Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

New evidence pertaining to the prediction of operating cash flows

Predictive ability, Operating cash flows, Cross-sectional prediction models, Time-series, Prediction models, Operating cycles, M41,

The contextual nature of the predictive power of statistically-based quarterly earnings models

We present new empirical evidence on the contextual nature of the predictive power of five statistically-based quarterly earnings expectation models evaluated on a holdout period spanning the twelve quarters from 2000–2002. In marked contrast to extant time-series work, the random walk with drift (RWD) model provides significantly more accurate pooled, one-step-ahead quarterly earnings predictions for a sample of high-technology firms (n=202). In similar predictive comparisons, the Griffin-Watts (GW) ARIMA model provides significantly more accurate quarterly earnings predictions for a sample of regulated firms (n=218). Finally, the RWD and GW ARIMA models jointly dominate the other expectation models (i.e., seasonal random walk with drift, the Brown-Rozeff (BR) and Foster (F) ARIMA models) for a default sample of firms (n=796). We provide supplementary analyses that document the: (1) increased frequency of the number of loss quarters experienced by our sample firms in the holdout period (2000–2002) vis-à-vis the identification period (1990–1999); (2) reduced levels of earnings persistence for our sample firms relative to earnings persistence factors computed by Baginski et al. ( 2003 ) during earlier time periods (1970s–1980s); (3) relative impact on the predictive ability of the five expectation models conditioned upon the extent of analyst coverage of sample firms (i.e., no coverage, moderate coverage, and extensive coverage); and (4) sensitivity of predictive performance across subsets of regulated firms with the BR ARIMA model providing the most accurate predictions for utilities (n=87) while the RWD model is superior for financial institutions (n=131). Copyright Springer Science+Business Media, LLC 2007

Statistically based quarterly earnings expectation models for nonseasonal firms

Nonseasonal quarterly earnings, ARIMA models, Random walk model, Analyst coverage, C22,