American Journal of Economics and Business Administration

Forecasting Financial Market Annual Performance Measures: Further Evidence+

Edward J. Lusk, Michael Halperin, Atanas Tetikov and Niya Stefanova

DOI : 10.3844/ajebasp.2010.300.306

American Journal of Economics and Business Administration

Volume 2, Issue 3

Pages 300-306


Problem statement: Forecasting is simple; producing accurate forecasts is the essential task. Experience suggests that financial managers often assume that because models used in forecasting are appropriate that they are effective. This study addresses this assumption. Effective is taken to mean forecasts where the Absolute Percentage Error (APE) is equal to or less than 10%. It has been reported that forecasts of the CAPM-β using the Bloomberg heuristic did not provide effective forecasts. We were interested to determine if the lack of forecasting accuracy is peculiar to β or is more pervasive. Approach: We expanded the analysis to include three measures of Excess Market Return: Jensen’s α (Jα), the Sharpe Performance Index (SPI) and the Treynor Performance Index (TPI) and two measures of market risk: we once again consider β and also a measure of non-market risk called idiosyncratic Risk (iR). We used information on 58 firms continuously traded on the NYSE or the NASDAQ from 1980 to and including 2008 to evaluate the effectiveness of forecasts of: Jα, SPI, TPI, β and iR. Results: Using Exponential Smoothing or (1,0,0) ARIMA models, we found no evidence that effective forecasts of these five market measures can be derived from such forecasting models. Conclusion/Recommendations: The important implication is: Financial Managers should be aware that even though they are they are using appropriate models to generate forecasts of Jα, SPI, TPI, β and iR that is no guarantee that such forecasts are effective. Finally, the authors’ results are posted on Scholarly Commons.


© 2010 Edward J. Lusk, Michael Halperin, Atanas Tetikov and Niya Stefanova. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.