Philipp Mundt Livres

Theory suggests that competition tends to equalize profit rates through the process of capital reallocation, and numerous studies have confirmed that profit rates are indeed persistent and mean-reverting. Recent empirical evidence further shows that fluctuations in the profitability of surviving corporations are well approximated by a stationary Laplace distribution. Here we show that a parsimonious diffusion process of corporate profitability that accounts for all three features of the data achieves better out-of-sample forecasting performance across different time horizons than previously suggested time series and panel data models. As a consequence of replicating the empirical distribution of profit rate fluctuations, the model prescribes a particular strength or speed for the mean-reversion of all profit rates, which leads to superior forecasts of individual time series when we exploit information from the cross-sectional collection of firms. The new model should appeal to managers, analysts, investors and other groups of corporate stakeholders who are interested in accurate forecasts of profitability. To the extent that mean-reversion in profitability is the source of predictable variation in earnings, our approach can also be used in forecasts of earnings and is thus useful for firm valuation.

We propose a parsimonious statistical model of firm competition where structural differences in the strength of competitive pressure and the magnitude of return fluctuations above and below the system-wide benchmark translate into a skewed Subbotin or asymmetric exponential power (AEP) distribution of returns to capital. Empirical evidence from US data illustrates that the AEP distribution compares favorably to popular alternative models such as the symmetric or asymmetric Laplace density in terms of goodness of fit when entry and exit dynamics of markets are taken into account.

The core philosophy of Gibrat’s rule of proportionate effect seeks a common mechanism in the growth of business firms, suggesting that growth rates are independent of size and drawn from the same distribution. However, after extensive research, it appears that this "law" does not universally explain firm growth. We propose that Gibrat’s approach is more applicable to firm profitability than growth, aligning with the classical view of economic competition as a dynamic capital reallocation process. Analyzing over five hundred long-lived US corporations across various sectors over thirty years, we find that profit rates and their volatilities are independent of size, unlike growth rates. Both profitability and growth exhibit exponential power distributions, yet they differ significantly in parameterization and autocorrelation structures. We argue that a recently proposed diffusion process accurately reflects the cross-sectional distribution of profit rates and aligns with the empirical time series of individual firms. This scenario resembles statistical equilibrium in natural sciences, while econometricians refer to it as ergodicity and stationarity. Our interpretation suggests that all surviving firms face the same competitive pressures, regardless of industry, with a shared profitability benchmark and volatility. The idiosyncratic efforts of firms influence only the persistence of abnormal profits. Ultimately, we observe that t