Performance Evaluation with Stochastic Discount Factors

We study the use of stochastic discount factor (SDF) models in evaluating the investment performance of portfolio managers. By constructing artificial mutual funds with known levels of investment ability, we evaluate a large set of SDF models. We find that the measures of performance are not highly sensitive to the SDF model, and that most of the models have a mild negative bias when performance is neutral. We use the models to evaluate a sample of U.S. equity mutual funds. Adjusting for the observed bias, we find that the average mutual fund has enough ability to cover its transactions costs. Extreme funds are more likely to have good rather than poor risk adjusted performance. Our analysis also reveals a number of implementation issues relevant to other applications of SDF models.

Geometric phase and quantum phase transition in an inhomogeneous periodic XY spin-1/2 model

The notion of geometric phase has been recently introduced to analyze the quantum phase transitions of many-body systems from the geometrical perspective. In this work, we study the geometric phase of the ground state for an inhomogeneous period-two anisotropic XY model in a transverse field. This model encompasses a group of familiar spin models as its special cases and shows a richer critical behavior. The exact solution is obtained by mapping on a fermionic system through the Jordan-Wigner transformation and constructing the relevant canonical transformation to realize the diagonalization of the Hamiltonian coupled in the $k$-space. The results show that there may exist more than one quantum phase transition point at some parameter regions and these transition points correspond to the divergence or extremum properties of the Berry curvature.Comment: 6 pages, 3 figures. As a backup of a previous work and some typos in the published version are fixe

Post-Newtonian SPH calculations of binary neutron star coalescence. I. Method and first results

We present the first results from our Post-Newtonian (PN) Smoothed Particle Hydrodynamics (SPH) code, which has been used to study the coalescence of binary neutron star (NS) systems. The Lagrangian particle-based code incorporates consistently all lowest-order (1PN) relativistic effects, as well as gravitational radiation reaction, the lowest-order dissipative term in general relativity. We test our code on sequences of single NS models of varying compactness, and we discuss ways to make PN simulations more relevant to realistic NS models. We also present a PN SPH relaxation procedure for constructing equilibrium models of synchronized binaries, and we use these equilibrium models as initial conditions for our dynamical calculations of binary coalescence. Though unphysical, since tidal synchronization is not expected in NS binaries, these initial conditions allow us to compare our PN work with previous Newtonian results. We compare calculations with and without 1PN effects, for NS with stiff equations of state, modeled as polytropes with $\Gamma=3$. We find that 1PN effects can play a major role in the coalescence, accelerating the final inspiral and causing a significant misalignment in the binary just prior to final merging. In addition, the character of the gravitational wave signal is altered dramatically, showing strong modulation of the exponentially decaying waveform near the end of the merger. We also discuss briefly the implications of our results for models of gamma-ray bursts at cosmological distances.Comment: RevTeX, 37 pages, 17 figures, to appear in Phys. Rev. D, minor corrections onl

Multi-layer local optima networks for the analysis of advanced local search-based algorithms

A Local Optima Network (LON) is a graph model that compresses the fitness landscape of a particular combinatorial optimization problem based on a specific neighborhood operator and a local search algorithm. Determining which and how landscape features affect the effectiveness of search algorithms is relevant for both predicting their performance and improving the design process. This paper proposes the concept of multi-layer LONs as well as a methodology to explore these models aiming at extracting metrics for fitness landscape analysis. Constructing such models, extracting and analyzing their metrics are the preliminary steps into the direction of extending the study on single neighborhood operator heuristics to more sophisticated ones that use multiple operators. Therefore, in the present paper we investigate a twolayer LON obtained from instances of a combinatorial problem using bitflip and swap operators. First, we enumerate instances of NK-landscape model and use the hill climbing heuristic to build the corresponding LONs. Then, using LON metrics, we analyze how efficiently the search might be when combining both strategies. The experiments show promising results and demonstrate the ability of multi-layer LONs to provide useful information that could be used for in metaheuristics based on multiple operators such as Variable Neighborhood Search.Comment: Accepted in GECCO202

Leading Two-loop corrections to the mass of Higgs boson in the High scale Dirac gaugino supersymmetry

Precision measurements of the Higgs mass have become a powerful constraint on models of physics beyond the standard model. We revisit supersymmetric models with Dirac gauginos and study the contributions to the Higgs mass. We calculate the leading two-loop corrections to the SM-like Higgs mass by constructing a series of EFTs and iteratively integrating out heavy particles. We then apply these calculations to a variety of scenarios, including a simple Dirac gluino, and split Dirac models of supersymmetry. We present the detailed formulae for threshold corrections and compare with previous results, where available. In general, the contributions are small, but the additional precision allows us to make more concrete statements about the relevant scales in Dirac SUSY models.Comment: 27 pages, 9 figure

Continuous and Discrete (Classical) Heisenberg Spin Chain Revised

Most of the work done in the past on the integrability structure of the Classical Heisenberg Spin Chain (CHSC) has been devoted to studying the $su(2)$ case, both at the continuous and at the discrete level. In this paper we address the problem of constructing integrable generalized ''Spin Chains'' models, where the relevant field variable is represented by a $N\times N$ matrix whose eigenvalues are the $N^{th}$ roots of unity. To the best of our knowledge, such an extension has never been systematically pursued. In this paper, at first we obtain the continuous $N\times N$ generalization of the CHSC through the reduction technique for Poisson-Nijenhuis manifolds, and exhibit some explicit, and hopefully interesting, examples for $3\times 3$ and $4\times 4$ matrices; then, we discuss the much more difficult discrete case, where a few partial new results are derived and a conjecture is made for the general case.Comment: This is a contribution to the Proc. of workshop on Geometric Aspects of Integrable Systems (July 17-19, 2006; Coimbra, Portugal), published in SIGMA (Symmetry, Integrability and Geometry: Methods and Applications) at http://www.emis.de/journals/SIGMA

A Questioning Agent for Literary Discussion

Developing a compelling and cohesive thesis for analytical writing can be a daunting task, even for those who have produced many written works, and finding others to engage with in literary discussion can be equally challenging. In this paper, we describe our solution: Questioner, a discussion tool that engages users in conversation about an academic topic of their choosing for the purpose of collecting thoughts on a subject and constructing an argument. This system will ask informed questions that prompt further discussion about the topic and provide a discussion report after the conversation has ended. We found that our system is effective in providing users with unique questions and excerpts that are relevant, significant, and engaging. Such a discussion tool can be used by writers building theses, students looking for study tools, and instructors who want to create individualized in-class discussions. Once more data is gathered, efficient and accurate machine learning models can be used to further improve the quality of question and excerpt recommendations. Co-creative discussion tools like Questioner are useful in assisting users in developing critical analyses of written works, helping to maximize human creativity

Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis

Background. In Traditional Chinese Medicine (TCM), most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning. However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs). Methods. We employed a multilabel learning using the relevant feature for each label (REAL) algorithm to construct a syndrome diagnostic model for chronic gastritis (CG) in TCM. REAL combines feature selection methods to select the significant symptoms (signs) of CG. The method was tested on 919 patients using the standard scale. Results. The highest prediction accuracy was achieved when 20 features were selected. The features selected with the information gain were more consistent with the TCM theory. The lowest average accuracy was 54% using multi-label neural networks (BP-MLL), whereas the highest was 82% using REAL for constructing the diagnostic model. For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively. Conclusion. REAL extracts the relevant symptoms (signs) for each syndrome and improves its recognition accuracy. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice