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Historical Average (historical + average)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	50%

Selected Abstracts

Estimating the Equity Risk Premium Using Accounting Fundamentals

JOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 9-10 2000
John O'Hanlon
This study uses recent developments in the theoretical modelling of the links between unrecorded accounting goodwill, accounting profitability and the cost of equity, together with Capital Asset Pricing Model (CAPM) betas, to estimate the ex-ante equity risk premium in the UK. The results suggest that, over our sample period from 1968 to 1995, the premium has been in the region of 5%. Our estimate lends support to the view that the ex-ante equity risk premium is substantially less than the historical average of the excess of equity returns over the risk-free rate, and is similar to the rates applied recently by UK competition regulators. [source]

Forecasting natural gas prices using cointegration technique

OPEC ENERGY REVIEW, Issue 4 2006
Dr Salman Saif Ghouri
This paper uses Augmented Dickey-Fuller and Phillips-Perron technique for determining whether individual crude oil prices (West Texas Intermediate, Brent, Japan crude cocktail) and natural gas prices- Henry Hub (HH), National Balancing Point (NBP), European and Japanese liquefied natural gas (LNG) prices are stationary or non-stationary. It then applies Johansen and Juselius cointegration technique for establishing long-run correlation between respective oil prices and natural gas prices. The paper concludes that all individual series pertaining to oil and natural gas prices are non-stationary and indeed having long-run relationship, despite short term drift. Ordinary least square method was used to forecast individual natural gas prices in various markets, assuming of course, that historical relationship continues to hold with respective oil prices throughout the forecasting period. Natural gas prices in each of the markets are expected to be stronger during 2005,25 as compared to respective historical average prices showing the tightness of the market. The mean NBP and HH forecast during 2005,25 are expected to be 92 and 84 per cent stronger than the historical average, whilst LNG prices in Japan continue to exhibit stronger trends during the forecast period as compared to rest of the markets in Europe and North America - showing greater dependency of imports and security of supply considerations. [source]

Forecasting Models of Emergency Department Crowding

ACADEMIC EMERGENCY MEDICINE, Issue 4 2009
Lisa M. Schweigler MD
Abstract Objectives:, The authors investigated whether models using time series methods can generate accurate short-term forecasts of emergency department (ED) bed occupancy, using traditional historical averages models as comparison. Methods:, From July 2005 through June 2006, retrospective hourly ED bed occupancy values were collected from three tertiary care hospitals. Three models of ED bed occupancy were developed for each site: 1) hourly historical average, 2) seasonal autoregressive integrated moving average (ARIMA), and 3) sinusoidal with an autoregression (AR)-structured error term. Goodness of fits were compared using log likelihood and Akaike's Information Criterion (AIC). The accuracies of 4- and 12-hour forecasts were evaluated by comparing model forecasts to actual observed bed occupancy with root mean square (RMS) error. Sensitivity of prediction errors to model training time was evaluated, as well. Results:, The seasonal ARIMA outperformed the historical average in complexity adjusted goodness of fit (AIC). Both AR-based models had significantly better forecast accuracy for the 4- and the 12-hour forecasts of ED bed occupancy (analysis of variance [ANOVA] p < 0.01), compared to the historical average. The AR-based models did not differ significantly from each other in their performance. Model prediction errors did not show appreciable sensitivity to model training times greater than 7 days. Conclusions:, Both a sinusoidal model with AR-structured error term and a seasonal ARIMA model were found to robustly forecast ED bed occupancy 4 and 12 hours in advance at three different EDs, without needing data input beyond bed occupancy in the preceding hours. [source]