PhD Finance 10 Articles

hat utilize Occam’s razor to simplify variable selection, while for the modeling process itself improvements in the predictions or the estimations based on a single point benefitted from the addition of probability distributions. Loss functions on the other hand were captured as actual quantities by making use of Bayesian decision theory as a philosophical approach to the derivation of the modeling error (Aziz and Percy, 2009).
The assumptions are that the use of appropriate modeling approaches, which apply rigorous math to arrive at the errors through Bayesian theory, will yield better forecasts. Relating the work to the consulted sources for the course, this present paper looks at financial forecasting via the investigation of modeling approaches based on Bayesian decision theory and the use of statistical probability distributions to improve the models and therefore improve the forecasts. This joins the other literature on financial forecasting. The limitations include bounds in the derivation of the modeling error based on Bayesian theory. There are opportunities to further refine the modeling approaches and to further improve the forecasting power of the models by tweaking the math further (Aziz and Percy, 2009).
The scope of the work is that of presenting a philosophy of management of the forecasting process through the presentation of a forecasting checklist. The goal is to improve forecasts. The paper presents insights into modeling consistency and integrity. The philosophical approach and purpose are linked to championing modeling integrity that is not shaped by specific circumstances and perspectives but are rather tied to excellence in the structuring of the process of forecasting. Consistency lies in modeling processes that are stable and that yield forecasts with high levels of accuracy. The focus is on excelling at forecasting to the point of being able to come up with highly reliable forecasts (Elikai, Hill an Elikai, 1999).
The assumptions