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Full-Text Articles in Business
An Examination Of Value Line’S Long-Term Projection, Andrew Szakmary, C. Mitchell Conover, Carol Lancaster
An Examination Of Value Line’S Long-Term Projection, Andrew Szakmary, C. Mitchell Conover, Carol Lancaster
Finance Faculty Publications
Unlike previous papers, which have focused on the timeliness ranks, we examine Value Line’s 3–5 year projections for stock returns, earnings, sales and related measures. We find that Value Line’s stock return and earnings forecasts exhibit large positive bias, although their sales predictions do not. For stock returns, Value Line’s projections lack predictive power; for other variables predictive power may exist to some degree. Our findings suggest the spectacular past performance of the timeliness indicator reflects either close alignment with other known anomalies or data mining, and that investors and researchers should use Value Line’s long-term projections with caution.
Duration Measures For Corporate Project Valuation, Tom Arnold, David S. North
Duration Measures For Corporate Project Valuation, Tom Arnold, David S. North
Finance Faculty Publications
Sensitivity analysis is a very common exercise performed with the forecasting of project cash flows. In this paper, a duration-type measure is generated that provides a single number for the assessment of project cash flows relative to changes in the discount rate (or adjusted for changes in a particular cash flow model parameter). The calculation is no more difficult than the duration measures that already exist for bonds. Yet, the calculation provides valuable insight that many times is lost when performing sensitivity analysis. Further, at a minimum, the measure provides a gauge for the consequences of mis-specifiying the discount rate …
A Simplified Approach To Understanding The Kalman Filter Technique, Tom Arnold, Mark J. Bertus, Jonathan Godbey
A Simplified Approach To Understanding The Kalman Filter Technique, Tom Arnold, Mark J. Bertus, Jonathan Godbey
Finance Faculty Publications
The Kalman filter is a time series estimation algorithm that is applied extensively in the field of engineering and recently (relative to engineering) in the field of finance and economics. However, presentations of the technique are somewhat intimidating despite the relative ease of generating the algorithm. This article presents the Kalman filter in a simplified manner and produces an example of an application of the algorithm in Excel. This scaled-down version of the Kalman filter can be introduced in the (advanced) undergraduate classroom as well as the graduate classroom.