G32 - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; GoodwillReturn

This paper focuses on using survival analysis models in the area of credit risk and on the modelling of the probability of default (i.e. a situation where the debtor is unwilling or unable to repay the loan in time) in particular. Most of the relevant scholarly literature argues that the survival models produce similar results to the commonly used logistic regression models for the development or testing of samples. However, this paper challenges the standard performance criteria measuring precision and performs a comparison using a new prediction-based method. This method gives more weight to the predictive power of the models measured on an ex-ante validation sample rather than the standard precision of the random testing sample. This new scheme shows that the predictive power of the survival model outperforms the logistic regression model in terms of Gini and lift coefficients. This finding opens up the prospect for the survival models to be further studied and considered as relevant alternatives in financial modelling.

The purpose of this article is to present an industrial application of the Reference Class Forecasting Method (RCFM) developed by Kahneman and Tversky for planning and decision-making under uncertainty. Project plans are usually prepared on the basis of detailed calculations and arrangements according to selected project management methodology. Undertakings that are planned in this manner often fail and do not achieve their goals. However, the American Planning Association recommends using the RCFM as an additional method. The article presents four groups of projects implemented by a chemical industry company over four years. A few of the projects were accomplished according to the plan in terms of triple constraint i.e. time, cost, and scope. The cost aspect was taken into account in the paper. During the study, the planned and implemented costs of 222 projects were analysed. On the basis of the distribution of cost overruns, according to RCFM, new patterns of planned costs were prepared. The Reference Class Forecasting Method, which was effective for large homogeneous projects turned out to be completely useless for various projects implemented by the chemical company.

Over the past few years, the Value at Risk indicator (VaR) has evolved, without doubt, into the most frequently used comprehensive tool for assessment of potential losses caused by adverse changes in market rates. However, the common models used for VaR assessment are based only on mid prices and do not take into account the existence of time-varying bid-ask spreads. In addition, they assume that any amount of instruments can be sold almost immediately without an adverse impact on prices. Thus, they focus only on pure market risks without taking into account the market liquidity. As a consequence, they underestimate the total risk.
This paper focuses on the importance of market liquidity and describes ways to integrate it into the VaR calculation.

Trade credit management should contribute to realization of basic financial purpose of an enterprise which is the is maximization of its value. Many of the current asset management models that are found in financial management literature assume book profit maximization as the basic financial purpose. These book profit-based models could be lacking in what relates to maximization of enterprise value. The enterprise value maximization strategy is executed with a focus on risk and uncertainty. This article presents the consequences that can result from operating risk that is related to purchasers using payment postponement for goods and/or services. The present article offers a method that uses portfolio management theory to determine the level of accounts receivable in a firm. An increase in the level of accounts receivables in a firm increases both the net working capital and the costs of holding and managing accounts receivables.

Income valuation is a fundamental valuation approach. One question still open is the discount rate calculation. One factor impacting the discount rate is a capital structure. Nevertheless, the structure has to be in market values. It means that the knowledge of the result is a precondition for the calculation. The solution lies in an iterative technique based on recursive progress. It is also necessary to select suitable reagency functions expressing relations between equity cost and leverage ratio. The traditional reagency function is employed according to MM II, whereas the modified reagency function is derived by other authors. In addition, the article analyzes main factors impacting the size of error arising when discount rate calculation using a DCF method is not based on capital structure in market values. This analysis is made for both MM and modified reagency functions.

At present time, when the rate of risk is increasing, achieving higher quality of investment decision-making by integrating risk and uncertainty factors could positively affect economical results and firm's prosperity. Firstly this article characterizes shortcomings of investment decision making connected with over optimism of people, preparing and evaluating investment projects. Important tool, which could contribute to removing, or at least weakening of these shortcomings, represents Monte Carlo simulation. To description of content Monte Carlo simulation in framework of risk analysis, its problem aspects, advantages and disadvantages is devoted main part of the article. The practical illustration of solving real investment problem from the field of oil industry through Monte Carlo simulation is also involved.