Interest Rate Sensitivities of REIT Returns

In order to identify effective interest rate proxies for equity and mortgage REITs, this study analyzes seven different interest rate proxies that have been widely used in the REIT literature. They are the monthly holding period returns on long-term U.S. government bonds and high-grade corporate bonds, the percentage changes in yields for long-term U.S. government bonds and high-yield (Baa) corporate bonds, the difference between returns on long-term U.S. government bonds and T-bill rates, the spread between yields on high-yield (Baa) corporate bonds and returns on long-term U.S. government bonds, and the spread between returns on high-grade corporate bonds and returns on long-term U.S. government bonds. The overall OLS results suggest that mortgage REITs are sensitive to all proxies, while equity REITs are significantly affected by only changes in yields on long-term U.S. government bonds and high-yield corporate bonds. The time variation paths for sensitivities indicate that all interest rate sensitivities are time specific. Overall, the changes in yields on high-yield corporate bonds (Baa) has the strongest explanatory power for returns of equity and mortgage REITs for most of the 27-year sample period (1972 through 1998).

The Sensitivity of Bank Stocks to Mortgage Portfolio Composition

Previous studies have found that bank stock returns are very sensitive to changes in real estate returns in general. But how the composition and quality of bank real estate portfolios affect the sensitivity of bank stocks to real estate returns has not been rigorously examined. The purpose of this study is to empirically examine this important question. The results indicate that commercial mortgages contribute the most to the sensitivity of bank stock returns. Farmland loans have a negative impact on bank real estate return sensitivity. Thus, farmland loans could play a diversification role in terms of reducing the sensitivity of banks to real estate returns, if used appropriately.

Stealing Links from Graph Neural Networks

Graph data, such as chemical networks and social networks, may be deemed confidential/private because the data owner often spends lots of resources collecting the data or the data contains sensitive information, e.g., social relationships. Recently, neural networks were extended to graph data, which are known as graph neural networks (GNNs). Due to their superior performance, GNNs have many applications, such as healthcare analytics, recommender systems, and fraud detection. In this work, we propose the first attacks to steal a graph from the outputs of a GNN model that is trained on the graph. Specifically, given a black-box access to a GNN model, our attacks can infer whether there exists a link between any pair of nodes in the graph used to train the model. We call our attacks link stealing attacks. We propose a threat model to systematically characterize an adversary's background knowledge along three dimensions which in total leads to a comprehensive taxonomy of 8 different link stealing attacks. We propose multiple novel methods to realize these 8 attacks. Extensive experiments on 8 real-world datasets show that our attacks are effective at stealing links, e.g., AUC (area under the ROC curve) is above 0.95 in multiple cases. Our results indicate that the outputs of a GNN model reveal rich information about the structure of the graph used to train the model.Comment: To appear in the 30th Usenix Security Symposium, August 2021, Vancouver, B.C., Canad

The Wealth Effects of Domestic vs International Joint Ventures: The Case of Real Estate

This study examines the wealth effect of international versus domestic real estate joint ventures on the U.S. participating firm's shareholders. This is done using traditional even study methodology for real estate joint venture announcements. The results suggest that domestic real estate joint ventures generally result in a significant increase in the firm's value, while international real estate joint ventures usually have a much less significant to nonsignificant wealth impact. This may be due to the immovability of real properties in foreign countries and the large amount of initial investment in real estate that increase both political and economic risks for international real estate joint ventures. This study also finds that hotel joint ventures generally have a weaker wealth effect than non-hotel real estate joint ventures.

Discovering New Gauge Bosons of Electroweak Symmetry Breaking at LHC-8

We study the physics potential of the 8TeV LHC (LHC-8) to discover, during its 2012 run, a large class of extended gauge models or extra dimensional models whose low energy behavior is well represented by an SU(2)^2 x U(1) gauge structure. We analyze this class of models and find that with a combined integrated luminosity of 40-60/fb at the LHC-8, the first new Kaluza-Klein mode of the W gauge boson can be discovered up to a mass of about 370-400 GeV, when produced in association with a Z boson.Comment: PRD final version (only minor refinements showing the consistency with new LHC data), 11 pages, 5 Figs, 2 Table

Identifying Better Effective Higgsless Theories via W_L W_L Scattering

The three site Higgsless model has been offered as a benchmark for studying the collider phenomenology of Higgsless models. In this talk, we present how well the three site Higgsless model performs as a general representative of Higgsless models in describing W_L W_L scattering, and which modifications can make it more representative. We employ general sum rules relating the masses and couplings of the Kaluza-Klein (KK) modes of the gauge fields in continuum and deconstructed Higgsless models as a way to compare the different theories. After comparing the three site Higgsless model to flat and warped continuum Higgsless models, we analyze an extensions of the three site Higgsless model, namely, the Hidden Local Symmetry (HLS) Higgsless model. We demonstrate that W_LW_L scattering in the HLS Higgsless model can very closely approximate scattering in the continuum models, provided that the parameter `a' is chosen to mimic rho-meson dominance of pi-pi scattering in QCD

Overcoming myelosuppression due to synthetic lethal toxicity for FLT3-targeted acute myeloid leukemia therapy.

Activating mutations in FLT3 confer poor prognosis for individuals with acute myeloid leukemia (AML). Clinically active investigational FLT3 inhibitors can achieve complete remissions but their utility has been hampered by acquired resistance and myelosuppression attributed to a 'synthetic lethal toxicity' arising from simultaneous inhibition of FLT3 and KIT. We report a novel chemical strategy for selective FLT3 inhibition while avoiding KIT inhibition with the staurosporine analog, Star 27. Star 27 maintains potency against FLT3 in proliferation assays of FLT3-transformed cells compared with KIT-transformed cells, shows no toxicity towards normal human hematopoiesis at concentrations that inhibit primary FLT3-mutant AML blast growth, and is active against mutations that confer resistance to clinical inhibitors. As a more complete understanding of kinase networks emerges, it may be possible to define anti-targets such as KIT in the case of AML to allow improved kinase inhibitor design of clinical agents with enhanced efficacy and reduced toxicity