Earnings Management and Long-Run Stock Underperformance of Private Placements

The study investigates whether private placement issuers manipulate their earnings around the time of issuance and the effect of earnings management on the long-run stock performance. We find that managers of U.S. private placement issuers tend to engage in income-increasing earnings management in the year prior to the issuance of private placements. We further speculate that earnings management serves as a likely source of investor over-optimism at the time of private placements. To support this speculation, we find evidence suggesting that the income-increasing accounting accruals made at the time of private placements predict the post-issue long-term stock underperformance. The study contributes to the large body of literature on earnings manipulation around the time of securities issuance

Window Dressing in Reported Earnings

The article discusses the use of the term window dressing, a wide range of techniques for auditing, by audit clients to enhance the financial position of an entity through manipulated disclosures. The term refers to the reporting practices adopted by firms to distort earnings by changing the way stakeholders perceived the financial figures. A research suggests that firms must engage in the type of manipulative behavior for the purpose of economic incentives

A new collective mode in the fractional quantum Hall liquid

We apply the methods of continuum mechanics to the study of the collective modes of the fractional quantum Hall liquid. Our main result is that at long wavelength there are {\it two} distinct modes of oscillations, while previous theories predicted only {\it one}. The two modes are shown to arise from the internal dynamics of shear stresses created by the Coulomb interaction in the liquid. Our prediction is supported by recent light scattering experiments, which report the observation of two long-wavelength modes in a quantum Hall liquid.Comment: 4 pages, 1 Figur

Radiance and Doppler shift distributions across the network of the quiet Sun

The radiance and Doppler-shift distributions across the solar network provide observational constraints of two-dimensional modeling of transition-region emission and flows in coronal funnels. Two different methods, dispersion plots and average-profile studies, were applied to investigate these distributions. In the dispersion plots, we divided the entire scanned region into a bright and a dark part according to an image of Fe xii; we plotted intensities and Doppler shifts in each bin as determined according to a filtered intensity of Si ii. We also studied the difference in height variations of the magnetic field as extrapolated from the MDI magnetogram, in and outside network. For the average-profile study, we selected 74 individual cases and derived the average profiles of intensities and Doppler shifts across the network. The dispersion plots reveal that the intensities of Si ii and C iv increase from network boundary to network center in both parts. However, the intensity of Ne viii shows different trends, namely increasing in the bright part and decreasing in the dark part. In both parts, the Doppler shift of C iv increases steadily from internetwork to network center. The average-profile study reveals that the intensities of the three lines all decline from the network center to internetwork region. The binned intensities of Si ii and Ne viii have a good correlation. We also find that the large blue shift of Ne viii does not coincide with large red shift of C iv. Our results suggest that the network structure is still prominent at the layer where Ne viii is formed in the quiet Sun, and that the magnetic structures expand more strongly in the dark part than in the bright part of this quiet Sun region.Comment: 10 pages,9 figure

Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders

Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled representations is a challenging problem, especially when certain factors of variation are difficult to label. In this paper, we introduce a novel architecture that disentangles the latent space into two complementary subspaces by using only weak supervision in form of pairwise similarity labels. Inspired by the recent success of cycle-consistent adversarial architectures, we use cycle-consistency in a variational auto-encoder framework. Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations. We show compelling results of disentangled latent subspaces on three datasets and compare with recent works that leverage adversarial training

Bubbles created from vacuum fluctuation

We show that the bubbles $S^2\times S^2$can be created from vacuum fluctuation in certain De Sitter universe, so the space-time foam-like structure might really be constructed from bubbles of $S^2\times S^2$ in the very early inflating phase of our universe. But whether such foam-like structure persisted during the later evolution of the universe is a problem unsolved now.Comment: 6 page