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This paper investigates the relation between lunar phases and stock market returns of 48 countries. The findings indicate that stock returns are lower on the days around a full moon than on the days around a new moon. The magnitude of the return difference is 3% to 5% per annum based on analyses of two global portfolios: one equal-weighted and the other value-weighted. The return difference is not due to changes in stock market volatility or trading volumes. The data show that the lunar effect is not explained away by announcements of macroeconomic indicators, nor is it driven by major global shocks. Moreover, the lunar effect is independent of other calendar-related anomalies such as the January effect, the day-of-week effect, the calendar month effect, and the holiday effect (including lunar holidays). Classification Code: G12, G14

This study focused on producing flash flood hazard susceptibility maps (FFHSM) using frequency ratio (FR) and statistical index (SI) models in the Xiqu Gully (XQG) of Beijing, China. First, a total of 85 flash flood hazard locations (n = 85) were surveyed in the field and plotted using geographic information system (GIS) software. Based on the flash flood hazard locations, a flood hazard inventory map was built. Seventy percent (n = 60) of the flooding hazard locations were randomly selected for building the models. The remaining 30% (n = 25) of the flooded hazard locations were used for validation. Considering that the XQG used to be a coal mining area, coalmine caves and subsidence caused by coal mining exist in this catchment, as well as many ground fissures. Thus, this study took the subsidence risk level into consideration for FFHSM. The ten conditioning parameters were elevation, slope, curvature, land use, geology, soil texture, subsidence risk area, stream power index (SPI), topographic wetness index (TWI), and short-term heavy rain. This study also tested different classification schemes for the values for each conditional parameter and checked their impacts on the results. The accuracy of the FFHSM was validated using area under the curve (AUC) analysis. Classification accuracies were 86.61%, 83.35%, and 78.52% using frequency ratio (FR)-natural breaks, statistical index (SI)-natural breaks and FR-manual classification schemes, respectively. Associated prediction accuracies were 83.69%, 81.22%, and 74.23%, respectively. It was found that FR modeling using a natural breaks classification method was more appropriate for generating FFHSM for the Xiqu Gully.

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Crowding in public transport is becoming a growing concern as demand grows at a rate that is outstripping available capacity.