1,804 research outputs found

    Behavioral investment strategy matters: a statistical arbitrage approach

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    In this study, we employ a statistical arbitrage approach to demonstrate that momentum investment strategy tend to work better in periods longer than six months, a result different from findings in past literature. Compared with standard parametric tests, the statistical arbitrage method produces more clearly that momentum strategies work only in longer formation and holding periods. Also they yield positive significant returns in an up market, but negative yet insignificant returns in a down market. Disposition and over-confidence effects are important factors contributing to the phenomenon. The over-confidence effect seems to dominate the disposition effect, especially in an up market. Moreover, the over-confidence investment behavior of institutional investors is the main cause for significant momentum returns observed in an up market. In a down market, the institutional investors tend to adopt a contrarian strategy while the individuals are still maintaining momentum behavior within shorter periods. The behavior difference between investor groups explains in part why momentum strategies work differently between up and down market states. Robustness tests confirm that the momentum returns do not come from firm size, overlapping execution periods, market states definition or market frictions

    High order quantum decoherence via multi-particle amplitude for boson system

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    In this paper we depict the high order quantum coherence of a boson system by using the multi-particle wave amplitude, whose norm square is just the high order correlation function. This multi-time amplitude can be shown to be a superposition of several "multi-particle paths". When the environment or a apparatus entangles with them to form a generalized "which-way" measurement for many particle system, the quantum decoherence happens in the high order case dynamically. An explicit illustration is also given with an intracavity system of two modes interacting with a moving mirror.Comment: 7 pages, revtex, 4 eps figure

    Developmental Acquisition of Regulomes Underlies Innate Lymphoid Cell Functionality

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    Innate lymphoid cells (ILCs) play key roles in host defense, barrier integrity, and homeostasis and mirror adaptive CD4(+) T helper (Th) cell subtypes in both usage of effector molecules and transcription factors. To better understand the relationship between ILC subsets and their Th cell counterparts, we measured genome-wide chromatin accessibility. We find that chromatin in proximity to effector genes is selectively accessible in ILCs prior to high-level transcription upon activation. Accessibility of these regions is acquired in a stepwise manner during development and changes little after in vitro or in vivo activation. Conversely, dramatic chromatin remodeling occurs in naive CD4(+) T cells during Th cell differentiation using a type-2-infection model. This alteration results in a substantial convergence of Th2 cells toward ILC2 regulomes. Our data indicate extensive sharing of regulatory circuitry across the innate and adaptive compartments of the immune system, in spite of their divergent developing pathways

    Seg2Reg: Differentiable 2D Segmentation to 1D Regression Rendering for 360 Room Layout Reconstruction

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    State-of-the-art single-view 360-degree room layout reconstruction methods formulate the problem as a high-level 1D (per-column) regression task. On the other hand, traditional low-level 2D layout segmentation is simpler to learn and can represent occluded regions, but it requires complex post-processing for the targeting layout polygon and sacrifices accuracy. We present Seg2Reg to render 1D layout depth regression from the 2D segmentation map in a differentiable and occlusion-aware way, marrying the merits of both sides. Specifically, our model predicts floor-plan density for the input equirectangular 360-degree image. Formulating the 2D layout representation as a density field enables us to employ `flattened' volume rendering to form 1D layout depth regression. In addition, we propose a novel 3D warping augmentation on layout to improve generalization. Finally, we re-implement recent room layout reconstruction methods into our codebase for benchmarking and explore modern backbones and training techniques to serve as the strong baseline. Our model significantly outperforms previous arts. The code will be made available upon publication
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