In this article, we propose a model that incorporates the preference of multiple decision makers in to a decision making process using multiple criteria and cardinal method in the Capital budgeting context. In this model, all characteristics represent a significant improvement compared with traditional Capital budgeting techniques.

Current paper examines the effect of retrospective source of finance on the Capital budgeting decisions and investigates the possibility of costly decision errors in the case of using mental accounting by managers. This experimental study has utilized a 2*2 between subjects factorial design. Independent variables that are manipulated in this paper are retrospective source of finance and prospective source of finance. The sample consists of 160 participants including executive experts and academic persons assigned to one of the four groups through random assignment. Results show that managers are less likely to part with a debt financed asset in comparison with an identical equity financed asset. The reason behind above reluctance is related to the managers’ perceptions about the asset’ s past benefits. This study shows that a psychological factor may cause managers to forego investments that they should invest according to the Capital budgeting techniques. In this way, the results can contribute to the accounting literature through providing valuable information for Capital budgeting studies.

Investors & creditors and others needs to relevant and accurate financial information for make decisions. this research intended to study the relevant between inflation & assessment criteria's Capital budgeting in listed companies in Tehran Stock Exchange among 1383 to 1388. Sampling method is simple sample method. In this research selected 30 companies. Results of research show that countries with inflation economic when companies applies cost method accounting for assessment of criteria's Capital budgeting is not provides useful and relevant financial information for investment decisions and decision makers has in wrong. Findings shows that for assessment of criteria's Capital budgeting we must adjusted the cash in and out flows with included of inflation rate in our calculations.

With the existence of ambiguity in the financial processes of projects, fuzzy concepts are being implemented into the foundation and essence of the current article. Authors have employed chance constrained programming and simulated annealing as appropriate tools for determining the net present worth value of several projects. At the end, the most economical project was chosen. In this article, the investment expenses and annual net financial processes are considered fuzzy taking the credibility definition into consideration. The proposed model is then solved by simulated annealing program and to check its credibility we have compared the results of our model with the results obtained by the Genetic Algorithm as well as the Branch and Bound routine. The results indicate that for small sized problems, Genetic Algorithm has worked relatively with less error amounts and produces better results. All developed algorithms such as simulated annealing and fuzzy simulation are coded in MATLAB R2009a environment and on the personal computer with266GHZ.

Daily increasing complexity and specialization of financial management’s tasks in companies as well as great significant of Capital expenditure in the companies’ success or lack of success have led to the emergence of methods and techniques of Capital budgeting. Complicated Capital budgeting is accompanied with several devices and various methods (such as Monte Carlo Simulation, certainty equivalents, Game Theory Decision Principles, and Real Choice Reasoning). Recent literature shows that complicated Capital budgeting methods cannot supplant traditional Capital budgeting methods and they, indeed, are a kind of the latter one.Capital budgeting methods and their various devices (complicated and traditional) may supplement each other or provide further information with regard to how investment theorems are solved. The current study is aimed at examining application of complicated Capital budgeting techniques in the petrochemical industry. It is notable that novel financial techniques and even traditional ones are mostly applied in the petrochemical industry and that the largest and most complicated projects are presently executed in this field.Eventually, study findings imply an increase in peculiar uncertainty is not accompanied with application of complicated Capital budgeting methods (including certainty equivalents, Game Theory Decision Principles, and Real Choice Reasoning Technique), while in the case of Monte Carlo Technique, an increase in peculiar uncertainty results from complicated Capital budgeting methods (i.e. Monte Carlo Technique) being used. It can also be discussed that financial uncertainty (profit uncertainty, inflation uncertainty, and inflation rate uncertainty) is regarded as a factor affecting the application of complicated Capital budgeting methods; amongst these three factors, inflation rate uncertainty acts the main role of influencing complicated Capital budgeting methods.

This paper examines the results of a survey of executives of companies listed on the "Tehran Stock Exchange regarding their companies' Capital budgeting practices and the major factors influencing these practices. The results show that the majority of responding companies use formal techniques to evaluate proposed Capital investments. Among these companies, the majority use discounted cash flow techniques as the primary measure for evaluating Capital investment proposals. Sensitivity analysis and decision tree analysis are the most commonly used risk assessment techniques. Accounting return on equity and the CAPM are the most popular used techniques in calculating the cost of equity in Iran. Increasing future cash flow and growth in market shares followed by growth in long term earnings are the most important objectives in Capital budgeting for the responding companies. The results also show that chief financial officer's educational background and the size of annual Capital investment (the increase in net fixed assets) influence the adoption of Capital budgeting analysis technique.

In this paper, the researchers have proposed a multi-dimensional knapsack model for project Capital budgeting problem in uncertain situation which has been modeled through fuzzy sets. The optimistic and pessimistic situations were considered and associated deterministic models were yielded. Numerical example has been supplied toillustrate the performance of proposed model. The results were promising in the sense of helping the decision makers.

The major goal of the current study is to investigate whether using the real option method along with the discounted cash flow techniques can reduce the decision makers’ escalation of commitment (EC hereafter) or their desire to keep up their commitment to a failed project. The sample size was 100 Data were gathered via the use of questionnaries. The results revealed that using the real option method in Capital budgeting can affect the users’ behavior and decisions and lead to better decision-making in the long-term projects.

The use of artificial intelligence (AI) in financial management, particularly in Capital budgeting, has emerged as a transformative technology due to its ability to analyze large and complex datasets. AI can significantly improve the accuracy of forecasts and the management of financial risks. However, its adoption comes with both challenges and opportunities, which are explored in this paper. This study aims to examine the role of AI in enhancing the Capital budgeting process and analyze the associated challenges and opportunities. This is a review study based on a comprehensive literature review and case studies from various economic and industrial sectors that have implemented AI technologies. Data were collected through content analysis of relevant academic articles and reports. The findings indicate that AI improves the accuracy and speed of financial decision-making and reduces human errors. Additionally, AI enhances risk management and optimizes resource allocation. However, challenges such as ethical and security concerns and the need for high-quality data for precise analysis remain significant. AI can serve as a powerful tool in the Capital budgeting process, but to fully harness its potential, greater attention must be given to addressing technical, ethical, and security-related challenges. Future research should focus on improving transparency in decision-making processes and developing more advanced algorithms.