Predicting Stock Volatility Using After-Hours Information

We use realized volatilities based on after hours high frequency returns to predict next day volatility. We extend GARCH and long-memory forecasting models to include additional information: the whole night, the preopen, the postclose realized variance, and the overnight squared return. For four NASDAQ stocks (MSFT, AMGN, CSCO, and YHOO) we find that the inclusion of the preopen variance can improve the out-of-sample forecastability of the next day conditional day volatility. Additionally, we find that the postclose variance and the overnight squared return do not provide any predictive power for the next day conditional volatility. Our findings support the results of prior studies that traders trade for non-information reasons in the postclose period and trade for information reasons in the preopen period.

Locoregional therapy in luminal-like and HER2-enriched patients with de novo stage IV breast cancer

BACKGROUND: Locoregional therapy is rarely the standard of care for De Novo stage IV breast cancer but usually used for palliation of symptoms. This retrospective study aimed to determine whether surgery or radiation would contribute to survival benefit for this group of patients by examining the survival outcome through the disease molecular subtypes. MATERIALS AND METHODS: We reviewed 246 patients with de novo stage IV (M1) breast cancer treated at our hospital between 1990 and 2009. Multivariable Cox Analysis was used to evaluate the survival association with subtypes and clinicopathologic factors. RESULTS: Patients with luminal-like subtype are mostly premonopausal (66.9%, P = 0.0002), with abnormal CA 15–3 level at initial diagnosis (58.7%, P = 0.01), a higher rate of bony metastases (78.5%, P = 0.02), and a lower rate of liver metastases (22.3%, P < 0.0001). Patients with HER2-enriched and triple negative showed higher rate of nuclear grade III, up to 35% and 40%, respectively (P = 0.01). There is no difference in treatment options patient received: systemic chemotherapy up to 82.2 ~ 95% (p = 0.0705), locoregional treatment up to 40.0 ~ 51.2% (P-0.2571). The median overall survival was 23.1 months: luminal-like subtype 39.6 months, HER2-enriched subtype 17.9 months, and triple negative subtype 13.3 months, respectively (P < 0.0001). In multivariate analysis, poor prognostic factors included HER2-enriched (HR 2.2, P < 0.0001) and triple negative subtype (HR 4.3, P < 0.0001), liver metastasis (HR 1.9, P < 0.0001), lung metastasis (HR 1.4, P = 0.0153), and bone metastasis (HR 1.8, P = 0.0007). Subgroup analysis revealed that local treatments (surgery or radiotherapy) to primary/regional tumors achieved better survival in patients with luminal-like (3-year survival 66.4% vs. 34.4%, p = 0.0001) and HER2-enriched (3-year survival 41.6% vs. 8.8%, p = 0.0012) subtypes, but not in triple negative subtype (P = 0.9575). CONCLUSIONS: For better survival outcome, De Novo Stage IV breast cancer patients with luminal-like or HER2-enriched subtype should be offered local treatments when surgery and/or radiotherapy presents an option for proper control of the primary and regional tumors

GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization

Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets' ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets. Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.Comment: Presented in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA and published in BMC Systems Biology 2018, 12(Suppl 8):14

Zero on-axis backscattering of an anisotropically coated shell of revolution

This report was sponsored by Sandia National Laboratories