The objective of this research is to design an optimal model for Build-Operate-Transfer (B.O.T) contracts, considering uncertainty conditions. In this study, mathematical modeling was conducted using MATLAB software and the Particle Swarm Optimization method. Hypothetical data related to a combined cycle power plant project were analyzed as a case study. The construction cost of the power plant was estimated at $120 million, and it was projected that the project’s annual revenue would increase from $10.74 million in the fifth year to $46.64 million by the eighteenth year. Operating costs also rose from $1.68 million in the fifth year to $19.90 million by the thirtieth year. The results showed that the cumulative net financial flow for the government reached $273.32 million by the thirty-third year, while the private sector’s cumulative net financial flow increased to $157.66 million by the thirty-second year. The proposed model, using historical data and information obtained from similar projects, was able to reduce risks associated with revenue fluctuations and provide a more accurate prediction of annual profits. Based on the analysis, the internal rate of return (IRR) was calculated at 12% for the government and 25% for the private sector. Using the proposed model, the economic lifespan of the project was estimated to be 33 years from the government’s perspective and 32 years from the private sector’s perspective. The optimal point for transferring ownership of the project was determined to be in year 20.8. The findings indicated that the proposed model for B.O.T contracts, by reducing uncertainty and accurately forecasting financial flows and profits, serves as a suitable tool for improving financial decision-making in infrastructure projects.

Companies pay attention to the value level of financial flexibility in making decisions related to optimizing investments and applying their net working capital policies. This issue will make profitable investment opportunities for companies more efficient and enable companies to gain more efficiency, as well as apply more optimal policies to keep cash. The purpose of this research is to investigate the effect of financial flexibility value and financial constraint on the speed of adjustment of net working capital, as well as the effect of financial constraint on the relationship between the value of financial flexibility and the speed of adjustment of net working capital in companies listed on the Tehran Stock Exchange. The appropriate pattern recognition test in combined data indicates the use of the regression model of the research using the panel data method with the fixed and random effects approach for the panel and pooled data patterns to estimate the regression model. The statistical sample includes 100 companies accepted to the Tehran Stock Exchange during the period from 2005 to 2020. The findings indicate that the value of financial flexibility has a positive and significant effect on the speed of adjustment of net working capital in the models of partial adjustments and error correction. financial constraint has a positive and significant effect on the speed of net working capital adjustment, and it also has a positive and significant effect on the relationship between the value of financial flexibility and the speed of net working capital adjustment.

The purpose of this study is a new understanding behaviour and net cash flow forecasting. The data of this research contains the daily trial balance for one year, which has been received from 48 bank branches. For this purpose, the optimal model out of 4 models; Geometric Brownie, Arithmetic Brownie, Vasicek and Modified Square Root at three levels of microscopic, mesoscopic and macroscopic have been investigated and the Geometric Brownie model has been approved as the optimal model in microscopic level . The results show that Geometric Brownian Motion model can simulate the net cash flow highly accurate in accordance with the criteria of mean absolute percentage error. also forecasting net cash flow for each under study time series has been done in various forecasting horizons involved 7, 14, 21, 30, 60, 90 and 180 day time period accordance with the criteria of mean absolute percentage error. Also The other results obtained from this study is that according to 8 different prediction accuracy criteria, By increasing the forecast hori-zon, ability of the GBM model in simulation and forecasting the net cash flow de-creases.

Based on valuation model of residual earnings, we cannot use earnings and losses of balance sheet items recorded in fair value for valuation purposes, for the balance sheet provides a perfect estimate of such items’ value. The purpose of this study is to examine whether net financial expenses are related to the market price of stocks in Iran, because after initial recording of financial debts, no adjustments are done in historical cost regime. We expect to see an improvement in this relationship during severe fluctuations in the country’s economy.35 companies were selected from firms listed in Tehran Stock Exchange and we a period of eleven years, 2005-2015 was studied. The statistical test for data analysis is regression testing. The results show that net financial expenses are value relevant and there is an increasing trend in value relevance of these expenses during imposing the sanctions. This trend is particularly strong from 2009 onwards