Portfolios Valuation

about us

StatDec provides Independent Portfolio Valuation services for Buyers or Sellers of Retail Portfolios.

Whether as a part of merger and acquisition due diligence, the sale/purchase of distressed assets or preparation for the secondary market, it is essential to be able to quantify future cash-flows from both anticipated income, likely credit losses and, especially in the case of distressed portfolios, recovery streams.

Determination of the expected future revenue of a portfolio allows appropriate pricing for asset sales.

Using StatDec’s broad experience in analysing retail portfolio behaviour, the methodology that best fits the given situation is selected.

Approaches taken include:

- Data quality and process review to confirm that the available data represent full and concise information for the analysis
- Identification of key drivers of future cash-flows or recoveries
- Benchmarking expectations vs. reference portfolios
- Model and/or segmentation approach for portfolio cash-flows estimation for lifetime or fixed time horizon

Why StatDec?

StatDec's Valuation Solutions offer a trustworthy and independent assessment of a portfolio value, from a firm renowned for its experience and technical expertise in Retail Credit Risk modelling.

StatDec's Portfolio Valuation solutions can be based on both Bottom-Up and Top-Down approaches, depending on the information depth and the objective on the accuracy of the estimate. Bottom-up approaches require more extensive data management but provide better estimations, since they capture the portfolio dynamics based on risk criteria in greater detail. Recovery and PD models are used or developed for the purposes of the Valuations, while results can be macroeconomically adjusted to represent scenarios on future economic conditions

In Countries with extensive market experience of retail portfolios Behaviour patterns, such as in the Greek and Romanian, StatDec's Valuations can be benchmarked against relevant portfolios. Thus, Portfolio Valuations can be enriched with expert judgement even in situations with data limitations.