There is much debate for both the academic community and accounting professionals with respect to the use of fair value and cost accounting, as well as the application of impairment to current and non-current assets. Fair value and impairment are two related concepts, the reason being that in order to proceed with the latter, the current market price of an asset should first be measured. IAS 36 came into force to stipulate that no asset should be valued above its current actual value. Assets’ revaluation affects not only the companies’ outcome but also the applied depreciation method, which must be adjusted accordingly to the new data. Assets that cannot be measured to their fair value, in accordance with the IAS instructions, are grouped to form identifiable units within the company that was able to generate cash inflows and be tested for impairment as a whole. In this article, we focused on presenting a methodology from a technical approach on these issues, whilst at the same time remaining compatible with the principles of both accounting and finance. Real-life data from existing companies have been used not only for the valuation of the same following their transformation into cash-generated units, but also for non-current assets by controlling both the impairment and the depreciation process. We use cash flow generation models through the business plan process and apply certainty and uncertainty techniques such as sensitivity analysis and Monte Carlo simulation. After having reviewed the estimations and bearing in mind the structure of the model, we have concluded that specific parameters are affecting the fair value measurement on non-current assets. The value of this article is to develop a methodology that can be easily applied to different companies and is compatible with the spirit and provisions of both the international accounting standards as well as those of financial accounting. Keywords: FVA, cost accounting, finance, impairment, OR

This paper aims to develop a comprehensive procedure for calculating the fair value of a company by predicting its future values using historical data of key ratios and applying dynamic algorithms to improve the selection of forecasting methods. The most important business valuation methodologies are based on discounting a firm’s future variables, and there are many ways to predict them through financial and quantitative methodologies. This paper provides the most important and commonly used time series forecasting methodologies that can be used for variables, such as financial ratios, and proposes three different algorithms to help and improve the selection of the best-fit method for each of the model’s variables. Another, more indirect way of predicting values is using operational research methodologies, such as Monte Carlo simulation, where the output of the sensitivity analysis gives the most likely firm value, taking into account the distribution of each variable. This paper includes a complete example of using the above procedures in a real Greek company to calculate its fair value. It offers alternative approaches to the problem that exists around the process of predicting variables, with the help of technology. We hope this will be a useful tool for future use.