Fundamental Information in Technical Trading Strategies

Technical trading strategies assume that past changes in prices help predict future changes. This makes sense if the past price trend reflects fundamental information that has not yet been fully incorporated in the current price. However, if the past price trend only reflects temporary pricing pressures, the technical trading strategy is doomed to fail. We demonstrate that this failure can be avoided by using financial statements as additional sources of information. We implement a trading strategy that invests in stocks with high past returns and high operating cash flows. This combination strategy yields a 3-factor alpha of 15% per year, which is much higher than that of the pure momentum strategy that invests in stocks with high past returns without considering operating cash flows. The combination strategy outperforms the momentum strategy in almost all years. The outperformance can be traced back to a higher probability of picking outperforming stocks. These are stocks that yield high future cash flows and hardly ever delist due to poor performance. The combination strategy is easily implemented: the information used is publicly available, the stocks chosen are liquid, and even high transaction costs do not erode the outperformance

Do Hospitals Respond to Increasing Prices by Supplying Fewer Services?

Medical providers often have a significant influence on treatment decisions which they can use in their own financial interest. Classical models of supplier-induced demand predict that medical providers will supply fewer services if they face increasing prices. We test this prediction based on a reform of hospital financing in Germany. Uniquely, this reform changed the overall level of reimbursement - with increasing prices for some hospitals and decreasing prices for others - without affecting the relative prices for different types of patients. Based on administrative data, we find that hospitals do indeed react to increasing prices by reducing service supply.Anbieter von medizinischen Leistungen treffen häufig Behandlungsentscheidungen für ihre Patienten und haben die Möglichkeit, bei diesen Entscheidungen ihre eigenen finanziellen Interessen zu berücksichtigen. Klassische Modelle der Theorie der 'angebotsinduzierten Nachfrage' prognostizieren, dass medizinische Anbieter auf höhere Preise reagieren, indem sie weniger Leistungen erbringen. Wir testen diese Vorhersage auf Grundlage einer Reform der Krankenhausfinanzierung in Deutschland. Das Besondere an der Finanzierungsreform in Deutschland ist, dass die Reform die Preise für Krankenhäuser verändert hat - mit steigenden Preisen für einige Krankenhäuser und sinkenden Preisen für andere - ohne dabei die relativen Preise für die Behandlung unterschiedlicher Patientengruppen oder unterschiedlicher Krankheiten zu beeinflussen. Unter Nutzung administrativer Daten finden wir, dass Krankenhäuser tatsächlich weniger Leistungen erbringen, wenn die Preise steigen

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Correction to: First results on survival from a large Phase 3 clinical trial of an autologous dendritic cell vaccine in newly diagnosed glioblastoma

Following publication of the original article [1], the authors reported an error in the spelling of one of the author names. In this Correction the incorrect and correct author names are indicated and the author name has been updated in the original publication. The authors also reported an error in the Methods section of the original article. In this Correction the incorrect and correct versions of the affected sentence are indicated. The original article has not been updated with regards to the error in the Methods section.https://deepblue.lib.umich.edu/bitstream/2027.42/144529/1/12967_2018_Article_1552.pd

Genome-Wide Analyses Characterize Shared Heritability Among Cancers and Identify Novel Cancer Susceptibility Regions

BACKGROUND: The shared inherited genetic contribution to risk of different cancers is not fully known. In this study, we leverage results from 12 cancer genome-wide association studies (GWAS) to quantify pairwise genome-wide genetic correlations across cancers and identify novel cancer susceptibility loci. METHODS: We collected GWAS summary statistics for 12 solid cancers based on 376 759 participants with cancer and 532 864 participants without cancer of European ancestry. The included cancer types were breast, colorectal, endometrial, esophageal, glioma, head and neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancers. We conducted cross-cancer GWAS and transcriptome-wide association studies to discover novel cancer susceptibility loci. Finally, we assessed the extent of variant-specific pleiotropy among cancers at known and newly identified cancer susceptibility loci. RESULTS: We observed widespread but modest genome-wide genetic correlations across cancers. In cross-cancer GWAS and transcriptome-wide association studies, we identified 15 novel cancer susceptibility loci. Additionally, we identified multiple variants at 77 distinct loci with strong evidence of being associated with at least 2 cancer types by testing for pleiotropy at known cancer susceptibility loci. CONCLUSIONS: Overall, these results suggest that some genetic risk variants are shared among cancers, though much of cancer heritability is cancer-specific and thus tissue-specific. The increase in statistical power associated with larger sample sizes in cross-disease analysis allows for the identification of novel susceptibility regions. Future studies incorporating data on multiple cancer types are likely to identify additional regions associated with the risk of multiple cancer types