Implications of Cost of Capital and Capital Budgeting
Capital budgeting is subject to the validity of forecasted data. However, understanding why the validity of data plays a significant part in capital budgeting is required because of the nature of decisions made in capital budgeting. Such decisions entail investment and financing decisions (Siziba & Hall, 2021). The decisions that are made in capital budgeting are, by nature, irreversible. Thus, using forecasted data that is not valid can result in irreversible commitments, resulting in losses for the company. In addition, capital budgeting decisions involve vast amounts of funds, and thus, it is essential to consider the validity of forecasted data to avoid losing the massive amount of funds invested (Boland & Purda, 2021). For instance, invalid data can project stable future economic conditions, and investors may make huge investments; however, losses will be incurred in the event of unstable conditions in the economy (Angelo et al., 2018). Further, invalid forecasted data is not in line with the objective of capital budgeting of maximizing wealth for shareholders.
The use of capital budgeting tools and their reliability will be influenced significantly by the validity of the forecasted data, as argued above. Therefore, the reliability of capital budgeting tools will be affected without valid forecasted data. Various alternatives are available and can be considered for use instead of capital budgeting techniques. Such methods include the capital asset pricing model and the arbitrage pricing theory (Sengul et al., 2019). While these alternatives are useful, most investors prefer to use CAPM because it is a single-factor pricing model. The APT model requires multiple factors to quantify, making it complicated to use. Notably, the two methods have been criticized for using many assumptions, which can spark reliability questions (Kengatharan, 2018). For that reason, there is a need to critically compare capital budgeting tools and their possible alternatives.
References
Angelo, B., Ayres, D., & Stanfield, J. (2018). Power from the ground up: Using data analytics in capital budgeting. Journal of Accounting Education, 42, 27-39.
Boland, M., & Purda, L. D. (2021). Close Enough! Exploring the Consequences and Motivations Behind Estimates in Capital Budgeting. Exploring the Consequences and Motivations Behind Estimates in Capital Budgeting (March 26, 2021).
Kengatharan, L. (2018). Capital Budgeting Theory and Practice: A review and agenda for future research. American Journal of economics and business management, 1(1), 20-53.
Sengul, M., Costa, A. A., & Gimeno, J. (2019). The allocation of capital within firms. Academy of Management Annals, 13(1), 43-83.
Siziba, S., & Hall, J. H. (2021). The evolution of the application of capital budgeting techniques in enterprises. Global Finance Journal, 47, 100504.
ORDER A PLAGIARISM-FREE PAPER HERE
We’ll write everything from scratch
Question 
Unit 6 Discussion: Implications of Cost of Capital and Capital BudgetingUnit 6 Discussion: Implications of Cost of Capital and Capital Budgeting
Implications of Cost of Capital and Capital Budgeting
In considering what we have learned thus far regarding the importance of determining the cost of capital as well as using capital budgeting tools, explain why it is important to understand that capital budgeting is subject to the validity of the forecasted data. Additionally, explain whether this reduces the reliability of these types of tools. Are there any other alternatives, or are these tools some of the most reliable that currently exist?