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See detailWhy is the Market Skewness-Return Relationship Negative?
Lehnert, Thorsten UL

Scientific Conference (2019, December 13)

The observed negative relationship between market skewness and excess return or the negative price of market skewness risk in the cross-section of stock returns is somewhat counterintuitive when we ... [more ▼]

The observed negative relationship between market skewness and excess return or the negative price of market skewness risk in the cross-section of stock returns is somewhat counterintuitive when we consider the usual interpretation of e.g. option-implied skewness as an indicator of jump risk or downside risk. One possible explanation for this inconsistency is that there are factors affecting option-implied market skewness other than jump risk in the stock market. In this paper, I find that price pressure associated with “crowded trades” of mutual funds is an important endogenous factor. Given that retail investors are prone to herding, the directional trading of mutual funds is correlated, and their collective actions can generate short-term price pressure on aggregate stock prices. Short sellers systematically exploit these patterns not only in the equity lending market, but also in the options market. In line with this economic channel, I find that firstly, the significant negative relationship between market skewness and returns becomes insignificant, once I control for price pressure. Secondly, the negative relationship is only present for the “bad” downside component of risk-neutral skewness, associated with out-of-the-money put options. For the “good” upside component of risk-neutral skewness, associated with out-of-the-money call options, the relationship is always positive. Thirdly, price pressure affects the skewness-return relationship, which can be clearly distinguished from the impact of flows on the volatility-return relationship in terms of the leverage effect. [less ▲]

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See detailWhy is the Market Skewness-Return Relationship Negative?
Lehnert, Thorsten UL

Scientific Conference (2019, December 13)

The observed negative relationship between market skewness and excess return or the negative price of market skewness risk in the cross-section of stock returns is somewhat counterintuitive when we ... [more ▼]

The observed negative relationship between market skewness and excess return or the negative price of market skewness risk in the cross-section of stock returns is somewhat counterintuitive when we consider the usual interpretation of e.g. option-implied skewness as an indicator of jump risk or downside risk. One possible explanation for this inconsistency is that there are factors affecting option-implied market skewness other than jump risk in the stock market. In this paper, I find that price pressure associated with “crowded trades” of mutual funds is an important endogenous factor. Given that retail investors are prone to herding, the directional trading of mutual funds is correlated, and their collective actions can generate short-term price pressure on aggregate stock prices. Short sellers systematically exploit these patterns not only in the equity lending market, but also in the options market. In line with this economic channel, I find that firstly, the significant negative relationship between market skewness and returns becomes insignificant, once I control for price pressure. Secondly, the negative relationship is only present for the “bad” downside component of risk-neutral skewness, associated with out-of-the-money put options. For the “good” upside component of risk-neutral skewness, associated with out-of-the-money call options, the relationship is always positive. Thirdly, price pressure affects the skewness-return relationship, which can be clearly distinguished from the impact of flows on the volatility-return relationship in terms of the leverage effect. [less ▲]

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See detailUnifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
Schütt, Kristof; Gastegger, Michael; Tkatchenko, Alexandre UL et al

in Nature Communications (2019), 10(1), 5024

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate ... [more ▼]

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry. [less ▲]

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See detailImpact of nuclear vibrations on van der Waals and Casimir interactions at zero and finite temperature
Venkataram, Prashanth; Hermann, Jan; Vongkovit, Teerit et al

in Science Advances (2019), 5(1), 0456

Recent advances in measuring van der Waals (vdW) interactions have probed forces on molecules at nanometric separations from metal surfaces and demonstrated the importance of infrared nonlocal ... [more ▼]

Recent advances in measuring van der Waals (vdW) interactions have probed forces on molecules at nanometric separations from metal surfaces and demonstrated the importance of infrared nonlocal polarization response and temperature effects, yet predictive theories for these systems remain lacking. We present a theoretical framework for computing vdW interactions among molecular structures, accounting for geometry, short-range electronic delocalization, dissipation, and collective nuclear vibrations (phonons) at atomic scales, along with long-range electromagnetic interactions in arbitrary macroscopic environments. We primarily consider experimentally relevant low-dimensional carbon allotropes, including fullerenes, carbyne, and graphene, and find that phonons couple strongly with long-range electromagnetic fields depending on molecular dimensionality and dissipation, especially at nanometric scales, creating delocalized phonon polaritons that substantially modify infrared molecular response. These polaritons, in turn, alter vdW interaction energies between molecular and macroscopic structures, producing nonmonotonic power laws and nontrivial temperature variations at nanometric separations feasible in current experiments. [less ▲]

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See detailA New Optimization Tool for Mega-Constellation Design and its Application to Trunking Systems
Kisseleff, Steven UL; Shankar, Bhavani UL; Spano, Danilo UL et al

in 37th International Communications Satellite Systems Conference (ICSSC), Japan, October 2019 (2019, November)

Detailed reference viewed: 27 (2 UL)
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See detailUser Terminal Wideband Modem for Very High Throughput Satellites
Kisseleff, Steven UL; Maturo, Nicola UL; Chatzinotas, Symeon UL et al

in 37th International Communications Satellite Systems Conference (ICSSC), Japan, October 2019 (2019, November)

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See detailTHE MARKET SKEWNESS-RETURN RELATIONSHIP
Lehnert, Thorsten UL

Presentation (2019, October 29)

The observed negative relationship between market skewness and excess return or the negative price of market skewness risk in the cross-section of stock returns is somewhat counterintuitive when we ... [more ▼]

The observed negative relationship between market skewness and excess return or the negative price of market skewness risk in the cross-section of stock returns is somewhat counterintuitive when we consider the usual interpretation of e.g. option-implied skewness as an indicator of jump risk or downside risk. One possible explanation for this inconsistency is that there are factors affecting option-implied market skewness other than jump risk in the stock market. In this paper, I find that price pressure associated with “crowded trades” of mutual funds is an important endogenous factor. Given that retail investors are prone to herding, the directional trading of mutual funds is correlated, and their collective actions can generate short-term price pressure on aggregate stock prices. Short sellers systematically exploit these patterns not only in the equity lending market, but also in the options market. In line with this economic channel, I find that firstly, the significant negative relationship between market skewness and returns becomes insignificant, once I control for price pressure. Secondly, the negative relationship is only present for the “bad” downside component of risk-neutral skewness, associated with out-of-the-money put options. For the “good” upside component of risk-neutral skewness, associated with out-of-the-money call options, the relationship is always positive. Thirdly, price pressure affects the skewness-return relationship, which can be clearly distinguished from the impact of flows on the volatility-return relationship in terms of the leverage effect. [less ▲]

Detailed reference viewed: 14 (1 UL)
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See detailLearning-Assisted Optimization for Energy-Efficient Scheduling in Deadline-Aware NOMA Systems
Lei, Lei UL; You, Lei; He, Qing et al

in IEEE Transactions on Green Communications and Networking (2019)

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See detailOptimal Resource Allocation for NOMA-Enabled Cache Replacement and Content Delivery
Lei, Lei UL; Vu, Thang Xuan UL; Xiang, Lin UL et al

in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2019) (2019, September)

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See detailJoint User Grouping and Power Allocation for MISO Systems: Learning to Schedule
Yuan, Yaxiong; Vu, Thang Xuan UL; Lei, Lei UL et al

in IEEE European Signal Processing Conference 2019 (2019, September)

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See detailLoad Coupling and Energy Optimization in Multi-Cell and Multi-Carrier NOMA Networks
Lei, Lei UL; You, Lei; Yang, Yang et al

in IEEE Transactions on Vehicular Technology (2019)

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See detailLearning-Based Resource Allocation: Efficient Content Delivery Enabled by Convolutional Neural Network
Lei, Lei UL; Yaxiong, Yuan UL; Vu, Thang Xuan UL et al

in IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019 (2019, July)

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See detailOn the Successful Delivery Probability of Full-Duplex-Enabled Mobile Edge Caching
Vu, Thang Xuan UL; Lei, Lei UL; Chatzinotas, Symeon UL et al

in IEEE Communications Letters (2019)

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See detailMachine Learning based Antenna Selection and Power Allocation in Multi-user MISO Systems
Vu, Thang Xuan UL; Lei, Lei UL; Chatzinotas, Symeon UL et al

in 2019 IEEE International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) (2019, June)

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See detailUltrareliable SWIPT using Unscheduled Short Packet Transmissions
Kisseleff, Steven UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in IEEE International Conference on Communications (ICC), B5G-URLLC Workshop, Shanghai, May 2019 (2019, May 20)

Large communication networks, e.g. Internet of Things (IoT), are known to be vulnerable to the co-channel interference from simultaneous transmissions. In the recent time, this problem has been ... [more ▼]

Large communication networks, e.g. Internet of Things (IoT), are known to be vulnerable to the co-channel interference from simultaneous transmissions. In the recent time, this problem has been extensively studied in various contexts. Due to a potentially very long duty cycle, orthogonal multiple access techniques are not well suited for such schemes. Instead, random medium access (RMA) seems promising, since it guarantees a lower bound for the network throughput even in presence of an infinite number of simultaneous transmissions while reducing the average length of the duty cycle. Such an RMA scheme is based on transmission of short data packets with unknown scheduling. Of course, a reliable symbol detection for this type of communication is very challenging not only due to a large amount of interference from the adjacent nodes, but also because of the uncertainty related to the presence or absence of overlapping packets. Interestingly, with increasing number of network nodes also the amount of energy, which can be harvested from the received signal, increases. This is especially beneficial for powering of a relay device, which may utilize the energy for further information processing and retransmission. In this paper, we address the design of a simultaneous information and power transfer scheme based on unscheduled short packet transmissions for ultrareliable communication. [less ▲]

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See detailMagnetic ordering of the martensite phase in Ni-Co-Mn-Sn-based ferromagnetic shape memory alloys
Sarkar, Sudip Kumar; Ahlawat, Sarita; Kaushik, Som Datta et al

in Journal of Physics: Condensed Matter (2019)

The magnetic state of low temperature martensite phase in Co-substituted Ni-Mn-Sn-based ferromagnetic shape memory alloys (FSMAs) has been investigated, in view of numerous conflicting reports of ... [more ▼]

The magnetic state of low temperature martensite phase in Co-substituted Ni-Mn-Sn-based ferromagnetic shape memory alloys (FSMAs) has been investigated, in view of numerous conflicting reports of occurrences of spin glass (SG), superparamagnetism (SPM) or long range anti-ferromagnetic (AF) ordering. Combination of DC magnetization, AC susceptibility and small angle neutron scattering (SANS) studies provide a clear evidence for AF order in martensitic phase of Ni45Co5Mn38Sn12 alloy and rule out SPM and SG orders. Identical studies on another alloy of close composition of Ni44Co6Mn40Sn10 point to presence of SG order in martensitic phase and absence of SPM behavior, contrary to earlier report. SANS results do show presence of nanometre-sized clusters but they are found to grow in size from 3 nm at 30 K to 11 nm at 300 K, and do not correlate with magnetism in these alloys. [less ▲]

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See detailLinear Precoding Design for Cache-aided Full-duplex Networks
Vu, Thang Xuan UL; Vu, Trinh Anh; Lei, Lei UL et al

in 2019 IEEE Wireless Communications and Networking Conference (WCNC) (2019)

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See detailEnergy Efficient Design for Coded Caching Delivery Phase
Vu, Thang Xuan UL; Lei, Lei UL; Chatzinotas, Symeon UL et al

in 2019 IEEE International Conference on Recent Advances in Signal Processing, Telecommunications & Computing (SigTelCom) (2019)

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See detailDemonstrator of Precoding Technique for a Multi-Beams Satellite System
Maturo, Nicola UL; Merlano Duncan, Juan Carlos UL; Krivochiza, Jevgenij UL et al

in 2019 8th International Workshop on Tracking, Telemetry and Command Systems for Space Applications (TTC) (2019)

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See detailTransverse and longitudinal spin-fluctuations in INVAR Fe0.65Ni0.35.
Stewart, J. Ross; Giblin, Sean R.; Honecker, Dirk UL et al

in Journal of physics. Condensed matter : an Institute of Physics journal (2019), 31(2), 025802

The presence of spin-fluctuations deep within the ordered state of ferromagnetic [Formula: see text] alloy [Formula: see text] has long been suspected but seldom directly observed. Inhomogeneities of one ... [more ▼]

The presence of spin-fluctuations deep within the ordered state of ferromagnetic [Formula: see text] alloy [Formula: see text] has long been suspected but seldom directly observed. Inhomogeneities of one type or another have been cited as important in stabilizing [Formula: see text] behaviour-either longitudinal spin-fluctuations associated with the [Formula: see text]-state (local environment) model or transverse magnetisation arising from non-collinear spin structures. In this study we employ small-angle neutron scattering with neutron polarization analysis to distinguish between the two possibilities. Surprisingly we in fact find evidence of dominant but uncorrelated longitudinal spin-fluctuations coexisting with transverse magnetisation which exists in short-range clusters of size ~[Formula: see text]. This finding supports recent first principles calculations of [Formula: see text] in which both longitudinal spin-fluctuations and magnetic short-range order are identified as important ingredients in reproducing the equilibrium [Formula: see text] lattice. [less ▲]

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