empirical study has investigated the nexus between the analyst forecast dispersion (AFD) and excess returns surrounding stock …

available forecast for each forecaster and the difference between the average and the forecast that this forecaster previously … made. We extended the knowledge base by analyzing the unpredictable component of the earnings forecast. We found that for … some forecasters the unpredictable component can be used to improve upon the predictable forecast, but we also found that …

better understand how financial analysts forecast earnings. We focus on forecasts for Real Estate Investment Trusts (REITs … regression analysis finds that the severity of the pandemic increases analysts' forecast error and dispersion. Government … forecast error rises by more, for REITs, when focusing on Hospitality and Industrial properties, and dispersion rises by more …

network autoregression (NNAR) models. Employing the static forecast approach, we forecast next-day Bitcoin price both with and … without re-estimation of the forecast model for each step. For cross-validation of forecast results, we consider two different …-sample forecast periods. The Diebold Mariano test confirms the superiority of forecast results of ARIMA model over NNAR in the test …

forecasting future price changes, and the nature of their confidence when price changes are forecast, determine whether price … involving multiple investors who participated in a market for a virtual asset. Our goal is to study investors’ forecast … forecast formation to the observed data. There is strong evidence that the investors forecast future prices by extrapolating …

-learning approaches can provide more accurate forecasts of short-term oil prices than those of the benchmark Naive Forecast (NF) model. We …

-homogeneous Markov chain method as the basis for a COVID-19-related sovereign default risk forecast model. It demonstrates the estimation …