abstract. Traditional valuation methods are less viable under uncertainty. Hence, other methods such as real options valuation models, which can minimize uncertainty, have become more important. In this study, the hybrid approach suggested by Carlsson and Fuller is examined for the case of discrete compounding as this approach better models risky cash flows. A new real options valuation model that will evaluate the investment in a more realistic way is suggested by postponing the defuzzification of parameters in early stages. The suggested model has been applied to the data of an oil field investment and in conclusion the loss of information caused by early-defuzzification has been determined.

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Forecasting techniques have a widespread area from simple regression to complex metaheuristics like neural networks and genetic algorithms. Economic forecasting is the process of attempting to predict the future condition of the economy. It is the projection or estimation of statistical measures of the performance of a country, group of countries, industry, firm or community. This involves the use of these techniques utilizing variables sometimes called indicators. Some of the most well-known economic indicators include inflation and interest rates, GDP growth/decline, retail sales and unemployment rates. While economic forecasting is not an exact science, it remains an important decision-making tool for businesses and governments as they formulate financial policy and strategy. Concepts forecasted are often standard measures of economic or business results such as production, employment, prices, incomes, spending, sales, profits and other similar statistics. This chapter summarizes and classifies the forecasting techniques from classical logic to fuzzy logic and from metaheuristic techniques (e.g. neural networks or ant colony optimization) to integrated metaheuristics (e.g. neuro-fuzzy). The chapter also includes numerical examples.

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