Analyst Compensation and Forecast Bias

1999 ◽  
Author(s):  
Dan Bernhardt ◽  
Murillo Campello ◽  
Edward Kutsoati
Keyword(s):  
Forecast Bias

Analyst sentiment and earning forecast bias in financial markets

2021 ◽  
pp. 126601
Author(s):  
Xiong-Fei Jiang ◽  
Long Xiong ◽  
Tao Cen ◽  
Ling Bai ◽  
Na Zhao ◽  
...  
Keyword(s):  
Financial Markets ◽  
Earning Forecast ◽  
Forecast Bias

scholarly journals Impacts of AMSU-A, MHS and IASI data assimilation on temperature and humidity forecasts with GSI–WRF over the western United States

2015 ◽  
Vol 8 (10) ◽  
pp. 4231-4242 ◽  
Author(s):  
Y. Bao ◽  
J. Xu ◽  
A. M. Powell Jr. ◽  
M. Shao ◽  
J. Min ◽  
...  
Keyword(s):  
United States ◽  
Data Assimilation ◽  
Lower Troposphere ◽  
Western United States ◽  
Observation Data ◽  
Positive Contribution ◽  
Negative Contribution ◽  
Infrared Observation ◽  
Forecast Bias ◽  
Infrared Data

Abstract. Using NOAA's Gridpoint Statistical Interpolation (GSI) data assimilation system and NCAR's Advanced Research WRF (Weather Research and Forecasting) (ARW-WRF) regional model, six experiments are designed by (1) a control experiment (CTRL) and five data assimilation (DA) experiments with different data sets, including (2) conventional data only (CON); (3) microwave data (AMSU-A + MHS) only (MW); (4) infrared data (IASI) only (IR); (5) a combination of microwave and infrared data (MWIR); and (6) a combination of conventional, microwave and infrared observation data (ALL). One-month experiments in July 2012 and the impacts of the DA on temperature and moisture forecasts at the surface and four vertical layers over the western United States have been investigated. The four layers include lower troposphere (LT) from 800 to 1000 hPa, middle troposphere (MT) from 400 to 800 hPa, upper troposphere (UT) from 200 to 400 hPa, and lower stratosphere (LS) from 50 to 200 hPa. The results show that the regional GSI–WRF system is underestimating the observed temperature in the LT and overestimating in the UT and LS. The MW DA reduced the forecast bias from the MT to the LS within 30 h forecasts, and the CON DA kept a smaller forecast bias in the LT for 2-day forecasts. The largest root mean square error (RMSE) is observed in the LT and at the surface (SFC). Compared to the CTRL, the MW DA produced the most positive contribution in the UT and LS, and the CON DA mainly improved the temperature forecasts at the SFC. However, the IR DA gave a negative contribution in the LT. Most of the observed humidity in the different vertical layers is overestimated in the humidity forecasts except in the UT. The smallest bias in the humidity forecast occurred at the SFC and in the UT. The DA experiments apparently reduced the bias from the LT to UT, especially for the IR DA experiment, but the RMSEs are not reduced in the humidity forecasts. Compared to the CTRL, the IR DA experiment has a larger RMSE in the moisture forecast, although the smallest bias is found in the LT and MT.

scholarly journals Interpreting Tests of School VAM Validity

2016 ◽  
Vol 106 (5) ◽  
pp. 388-392 ◽  
Author(s):  
Joshua Angrist ◽  
Peter Hull ◽  
Parag Pathak ◽  
Christopher Walters
Keyword(s):  
School District ◽  
Predictive Value ◽  
Degrees Of Freedom ◽  
School Attendance ◽  
Value Added ◽  
School Effects ◽  
Test Statistic ◽  
Forecast Bias ◽  
Value Added Models

We develop over-identification tests that use admissions lotteries to assess the predictive value of regression-based value-added models (VAMs). These tests have degrees of freedom equal to the number of quasi-experiments available to estimate school effects. By contrast, previously implemented VAM validation strategies look at a single restriction only, sometimes said to measure forecast bias. Tests of forecast bias may be misleading when the test statistic is constructed from many lotteries or quasi-experiments, some of which have weak first stage effects on school attendance. The theory developed here is applied to data from the Charlotte-Mecklenberg School district analyzed by Deming (2014).

scholarly journals Evaluation and Bias Correction in WRF Model Forecasting of Precipitation and Potential Evapotranspiration

2019 ◽  
Vol 20 (5) ◽  
pp. 965-983 ◽  
Author(s):  
Theodor Bughici ◽  
Naftali Lazarovitch ◽  
Erick Fredj ◽  
Eran Tas
Keyword(s):  
Bias Correction ◽  
Potential Evapotranspiration ◽  
Irrigation Management ◽  
Hybrid Approach ◽  
Wrf Model ◽  
Irrigation Scheduling ◽  
Weather Research And Forecasting ◽  
Forecast Bias ◽  
Synoptic Conditions ◽  
High Bias

Abstract A reliable forecast of potential evapotranspiration (ET0) is key to precise irrigation scheduling toward reducing water and agrochemical use while optimizing crop yield. In this study, we examine the benefits of using the Weather Research and Forecasting (WRF) Model for ET0 and precipitation forecasts with simulations at a 3-km grid spatial resolution and an hourly temporal resolution output over Israel. The simulated parameters needed to calculate ET0 using the Penman–Monteith (PM) approach, as well as calculated ET0 and precipitation, were compared to observations from a network of meteorological stations. WRF forecasts of all PM meteorological parameters, except wind speed Ws, were significantly sensitive to seasonality and synoptic conditions, whereas forecasts of Ws consistently showed high bias associated with strong local effects, leading to high bias in the evaluated PM–ET0. Local Ws bias correction using observations on days preceding the forecast and interpolation of the resulting PM–ET0 to other locations led to significant improvement in ET0 forecasts over the investigated area. By using this hybrid forecast approach (WRFBC) that combines WRF numerical simulations with statistical bias corrections, daily ET0 forecast bias was reduced from an annual mean of 13% with WRF to 3% with WRFBC, while maintaining a high model–observation correlation. WRF was successful in predicting precipitation events on a daily event basis for all four forecast lead days. Considering the benefit of the hybrid approach for forecasting ET0, the WRF Model was found to be a high-potential tool for improving crop irrigation management.

First Impressions and Analyst Forecast Bias

2019 ◽  
Author(s):  
David A. Hirshleifer ◽  
Ben Lourie ◽  
Thomas Ruchti ◽  
Phong Truong
Keyword(s):  
First Impressions ◽  
Analyst Forecast ◽  
Forecast Bias

Setting Safety-Stock Targets at Intel in the Presence of Forecast Bias

2008 ◽  
Vol 38 (2) ◽  
pp. 112-122 ◽  
Author(s):  
Matthew P. Manary ◽  
Sean P. Willems
Keyword(s):  
Safety Stock ◽  
Forecast Bias
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