we discuss possible extensions. We also consider alternative propagation methods based on existing constraints in CP toolkits. Our experimental results on a number of benchmark problems demonstrate that propagating the multiset ordering constraint via a dedicated algorithm can be very beneficial. (C) 2008 Elsevier B.V. All rights reserved.
We consider approximate inference in hybrid Bayesian Networks (BNs) and present a new iterative algorithm that efficiently combines dynamic discretization with robust propagation algorithms on junction trees. Our appr...
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We consider approximate inference in hybrid Bayesian Networks (BNs) and present a new iterative algorithm that efficiently combines dynamic discretization with robust propagation algorithms on junction trees. Our approach offers a significant extension to Bayesian Network theory and practice by offering a flexible way of modeling continuous nodes in BNs conditioned on complex configurations of evidence and intermixed with discrete nodes as both parents and children of continuous nodes. Our algorithm is implemented in a commercial Bayesian Network software package, AgenaRisk, which allows model construction and testing to be carried out easily. The results from the empirical trials clearly show how our software can deal effectively with different type of hybrid models containing elements of expert judgment as well as statistical inference. In particular, we show how the rapid convergence of the algorithm towards zones of high probability density, make robust inference analysis possible even in situations where, due to the lack of information in both prior and data, robust sampling becomes unfeasible.
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