On the Structure of Analyst Research Portfolios and Forecast Accuracy

CFA Digest ◽  
2010 ◽  
Vol 40 (1) ◽  
pp. 74-76
Author(s):  
Stephen Phillip Huffman
Keyword(s):  
Forecast Accuracy

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

Feedback structure and EFAS flood warning verification for the rivers in the northwestern Russian Federation

2020 ◽  
Vol 4 ◽  
pp. 96-109
Author(s):  
A.V. Romanov ◽  
◽  
M.V. Yachmenova ◽  
Keyword(s):  
Russian Federation ◽  
Forecast Accuracy ◽  
Warning System ◽  
General Description ◽  
System Quality ◽  
Flood Warning ◽  
Forecast Lead Time ◽  
Feedback Structure ◽  
The Russian Federation ◽  
Flood Warning System

Based on the example of flood warning data provided by EFAS for the territory of Northwestern Administration for Hydrometeorology and Environmental Monitoring in 2018-2020, the structure of the systematized issues of the EFAS portal is analyzed. The issues determine a feedback for the year-round monitoring of the accuracy of flood forecasting using the LISFLOOD base model, as well as its calibration. Several most important feedback sections are highlighted, that allow improving significantly a procedure for the quantitative and qualitative differentiated assessment of short- and medium-range flood forecasts. Using the results of the numerical analysis, a general description of the EFAS flood warning system quality and the prospects for the participation of the Russian Federation in it are given. Keywords: flooding, hydrological forecasts, forecast lead time, feedback, forecast accuracy

What Do Questions Reveal? Analyst Topic-Specific Skill and Forecast Accuracy

2020 ◽  
Author(s):  
Ling Cen ◽  
Yanru Han ◽  
Jarrad Harford
Keyword(s):  
Forecast Accuracy ◽  
Specific Skill

Analyst Research Portfolios and Forecast Accuracy: Accounting for Endogeneity in Portfolio Structure

2006 ◽  
Author(s):  
Omesh Kini ◽  
Michael J. Rebello ◽  
Shehzad L. Mian ◽  
Anand Venkateswaran
Keyword(s):  
Forecast Accuracy ◽  
Portfolio Structure
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