The interplay of time and performance : long-term discounting and private equity benchmarks
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My essay 1--Forecasting the Term Structure of Equity Betas: Implications for Valuation--studies the impact of incorporating term structure movements of systematic risk exposures (betas) to asset valuations. In a sample of more than 20, 000 firms from 1965 to 2023 betas vary significantly over the term structure. These variations can be predicted using a term-structure-adjusted ordinary least squares approach (OLS-TS), which reduces median squared errors by up to 25 percent compared to the traditional approach of using a rolling beta estimate. In addition, OLS-TS produces beta forecasts whose accuracy is more stable over time. Converting long-term beta expectations into costs of equity reveals that firms' implied costs of equity vary by 4.5 percentage points between extreme deciles, providing evidence contrary to the commonly held believe that discount rates do not vary across firms. In addition, applying OLS-TS beta estimates to discounted cash flow valuation reduces pricing errors compared to using rolling betas for both analyst target prices and observed market prices and reduces beta errors in long-term hedging portfolios. The OLS-TS forecasts are then applied to long-term portfolios with a holding period of five years, mimicking the typical holding window in private equity (PE). My results show that term-structure adjusted beta predictions are more accurate in predicting realized factor exposures for long-term portfolios and thus have implications for private equity planning and benchmarking. The end of the essay introduces a unified term-structure prediction framework utilizing a multi-output neural network.
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Ph. D.