Forking paths in financial economics

We argue that spanning large numbers of degrees of freedom in empirical analysis allows better characterizations of effects and thus improves the trustworthiness of conclusions. Our ideas are illustrated in three studies: equity premium prediction, asset pricing anomalies and risk premia estimation. In the first, we find that each additional degree of freedom in the protocol expands the average range of $t$-statistics by at least 30%. In the second, we show that resorting to forking paths instead of bootstrapping in multiple testing raises the bar of significance for anomalies: at the 5% confidence level, the threshold for bootstrapped statistics is 4.5, whereas with paths, it is at least 8.2, a bar much higher than those currently used in the literature. In our third application, we reveal the importance of particular steps in the estimation of premia. In addition, we use paths to corroborate prior findings in the three topics. We document heterogeneity in our ability to replicate prior studies: some conclusions seem robust, others do not align with the paths we were able to generate.