Diversifikationseffekte durch small und mid caps? : Eine empirische Untersuchung basierend auf europäischen Aktienindizes

Im Laufe der Zeit ist die Korrelation von internationalen Aktienindizes tendenziell gestiegen, was die Möglichkeit einer Diversifikation von Aktieninvestments über verschiedene Länder einschränkt. Diese Erkenntnisse wurden allerdings erzielt auf der Basis von empirischen Analysen von Large Cap Indizes. Ob das Ergebnis allerdings auch für Small und Mid Cap Indizes vorliegt, ist bisher unbekannt. Ziel dieser Arbeit ist es deshalb zu überprüfen, ob bei Investition in Small und Mid Caps stärkere Diversifikationseffekte erzielt werden können. Die Arbeit beinhaltet eine empirische Analyse auf Basis von europäischen Aktienindizes, berechnet von den nationalen Börsen bzw. MSCI, für den Zeitraum von 1994 bis 2003. Unsere wesentlichen Ergebnisse können wie folgt zusammengefasst werden. Small Cap Indizes sind sowohl untereinander also auch mit Large Caps relativ niedrig korreliert. Allerdings waren alle Korrelationen in der Baisse signifikant höher als in der Hausse. Small Cap Indexrenditen können nicht vollständig durch Large Cap Indexrenditen dupliziert werden. Während Large Cap Renditen hauptsächlich durch globale Faktoren beeinflusst werden, spielen bei den Small Cap Renditen unternehmensindividuelle Faktoren eine größere Rolle. Außerdem bestehen Unterschiede in der Branchenzugehörigkeit zwischen Small und Large Caps. Eine Beimischung von Small und Mid Caps hat generell zu einer Senkung des Portfoliorisikos geführt

Diversifikationseffekte durch Small und Mid Caps?

In this paper, we analyze the potential benefits of international diversification with small and mid caps. Based on an empirical analysis of European large, small and mid cap stock indices, we find that small caps have relatively low correlations not only with large caps but also with each other. We show, that small cap stock returns cannot be spanned by large cap stock returns. Furthermore, we find that diversification in Europe is likely to be more effective with a combination of small and large caps than with large caps alone. In the paper, we present several robustness checks and show that our results are also valid in sub-periods as well as for upside and downside stock market moves. Moreover, we find that large cap returns are mainly driven by global factors whereas returns on small cap stocks are primarily driven by local and idiosyncratic factors. Furthermore, the different industry classification of large and small caps explain parts of our results.

Depth Super-Resolution from Explicit and Implicit High-Frequency Features

We propose a novel multi-stage depth super-resolution network, which progressively reconstructs high-resolution depth maps from explicit and implicit high-frequency features. The former are extracted by an efficient transformer processing both local and global contexts, while the latter are obtained by projecting color images into the frequency domain. Both are combined together with depth features by means of a fusion strategy within a multi-stage and multi-scale framework. Experiments on the main benchmarks, such as NYUv2, Middlebury, DIML and RGBDD, show that our approach outperforms existing methods by a large margin (~20% on NYUv2 and DIML against the contemporary work DADA, with 16x upsampling), establishing a new state-of-the-art in the guided depth super-resolution task

Rethinking Range View Representation for LiDAR Segmentation

LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point- or voxel-based methods as they often yield better performance than the traditional range view representation. In this work, we unveil several key factors in building powerful range view models. We observe that the "many-to-one" mapping, semantic incoherence, and shape deformation are possible impediments against effective learning from range view projections. We present RangeFormer -- a full-cycle framework comprising novel designs across network architecture, data augmentation, and post-processing -- that better handles the learning and processing of LiDAR point clouds from the range view. We further introduce a Scalable Training from Range view (STR) strategy that trains on arbitrary low-resolution 2D range images, while still maintaining satisfactory 3D segmentation accuracy. We show that, for the first time, a range view method is able to surpass the point, voxel, and multi-view fusion counterparts in the competing LiDAR semantic and panoptic segmentation benchmarks, i.e., SemanticKITTI, nuScenes, and ScribbleKITTI.Comment: ICCV 2023; 24 pages, 10 figures, 14 tables; Webpage at https://ldkong.com/RangeForme

MSSNet: Multi-Scale-Stage Network for Single Image Deblurring

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