Peer-implied Value Drivers: A Novel Approach to the DCF Model

Abstract

This thesis introduces a novel methodology for identifying value drivers in a Discounted Cash Flow model that replicates the practical applicability of relative valuation techniques. We aim to enhance valuation accuracy by drawing value drivers from a peer group selected for its fundamental comparability on growth, profitability, and risk levels. Our approach is to construct a pro-forma model structure that allows us to minimise effects outside of the scope. We optimise the composition of peer groups by examining 288 different configurations that vary decision variables of ranking systems, peer group size, data criteria, and industry criteria. Then, we compare the peer- implied models to a baseline model deriving its input from consensus estimates.

We find that peer-implied models based on two of our four decision variables reduce median valuation errors by a range of 2.14% to 4.37%-points and reduce the median absolute deviation by a range of 6.92% to 8.30%-points. Additionally, we find that the peer-implied models outperform the baseline in approximately half of the 407 firms we analyse and across a majority of industries. Statistical analysis confirms the robustness of our models, with consistent improvements across peer group sizes and data criteria. When implementing an industry criterion, we identify a statistically significant model that matches peers on a broad industry classification. However, we find that its reduction in median valuation errors is only 0.75%-points relative to the model with no industry criterion, and it is dependent on a specific model configuration. We find no improvement in valuation accuracy when implementing more narrow industry criteria. Our results indicate a need for a more dynamic and target firm industry-tailored approach to combining industry and fundamental peer selection and invite future research to advance this. Future research should explore further optimisation of our methodologies and investigate their applicability both geographically and temporally. In conclusion, we find that peer-implied models within a DCF framework provide a more accurate and consistent method of valuation, which could significantly benefit investors, analysts, and financial stakeholders.